Recommendation engine which provides recommendation based on customer id, item id and the preference of the customer for a particular Item. Recommendations can be fetched based on User Similarity i.e. It finds similarity based on on Users and Item Similarity which finds similarity based on Items. The Recommendation Engine currently supports two types of Similarity Algorithms i.e. EuclideanDistanceSimilarity and PearsonCorrelationSimilarity. By default when similarity is not specified PearsonCorrelationSimilarity is used e.g. in the method ItemBased(Double userId, int howMany) it uses PearsonCorrelationSimilarity. In the method ItemBasedBySimilarity(String similarity,Double userId, int howMany) one can specify which similarity algorithm has to be used e.g. Recommender.EUCLIDEAN_DISTANCE or Recommender.PEARSON_CORRELATION. Preference file can be loaded using the method LoadPreferenceFile(String preferenceFilePath) in csv format. This prefernce file has to be uploaded once which can be a batch process

The csv format for the file is given below. customerId, itemId, preference e.g. 1,101,5.0: 1,102,3.0: 1,103,2.5: 2,101,2.0: 2,102,2.5: 2,103,5.0: 2,104,2.0: 3,101,2.5: 3,104,4.0: 3,105,4.5: 3,107,5.0: 4,101,5.0: 4,103,3.0: 4,104,4.5: 4,106,4.0: 5,101,4.0: 5,102,3.0: 5,103,2.0: 5,104,4.0: 5,105,3.5: 5,106,4.0:

The Customer Id and Item id can be any alphanumaric character(s) and Preference values can be in any range. If app developers have used the Review Service. The Recommendation Engine can be used in conjunction with Review. In this case a CSV preference file need not be uploaded. The CustomerId, ItemId and Preference will be taken from Review where customerId is mapped with userName, ItemId is mapped with itemId and preference with rating. The methods for recommendations based on Reviews are part of the Review service.

• Initialize
• Build Service
• Import Statement
• Load Preference File
• Load Preference File With Input Stream
• User Based Neighbourhood
• User Based Neighbourhood By Similarity
• User Based Neighbourhood For All
• User Based Neighbourhood By Similarity For All
• User Based Threshold
• User Based Threshold For All
• User Based Threshold By Similarity
• User Based Threshold By Similarity For All
• Item Based
• Item Based For All
• Item Based By Similarity
• Item Based By Similarity For All
• Slope One For All
• Delete All Preferences
• Exception Handling
• Error Codes

Initialize

In order to use various functions available in a specific API, a developer has to create an instance of ServiceAPI by passing the apiKey and secretKey which will be created after the app creation from AppHQ dashboard.

Required Parameters

apiKey - The Application key given when the application was created. secretKey - The secret key corresponding to the application key given when the application was created.

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ServiceAPI api = new ServiceAPI("API_KEY","SECRET_KEY");

ServiceAPI api = new ServiceAPI("API_KEY","SECRET_KEY");

ServiceAPI *api = [[ServiceAPI alloc]init];
api.apiKey = @"<API_KEY>";
api.secretKey = @"<SECRET_KEY>"; 

ServiceAPI api = new ServiceAPI("API_KEY","SECRET_KEY");

ServiceAPI api = new ServiceAPI("API_KEY","SECRET_KEY");

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App42.initialize("API_KEY","SECRET_KEY");

ServiceAPI api = new ServiceAPI("API_KEY","SECRET_KEY");

$api = new ServiceAPI("API_KEY","SECRET_KEY"); 

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Build Service

After initialization, developer needs to call the buildXXXService method on ServiceAPI instance to get the instance of the particular API that you wish to build. For example, To build an instance of RecommenderService, buildRecommenderService() method needs to be called.

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RecommenderService recommenderService = api.buildRecommenderService(); 

RecommenderService recommenderService = api.BuildRecommenderService();  

RecommenderService *recommenderService = [api buildRecommenderService]; 

RecommenderService recommenderService = api.buildRecommenderService(); 

RecommenderService recommenderService = api.buildRecommenderService(); 

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RecommenderService recommenderService = api.BuildRecommenderService(); 

$recommenderService = $api->buildRecommenderService(); 

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Import Statement

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import com.shephertz.app42.paas.sdk.android.ServiceAPI;
import com.shephertz.app42.paas.sdk.android.App42Response;
import com.shephertz.app42.paas.sdk.android.App42Exception;
import com.shephertz.app42.paas.sdk.android.App42BadParameterException;
import com.shephertz.app42.paas.sdk.android.App42NotFoundException;
import com.shephertz.app42.paas.sdk.android.recommend.PreferenceData;
import com.shephertz.app42.paas.sdk.android.recommend.Recommender;
import com.shephertz.app42.paas.sdk.android.recommend.RecommenderService;
import com.shephertz.app42.paas.sdk.android.recommend.RecommenderSimilarity;

using com.shephertz.app42.paas.sdk.windows;  
using com.shephertz.app42.paas.sdk.windows.recommend;   

#import "Shephertz_App42_iOS_API/Shephertz_App42_iOS_API.h"   

import com.shephertz.app42.paas.sdk.jme.ServiceAPI;
import com.shephertz.app42.paas.sdk.jme.App42Response;
import com.shephertz.app42.paas.sdk.jme.App42Exception;
import com.shephertz.app42.paas.sdk.jme.App42BadParameterException;
import com.shephertz.app42.paas.sdk.jme.App42NotFoundException;
import com.shephertz.app42.paas.sdk.jme.recommend.PreferenceData;
import com.shephertz.app42.paas.sdk.jme.recommend.Recommender;
import com.shephertz.app42.paas.sdk.jme.recommend.RecommenderService;
import com.shephertz.app42.paas.sdk.jme.recommend.RecommenderSimilarity;

import com.shephertz.app42.paas.sdk.java.ServiceAPI;
import com.shephertz.app42.paas.sdk.java.App42Response;
import com.shephertz.app42.paas.sdk.java.App42Exception;
import com.shephertz.app42.paas.sdk.java.App42BadParameterException;
import com.shephertz.app42.paas.sdk.java.App42NotFoundException;
import com.shephertz.app42.paas.sdk.java.recommend.Recommender;
import com.shephertz.app42.paas.sdk.java.recommend.PreferenceData;
import com.shephertz.app42.paas.sdk.java.recommend.RecommenderService;
import com.shephertz.app42.paas.sdk.java.recommend.RecommenderSimilarity;

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using com.shephertz.app42.paas.sdk.csharp;  
using com.shephertz.app42.paas.sdk.csharp.recommend;  

use com\shephertz\app42\paas\sdk\php\App42Response;  
use com\shephertz\app42\paas\sdk\php\App42Exception;  
use com\shephertz\app42\paas\sdk\php\App42BadParameterException;  
use com\shephertz\app42\paas\sdk\php\App42NotFoundException;  
use com\shephertz\app42\paas\sdk\php\recommend\RecommenderService;  
use com\shephertz\app42\paas\sdk\php\ServiceAPI;  
include_once '../RecommenderService.php';  
include_once '../ServiceAPI.php';   
include_once '../App42Response.php';  
include_once '../App42Exception.php';   
include_once '../App42BadParameterException.php';  
include_once '../App42NotFoundException.php';  

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Load Prefrence File

Uploads preference file on the cloud. The preference file should be in CSV format. This preference file has to be uploaded once which can be a batch process. New versions of preference file either can be uploaded in a different name or the older one has to be removed and the uploaded in the same name. The csv format for the file is given below. customerId, itemId, preference e.g. 1,101,5.0: 1,102,3.0: 1,103,2.5: 2,101,2.0: 2,102,2.5: 2,103,5.0: 2,104,2.0: 3,101,2.5: 3,104,4.0: 3,105,4.5: 3,107,5.0: 4,101,5.0: 4,103,3.0: 4,104,4.5: 4,106,4.0: 5,101,4.0: 5,102,3.0: 5,103,2.0: 5,104,4.0: 5,105,3.5: 5,106,4.0:

The customer Id and item id can be any alphanumeric character(s) and preference values can be in any range. If the recommendations have to be done based on Reviews then this file need not be uploaded.

