nodejs实现协同过滤算法

基于协同过滤算法的推荐系统可以使用Node.js来实现。下面是一个简单的实现步骤：

1.首先，需要收集用户的历史数据，包括用户的评分和偏好等信息。

2.然后，需要计算用户之间的相似度。可以使用余弦相似度或皮尔逊相关系数等方法来计算用户之间的相似度。

3.接下来，需要根据用户之间的相似度来预测用户对未评分物品的评分。可以使用加权平均值等方法来预测用户对未评分物品的评分。

4.最后，根据预测的评分来推荐物品给用户。

下面是一个简单的Node.js实现示例：

// 导入依赖模块
const _ = require('lodash');

// 用户历史数据
const userHistory = {
  'user1': {
    'item1': 3,
    'item2': 4,
    'item3': 1,
    'item4': 5,
  },
  'user2': {
    'item1': 4,
    'item2': 3,
    'item3': 5,
    'item4': 2,
  },
  'user3': {
    'item1': 1,
    'item2': 5,
    'item3': 2,
    'item4': 4,
  },
};

// 计算用户之间的相似度
function getUserSimilarity(user1, user2) {
  const user1Ratings = _.values(user1);
  const user2Ratings = _.values(user2);
  const dotProduct = _.sum(_.zipWith(user1Ratings, user2Ratings, (a, b) => a * b));
  const user1Norm = Math.sqrt(_.sum(_.map(user1Ratings, (rating) => rating * rating)));
  const user2Norm = Math.sqrt(_.sum(_.map(user2Ratings, (rating) => rating * rating)));
  const similarity = dotProduct / (user1Norm * user2Norm);
  return similarity;
}

// 预测用户对物品的评分
function predictRating(user, item, userSimilarity) {
  let rating = 0;
  let similaritySum = 0;
  _.forEach(userHistory, (otherUserRatings, otherUserId) => {
    if (otherUserId !== user) {
      const similarity = userSimilarity[user][otherUserId];
      const otherUserRating = otherUserRatings[item];
      if (otherUserRating) {
        rating += similarity * otherUserRating;
        similaritySum += similarity;
      }
    }
  });
  if (similaritySum > 0) {
    rating /= similaritySum;
  }
  return rating;
}

// 推荐物品给用户
function recommendItems(user, userSimilarity) {
  const items = [];
  _.forEach(userHistory, (otherUserRatings, otherUserId) => {
    if (otherUserId !== user) {
      _.forEach(otherUserRatings, (rating, item) => {
        if (!userHistory[user][item]) {
          const predictedRating = predictRating(user, item, userSimilarity);
          items.push({
            item,
            predictedRating,
          });
        }
      });
    }
  });
  return _.orderBy(items, ['predictedRating'], ['desc']);
}

// 计算用户之间的相似度矩阵
const userSimilarity = {};
_.forEach(userHistory, (user1Ratings, user1Id) => {
  userSimilarity[user1Id] = {};
  _.forEach(userHistory, (user2Ratings, user2Id) => {
    if (user1Id !== user2Id) {
      const similarity = getUserSimilarity(user1Ratings, user2Ratings);
      userSimilarity[user1Id][user2Id] = similarity;
    }
  });
});

// 推荐物品给用户
const user = 'user1';
const recommendedItems = recommendItems(user, userSimilarity);
console.log(`Recommended items for ${user}:`);
console.log(recommendedItems);