Rekomendasi non-personal

Membangun Recommendation Engine di Python

Rob O'Callaghan

Director of Data

Rating non-personal

Contoh tiga buku yang sering dibeli bersama.

Membangun Recommendation Engine di Python

Menemukan item paling populer

book_df DataFrame:

Pengguna Buku
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
... ...
Membangun Recommendation Engine di Python

Menemukan item paling populer

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
                                     ...
Membangun Recommendation Engine di Python

Menemukan item paling populer

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')
Membangun Recommendation Engine di Python

Menemukan item paling disukai

user_ratings DataFrame:

Pengguna Buku 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
... ... ...
Membangun Recommendation Engine di Python

Menemukan item paling disukai

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
                                          ...
Membangun Recommendation Engine di Python

Menemukan item paling disukai

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
                                          ...
Membangun Recommendation Engine di Python

Menemukan item paling disukai

(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
Membangun Recommendation Engine di Python

Menemukan item populer paling disukai

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'])
Membangun Recommendation Engine di Python

Menemukan item populer paling disukai

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
                                          ...
Membangun Recommendation Engine di Python

Ayo berlatih!

Membangun Recommendation Engine di Python

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