Required Parameters

filePath - Path of the preference file to be loaded

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String filePath = "Your File Path";
App42Response response = recommenderService.loadPreferenceFile(filePath); 
boolean  success = response.isResponseSuccess();
String jsonResponse = response.toString(); 

public class Callback : App42Callback  
{  
String filePath = "Your File Path";
recommenderService.LoadPreferenceFile(filePath,this);  
	void App42Callback.OnException(App42Exception exception)  
	{  
		Console.WriteLine("Exception Message");  
	}  
	void App42Callback.OnSuccess(Object object)  
	{  
		App42Response response = (App42Response) object;     
		String jsonResponse = response.ToString();  
	}  
}  

NSString *filePath = @"Your File Path";
App42Response *response = [recommenderService loadPreferenceFile:filePath]; 
NSString *success = response.isResponseSuccess;
NSString *jsonResponse = [response toString];                             

String filePath = "Your File Path";
App42Response response = recommenderService.loadPreferenceFile(filePath); 
boolean  success = response.isResponseSuccess();
String jsonResponse = response.toString(); 

String filePath = "Your File Path";
App42Response response = recommenderService.loadPreferenceFile(filePath);
boolean  success = response.isResponseSuccess();
String jsonResponse = response.toString(); 

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String filePath = "Your File Path";
App42Response response = recommenderService.LoadPreferenceFile(filePath); 
Boolean  success = response.IsResponseSuccess();
String jsonResponse = response.ToString(); 

$filePath = "Your File Path";
$response = $recommenderService->loadPreferenceFile($filePath); 
$success = $respons->isResponseSuccess();
$jsonResponse = $respons->toString(); 

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Load Prefrence File With Inputstream

Uploads preference file on the cloud. The preference file should be in CSV format. This preference file has to be uploaded once which can be a batch process. New versions of preference file either can be uploaded in a different name or the older one has to be removed and the uploaded in the same name. The csv format for the file is given below. customerId, itemId, preference e.g. 1,101,5.0: 1,102,3.0: 1,103,2.5: 2,101,2.0: 2,102,2.5: 2,103,5.0: 2,104,2.0: 3,101,2.5: 3,104,4.0: 3,105,4.5: 3,107,5.0: 4,101,5.0: 4,103,3.0: 4,104,4.5: 4,106,4.0: 5,101,4.0: 5,102,3.0: 5,103,2.0: 5,104,4.0: 5,105,3.5: 5,106,4.0: The customer Id and item id can be any alphanumeric character(s) and preference values can be in any range. If the recommendations have to be done based on Reviews then this file need not be uploaded.

Required Parameters

inputStream - InputStream of the file to load.

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InputStream inputStream = null; /*Get input stream from your source*/
App42Response response = recommenderService.loadPreferenceFile(inputStream); 
boolean  success = response.isResponseSuccess();
String jsonResponse = response.toString(); 

public class Callback : App42Callback  
{  
Stream inputStream = null; /*Get input stream from your source*/
recommenderService.LoadPreferenceFile(inputStream,this);  
	void App42Callback.OnException(App42Exception exception)  
	{  
		Console.WriteLine("Exception Message");  
	}  
	void App42Callback.OnSuccess(Object object)  
	{  
		App42Response response = (App42Response) object;     
		String jsonResponse = response.ToString();  
	}  
}  

App42Response *response = [recommenderService loadPreferenceFile:@"Get NSData from your source"]; 
NSString *success = response.isResponseSuccess;
NSString *jsonResponse = [response toString];                             

InputStream inputStream = null; /*Get input stream from your source*/
App42Response response = recommenderService.loadPreferenceFile(inputStream); 
boolean  success = response.isResponseSuccess();
String jsonResponse = response.toString(); 

InputStream inputStream = null; /*Get input stream from your source*/
App42Response response = recommenderService.loadPreferenceFile(inputStream);
boolean  success = response.isResponseSuccess();
String jsonResponse = response.toString(); 

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Stream inputStream = null; /*Get input stream from your source*/
App42Response response = recommenderService.LoadPreferenceFile(inputStream); 
Boolean  success = response.IsResponseSuccess();
String jsonResponse = response.ToString(); 

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User Based Neighbourhood

User based recommendations based on Neighborhood. Recommendations are found based on similar users in the Neighborhood of the given user. The size of the neighborhood can be found.

Required Parameters

userId - The user Id for whom recommendations have to be found. size - Size of the Neighborhood. howMany - Specifies that how many recommendations have to be found

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long userId = 3;
int size = 4567788 ;
int howMany = 2;
Recommender recommender = recommenderService.userBasedNeighborhood(userId, size, howMany);  
ArrayList<Recommender.RecommendedItem> recommendedItemList =  recommender.getRecommendedItemList(); 
for(Recommender.RecommendedItem recommendedItem : recommendedItemList)
{
    System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());
}
String jsonResponse = recommender.toString(); 

public class Callback : App42Callback  
{
long userId = 3;
int size = 4567788 ;
int howMany = 2;
recommenderService.UserBasedNeighborhood(userId, size, howMany,this);  
	void App42Callback.OnException(App42Exception exception)  
	{  
		Console.WriteLine("Exception Message");  
	}  
	void App42Callback.OnSuccess(Object response)  
	{  
		Recommender recommender = (Recommender) response;     
		String jsonResponse = recommender.ToString();  
	}  
}  

long userId = 3;
int size = 4567788 ;
int howMany = 2;
Recommender *recommender =  [recommenderService userBasedNeighborhood:userId size:size howMany:howMany];
NSMutableArray *recommendedItemList =  recommender.recommendedItemList; 
for(RecommendedItem *recommendedItem in recommendedItemList){
	NSLog(@"item is = %@",recommendedItem.userId);
	NSLog(@"value is = %f",recommendedItem.value);
}
NSString *jsonResponse = [recommender toString];
                            

long userId = 3;
int size = 4567788 ;
int howMany = 2;
Recommender recommender = recommenderService.userBasedNeighborhood(userId, size, howMany);
Vector recommendedItemList =  recommender.getRecommendedItemList(); 
for(int i=0;i< recommendedItemList.size();i++)
{  
	Recommender.RecommendedItem recommendedItem = (Recommender.RecommendedItem) recommendedItemList.elementAt(i);  
	System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());  
} 
String jsonResponse = recommender.toString(); 

long userId = 3;
int size = 4567788 ;
int howMany = 2;
Recommender recommender = recommenderService.userBasedNeighborhood(userId, size, howMany);  
ArrayList<Recommender.RecommendedItem> recommendedItemList =  recommender.getRecommendedItemList(); 
for(Recommender.RecommendedItem recommendedItem : recommendedItemList)
{
    System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());
}
String jsonResponse = recommender.toString(); 

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Int64 userId = 3;
int size = 4567788 ;
int howMany = 2;
Recommender recommender = recommenderService.UserBasedNeighborhood(userId, size, howMany);  
IList<Recommender.RecommendedItem> recommendedItemList =  recommender.GetRecommendedItemList(); 
for(int i = 0; i < recommendedItemList.Count; i++)
{
	Console.WriteLine("item is " + recommendedItemList[0].GetItem());
	Console.WriteLine("value is " + recommendedItemList[0].GetValue());
}
String jsonResponse = recommender.ToString(); 

$userId = 3;
$size = 4567788 ;
$howMany = 2;
$recommender = $recommenderService->userBasedNeighborhood($userId, $size, $howMany);  
$recommendedItemList =  $recommender->getRecommendedItemList(); 
foreach( $recommendedItemList as $recommendedItem ){
print_r("value is" . $recommendedItem->getValue());
print_r("item is" . $recommendedItem->getItem());
}
$jsonResponse = $recommender->toString(); 

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User Based Neighbourhood By Similarity

User based recommendations based on Neighborhood and Similarity. Recommendations and found based on the similar users in the Neighborhood with the specified Similarity Algorithm. Algorithim can be specified using the constants Recommender.EUCLIDEAN_DISTANCE and Recommender.PEARSON_CORRELATION

Required Parameters

recommenderSimilarity - Similarity algorithm e.g. Recommender.EUCLIDEAN_DISTANCE and Recommender.PEARSON_CORRELATION. userId - The user Id for whom recommendations have to be found. size - Size of the Neighborhood. howMany - Specifies that how many recommendations have to be found.

