用户个性化推荐

最近在了解下电子商务方面的网站，很多网站都在走个性化定制的线路，个性化定制在信息系统里就是系统推荐，把符合用户喜好或用户操作形为相关的的信息

以订阅或网页的形式展示出来，如何实现这个推荐,有很多种算法和处理方式，不过我认为比较靠谱的一种做法就是隐式对商品或信息打分，然后推荐，这种做

法相对而己不需要用户太高的参与性，不需要用户手工给某一个产品打分，而是通过一些用户的操作，比如查看，购买，评论等方式来评分。

这个方法就是Slope one，我把其它博友的C#代码贴出来给大家参考下。

Here's the source code of my C# implementation of Weighted Slope One. Tested on .net 3.5; The instruction in Chinese can be find here and here.
Reference:
Tutorial about how to implement Slope One in Python
Slope One Predictors for Online Rating-Based Collaborative Filtering
Recommender Systems: Slope One


using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;

namespace SlopeOne
{
    public class Rating
    {
        public float Value { get; set; }
        public int Freq { get; set; }

        public float AverageValue
        {
            get { return Value / Freq; }
        }
    }

    public class RatingDifferenceCollection : Dictionary<string, Rating>
    {
        private string GetKey(int Item1Id, int Item2Id)
        {
            return (Item1Id < Item2Id) ? Item1Id + "/" + Item2Id : Item2Id + "/" + Item1Id ;
        }

        public bool Contains(int Item1Id, int Item2Id)
        {
            return this.Keys.Contains<string>(GetKey(Item1Id, Item2Id));
        }

        public Rating this[int Item1Id, int Item2Id]
        {
            get {
                    return this[this.GetKey(Item1Id, Item2Id)];
            }
            set { this[this.GetKey(Item1Id, Item2Id)] = value; }
        }
    }

     public class SlopeOne
    {       
        public RatingDifferenceCollection _DiffMarix = new RatingDifferenceCollection();  // The dictionary to keep the diff matrix
        public HashSet<int> _Items = new HashSet<int>();  // Tracking how many items totally

        public void AddUserRatings(IDictionary<int, float> userRatings)
        {
            foreach (var item1 in userRatings)
            {
                int item1Id = item1.Key;
                float item1Rating = item1.Value;
                _Items.Add(item1.Key);

                foreach (var item2 in userRatings)
                {
                    if (item2.Key <= item1Id) continue; // Eliminate redundancy
                    int item2Id = item2.Key;
                    float item2Rating = item2.Value;

                    Rating ratingDiff;
                    if (_DiffMarix.Contains(item1Id, item2Id))
                    {
                        ratingDiff = _DiffMarix[item1Id, item2Id];
                    }
                    else
                    {
                        ratingDiff = new Rating();
                        _DiffMarix[item1Id, item2Id] = ratingDiff;
                    }

                    ratingDiff.Value += item1Rating - item2Rating;
                    ratingDiff.Freq += 1;
                }
            }
        }

        // Input ratings of all users
        public void AddUerRatings(IList<IDictionary<int, float>> Ratings)
        {
            foreach(var userRatings in Ratings)
            {
                AddUserRatings(userRatings);
            }
        }

        public IDictionary<int, float> Predict(IDictionary<int, float> userRatings)
        {
            Dictionary<int, float> Predictions = new Dictionary<int, float>();
            foreach (var itemId in this._Items)
            {
                if (userRatings.Keys.Contains(itemId))    continue; // User has rated this item, just skip it

                Rating itemRating = new Rating();

                foreach (var userRating in userRatings)
                {
                    if (userRating.Key == itemId) continue;
                    int inputItemId = userRating.Key;
                    if (_DiffMarix.Contains(itemId, inputItemId))
                    {
                        Rating diff = _DiffMarix[itemId, inputItemId];
                        itemRating.Value += diff.Freq * (userRating.Value + diff.AverageValue * ((itemId < inputItemId) ? 1 : -1));
                        itemRating.Freq += diff.Freq;
                    }
                }
                Predictions.Add(itemId, itemRating.AverageValue);               
            }
            return Predictions;
        }

        public static void Test()
        {
            SlopeOne test = new SlopeOne();

            Dictionary<int, float> userRating = new Dictionary<int, float>();
            userRating.Add(1, 5);
            userRating.Add(2, 4);
            userRating.Add(3, 4);
            test.AddUserRatings(userRating);

            userRating = new Dictionary<int, float>();
            userRating.Add(1, 4);
            userRating.Add(2, 5);
            userRating.Add(3, 3);
            userRating.Add(4, 5);
            test.AddUserRatings(userRating);

            userRating = new Dictionary<int, float>();
            userRating.Add(1, 4);
            userRating.Add(2, 4);
            userRating.Add(4, 5);
            test.AddUserRatings(userRating);

            userRating = new Dictionary<int, float>();
            userRating.Add(1, 5);
            userRating.Add(3, 4);

            IDictionary<int, float> Predictions = test.Predict(userRating);
            foreach (var rating in Predictions)
            {
                Console.WriteLine("Item " + rating.Key + " Rating: " + rating.Value);
            }
        }
    }
}