Recommandations non personnalisées

Créer des moteurs de recommandation en Python

Rob O'Callaghan

Director of Data

Notes non personnalisées

Exemple de trois livres souvent achetés ensemble.

Identifier les articles les plus populaires

DataFrame book_df :

Utilisateur	Livre
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
...	...

Identifier les articles les plus populaires

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
                                     ...

Identifier les articles les plus populaires

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')

Identifier les articles les mieux notés

DataFrame user_ratings :

Utilisateur	Livre	Note
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
...	...	...

Identifier les articles les mieux notés

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
                                          ...

Identifier les articles les mieux notés

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
                                          ...

Identifier les articles les mieux notés

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

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

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

Identifier les articles populaires les mieux notés

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'])

Identifier les articles populaires les mieux notés

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 !

Créer des moteurs de recommandation en Python