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RecommenderSimilarity recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;		
long userId = 1;
int size = 2;
int howMany = 2;						
Recommender recommender = recommenderService.userBasedNeighborhoodBySimilarity(recommenderSimilarity, userId, size, howMany); 
ArrayList<Recommender.RecommendedItem> recommendedItemList =  recommender.getRecommendedItemList(); 
for(Recommender.RecommendedItem recommendedItem : recommendedItemList)
{
    System.out.println("item is " + recommendedItem.getItem());
    System.out.println("value is " + recommendedItem.getValue());
}
String jsonResponse = recommender.toString();

public class Callback : App42Callback  
{
String recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;		
long userId = 1;
int size = 2;
int howMany = 2;	
recommenderService.UserBasedNeighborhoodBySimilarity(recommenderSimilarity,userId, size, howMany,this); 
void App42Callback.OnException(App42Exception exception)  
	{  
		Console.WriteLine("Exception Message");  
	}  
	void App42Callback.OnSuccess(Object response)  
	{  
		Recommender recommender = (Recommender) response;     
		String jsonResponse = recommender.ToString();  
	}   
}  

NSString *recommenderSimilarity = EUCLIDEAN_DISTANCE;		
long userId = 1;
int size = 2;
int howMany = 2;						
Recommender *recommender =  [recommenderService userBasedNeighborhoodBySimilarity:EUCLIDEAN_DISTANCE userId:userId size:size howMany:howMany];
NSMutableArray *recommendedItemList =  recommender.recommendedItemList; 
for(RecommendedItem *recommendedItem in recommendedItemList){
	NSLog(@"item is = %@",recommendedItem.userId);
	NSLog(@"value is = %f",recommendedItem.value);
}
NSString *jsonResponse = [recommender toString];

String recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;		
long userId = 1;
int size = 2;
int howMany = 2;
Recommender recommender = recommenderService.userBasedNeighborhoodBySimilarity(recommenderSimilarity,userId, size, howMany); 
Vector recommendedItemList =  recommender.getRecommendedItemList(); 
for(int i=0;i< recommendedItemList.size();i++)
{  
	Recommender.RecommendedItem recommendedItem = (Recommender.RecommendedItem) recommendedItemList.elementAt(i);  
	System.out.println("item is " + recommendedItem.getItem());
  	System.out.println("value is " + recommendedItem.getValue());   
}
String jsonResponse = recommender.toString(); 

RecommenderSimilarity recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;		
long userId = 1;
int size = 2;
int howMany = 2;						
Recommender recommender = recommenderService.userBasedNeighborhoodBySimilarity(recommenderSimilarity, userId, size, howMany);  
ArrayList<Recommender.RecommendedItem> recommendedItemList =  recommender.getRecommendedItemList(); 
for(Recommender.RecommendedItem recommendedItem : recommendedItemList)
{
    System.out.println("item is " + recommendedItem.getItem());
    System.out.println("value is " + recommendedItem.getValue());
}
String jsonResponse = recommender.toString(); 

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String recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;		
Int64 userId = 1;
int size = 2;
int howMany = 2;
Recommender recommender = recommenderService.UserBasedNeighborhoodBySimilarity(recommenderSimilarity, userId, size, howMany);  
IList<Recommender.RecommendedItem> recommendedItemList =  recommender.GetRecommendedItemList(); 
for(int i = 0; i < recommendedItemList.Count; i++)
{
	Console.WriteLine("item is " + recommendedItemList[0].GetItem());
    Console.WriteLine("value is " + recommendedItemList[0].GetValue());
}
String jsonResponse = recommender.ToString(); 

recommenderSimilarity = RecommenderSimilarity::EUCLIDEAN_DISTANCE;		
$userId = 1;
$size = 2;
$howMany = 2;
$recommender = $recommenderService->userBasedNeighborhoodBySimilarity($recommenderSimilarity,$userId, $size, $howMany); 
$recommendedItemList =  $recommender->getRecommendedItemList(); 
foreach( $recommendedItemList as $recommendedItem ){
print_r("value is" . $recommendedItem->getValue());
print_r("item is" . $recommendedItem->getItem());
}
$jsonResponse = $recommender->toString();

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User Based Neighbourhood For All

User based recommendations based on Neighborhood for All Users. Recommendations and found based similar users in the Neighborhood of the given user. The size of the neighborhood can be found.

Required Parameters

size - Size of the Neighborhood. howMany - Specifies that how many recommendations have to be found.

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int size = 1;
int howMany = 1;						
Recommender recommender = recommenderService.userBasedNeighborhoodForAll(size, howMany);  
boolean success = recommender.isResponseSuccess();  
String jsonResponse = recommender.toString(); 

public class Callback : App42Callback  
{
int size = 1;
int howMany = 1;
recommenderService.UserBasedNeighborhoodForAll(size, howMany,this); 
void App42Callback.OnException(App42Exception exception)  
	{  
		Console.WriteLine("Exception Message");  
	}  
	void App42Callback.OnSuccess(Object response)  
	{  
		Recommender recommender = (Recommender) response;     
		String jsonResponse = recommender.ToString();  
	}   
}  

int size = 1;
int howMany = 1;						
Recommender *recommender =  [recommenderService userBasedNeighborhoodForAll:size howMany:howMany]
NSMutableArray *recommendedItemList =  recommender.recommendedItemList; 
for(RecommendedItem *recommendedItem in recommendedItemList){
	NSLog(@"item is = %@",recommendedItem.userId);
	NSLog(@"value is = %f",recommendedItem.value);
}
NSString *jsonResponse = [recommender toString];

int size = 1;
int howMany = 1;		
Recommender recommender = recommenderService.userBasedNeighborhoodForAll(size, howMany);  
Vector recommendedItemList =  recommender.getRecommendedItemList(); 
for(int i=0;i< recommendedItemList.size();i++)
{  
	Recommender.RecommendedItem recommendedItem = (Recommender.RecommendedItem) recommendedItemList.elementAt(i);  
	System.out.println("userId is " + recommendedItem.getUserId());
	System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());
}
String jsonResponse = recommender.toString();  

int size = 1;
int howMany = 1;						
Recommender recommender = recommenderService.userBasedNeighborhoodForAll(size, howMany);  
boolean success = recommender.isResponseSuccess();  
String jsonResponse = recommender.toString(); 

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int size = 1;
int howMany = 1;		
Recommender recommender = recommenderService.UserBasedNeighborhoodForAll(size, howMany);  
IList<Recommender.RecommendedItem> recommendedItemList =  recommender.GetRecommendedItemList(); 
for(int i = 0; i < recommendedItemList.Count; i++)
{
	Console.WriteLine("userId is " + recommendedItemList[0].GetUserId());
	Console.WriteLine("item is " + recommendedItemList[0].GetItem());
	Console.WriteLine("value is " + recommendedItemList[0].GetValue());
}
String jsonResponse = recommender.ToString(); 

$size = 1;
$howMany = 1;
$recommender = $recommenderService->userBasedNeighborhoodForAll($size, $howMany);  
$success = $recommender->isResponseSuccess();  
$jsonResponse = $recommender->toString(); 

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User Based Neighbourhood By Similarity For All

User based recommendations based on Neighborhood and Similarity for all Users. Recommendations and found based similar users in the Neighborhood with the specified Similarity Algorithm. Algorithim can be specified using the constants Recommender.EUCLIDEAN_DISTANCE and Recommender.PEARSON_CORRELATION

Required Parameters recommenderSimilarity - Similarity algorithm e.g. Recommender.EUCLIDEAN_DISTANCE and Recommender.PEARSON_CORRELATION size - Size of the Neighborhood howMany - Specifies that how many recommendations have to be found

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RecommenderSimilarity recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;
int size = 2;
int howMany = 3;						
Recommender recommender = recommenderService.userBasedNeighborhoodBySimilarityForAll(recommenderSimilarity, size, howMany); 
ArrayList<Recommender.RecommendedItem> recommendedItemList =  recommender.getRecommendedItemList(); 
for(Recommender.RecommendedItem recommendedItem : recommendedItemList)
{
    System.out.println("userId is " + recommendedItem.getUserId());
	System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());
} 
String jsonResponse = recommender.toString(); 

public class Callback : App42Callback  
{
String recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;
int size = 2;
int howMany = 3;
recommenderService.UserBasedNeighborhoodBySimilarityForAll(recommenderSimilarity, size, howMany,this); 
void App42Callback.OnException(App42Exception exception)  
	{  
		Console.WriteLine("Exception Message");  
	}  
	void App42Callback.OnSuccess(Object response)  
	{  
		Recommender recommender = (Recommender) response;     
		String jsonResponse = recommender.ToString();  
	}   
}  

NSString *recommenderSimilarity = EUCLIDEAN_DISTANCE;
int size = 2;
int howMany = 3;						
Recommender *recommender =  [recommenderService userBasedNeighborhoodBySimilarityForAll:EUCLIDEAN_DISTANCE size:size howMany:howMany] 
NSMutableArray *recommendedItemList =  recommender.recommendedItemList; 
for(RecommendedItem *recommendedItem in recommendedItemList){
	NSLog(@"item is = %@",recommendedItem.item);
	NSLog(@"userId is = %@",recommendedItem.userId);
	NSLog(@"value is = %f",recommendedItem.value);
}
NSString *jsonResponse = [recommender toString];

String recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;
int size = 2;
int howMany = 3;						
Recommender recommender = recommenderService.userBasedNeighborhoodBySimilarityForAll(recommenderSimilarity,size, howMany);
Vector recommendedItemList =  recommender.getRecommendedItemList(); 
for(int i=0;i< recommendedItemList.size();i++)
{  
	Recommender.RecommendedItem recommendedItem = (Recommender.RecommendedItem) recommendedItemList.elementAt(i);  
	System.out.println("userId is " + recommendedItem.getUserId());
	System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());
}
String jsonResponse = recommender.toString();

