现在我们构建了推荐引擎的所有不同部分, 我们已经准备好生成电影建议. 我们将使用我们在之前配方中构建的所有功能来构建电影推荐引擎. 让我们看看如何建立它.

怎么做...?

• 创建文件并导入需要的包:

import json
import numpy as np

from pearson_score import pearson_score

• 我们将定义一个函数来为给定的用户生成电影推荐. 第一步是检查用户是否存在于数据集中:

# Generate recommendations for a given user


def generate_recommendations(dataset, user):
    if user not in dataset:
        raise TypeError('User ' + user + ' not present in the dataset')

    total_scores = {}
    similarity_sums = {}

    for u in [x for x in dataset if x != user]:
        similarity_score = pearson_score(dataset, user, u)

        if similarity_score <= 0:
            continue

        for item in [
                x for x in dataset[u]
                if x not in dataset[user] or dataset[user][x] == 0]:
            total_scores.update({item: dataset[u][item] * similarity_score})
            similarity_sums.update({item: similarity_score})

    if len(total_scores) == 0:
        return ['No recommendations possible']

    # Create the normalized list
    movie_ranks = np.array(
        [
            [total / similarity_sums[item], item]
            for item, total in total_scores.items()
        ]
    )

    # Sort in decreasing order based on the first column
    movie_ranks = movie_ranks[np.argsort(movie_ranks[:, 0])[::-1]]

    # Extract the recommended movies
    recommendations = [movie for _, movie in movie_ranks]

    return recommendations

• 我们来定义主函数并加载数据集

if __name__ == '__main__':
    data_file = 'movie_ratings.json'

    with open(data_file, 'r') as f:
        data = json.loads(f.read())

    user = 'Michael Henry'
    print ("Recommendations for " + user + ":")
    movies = generate_recommendations(data, user)
    for i, movie in enumerate(movies):
        print (str(i + 1) + '. ' + movie)

    user = 'John Carson'
    print ("Recommendations for " + user + ":")
    movies = generate_recommendations(data, user)
    for i, movie in enumerate(movies):
        print (str(i + 1) + '. ' + movie)

• 运行结果如下: