Skip to content

Latest commit

 

History

History
192 lines (152 loc) · 7.97 KB

README.md

File metadata and controls

192 lines (152 loc) · 7.97 KB

Recombee Async API Client (because it's no longer 2003)

Important Note:

  • Recombee SDK now supports Async. If you're only interested in Async support, then there's no need to use this fork.

What's different from the orginal SDK

  • All requests are now Async (SendAsync instead of Send)
  • Possibility to supply your own HttpClient instance in the constructor
  • Bug fixes

.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

Run the following command in the Package Manager Console

Install-Package RecombeeAsyncApiClient

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 async Task 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");
            await client.SendAsync(new Batch(purchases)); //Use Batch for faster processing of larger data

            // Get 5 recommendations for user 'user-25'
            RecommendationResponse recommendationResponse = await client.SendAsync(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 async Task 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
         {
            await client.SendAsync(new ResetDatabase());
            await client.SendAsync(new AddItemProperty("price", "double"));
            await client.SendAsync(new AddItemProperty("num-cores", "int"));
            await client.SendAsync(new AddItemProperty("description", "string"));
            await client.SendAsync(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);
            }
            
            await client.SendAsync(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                                                               
                );
            }

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

            // Get 5 recommendations for user-42, who is currently viewing computer-6
            var recommendationResponse = await client.SendAsync(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 = await client.SendAsync(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 = await client.SendAsync(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.