RecommenderSimilarity recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;
int size = 2;
int howMany = 3;						
Recommender recommender = recommenderService.userBasedNeighborhoodBySimilarityForAll(recommenderSimilarity, size, howMany); 
ArrayList<Recommender.RecommendedItem> recommendedItemList =  recommender.getRecommendedItemList(); 
for(Recommender.RecommendedItem recommendedItem : recommendedItemList)
{
    System.out.println("userId is " + recommendedItem.getUserId());
	System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());
} 
String jsonResponse = recommender.toString(); 

Coming Soon

Not Available

String recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;
int size = 2;
int howMany = 3;		
Recommender recommender = recommenderService.UserBasedNeighborhoodBySimilarityForAll(recommenderSimilarity, size, howMany); 
IList<Recommender.RecommendedItem> recommendedItemList =  recommender.GetRecommendedItemList(); 
for(int i = 0; i < recommendedItemList.Count; i++)
{
	Console.WriteLine("userId is " + recommendedItemList[0].GetUserId());
	Console.WriteLine("item is " + recommendedItemList[0].GetItem());
	Console.WriteLine("value is " + recommendedItemList[0].GetValue());
}
String jsonResponse = recommender.ToString();

$recommenderSimilarity = RecommenderSimilarity::EUCLIDEAN_DISTANCE;
$size = 2;
$howMany = 3;		
$recommender = $recommenderService->userBasedNeighborhoodBySimilarityForAll($recommenderSimilarity,$size, $howMany);  
$recommendedItemList =  $recommender->getRecommendedItemList(); 
foreach( $recommendedItemList as $recommendedItem ){
print_r("value is" . $recommendedItem->getValue());
print_r("item is" . $recommendedItem->getItem());
}
$jsonResponse = $recommender->toString(); 

Coming Soon

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Coming Soon

User Based Threshold

User based neighborhood recommendations based on Threshold. Recommendations are found based on Threshold where thereshold represents similarity threshold where user are at least that similar. Threshold values can vary from -1 to 1.

Required Parameters

userId - The user Id for whom recommendations have to be found threshold - Threshold size. Values can vary from -1 to 1 howMany - Specifies that how many recommendations have to be found

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long userId = 1;
double threshold = 0.5;
int howMany = 2;					
Recommender recommender = recommenderService.userBasedThreshold(userId, threshold, howMany); 
ArrayList<Recommender.RecommendedItem> recommendedItemList =  recommender.getRecommendedItemList(); 
for(Recommender.RecommendedItem recommendedItem : recommendedItemList)
{
	System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());
}
String jsonResponse = recommender.toString(); 

public class Callback : App42Callback  
{
long userId = 1;
double threshold = 0.5;
int howMany = 2;	
recommenderService.UserBasedThreshold(userId, threshold, howMany,this);
void App42Callback.OnException(App42Exception exception)  
	{  
		Console.WriteLine("Exception Message");  
	}  
	void App42Callback.OnSuccess(Object response)  
	{  
		Recommender recommender = (Recommender) response;     
		String jsonResponse = recommender.ToString();  
	}   
}  

long userId = 3;
int size = 4567788 ;
int howMany = 2;
Recommender *recommender =  [recommenderService userBasedNeighborhood:userId size:size howMany:howMany]; 
NSMutableArray *recommendedItemList =  recommender.recommendedItemList; 
for(RecommendedItem *recommendedItem in recommendedItemList){
	NSLog(@"item is = %@",recommendedItem.userId);
	NSLog(@"value is = %f",recommendedItem.value);
}
NSString *jsonResponse = [recommender toString];

long userId = 1;
double threshold = 0.5;
int howMany = 2;		
Recommender recommender = recommenderService.userBasedThreshold(userId, threshold, howMany);  
Vector recommendedItemList =  recommender.getRecommendedItemList(); 
for(int i=0;i< recommendedItemList.size();i++)
{  
	Recommender.RecommendedItem recommendedItem = (Recommender.RecommendedItem) recommendedItemList.elementAt(i);  
	System.out.println("item is " + recommendedItem.getItem());
    System.out.println("value is " + recommendedItem.getValue());     
}
String jsonResponse = recommender.toString();

long userId = 1;
double threshold = 0.5;
int howMany = 2;					
Recommender recommender = recommenderService.userBasedThreshold(userId, threshold, howMany); 
ArrayList<Recommender.RecommendedItem> recommendedItemList =  recommender.getRecommendedItemList(); 
for(Recommender.RecommendedItem recommendedItem : recommendedItemList)
{
	System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());
}
String jsonResponse = recommender.toString(); 

Coming Soon

Not Available

Int64 userId = 1;
Double threshold = 0.5;
int howMany = 2;	
Recommender recommender = recommenderService.UserBasedThreshold(userId, threshold, howMany); 
IList<Recommender.RecommendedItem> recommendedItemList =  recommender.GetRecommendedItemList(); 
for(int i = 0; i < recommendedItemList.Count; i++)
{
	Console.WriteLine("item is " + recommendedItemList[0].GetItem());
	Console.WriteLine("value is " + recommendedItemList[0].GetValue());
}
String jsonResponse = recommender.ToString(); 

$userId = 1;
$threshold = 0.5;
$howMany = 2;
$recommender = $recommenderService-> userBasedThreshold($userId, $threshold, $howMany);  
$recommendedItemList =  $recommender-> getRecommendedItemList(); 
foreach( $recommendedItemList as $recommendedItem ){
print_r("value is" . $recommendedItem->getValue());
print_r("item is" . $recommendedItem->getItem());
}
$jsonResponse = $recommender->toString(); 

Coming Soon

Coming Soon

Coming Soon 

Coming Soon

User Based Threshold For All

User based neighborood recommendations based on Threshold for all Users. Recommendations are found based on Threshold where thereshold represents similarity threshold where user are at least that similar. Threshold values can vary from -1 to 1

Required Parameters

threshold - Threshold size. Values can vary from -1 to 1 howMany - Specifies that how many recommendations have to be found

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double threshold = 1;
int howMany = 3;
Recommender recommender = recommenderService.userBasedThresholdForAll(threshold, howMany);  
boolean success = recommender.isResponseSuccess(); 
String jsonResponse = recommender.toString(); 

public class Callback : App42Callback  
{
double threshold = 1;
int howMany = 3;
recommenderService.UserBasedThresholdForAll(threshold, howMany,this); 
void App42Callback.OnException(App42Exception exception)  
	{  
		Console.WriteLine("Exception Message");  
	}  
	void App42Callback.OnSuccess(Object response)  
	{  
		Recommender recommender = (Recommender) response;     
		String jsonResponse = recommender.ToString();  
	}   
}  

double threshold = 1;
int howMany = 3;
Recommender *recommender =  [recommenderService userBasedThresholdForAll:threshold howMany:howMany];
NSMutableArray *recommendedItemList =  recommender.recommendedItemList; 
for(RecommendedItem *recommendedItem in recommendedItemList){
	NSLog(@"item is = %@",recommendedItem.item);
	NSLog(@"userId is = %@",recommendedItem.userId);
	NSLog(@"value is = %f",recommendedItem.value);
}
NSString *jsonResponse = [recommender toString];               

double threshold = 1;
int howMany = 3;
Recommender recommender = recommenderService.userBasedThresholdForAll(threshold, howMany);
Vector recommendedItemList =  recommender.getRecommendedItemList(); 
for(int i=0;i< recommendedItemList.size();i++)
{  
	Recommender.RecommendedItem recommendedItem = (Recommender.RecommendedItem) recommendedItemList.elementAt(i);  
	System.out.println("userId is " + recommendedItem.getUserId());
	System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());
}
String jsonResponse = recommender.toString(); 

double threshold = 1;
int howMany = 3;
Recommender recommender = recommenderService.userBasedThresholdForAll(threshold, howMany);  
boolean success = recommender.isResponseSuccess(); 
String jsonResponse = recommender.toString(); 

Coming Soon

Not Available

 
Double threshold = 1;
int howMany = 3;
Recommender recommender = recommenderService.UserBasedThresholdForAll(threshold, howMany);  
IList<Recommender.RecommendedItem> recommendedItemList =  recommender.GetRecommendedItemList(); 
for(int i = 0; i < recommendedItemList.Count; i++)
{
	Console.WriteLine("userId is " + recommendedItemList[0].GetUserId());
	Console.WriteLine("item is " + recommendedItemList[0].GetItem());
	Console.WriteLine("value is " + recommendedItemList[0].GetValue());
}
String jsonResponse = recommender.ToString(); 

