非パーソナライズ推奨
Pythonでつくるレコメンデーションエンジン
Rob O'Callaghan
Director of Data
非パーソナライズ評価
最も人気のアイテムを探す
book_df データフレーム:
| 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 |
| ... | ... |
最も人気のアイテムを探す
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
...
最も人気のアイテムを探す
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')
最も高評価のアイテムを探す
user_ratings データフレーム:
| 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 |
| ... | ... | ... |
最も高評価のアイテムを探す
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
...
最も高評価のアイテムを探す
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
...
最も高評価のアイテムを探す
(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
人気アイテムの中で最も高評価を探す
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'])
人気アイテムの中で最も高評価を探す
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
...
Passons à la pratique !
Pythonでつくるレコメンデーションエンジン
