非個人化推薦

用 Python 建立推薦引擎

Rob O'Callaghan

Director of Data

非個人化評分

常被一起購買的三本書範例。

用 Python 建立推薦引擎

找出最受歡迎的項目

book_df DataFrame：

User Book
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 建立推薦引擎

找出最受歡迎的項目

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 建立推薦引擎

找出最受歡迎的項目

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 建立推薦引擎

找出最受喜愛的項目

user_ratings DataFrame：

User Book Rating
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 建立推薦引擎

找出最受喜愛的項目

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 建立推薦引擎

找出最受喜愛的項目

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 建立推薦引擎

找出最受喜愛的項目

(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 建立推薦引擎

找出最受喜愛的熱門項目

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 建立推薦引擎

找出最受喜愛的熱門項目

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 建立推薦引擎

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用 Python 建立推薦引擎

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