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README.md

Recombee API Client

A Java client (SDK) for easy use of the Recombee recommendation API.

If you don't have an account at Recombee yet, you can create a free account here.

Documentation of the API can be found at docs.recombee.com.

Installation

The client is available in the Maven Central Repository, so you just need to add the following <dependency> entry to your project's POM:

    <dependency>
        <groupId>com.recombee</groupId>
        <artifactId>api-client</artifactId>
        <version>3.0.0</version>
    </dependency>

Examples

Basic example

Examples are located in src/examples.

package com.recombee.api_client.examples;

import com.recombee.api_client.RecombeeClient;
import com.recombee.api_client.api_requests.*;
import com.recombee.api_client.bindings.RecommendationResponse;
import com.recombee.api_client.bindings.Recommendation;
import com.recombee.api_client.exceptions.ApiException;

import java.util.ArrayList;
import java.util.Random;

public class BasicExample {
    public static void main(String[] args) {

        RecombeeClient client = new RecombeeClient("--my-database-id--", "--db-private-token--");
        try {
            client.send(new ResetDatabase());
            final int NUM = 100;
            // Generate some random purchases of items by users
            final double PROBABILITY_PURCHASED = 0.1;
            Random r = new Random();
            ArrayList<Request> addPurchaseRequests = new ArrayList<Request>();
            for (int i = 0; i < NUM; i++)
                for (int j = 0; j < NUM; j++)
                    if (r.nextDouble() < PROBABILITY_PURCHASED) {

                        AddPurchase request = new AddPurchase(String.format("user-%s", i),String.format("item-%s", j))
                                                    .setCascadeCreate(true); // Use cascadeCreate parameter to create
                                                                             // the yet non-existing users and items
                        addPurchaseRequests.add(request);
                    }

            System.out.println("Send purchases");
            client.send(new Batch(addPurchaseRequests)); //Use Batch for faster processing of larger data

            // Get 5 recommendations for user 'user-25'
            RecommendationResponse recommendationResponse = client.send(new RecommendItemsToUser("user-25", 5));
            System.out.println("Recommended items:");
            for(Recommendation rec: recommendationResponse) System.out.println(rec.getId());

        } catch (ApiException e) {
            e.printStackTrace();
            //use fallback
        }
    }
}

Using property values

package com.recombee.api_client.examples;

import com.recombee.api_client.RecombeeClient;
import com.recombee.api_client.api_requests.*;
import com.recombee.api_client.bindings.RecommendationResponse;
import com.recombee.api_client.bindings.Recommendation;
import com.recombee.api_client.bindings.SearchResponse;
import com.recombee.api_client.exceptions.ApiException;

import java.util.ArrayList;
import java.util.HashMap;
import java.util.Random;

public class ItemPropertiesExample {
    public static void main(String[] args) {

        RecombeeClient client = new RecombeeClient("--my-database-id--", "--db-private-token--");

        try {
            client.send(new ResetDatabase()); // Clear everything from the database

            /*
            We will use computers as items in this example
            Computers have four properties
              - price (floating point number)
              - number of processor cores (integer number)
              - description (string)
              - image (url of computer's photo)
            */

            client.send(new AddItemProperty("price", "double"));
            client.send(new AddItemProperty("num-cores", "int"));
            client.send(new AddItemProperty("description", "string"));
            client.send(new AddItemProperty("image", "image"));

            // Prepare requests for setting a catalog of computers
            final ArrayList<Request> requests = new ArrayList<Request>();
            final int NUM = 100;
            final Random rand = new Random();

            for(int i=0; i<NUM; i++)
            {
                final String itemId = String.format("computer-%s",i);
                final SetItemValues req = new SetItemValues(
                        itemId,
                        //values:
                        new HashMap<String, Object>() {{
                            put("price", 600.0 + 400*rand.nextDouble());
                            put("num-cores", 1 + rand.nextInt(7));
                            put("description", "Great computer");
                            put("image", String.format("http://examplesite.com/products/%s.jpg", itemId));
                        }}
                ).setCascadeCreate(true);  // Use cascadeCreate for creating item
                // with given itemId, if it doesn't exist;
                requests.add(req);
            }
            client.send(new Batch(requests)); // Send catalog to the recommender system

            // Generate some random purchases of items by users
            final double PROBABILITY_PURCHASED = 0.02;
            ArrayList<Request> addPurchaseRequests = new ArrayList<Request>();
            for (int i = 0; i < NUM; i++)
                for (int j = 0; j < NUM; j++)
                    if (rand.nextDouble() < PROBABILITY_PURCHASED) {
                        AddPurchase req = new AddPurchase(String.format("user-%s", i),String.format("computer-%s", j))
                                .setCascadeCreate(true); //use cascadeCreate to create the users
                        addPurchaseRequests.add(req);
                    }
            client.send(new Batch(addPurchaseRequests)); // Send purchases to the recommender system


            // Get 5 recommendations for user-42, who is currently viewing computer-6
            // Recommend only computers that have at least 3 cores
            RecommendationResponse recommendationResponse = client.send(
                new RecommendItemsToItem("computer-6", "user-42", 5)
                    .setFilter(" 'num-cores'>=3 "));
            System.out.println("Recommended items with at least 3 processor cores:");
            for(Recommendation rec: recommendationResponse) System.out.println(rec.getId());

            // Recommend only items that are more expensive then currently viewed item (up-sell)
            recommendationResponse = client.send(new RecommendItemsToItem("computer-6", "user-42", 5)
                    .setFilter(" 'price' > context_item[\"price\"] "));

            System.out.println("Recommended up-sell items:");
            for(Recommendation rec: recommendationResponse) System.out.println(rec.getId());


            // Filters, boosters and other settings can be set also in the Admin UI (admin.recombee.com)
            // when scenario is specified
            recommendationResponse = client.send(
                new RecommendItemsToItem("computer-6", "user-42", 5).setScenario("product_detail")
            );

            // Perform personalized full-text search with a user's search query (e.g. "computers")
            SearchResponse searchResponse = client.send(
              new SearchItems("user-42", "computers", 5)
              );
            System.out.println("Search matches:");
            for(Recommendation rec: searchResponse) System.out.println(rec.getId());

        } catch (ApiException e) {
            e.printStackTrace();
            //Use fallback
        }
    }
}

Exception handling

Various errors can occur while processing request, for example because of adding an already existing item or submitting interaction of nonexistent user without setCascadeCreate(true). These errors lead to throwing the ResponseException by the send method of the client. Another reason for throwing an exception is a timeout. ApiException is the base class of both ResponseException and ApiTimeoutException.

We are doing our best to provide the fastest and most reliable service, but production-level applications must implement a fallback solution since errors can always happen. The fallback might be, for example, showing the most popular items from the current category, or not displaying recommendations at all.

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