$threshold = 1;
$howMany = 3;
$recommender = $recommenderService->userBasedThresholdForAll($threshold, $howMany);  
$recommendedItemList =  $recommender->getRecommendedItemList(); 
$success = $recommender->isResponseSuccess();
$jsonResponse = $recommender->toString(); 

Coming Soon

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Coming Soon

User Based Threshold By Similarity

User based neighborood recommendations based on Threshold. Recommendations are found based on Threshold where thereshold represents similarity threshold where user are at least that similar. Threshold values can vary from -1 to 1

Required Parameters

recommenderSimilarity - Similarity algorithm e.g. Recommender.EUCLIDEAN_DISTANCE and Recommender.PEARSON_CORRELATION. userId - The user Id for whom recommendations have to be found threshold - Threshold size. Values can vary from -1 to 1 howMany - Specifies that how many recommendations have to be found

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RecommenderSimilarity recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;
long userId = 1;
double threshold = 0.5;
int howMany = 1;
Recommender recommender = recommenderService.userBasedThresholdBySimilarity(recommenderSimilarity, userId, threshold, howMany); 
ArrayList<Recommender.RecommendedItem> recommendedItemList =  recommender.getRecommendedItemList(); 
for(Recommender.RecommendedItem recommendedItem : recommendedItemList)
{
    System.out.println("item is " + recommendedItem.getItem());
  	System.out.println("value is " + recommendedItem.getValue());
}
String jsonResponse = recommender.toString(); 

public class Callback : App42Callback  
{
String recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;		
long userId = 1;
int size = 2;
int howMany = 2;	
recommenderService.UserBasedNeighborhoodBySimilarity(recommenderSimilarity,userId, size, howMany,this);
void App42Callback.OnException(App42Exception exception)  
	{  
		Console.WriteLine("Exception Message");  
	}  
	void App42Callback.OnSuccess(Object response)  
	{  
		Recommender recommender = (Recommender) response;     
		String jsonResponse = recommender.ToString();  
	}   
}  

RecommenderSimilarity recommenderSimilarity = EUCLIDEAN_DISTANCE;
long userId = 1;
double threshold = 0.5;
int howMany = 1;

Recommender *recommender =  [recommenderService userBasedThresholdBySimilarity:EUCLIDEAN_DISTANCE userId:userId threshold:threshold howMany:howMany]; 
NSMutableArray *recommendedItemList =  recommender.recommendedItemList; 
for(RecommendedItem *recommendedItem in recommendedItemList){
	NSLog(@"item is = %@",recommendedItem.userId);
	NSLog(@"value is = %f",recommendedItem.value);
}
NSString *jsonResponse = [recommender toString];

 String recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;
long userId = 1;
double threshold = 0.5;
int howMany = 1;
Recommender recommender = recommenderService.userBasedThresholdBySimilarity(recommenderSimilarity,userId, threshold, howMany); 
Vector recommendedItemList =  recommender.getRecommendedItemList(); 
for(int i=0;i< recommendedItemList.size();i++)
{  
	Recommender.RecommendedItem recommendedItem = (Recommender.RecommendedItem) recommendedItemList.elementAt(i);  
	System.out.println("item is " + recommendedItem.getItem());
  	System.out.println("value is " + recommendedItem.getValue());   
}
String jsonResponse = recommender.toString(); 

RecommenderSimilarity recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;
long userId = 1;
double threshold = 0.5;
int howMany = 1;
Recommender recommender = recommenderService.userBasedThresholdBySimilarity(recommenderSimilarity, userId, threshold, howMany); 
ArrayList<Recommender.RecommendedItem> recommendedItemList =  recommender.getRecommendedItemList(); 
for(Recommender.RecommendedItem recommendedItem : recommendedItemList)
{
    System.out.println("item is " + recommendedItem.getItem());
  	System.out.println("value is " + recommendedItem.getValue());
}
String jsonResponse = recommender.toString(); 

Coming Soon

Not Available

 String recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;
Int64 userId = 1;
Double threshold = 0.5;
int howMany = 1;
Recommender recommender = recommenderService.UserBasedThresholdBySimilarity(recommenderSimilarity, userId, threshold, howMany); 
IList<Recommender.RecommendedItem> recommendedItemList =  recommender.GetRecommendedItemList(); 
for(int i = 0; i < recommendedItemList.Count; i++)
{
	Console.WriteLine("item is " + recommendedItemList[0].GetItem());
  	Console.WriteLine("value is " + recommendedItemList[0].GetValue());
}
String jsonResponse = recommender.ToString();

recommenderSimilarity = RecommenderSimilarity::EUCLIDEAN_DISTANCE;
$userId = 1;
$threshold = 0.5;
$howMany = 1;					
$recommender = $recommenderService->userBasedThresholdBySimilarity($recommenderSimilarity,$userId, $threshold, $howMany);  
$recommendedItemList =  $recommender->getRecommendedItemList(); 
foreach( $recommendedItemList as $recommendedItem ){
print_r("value is" . $recommendedItem->getValue());
print_r("item is" . $recommendedItem->getItem());
}
$jsonResponse = $recommender->toString();

Coming Soon

Coming Soon

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Coming Soon

User Based Threshold By Similarity For All

User based neighborood recommendations based on Threshold for All. Recommendations are found based on Threshold where thereshold represents similarity threshold where user are at least that similar. Threshold values can vary from -1 to 1

Required Parameters

recommenderSimilarity - Similarity algorithm e.g. Recommender.EUCLIDEAN_DISTANCE and Recommender.PEARSON_CORRELATION. threshold - Threshold size. Values can vary from -1 to 1. howMany - Specifies that how many recommendations have to be found.

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RecommenderSimilarity recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;
double threshold = 0.5;
int howMany = 4;
Recommender recommender = recommenderService.userBasedThresholdBySimilarityForAll(recommenderSimilarity, threshold, howMany); 
ArrayList<Recommender.RecommendedItem> recommendedItemList =  recommender.getRecommendedItemList(); 
for(Recommender.RecommendedItem recommendedItem : recommendedItemList)
{
    System.out.println("userId is " + recommendedItem.getUserId());
	System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());
}	
String jsonResponse = recommender.toString();  

public class Callback : App42Callback  
{
String recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;
double threshold = 0.5;
int howMany = 4;
recommenderService.UserBasedThresholdBySimilarityForAll(recommenderSimilarity, threshold, howMany,this); 
void App42Callback.OnException(App42Exception exception)  
	{  
		Console.WriteLine("Exception Message");  
	}  
	void App42Callback.OnSuccess(Object response)  
	{  
		Recommender recommender = (Recommender) response;     
		String jsonResponse = recommender.ToString();  
	}   
}     

NSString *recommenderSimilarity = EUCLIDEAN_DISTANCE;
double threshold = 0.5;
int howMany = 4;
Recommender *recommender =  [recommenderService userBasedThresholdBySimilarityForAll:EUCLIDEAN_DISTANCE threshold:threshold howMany:howMany];
NSMutableArray *recommendedItemList =  recommender.recommendedItemList; 
for(RecommendedItem *recommendedItem in recommendedItemList){
	NSLog(@"item is = %@",recommendedItem.item);
	NSLog(@"userId is = %@",recommendedItem.userId);
	NSLog(@"value is = %f",recommendedItem.value);
}
NSString *jsonResponse = [recommender toString];

String recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;
double threshold = 0.5;
int howMany = 4;
Recommender recommender = recommenderService.userBasedThresholdBySimilarityForAll(recommenderSimilarity, preferenceFileName, threshold, howMany);  
Vector recommendedItemList =  recommender.getRecommendedItemList(); 
for(int i=0;i< recommendedItemList.size();i++)
{  
	Recommender.RecommendedItem recommendedItem = (Recommender.RecommendedItem) recommendedItemList.elementAt(i);  
	System.out.println("userId is " + recommendedItem.getUserId());
	System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());
}
String jsonResponse = recommender.toString(); 

RecommenderSimilarity recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;
double threshold = 0.5;
int howMany = 4;
Recommender recommender = recommenderService.userBasedThresholdBySimilarityForAll(recommenderSimilarity, threshold, howMany); 
ArrayList<Recommender.RecommendedItem> recommendedItemList =  recommender.getRecommendedItemList(); 
for(Recommender.RecommendedItem recommendedItem : recommendedItemList)
{
    System.out.println("userId is " + recommendedItem.getUserId());
	System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());
}	
String jsonResponse = recommender.toString(); 

Coming Soon

Not Available

 String recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;
Double threshold = 0.5;
int howMany = 4;
Recommender recommender = recommenderService.UserBasedThresholdBySimilarityForAll(recommenderSimilarity, threshold, howMany); 
IList<Recommender.RecommendedItem> recommendedItemList =  recommender.GetRecommendedItemList(); 
for(int i = 0; i < recommendedItemList.Count; i++)
{
	Console.WriteLine("userId is " + recommendedItemList[0].GetUserId());
	Console.WriteLine("item is " + recommendedItemList[0].GetItem());
	Console.WriteLine("value is " + recommendedItemList[0].GetValue());
}
String jsonResponse = recommender.ToString(); 

$recommenderSimilarity = RecommenderSimilarity::EUCLIDEAN_DISTANCE;
$threshold = 0.5;
$howMany = 4;
$recommender = $recommenderService->userBasedThresholdBySimilarityForAll($recommenderSimilarity,$threshold, $howMany); 
$recommendedItemList =  $recommender->getRecommendedItemList(); 
foreach( $recommendedItemList as $recommendedItem ){
print("userId is" . $recommendedItem->getUserId());
print_r("value is" . $recommendedItem->getValue());
print_r("item is" . $recommendedItem->getItem());
}
$jsonResponse = $recommender->toString(); 

Coming Soon

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Coming Soon

Item Based

Item based recommendations. Recommendations and found based item similarity of the given user. The size of the neighborhood can be found.

