.NET client for easy use of the Recombee recommendation API
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README.md

Recombee API Client

A .NET 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

To install Recombee.ApiClient, run the following command in the Package Manager Console

Install-Package Recombee.ApiClient

Examples

Basic example

using System;
using System.Collections.Generic;
using System.Linq;
using Recombee.ApiClient;
using Recombee.ApiClient.ApiRequests;
using Recombee.ApiClient.Bindings;

public class BasicExample
{
    static int Main(string[] args)
    {
        RecombeeClient client = new RecombeeClient("--my-database-id--", "--my-secret-token--");

        try
        {
            const int NUM = 100;
            var userIds = Enumerable.Range(0, NUM).Select(i => String.Format("user-{0}", i));
            var itemIds = Enumerable.Range(0, NUM).Select(i => String.Format("item-{0}", i));
            // Generate some random purchases of items by users
            const double PROBABILITY_PURCHASED = 0.1;
            Random r = new Random();
            var purchases = new List<Request>();

            foreach(var userId in userIds) {
                purchases.AddRange(
                    itemIds.Where(_ => r.NextDouble() < PROBABILITY_PURCHASED)
                           .Select(itemId =>
                                    new AddPurchase(userId, itemId, cascadeCreate: true) // Use cascadeCreate parameter to create
                           )                                                             // the yet non-existing users and items
                );
            }

            Console.WriteLine("Send purchases");
            client.Send(new Batch(purchases)); //Use Batch for faster processing of larger data

            // Get 5 recommendations for user 'user-25'
            RecommendationResponse recommendationResponse = client.Send(new RecommendItemsToUser("user-25", 5));
            Console.WriteLine("Recommended items:");
            foreach(Recommendation rec in recommendationResponse.Recomms) Console.WriteLine(rec.Id);

        }
        catch(ApiException e)
        {
         Console.WriteLine(e.ToString());
         // Use fallback
        }

        return 0;
    }
}

Using property values

using System;
using System.Collections.Generic;
using System.Linq;
using Recombee.ApiClient;
using Recombee.ApiClient.ApiRequests;
using Recombee.ApiClient.Bindings;

public class PropertiesExample
{
    static int Main(string[] args)
    {
        RecombeeClient client = new RecombeeClient("--my-database-id--", "--my-secret-token--");

        /*
        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)
        */

         try
         {
            client.Send(new ResetDatabase());
            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
            var requests = new List<Request>();
            const int NUM = 100;
            Random r = new Random();
            var itemIds = Enumerable.Range(0, NUM).Select(i => String.Format("computer-{0}", i));
            foreach(var itemId in itemIds)
            {
                var req = new SetItemValues(
                    itemId,
                    //values:
                    new Dictionary<string, object>() {
                        {"price", 600.0 + 400*r.NextDouble()},
                        {"num-cores", 1 + r.Next(7)},
                        {"description", "Great computer"},
                        {"image", String.Format("http://examplesite.com/products/{0}.jpg", itemId)}
                    },
                    cascadeCreate: 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
            var userIds = Enumerable.Range(0, NUM).Select(i => String.Format("user-{0}", i));
            const double PROBABILITY_PURCHASED = 0.1;
            var purchases = new List<Request>();

            foreach(var userId in userIds) {
                purchases.AddRange(
                    itemIds.Where(_ => r.NextDouble() < PROBABILITY_PURCHASED)
                           .Select(itemId => 
                                    new AddPurchase(userId, itemId, cascadeCreate: true) // Use cascadeCreate parameter to create
                           )                                                             // the yet non-existing users and items                                                               
                );
            }

            client.Send(new Batch(purchases)); // Send purchases to the recommender system
        

            // Get 5 recommendations for user-42, who is currently viewing computer-6
            var recommendationResponse = client.Send(new RecommendItemsToItem("computer-6", "user-42", 5));
            Console.WriteLine("Recommended items:");
            foreach(Recommendation rec in recommendationResponse.Recomms) Console.WriteLine(rec.Id);


            // Recommend only computers that have at least 3 cores
            recommendationResponse = client.Send(new RecommendItemsToItem("computer-6", "user-42", 5,
                                            filter: " 'num-cores'>=3 "));
            Console.WriteLine("Recommended items with at least 3 processor cores:");
            foreach(Recommendation rec in recommendationResponse.Recomms) Console.WriteLine(rec.Id);

            // Recommend only items that are more expensive then currently viewed item (up-sell)
            recommendationResponse = client.Send(new RecommendItemsToItem("computer-6", "user-42", 5,
                                            filter: " 'price' > context_item[\"price\"] "));
            Console.WriteLine("Recommended up-sell items:");
            foreach(Recommendation rec in recommendationResponse.Recomms) Console.WriteLine(rec.Id);
         }
         catch(ApiException e)
         {
             Console.WriteLine(e.ToString());
             // Use fallback
         }
        return 0;
    }
}

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 cascadeCreate set to 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 TimeoutException.

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.