プログラムにおける機械学習の一般的な推奨アルゴリズムを説明する例

using System;using System.Collections.Generic;class CollaborativeFiltering{static void Main(){// 用户-物品评分矩阵Dictionary<string dictionary double>> userItemRatings = new Dictionary<string dictionary double>>{{ "User1", new Dictionary<string double> { { "Item1", 5.0 }, { "Item2", 3.0 } } },{ "User2", new Dictionary<string double> { { "Item1", 4.0 }, { "Item3", 1.0 } } },{ "User3", new Dictionary<string double> { { "Item2", 4.5 }, { "Item4", 2.0 } } }};string targetUser = "User2";string targetItem = "Item2";// 计算与目标用户相似的其他用户var similarUsers = FindSimilarUsers(userItemRatings, targetUser);// 基于相似用户的评分预测double predictedRating = PredictRating(userItemRatings, similarUsers, targetUser, targetItem);Console.WriteLine($"预测用户 {targetUser} 对物品 {targetItem} 的评分为: {predictedRating}");}static Dictionary<string double> FindSimilarUsers(Dictionary<string dictionary double>> userItemRatings, string targetUser){Dictionary<string double> similarUsers = new Dictionary<string double>();foreach (var user in userItemRatings.Keys){if (user != targetUser){double similarity = CalculateSimilarity(userItemRatings[targetUser], userItemRatings[user]);similarUsers.Add(user, similarity);}}return similarUsers;}static double CalculateSimilarity(Dictionary<string double> ratings1, Dictionary<string double> ratings2){// 计算两个用户之间的相似性，可以使用不同的方法，如皮尔逊相关系数、余弦相似度等// 这里使用简单的欧氏距离作为示例double distance = 0.0;foreach (var item in ratings1.Keys){if (ratings2.ContainsKey(item)){distance += Math.Pow(ratings1[item] - ratings2[item], 2);}}return 1 / (1 + Math.Sqrt(distance));}static double PredictRating(Dictionary<string dictionary double>> userItemRatings, Dictionary<string double> similarUsers, string targetUser, string targetItem){double numerator = 0.0;double denominator = 0.0;foreach (var user in similarUsers.Keys){if (userItemRatings[user].ContainsKey(targetItem)){numerator += similarUsers[user] * userItemRatings[user][targetItem];denominator += Math.Abs(similarUsers[user]);}}if (denominator == 0){return 0; // 无法预测}return numerator / denominator;}}</string></string></string></string></string></string></string></string></string></string></string></string></string>

using System;using System.Collections.Generic;class ContentFiltering{static void Main(){// 物品-属性矩阵Dictionary<string dictionary double>> itemAttributes = new Dictionary<string dictionary double>>{{ "Item1", new Dictionary<string double> { { "Genre", 1.0 }, { "Year", 2010.0 } } },{ "Item2", new Dictionary<string double> { { "Genre", 2.0 }, { "Year", 2015.0 } } },{ "Item3", new Dictionary<string double> { { "Genre", 1.5 }, { "Year", 2020.0 } } }};string targetUser = "User1";// 用户历史喜好List<string> userLikedItems = new List<string> { "Item1", "Item2" };// 基于内容相似性的物品推荐var recommendedItems = RecommendItems(itemAttributes, userLikedItems, targetUser);Console.WriteLine($"为用户 {targetUser} 推荐的物品是: {string.Join(", ", recommendedItems)}");}static List<string> RecommendItems(Dictionary<string dictionary double>> itemAttributes, List<string> userLikedItems, string targetUser){Dictionary<string double> itemScores = new Dictionary<string double>();foreach (var item in itemAttributes.Keys){if (!userLikedItems.Contains(item)){double similarity = CalculateItemSimilarity(itemAttributes, userLikedItems, item, targetUser);itemScores.Add(item, similarity);}}// 根据相似性得分排序物品var sortedItems = itemScores.OrderByDescending(x => x.Value).Select(x => x.Key).ToList();return sortedItems;}static double CalculateItemSimilarity(Dictionary<string dictionary double>> itemAttributes, List<string> userLikedItems, string item1, string targetUser){double similarity = 0.0;foreach (var item2 in userLikedItems){similarity += CalculateJaccardSimilarity(itemAttributes[item1], itemAttributes[item2]);}return similarity;}static double CalculateJaccardSimilarity(Dictionary<string double> attributes1, Dictionary<string double> attributes2){// 计算Jaccard相似性，可以根据属性值的相似性定义不同的相似性度量方法var intersection = attributes1.Keys.Intersect(attributes2.Keys).Count();var union = attributes1.Keys.Union(attributes2.Keys).Count();return intersection / (double)union;}}</string></string></string></string></string></string></string></string></string></string></string></string></string></string></string></string>

// 请注意，此示例需要安装PyTorch.NET库using System;using System.Linq;using Python.Runtime;using torch = Python.Runtime.Torch;class DeepLearningRecommendation{static void Main(){// 启动Python运行时using (Py.GIL()){// 创建一个简单的神经网络模型var model = CreateRecommendationModel();// 模拟用户和物品的数据var userFeatures = torch.tensor(new double[,] { { 0.1, 0.2 }, { 0.4, 0.5 } });var itemFeatures = torch.tensor(new double[,] { { 0.6, 0.7 }, { 0.8, 0.9 } });// 计算用户和物品之间的交互var interaction = torch.mm(userFeatures, itemFeatures.T);// 使用模型进行推荐var recommendations = model.forward(interaction);Console.WriteLine("推荐得分：");Console.WriteLine(recommendations);}}static dynamic CreateRecommendationModel(){using (Py.GIL()){dynamic model = torch.nn.Sequential(torch.nn.Linear(2, 2),torch.nn.ReLU(),torch.nn.Linear(2, 1),torch.nn.Sigmoid());return model;}}}