Required Parameters

userId - The user Id for whom recommendations have to be found howMany - Specifies that how many recommendations have to be found

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long userId = 1;
int howMany = 1;
Recommender recommender = recommenderService.itemBased(userId, howMany); 
ArrayList<Recommender.RecommendedItem> recommendedItemList =  recommender.getRecommendedItemList(); 
for(Recommender.RecommendedItem recommendedItem : recommendedItemList)
{
    System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());
}
String jsonResponse = recommender.toString();  

public class Callback : App42Callback  
{
long userId = 1;
int howMany = 1;
recommenderService.ItemBased(preferenceFileName, userId, howMany,this); 
void App42Callback.OnException(App42Exception exception)  
	{  
		Console.WriteLine("Exception Message");  
	}  
	void App42Callback.OnSuccess(Object response)  
	{  
		Recommender recommender = (Recommender) response;     
		String jsonResponse = recommender.ToString();  
	}   
}             

long userId = 1;
int howMany = 1;
Recommender *recommender =  [recommenderService itemBased:preferenceFileName userId:userId howMany:howMany]; 
NSMutableArray *recommendedItemList =  recommender.recommendedItemList; 
for(RecommendedItem *recommendedItem in recommendedItemList){
	NSLog(@"item is = %@",recommendedItem.item);
	NSLog(@"value is = %f",recommendedItem.value);
}
NSString *jsonResponse = [recommender toString];

long userId = 1;
int howMany = 1;
Recommender recommender = recommenderService.itemBased(userId, howMany); 
Vector recommendedItemList =  recommender.getRecommendedItemList(); 
for(int i=0;i< recommendedItemList.size();i++)
{  
	Recommender.RecommendedItem recommendedItem = (Recommender.RecommendedItem) recommendedItemList.elementAt(i);  
	System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());     
}
String jsonResponse = recommender.toString();

long userId = 1;
int howMany = 1;
Recommender recommender = recommenderService.itemBased(userId, howMany); 
ArrayList<Recommender.RecommendedItem> recommendedItemList =  recommender.getRecommendedItemList(); 
for(Recommender.RecommendedItem recommendedItem : recommendedItemList)
{
    System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());
}
String jsonResponse = recommender.toString(); 

Coming Soon

Not Available

Int64 userId = 1;
int howMany = 1;
Recommender recommender = recommenderService.ItemBased(userId, howMany); 
IList<Recommender.RecommendedItem> recommendedItemList =  recommender.GetRecommendedItemList(); 
for(int i = 0; i < recommendedItemList.Count; i++)
{
	Console.WriteLine("item is " + recommendedItemList[0].GetItem());
	Console.WriteLine("value is " + recommendedItemList[0].GetValue());
}
String jsonResponse = recommender.ToString();  

$userId = 1;
$howMany = 1;
$recommender = $recommenderService->itemBased($userId, $howMany); 
$recommendedItemList =  $recommender->getRecommendedItemList(); 
foreach( $recommendedItemList as $recommendedItem ){
print_r("value is" . $recommendedItem->getValue());
print_r("item is" . $recommendedItem->getItem());
}
$jsonResponse = $recommender->toString(); 

Coming Soon

Coming Soon

Coming Soon 

Coming Soon

Item Based For All

Item based recommendations for all Users. Recommendations and found based item similarity of the given user. The size of the neighborhood can be found.

Required Parameters

howMany - Specifies that how many recommendations have to be found

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int howMany = 3;					
Recommender recommender = recommenderService.itemBasedForAll(howMany);  
ArrayList<Recommender.RecommendedItem> recommendedItemList =  recommender.getRecommendedItemList(); 
for(Recommender.RecommendedItem recommendedItem : recommendedItemList)
{
    System.out.println("userId is " + recommendedItem.getUserId());
	System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());
}
String jsonResponse = recommender.toString(); 

public class Callback : App42Callback  
{
int howMany = 3;
recommenderService.ItemBasedForAll(howMany,this);  
void App42Callback.OnException(App42Exception exception)  
	{  
		Console.WriteLine("Exception Message");  
	}  
	void App42Callback.OnSuccess(Object response)  
	{  
		Recommender recommender = (Recommender) response;     
		String jsonResponse = recommender.ToString();  
	}   
}                  

int howMany = 3;					
Recommender *recommender =  [recommenderService itemBasedForAll:howMany];
NSMutableArray *recommendedItemList =  recommender.recommendedItemList; 
for(RecommendedItem *recommendedItem in recommendedItemList){
	NSLog(@"item is = %@",recommendedItem.item);
	NSLog(@"userId is = %@",recommendedItem.userId);
	NSLog(@"value is = %f",recommendedItem.value);
}
NSString *jsonResponse = [recommender toString];

int howMany = 3;			
Recommender recommender = recommenderService.itemBasedForAll(howMany);
Vector recommendedItemList =  recommender.getRecommendedItemList(); 
for(int i=0;i< recommendedItemList.size();i++)
{  
	Recommender.RecommendedItem recommendedItem = (Recommender.RecommendedItem) recommendedItemList.elementAt(i);  
	System.out.println("userId is " + recommendedItem.getUserId());
	System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());
}
String jsonResponse = recommender.toString();

int howMany = 3;					
Recommender recommender = recommenderService.itemBasedForAll(howMany);  
ArrayList<Recommender.RecommendedItem> recommendedItemList =  recommender.getRecommendedItemList(); 
for(Recommender.RecommendedItem recommendedItem : recommendedItemList)
{
    System.out.println("userId is " + recommendedItem.getUserId());
	System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());
}
String jsonResponse = recommender.toString(); 

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 int howMany = 3;					
String recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;
int howMany = 5;
Recommender recommender = recommenderService.ItemBasedForAll(howMany); 
IList<Recommender.RecommendedItem> recommendedItemList =  recommender.GetRecommendedItemList(); 
for(int i = 0; i < recommendedItemList.Count; i++)
{
	Console.WriteLine("userId is " + recommendedItemList[0].GetUserId());
	Console.WriteLine("item is " + recommendedItemList[0].GetItem());
	Console.WriteLine("value is " + recommendedItemList[0].GetValue());
} 
String jsonResponse = recommender.ToString(); 

$howMany = 3;
$recommender = $recommenderService->itemBasedForAll($howMany);  
$recommendedItemList =  $recommender->getRecommendedItemList(); 
foreach( $recommendedItemList as $recommendedItem ){
print("userId is" . $recommendedItem->getUserId());
print_r("value is" . $recommendedItem->getValue());
print_r("item is" . $recommendedItem->getItem());
}
$jsonResponse = $recommender->toString(); 

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Item Based By Similarity

Item based recommendations. Recommendations and found based one item similarity. Similarity algorithm can be specified. of the given user. The size of the neighborhood can be found.

