非个性化推荐

用 Python 构建推荐引擎

Rob O'Callaghan

Director of Data

非个性化评分

经常一起购买的三本书示例。

用 Python 构建推荐引擎

查找最流行的项目

book_df 数据框：

用户 书籍
User_233 The Great Gatsby
User_651 The Catcher in the Rye
User_131 The Lord of the Rings
User_965 Little Women
User_651 Fifty Shades of Grey
... ...
用 Python 构建推荐引擎

查找最流行的项目

book_df['book'].value_counts()

40 Shades of Grey                      524
Harry Potter and the Sorcerer's Stone  487
The Da Vinci Code                      455
The Twilight Saga                      401
Lord of the Rings                      278
                                     ...
用 Python 构建推荐引擎

查找最流行的项目

print(book_df.value_counts().index)

Index(['40 Shades of Grey', 'Harry Potter and the Sorcerer's Stone',
       'The Da Vinci Code', 'The Twilight Saga',
       'The Lord of the Rings'],
      dtype='object')
用 Python 构建推荐引擎

查找最受欢迎的项目

user_ratings 数据框：

用户 书籍 评分
User_233 The Great Gatsby 3.0
User_651 The Catcher in the Rye 5.0
User_131 The Lord of the Rings 3.0
User_965 Little Women 4.0
User_651 Fifty Shades of Grey 2.0
... ... ...
用 Python 构建推荐引擎

查找最受欢迎的项目

avg_rating_df = user_ratings[["book", "rating"]].groupby(['book']).mean()
avg_rating_df.head()

                                      rating
title                                      
Hamlet                                   4.1
The Da Vinci Code                       2.1
Gone with the Wind                       4.2
Fifty Shades of Grey                     1.2
Wuthering Heights                        3.9
                                          ...
用 Python 构建推荐引擎

查找最受欢迎的项目

sorted_avg_rating_df = avg_rating_df.sort_values(by="rating", ascending=False)
sorted_avg_rating_df.head()

                                      rating
title                                      
The Girl in the Fog                      5.0
Behind the Bell                          5.0
Across the River and into the Trees      5.0
The Complete McGonagall                  5.0
What Is to Be Done?                      5.0
                                          ...
用 Python 构建推荐引擎

查找最受欢迎的项目

(user_ratings['title']=='The Girl in the Fog').sum()

1

(user_ratings['title']=='Valley of the Dolls').sum()

1

(user_ratings['title']=='Across the River and into the Trees').sum()

1
用 Python 构建推荐引擎

查找最受欢迎的热门项目

book_frequency = user_ratings["book"].value_counts()
print(book_frequency)

40 Shades of Grey                      524
Harry Potter and the Sorcerer's Stone  487
                                       ...

frequently_reviewed_books = book_frequency[book_frequency > 100].index
print(frequently_reviewed_books)

Index([u'The Lord of the Rings', u'To Kill a Mockingbird', u'Of Mice and Men',
       u'1984', u'Hamlet'])
用 Python 构建推荐引擎

查找最受欢迎的热门项目

frequent_books_df =  user_ratings_df[user_ratings_df["book"].isin(frequently_reviewed_books)]

frequent_books_avgs = frequently_reviewed_books[["title", "rating"]].groupby('title').mean()
print(frequent_books_avgs.sort_values(by="rating", ascending=False).head())

                                      rating
title                                      
To Kill a Mockingbird                    4.7
1984.                                    4.7
Harry Potter and the Sorcerer's Stone    4.6
                                          ...
用 Python 构建推荐引擎

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用 Python 构建推荐引擎

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