这篇文章介绍了C#实现协同过滤算法的实例代码，有需要的朋友可以参考一下 代码如下: 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 + \ }
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(\ } } } }
C#实现协同过滤算法的实例代码