Required Parameters

recommenderSimilarity - Similarity algorithm e.g. Recommender.EUCLIDEAN_DISTANCE and Recommender.PEARSON_CORRELATION userId - The user Id for whom recommendations have to be found howMany - Specifies that how many recommendations have to be found

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RecommenderSimilarity recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;
long userId = 1;
int howMany = 1;
Recommender recommender = recommenderService.itemBasedBySimilarity(recommenderSimilarity, userId, howMany); 
ArrayList<Recommender.RecommendedItem> recommendedItemList =  recommender.getRecommendedItemList(); 
for(Recommender.RecommendedItem recommendedItem : recommendedItemList)
{
   	System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());
}
String jsonResponse = recommender.toString(); 

public class Callback : App42Callback  
{
String recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;
long userId = 1;
int howMany = 1;
recommenderService.ItemBasedBySimilarity(recommenderSimilarity, userId, howMany,this);  
void App42Callback.OnException(App42Exception exception)  
	{  
		Console.WriteLine("Exception Message");  
	}  
	void App42Callback.OnSuccess(Object response)  
	{  
		Recommender recommender = (Recommender) response;     
		String jsonResponse = recommender.ToString();  
	}   
}  

NSString *recommenderSimilarity = EUCLIDEAN_DISTANCE;
long userId = 1;
int howMany = 1;
Recommender *recommender =  [recommenderService itemBased:userId howMany:howMany];
NSMutableArray *recommendedItemList =  recommender.recommendedItemList; 
for(RecommendedItem *recommendedItem in recommendedItemList){
	NSLog(@"item is = %@",recommendedItem.item);
	NSLog(@"value is = %f",recommendedItem.value);
}
NSString *jsonResponse = [recommender toString];

String recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;
long userId = 1;
int howMany = 1;
Recommender recommender = recommenderService.itemBasedBySimilarity(recommenderSimilarity, userId, howMany); 
Vector recommendedItemList =  recommender.getRecommendedItemList(); 
for(int i=0;i< recommendedItemList.size();i++)
{  
	Recommender.RecommendedItem recommendedItem = (Recommender.RecommendedItem) recommendedItemList.elementAt(i);  
	System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());  
}
String jsonResponse = recommender.toString(); 

RecommenderSimilarity recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;
long userId = 1;
int howMany = 1;
Recommender recommender = recommenderService.itemBasedBySimilarity(recommenderSimilarity, userId, howMany); 
ArrayList<Recommender.RecommendedItem> recommendedItemList =  recommender.getRecommendedItemList(); 
for(Recommender.RecommendedItem recommendedItem : recommendedItemList)
{
   	System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());
}
String jsonResponse = recommender.toString(); 

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String recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;
Int64 userId = 1;
int howMany = 1;
Recommender recommender = recommenderService.ItemBasedBySimilarity(recommenderSimilarity, userId, howMany); 
IList<Recommender.RecommendedItem> recommendedItemList =  recommender.GetRecommendedItemList(); 
for(int i = 0; i < recommendedItemList.Count; i++)
{
	Console.WriteLine("item is " + recommendedItemList[0].GetItem());
	Console.WriteLine("value is " + recommendedItemList[0].GetValue());
}
String jsonResponse = recommender.ToString();

recommenderSimilarity = RecommenderSimilarity::EUCLIDEAN_DISTANCE;
$userId = 1;
$howMany = 1;
$recommender = $recommenderService->itemBasedBySimilarity($recommenderSimilarity,$userId, $howMany); 
$recommendedItemList =  $recommender->getRecommendedItemList(); 
foreach( $recommendedItemList as $recommendedItem ){
print_r("value is" . $recommendedItem->getValue());
print_r("item is" . $recommendedItem->getItem());
}
$jsonResponse = $recommender->toString();                             

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Item Based By Similarity For All

Item based recommendations for all Users. Recommendations and found based item similarity of the given user. The size of the neighborhood can be found.

Required Parameters

howMany - Specifies that how many recommendations have to be found.

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RecommenderSimilarity recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;
int howMany = 5;
Recommender recommender = recommenderService.itemBasedBySimilarityForAll(recommenderSimilarity, howMany);  
ArrayList<Recommender.RecommendedItem> recommendedItemList =  recommender.getRecommendedItemList(); 
for(Recommender.RecommendedItem recommendedItem : recommendedItemList)
{
    System.out.println("userId is " + recommendedItem.getUserId());
	System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());
}
String jsonResponse = recommender.toString();  

public class Callback : App42Callback  
{
String recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;
int howMany = 5;
recommenderService.ItemBasedBySimilarityForAll(recommenderSimilarity,howMany,this); 
OnSuccess(Object obj){
Recommender recommender = (Recommender) obj;
IList<Recommender.RecommendedItem> recommendedItemList =  recommender.GetRecommendedItemList(); 
	for(int i = 0; i < recommendedItemList.Count; i++)
	{
		Console.WriteLine("user Id is " + recommendedItemList[i].GetUserId());
		Console.WriteLine("item is " + recommendedItemList[i].GetItem());
		Console.WriteLine("value is " + recommendedItemList[i].GetValue());
	}
String jsonResponse = recommendedItemList.ToString();
}

   
NSString *recommenderSimilarity = EUCLIDEAN_DISTANCE;
long userId = 1;
int howMany = 1;
Recommender *recommender =  [recommenderService itemBased:userId howMany:howMany];
NSMutableArray *recommendedItemList =  recommender.recommendedItemList; 
for(RecommendedItem *recommendedItem in recommendedItemList){
	NSLog(@"item is = %@",recommendedItem.item);
	NSLog(@"value is = %f",recommendedItem.value);
}
NSString *jsonResponse = [recommender toString];

 
String recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;
int howMany = 5;
Recommender recommender = recommenderService.itemBasedBySimilarityForAll(recommenderSimilarity,howMany); 
Vector recommendedItemList =  recommender.getRecommendedItemList(); 
for(int i=0;i< recommendedItemList.size();i++)
{  
	Recommender.RecommendedItem recommendedItem = (Recommender.RecommendedItem) recommendedItemList.elementAt(i);  
	System.out.println("userId is " + recommendedItem.getUserId());
	System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());    
}
String jsonResponse = recommender.toString(); 

 RecommenderSimilarity recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;
int howMany = 5;
Recommender recommender = recommenderService.itemBasedBySimilarityForAll(recommenderSimilarity, howMany);  
ArrayList<Recommender.RecommendedItem> recommendedItemList =  recommender.getRecommendedItemList(); 
for(Recommender.RecommendedItem recommendedItem : recommendedItemList)
{
    System.out.println("userId is " + recommendedItem.getUserId());
	System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());
}
String jsonResponse = recommender.toString(); 

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String recommenderSimilarity = RecommenderSimilarity.EUCLIDEAN_DISTANCE;
int howMany = 5;
Recommender recommender = recommenderService.ItemBasedBySimilarityForAll(recommenderSimilarity, howMany);  
IList<Recommender.RecommendedItem> recommendedItemList =  recommender.GetRecommendedItemList(); 
for(int i = 0; i < recommendedItemList.Count; i++)
{
	Console.WriteLine("userId is " + recommendedItemList[0].GetUserId());
	Console.WriteLine("item is " + recommendedItemList[0].GetItem());
	Console.WriteLine("value is " + recommendedItemList[0].GetValue());
}
String jsonResponse = recommender.ToString();

$recommenderSimilarity = RecommenderSimilarity::EUCLIDEAN_DISTANCE;
$howMany = 5;
$recommender = $recommenderService->itemBasedBySimilarityForAll($recommenderSimilarity,$howMany);  
$recommendedItemList =  $recommender->getRecommendedItemList(); 
foreach( $recommendedItemList as $recommendedItem ){
print("userId is" . $recommendedItem->getUserId());
print_r("value is" . $recommendedItem->getValue());
print_r("item is" . $recommendedItem->getItem());
}
$jsonResponse = $recommender->toString(); 

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Slope One For All

Recommendations based on SlopeOne Algorithm for all Users.

Required Parameters

howMany - Specifies that how many recommendations have to be found

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int howMany = 3;
Recommender recommender = recommenderService.slopeOneForAll(howMany);  
ArrayList<Recommender.RecommendedItem> recommendedItemList =  recommender.getRecommendedItemList(); 
for(Recommender.RecommendedItem recommendedItem : recommendedItemList)
{
    System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());
}
String jsonResponse = recommender.toString(); 

public class Callback : App42Callback  
{
int howMany = 3;
recommenderService.SlopeOneForAll(howMany,this);  
	void App42Callback.OnException(App42Exception exception)  
	{  
		Console.WriteLine("Exception Message");  
	}  
	void App42Callback.OnSuccess(Object response)  
	{  
		Recommender recommender = (Recommender) response;     
		String jsonResponse = recommender.ToString();  
	}  
}  

int howMany = 3;
Recommender *recommender =  [recommenderService slopeOneForAll:howMany];
NSMutableArray *recommendedItemList =  recommender.recommendedItemList; 
for(RecommendedItem *recommendedItem in recommendedItemList){
	NSLog(@"item is = %@",recommendedItem.userId);
	NSLog(@"value is = %f",recommendedItem.value);
}
NSString *jsonResponse = [recommender toString];
                            

int howMany = 3;
Recommender recommender = recommenderService.slopeOneForAll(howMany);
Vector recommendedItemList =  recommender.getRecommendedItemList(); 
for(int i=0;i< recommendedItemList.size();i++)
{  
	Recommender.RecommendedItem recommendedItem = (Recommender.RecommendedItem) recommendedItemList.elementAt(i);  
	System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());  
} 
String jsonResponse = recommender.toString(); 

int howMany = 3;
Recommender recommender = recommenderService.slopeOneForAll(howMany);  
ArrayList<Recommender.RecommendedItem> recommendedItemList =  recommender.getRecommendedItemList(); 
for(Recommender.RecommendedItem recommendedItem : recommendedItemList)
{
    System.out.println("item is " + recommendedItem.getItem());
	System.out.println("value is " + recommendedItem.getValue());
}
String jsonResponse = recommender.toString(); 

