cs-recommender 1.0.1

Collaborative Filtering Recommender

Install-Package cs-recommender -Version 1.0.1
dotnet add package cs-recommender --version 1.0.1
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paket add cs-recommender --version 1.0.1
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cs-recommender

Recommender based on Hidden Factor Analysis Collaborative Filtering in .NET 4.6.1

Install

Run the following command to get the nuget package:

Install-Package cs-recommender 

Usage

The sample code show show how to train and use the Collaborative Filtering Recommender:

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using MathNet.Numerics.LinearAlgebra.Generic;
using MathNet.Numerics.LinearAlgebra.Double;
using System.IO;
using ContinuousOptimization.LocalSearch;
using ContinuousOptimization;
using Recommender.Utils;

namespace Recommender
{
    public class Program
    {

        protected static List<string> LoadMovies()
        {
            List<string> movie_titles = new List<string>();

            string line;
            using (StreamReader reader = new StreamReader("movie_ids.txt"))
            {
                while ((line = reader.ReadLine()) != null)
                {
                    string[] texts = line.Split(new char[] { ' ' });
                    StringBuilder sb = new StringBuilder();
                    bool first_index = true;
                    foreach (string text in texts)
                    {
                        if (string.IsNullOrEmpty(text)) continue;
                        if (first_index)
                        {
                            first_index = false;
                            continue;
                        }
                        sb.AppendFormat("{0} ", text);
                    }
                    string title = sb.ToString().Trim();
                    movie_titles.Add(title);
                }
            }

            return movie_titles;
        }

        public static void Main(string[] args)
        {
            List<string> movie_titles = LoadMovies();
            int num_movies = movie_titles.Count;

            // Step 1: create my ratings with missing entries
            double[] my_ratings = new double[num_movies];
            int[] my_ratings_r = new int[num_movies];
            for (int i = 0; i < num_movies; ++i)
            {
                my_ratings[i] = 0;
            }

            my_ratings[1] = 4;
            my_ratings[98] = 2;
            my_ratings[7] = 3;
            my_ratings[12] = 5;
            my_ratings[54] = 4;
            my_ratings[64] = 5;
            my_ratings[66] = 3;
            my_ratings[69] = 5;
            my_ratings[183] = 4;
            my_ratings[226] = 5;
            my_ratings[355] = 5;

            for (int i = 0; i < num_movies; ++i)
            {
                my_ratings_r[i] = my_ratings[i] > 0 ? 1 : 0;
            }

            // Step 2: load the current ratings of all users, i.e., Y and R
            List<List<double>> Y = DblDataTableUtil.LoadDataSet("Y.txt");
            List<List<int>> R = IntDataTableUtil.LoadDataSet("R.txt");

            int num_users;
            DblDataTableUtil.GetSize(Y, out num_movies, out num_users);


            // Step 3: insert my ratings into the existing Y and R (as the first column)
            num_users++;
            List<RatedItem> records = new List<RatedItem>();
            for (int i = 0; i < num_movies; ++i)
            {
                double[] rec_Y = new double[num_users];
                bool[] rec_R = new bool[num_users];
                for (int j = 0; j < num_users; ++j)
                {
                    if (j == 0)
                    {
                        rec_Y[j] = my_ratings[i];
                        rec_R[j] = my_ratings_r[i] == 1;
                    }
                    else
                    {
                        rec_Y[j] = Y[i][j - 1];
                        rec_R[j] = R[i][j - 1] == 1;
                    }
                }
                RatedItem rec = new RatedItem(null, rec_Y, rec_R);
                records.Add(rec);
            }

            int num_features = 10;

            double lambda = 10;
            CollaborativeFilteringRS<RatedItem> algorithm = new CollaborativeFilteringRS<RatedItem>();
            algorithm.Stepped += (s, step) =>
            {
                Console.WriteLine("#{0}: {1}", step, s.Cost);
            };
            algorithm.RegularizationLambda = lambda;
            algorithm.MaxLocalSearchIteration = 100;
            GradientDescent local_search = algorithm.LocalSearch as GradientDescent;
            local_search.Alpha = 0.005;

            double[] Ymean;
            algorithm.DoMeanNormalization(records, out Ymean);

            algorithm.Compute(records, num_features);

            algorithm.UndoMeanNormalization(records, Ymean);

            int userId = 0;
            int topK = 10;
            List<RatedItem> highest_ranks = algorithm.SelectHigestRanked(userId, records, topK);

            for (int i = 0; i < highest_ranks.Count; ++i)
            {
                RatedItem rec = highest_ranks[i];
                Console.WriteLine("#{0}: ({1}) {2}", i + 1, rec.UserRanks[0], movie_titles[rec.ItemIndex]);
            }
        }


