Recomendações não personalizadas
Construindo mecanismos de recomendação em Python
Rob O'Callaghan
Director of Data
Avaliações não personalizadas
Encontrando os itens mais populares
DataFrame book_df:
| 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 |
| ... | ... |
Encontrando os itens mais populares
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
...
Encontrando os itens mais populares
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')
Encontrando os itens mais bem avaliados
DataFrame user_ratings:
| 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 |
| ... | ... | ... |
Encontrando os itens mais bem avaliados
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
...
Encontrando os itens mais bem avaliados
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
...
Encontrando os itens mais bem avaliados
(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
Encontrando os itens populares mais bem avaliados
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'])
Encontrando os itens populares mais bem avaliados
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
...
Vamos praticar!
Construindo mecanismos de recomendação em Python
