ML之RS之CF：基于用户的CF算法—利用大量用户的电影及其评分数据集对一个新用户Jason进行推荐电影+(已知Jason曾观看几十部电影及其评分)

ML之RS之CF：基于用户的CF算法—利用大量用户的电影及其评分数据集对一个新用户Jason进行推荐电影+(已知Jason曾观看几十部电影及其评分)

输出结果

先看推荐结果显示

实现代码

from math import sqrt

#pearson距离
def pearson_dis(rating1, rating2):
    sum_xy = 0
    sum_x = 0
    sum_y = 0
    sum_x2 = 0
    sum_y2 = 0
    n = 0
    for key in rating1:
        if key in rating2:
            n += 1
            x = rating1[key]
            y = rating2[key]
            sum_xy += x * y
            sum_x += x
            sum_y += y
            sum_x2 += pow(x, 2)
            sum_y2 += pow(y, 2)
    # now compute denominator
    denominator = sqrt(sum_x2 - pow(sum_x, 2) / n) * sqrt(sum_y2 - pow(sum_y, 2) / n)
    if denominator == 0:
        return 0
    else:
        return (sum_xy - (sum_x * sum_y) / n) / denominator

#查找最近邻函数
def computeNearestNeighbor(username, users):
    """在给定username的情况下，计算其他用户和它的距离并排序"""
    distances = []
    for user in users:  #全用户遍历，找到两个用户，计算pearson距离，依次添加到列表内
        if user != username:
            #distance = manhattan_dis(users[user], users[username])
            distance = pearson_dis(users[user], users[username])
            distances.append((distance, user))
    distances.sort()
    return distances

#进行推荐函数
def recommend(username, users):
    nearest = computeNearestNeighbor(username, users)[0][1]

    recommendations = []
    neighborRatings = users[nearest]
    userRatings = users[username]
    for artist in neighborRatings:
        if not artist in userRatings:
            recommendations.append((artist, neighborRatings[artist]))
    results = sorted(recommendations, key=lambda artistTuple: artistTuple[1], reverse = True)
    for result in results:
        print(result[0], result[1])

recommend('Jason', users)

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ML之RS之CF：基于用户的CF算法—利用大量用户的电影及其评分数据集对一个新用户Jason进行推荐电影+(已知Jason曾观看几十部电影及其评分)

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