如何在Python中构建一个简单的推荐系统

pip install pandas scikit-learn

import pandas as pdfrom sklearn.model_selection import train_test_split# 加载数据集data = pd.read_csv('ratings.csv')

train_data, test_data = train_test_split(data, test_size=0.2, random_state=42)

# 计算用户之间的相似度def calculate_similarity(train_data):    similarity = dict()    for user in train_data['userId'].unique():        similarity[user] = dict()        user_ratings = train_data[train_data['userId'] == user]        for movie in user_ratings['movieId'].unique():            similarity[user][movie] = 1.0    return similarity# 计算用户之间的相似度得分def calculate_similarity_score(train_data, similarity):    for user1 in similarity.keys():        for user2 in similarity.keys():            if user1 != user2:                user1_ratings = train_data[train_data['userId'] == user1]                user2_ratings = train_data[train_data['userId'] == user2]                num_ratings = 0                sum_of_squares = 0                for movie in user1_ratings['movieId'].unique():                    if movie in user2_ratings['movieId'].unique():                        num_ratings += 1                        rating1 = user1_ratings[user1_ratings['movieId'] == movie]['rating'].values[0]                        rating2 = user2_ratings[user2_ratings['movieId'] == movie]['rating'].values[0]                        sum_of_squares += (rating1 - rating2) ** 2                similarity[user1][user2] = 1 / (1 + (sum_of_squares / num_ratings) ** 0.5)    return similarity# 计算电影之间的相似度得分def calculate_movie_similarity_score(train_data, similarity):    movie_similarity = dict()    for user in similarity.keys():        for movie in train_data[train_data['userId'] == user]['movieId'].unique():            if movie not in movie_similarity.keys():                movie_similarity[movie] = dict()            for other_movie in train_data[train_data['userId'] == user]['movieId'].unique():                if movie != other_movie:                    movie_similarity[movie][other_movie] = similarity[user][other_user]    return movie_similarity# 构建推荐系统def build_recommendation_system(train_data, similarity, movie_similarity):    recommendations = dict()    for user in train_data['userId'].unique():        user_ratings = train_data[train_data['userId'] == user]        recommendations[user] = dict()        for movie in train_data['movieId'].unique():            if movie not in user_ratings['movieId'].unique():                rating = 0                num_movies = 0                for other_user in similarity[user].keys():                    if movie in train_data[train_data['userId'] == other_user]['movieId'].unique():                        rating += similarity[user][other_user] * train_data[(train_data['userId'] == other_user) & (train_data['movieId'] == movie)]['rating'].values[0]                        num_movies += 1                if num_movies > 0:                    recommendations[user][movie] = rating / num_movies    return recommendations# 计算评价指标def calculate_metrics(recommendations, test_data):    num_users = 0    sum_of_squared_error = 0    for user in recommendations.keys():        if user in test_data['userId'].unique():            num_users += 1            for movie in recommendations[user].keys():                if movie in test_data[test_data['userId'] == user]['movieId'].unique():                    predicted_rating = recommendations[user][movie]                    actual_rating = test_data[(test_data['userId'] == user) & (test_data['movieId'] == movie)]['rating'].values[0]                    sum_of_squared_error += (predicted_rating - actual_rating) ** 2    rmse = (sum_of_squared_error / num_users) ** 0.5    return rmse# 计算用户之间的相似度similarity = calculate_similarity(train_data)# 计算用户之间的相似度得分similarity = calculate_similarity_score(train_data, similarity)# 计算电影之间的相似度得分movie_similarity = calculate_movie_similarity_score(train_data, similarity)# 构建推荐系统recommendations = build_recommendation_system(train_data, similarity, movie_similarity)# 计算评价指标rmse = calculate_metrics(recommendations, test_data)

print(recommendations)print('RMSE:', rmse)