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int howMany = 3;
Recommender recommender = recommenderService.SlopeOneForAll(howMany);  
IList<Recommender.RecommendedItem> recommendedItemList =  recommender.GetRecommendedItemList(); 
for(int i = 0; i < recommendedItemList.Count; i++)
{
	Console.WriteLine("item is " + recommendedItemList[0].GetItem());
	Console.WriteLine("value is " + recommendedItemList[0].GetValue());
}
String jsonResponse = recommender.ToString(); 

$howMany = 3;
$recommender = $recommenderService->slopeOneForAll($howMany);  
$recommendedItemList =  $recommender->getRecommendedItemList(); 
foreach( $recommendedItemList as $recommendedItem ){
print_r("value is" . $recommendedItem->getValue());
print_r("item is" . $recommendedItem->getItem());
}
$jsonResponse = $recommender->toString(); 

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Delete All Prefrences

Delete existing preference file.

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App42Response response = recommenderService.deleteAllPreferences(); 
boolean  success = response.isResponseSuccess();
String jsonResponse = response.toString(); 

public class Callback : App42Callback  
{  
recommenderService.DeleteAllPreferences(this);  
	void App42Callback.OnException(App42Exception exception)  
	{  
		Console.WriteLine("Exception Message");  
	}  
	void App42Callback.OnSuccess(Object object)  
	{  
		App42Response response = (App42Response) object;     
		String jsonResponse = response.ToString();  
	}  
}  

App42Response *response = [recommenderService deleteAllPreferences]; 
NSString *success = response.isResponseSuccess;
NSString *jsonResponse = [response toString];                             

App42Response response = recommenderService.deleteAllPreferences(); 
boolean  success = response.isResponseSuccess();
String jsonResponse = response.toString(); 

App42Response response = recommenderService.deleteAllPreferences(); 
boolean  success = response.isResponseSuccess();
String jsonResponse = response.toString(); 

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App42Response response = recommenderService.DeleteAllPreferences(); 
Boolean  success = response.IsResponseSuccess();
String jsonResponse = response.ToString(); 

$response = $recommenderService->deleteAllPreferences(); 
$success = $respons->isResponseSuccess();
$jsonResponse = $respons->toString(); 

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Exception Handling

The functions available under Recommendation API can throw some exceptions in abnormal conditions. Example of the same has been given below. E.g. If App developer is uploading the Preference File which does not have any data, the loadPreferenceFile function will throw the App42Exception (as shown below) with message as “Bad Request” and the appErrorCode as “2805” and the details as “Preference Data is not valid.”

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try
{
	App42Response response = recommenderService.loadPreferenceFile("Your File Path");
}
catch(App42Exception ex) 
{
	int appErrorCode = ex.getAppErrorCode();
	int httpErrorCode = ex.getHttpErrorCode();
	if(appErrorCode == 2805)
	{
		// Handle here for Bad Request (Preference Data is not valid.)
	}
	else if(appErrorCode == 1401)
	{
		// handle here for Client is not authorized
	}
	else if(appErrorCode == 1500)
	{
		// handle here for Internal Server Error
	}
	String jsonText = ex.getMessage(); /* returns the Exception text in JSON format. (as shown below)*/	
}                          

public class Callback : App42Callback  
{
recommenderService.loadPreferenceFile("Your File Path",this)
	void App42Callback.OnException(App42Exception exception)  
	{  
		int appErrorCode = exception.GetAppErrorCode();
		int httpErrorCode = exception.GetHttpErrorCode();
		if(appErrorCode == 2805)
		{
			// Handle here for Bad Request (Preference Data is not valid.)
		}
		else if(appErrorCode == 1401)
		{
			// handle here for Client is not authorized
		}
		else if(appErrorCode == 1500)
		{
			// handle here for Internal Server Error
		}
	String jsonText = exception.GetMessage();  
	}  
	void App42Callback.OnSuccess(Object object)  
	{  
		App42Response response = (App42Response) object;
		String jsonResponse = response.ToString();  
	}  
}  

	
@try
{
	App42Response *response = [recommenderService loadPreferenceFile:@"Your File Path"];
} @catch(App42Exception *exception) {
	int appErrorCode = exception.appErrorCode;
	int httpErrorCode = exception.httpErrorCode;
	if(appErrorCode == 2805)
	{
		// Handle here for Bad Request (Preference Data is not valid.)
	}
	else if(appErrorCode == 1401)
	{
		// handle here for Client is not authorized
	}
	else if(appErrorCode == 1500)
	{
		// handle here for Internal Server Error
	}
	NSString *jsonText = exception.reason; 	
}                        

try
{
	App42Response response = recommenderService.loadPreferenceFile("Your File Path");
}
catch(App42Exception ex) 
{
	int appErrorCode = ex.getAppErrorCode();
	int httpErrorCode = ex.getHttpErrorCode();
	if(appErrorCode == 2805)
	{
		// Handle here for Bad Request (Preference Data is not valid.)
	}
	else if(appErrorCode == 1401)
	{
		// handle here for Client is not authorized
	}
	else if(appErrorCode == 1500)
	{
		// handle here for Internal Server Error
	}
	String jsonText = ex.getMessage(); /* returns the Exception text in JSON format. (as shown below)*/	
}                          

try
{
	App42Response response = recommenderService.loadPreferenceFile("Your File Path");
}
catch(App42Exception ex) 
{
	int appErrorCode = ex.getAppErrorCode();
	int httpErrorCode = ex.getHttpErrorCode();
	if(appErrorCode == 2805)
	{
		// Handle here for Bad Request (Preference Data is not valid.)
	}
	else if(appErrorCode == 1401)
	{
		// handle here for Client is not authorized
	}
	else if(appErrorCode == 1500)
	{
		// handle here for Internal Server Error
	}
	String jsonText = ex.getMessage(); /* returns the Exception text in JSON format. (as shown below)*/	
}                          

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try
{
	App42Response response = recommenderService.LoadPreferenceFile("Your File Path");
}
catch(App42Exception ex) 
{
	int appErrorCode = ex.GetAppErrorCode();
	int httpErrorCode = ex.GetHttpErrorCode();
	if(appErrorCode == 2805)
	{
		// Handle here for Bad Request (Preference Data is not valid.)
	}
	else if(appErrorCode == 1401)
	{
		// handle here for Client is not authorized
	}
	else if(appErrorCode == 1500)
	{
		// handle here for Internal Server Error
	}
	String jsonText = ex.GetMessage(); /* returns the Exception text in JSON format. (as shown below)*/	
}                              

try
{
	$response = $recommenderService->loadPreferenceFile("Your File Path")
} 
catch(App42Exception $exception) 
{
	$appErrorCode =$exception->getAppErrorCode();
	$httpErrorCode = $exception->getHttpErrorCode();
	if($appErrorCode == 2805)
	{
		// Handle here for Bad Request (Preference Data is not valid.)
	}
	else if($appErrorCode == 1401)
	{
		// handle here for Client is not authorized
	}
	else if($appErrorCode == 1500)
	{
		// handle here for Internal Server Error
	}
	$jsonText = $exception->getMessage(); 	
}

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Error Codes

Functions in User API might throw exceptions with following HTTP and Application Error Codes (along with their descriptions):

1400 - BAD REQUEST - The Request parameters are invalid 1401 - UNAUTHORIZED - Client is not authorized 1500 - INTERNAL SERVER ERROR - Internal Server Error. Please try again. 2800 - NOT FOUND - Preferences does not exist. 2801 - NOT FOUND - There are no recommendations for optimize values for 'size' and 'howMany'. 2802 - BAD REQUEST - InvalidArgumentException: '@e.getMessage()'. 2803 - NOT FOUND - There are no recommendations for optimize values for 'threshold' and 'howMany'. 2804 - BAD REQUEST - InvalidArgumentException: NoSuchUserException : UserId '@e.getMessage()'. 2805 - BAD REQUEST - Preference Data is not valid.