Recomandări nepersonalizate

Construirea motoarelor de recomandare în Python

Rob O'Callaghan

Director of Data

Evaluări nepersonalizate

Exemplu de trei cărți cumpărate frecvent împreună.

Găsirea elementelor cele mai populare

DataFrame book_df:

Utilizator	Carte
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
...	...

Găsirea elementelor cele mai populare

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
                                     ...

Găsirea elementelor cele mai populare

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')

Găsirea elementelor cu cele mai bune evaluări

DataFrame user_ratings:

Utilizator	Carte	Evaluare
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
...	...	...

Găsirea elementelor cu cele mai bune evaluări

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
                                          ...

Găsirea elementelor cu cele mai bune evaluări

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
                                          ...

Găsirea elementelor cu cele mai bune evaluări

(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()

Găsirea elementelor populare cu cele mai bune evaluări

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'])

Găsirea elementelor populare cu cele mai bune evaluări

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
                                          ...

Să exersăm!

Construirea motoarelor de recomandare în Python