    }
}

cs-recommender

Recommender based on Hidden Factor Analysis Collaborative Filtering in .NET 4.6.1

Install

Run the following command to get the nuget package:

Install-Package cs-recommender 

Usage

The sample code show show how to train and use the Collaborative Filtering Recommender:

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.Threading.Tasks;
using MathNet.Numerics.LinearAlgebra.Generic;
using MathNet.Numerics.LinearAlgebra.Double;
using System.IO;
using ContinuousOptimization.LocalSearch;
using ContinuousOptimization;
using Recommender.Utils;

namespace Recommender
{
    public class Program
    {

        protected static List<string> LoadMovies()
        {
            List<string> movie_titles = new List<string>();

            string line;
            using (StreamReader reader = new StreamReader("movie_ids.txt"))
            {
                while ((line = reader.ReadLine()) != null)
                {
                    string[] texts = line.Split(new char[] { ' ' });
                    StringBuilder sb = new StringBuilder();
                    bool first_index = true;
                    foreach (string text in texts)
                    {
                        if (string.IsNullOrEmpty(text)) continue;
                        if (first_index)
                        {
                            first_index = false;
                            continue;
                        }
                        sb.AppendFormat("{0} ", text);
                    }
                    string title = sb.ToString().Trim();
                    movie_titles.Add(title);
                }
            }

            return movie_titles;
        }

        public static void Main(string[] args)
        {
            List<string> movie_titles = LoadMovies();
            int num_movies = movie_titles.Count;

            // Step 1: create my ratings with missing entries
            double[] my_ratings = new double[num_movies];
            int[] my_ratings_r = new int[num_movies];
            for (int i = 0; i < num_movies; ++i)
            {
                my_ratings[i] = 0;
            }

            my_ratings[1] = 4;
            my_ratings[98] = 2;
            my_ratings[7] = 3;
            my_ratings[12] = 5;
            my_ratings[54] = 4;
            my_ratings[64] = 5;
            my_ratings[66] = 3;
            my_ratings[69] = 5;
            my_ratings[183] = 4;
            my_ratings[226] = 5;
            my_ratings[355] = 5;

            for (int i = 0; i < num_movies; ++i)
            {
                my_ratings_r[i] = my_ratings[i] > 0 ? 1 : 0;
            }

            // Step 2: load the current ratings of all users, i.e., Y and R
            List<List<double>> Y = DblDataTableUtil.LoadDataSet("Y.txt");
            List<List<int>> R = IntDataTableUtil.LoadDataSet("R.txt");

            int num_users;
            DblDataTableUtil.GetSize(Y, out num_movies, out num_users);


            // Step 3: insert my ratings into the existing Y and R (as the first column)
            num_users++;
            List<RatedItem> records = new List<RatedItem>();
            for (int i = 0; i < num_movies; ++i)
            {
                double[] rec_Y = new double[num_users];
                bool[] rec_R = new bool[num_users];
                for (int j = 0; j < num_users; ++j)
                {
                    if (j == 0)
                    {
                        rec_Y[j] = my_ratings[i];
                        rec_R[j] = my_ratings_r[i] == 1;
                    }
                    else
                    {
                        rec_Y[j] = Y[i][j - 1];
                        rec_R[j] = R[i][j - 1] == 1;
                    }
                }
                RatedItem rec = new RatedItem(null, rec_Y, rec_R);
                records.Add(rec);
            }

            int num_features = 10;

            double lambda = 10;
            CollaborativeFilteringRS<RatedItem> algorithm = new CollaborativeFilteringRS<RatedItem>();
            algorithm.Stepped += (s, step) =>
            {
                Console.WriteLine("#{0}: {1}", step, s.Cost);
            };
            algorithm.RegularizationLambda = lambda;
            algorithm.MaxLocalSearchIteration = 100;
            GradientDescent local_search = algorithm.LocalSearch as GradientDescent;
            local_search.Alpha = 0.005;

            double[] Ymean;
            algorithm.DoMeanNormalization(records, out Ymean);

            algorithm.Compute(records, num_features);

            algorithm.UndoMeanNormalization(records, Ymean);

            int userId = 0;
            int topK = 10;
            List<RatedItem> highest_ranks = algorithm.SelectHigestRanked(userId, records, topK);

            for (int i = 0; i < highest_ranks.Count; ++i)
            {
                RatedItem rec = highest_ranks[i];
                Console.WriteLine("#{0}: ({1}) {2}", i + 1, rec.UserRanks[0], movie_titles[rec.ItemIndex]);
            }
        }


    }
}

Release Notes

Collaborative Filtering Recommender in .NET 4.6.1

Dependencies

This package has no dependencies.

This package is not used by any popular GitHub repositories.

Version History

Version Downloads Last updated
1.0.1 296 5/1/2018