2.5.3推薦システムアルゴリズム---コンテンツに基づく映画推薦:ユーザーにTOP-N推薦結果を生成する

# ......

user_profile = create_user_profile()

watch_record = pd.read_csv("datasets/ml-latest-small/ratings.csv", usecols=range(2),dtype={"userId": np.int32, "movieId": np.int32})

watch_record = watch_record.groupby("userId").agg(list)

for uid, interest_words in user_profile.items():
    result_table = {} #   id:[0.2,0.5,0.7]
    for interest_word, interest_weight in interest_words:
        related_movies = inverted_table[interest_word]
        for mid, related_weight in related_movies:
            _ = result_table.get(mid, [])
            _.append(interest_weight)    #           
            # _.append(related_weight)    #               
            # _.append(interest_weight*related_weight)    #      
            result_table.setdefault(mid, _)

    rs_result = map(lambda x: (x[0], sum(x[1])), result_table.items())
    rs_result = sorted(rs_result, key=lambda x:x[1], reverse=True)[:100]
    print(uid)
    pprint(rs_result)
    break

    #       ==>         ==>                           
    #      ===>      (   )   ===>     ==>      100%  
    #   ：  1 、3 、7