使用者檔案推薦

用 Python 建立推薦引擎

Rob O'Callaghan

Director of Data

項目對項目推薦

根據尋找相似項目的推薦示意圖。

使用者檔案

tfidf_summary_df:

書籍	Adventure	Fantasy	Tragedy	Social commentary
The Hobbit	1	1	0	0
Macbeth	0	0	1	0
...	...	...	...	...

使用者檔案：

使用者檔案	Adventure	Fantasy	Tragedy	Social commentary
User_001	???	???	???	???

擷取使用者資料

list_of_books_read = ['The Hobbit', 'Foundation', 'Nudge']

user_books = tfidf_summary_df.reindex(list_of_books_read)

print(user_books)

               age   ancient   angry   brave   battle   fellow    ...
 The Hobbit   0.21      0.53    0.41    0.64     0.01     0.02    ...
 Foundation   0.31      0.90    0.42    0.33     0.64     0.04    ...
      Nudge   0.61      0.01    0.45    0.31     0.12     0.74    ...

建立使用者檔案

user_prof = user_movies.mean()

print(user_prof)

age      0.376667
ancient  0.480000
angry    0.426667
brave    0.256667
             ...

print(user_prof.values.reshape(1,-1))

[0.376667, .480000, 0.426667, 0.256667, ...]

為使用者找推薦

# Create a subset of only the non read books
non_user_movies = tfidf_summary_df.drop(list_of_movies_seen, axis=0)


# Calculate the cosine similarity between all rows
user_prof_similarities = cosine_similarity(user_prof.values.reshape(1, -1),
                                          non_user_movies)

# Wrap in a DataFrame for ease of use
user_prof_similarities_df = pd.DataFrame(user_prof_similarities.T,
                                        index=tfidf_summary_df.index,
                                        columns=["similarity_score"])

取得最高分推薦

sorted_similarity_df = user_prof_similarities.sort_values(by="similarity_score",
                                                         ascending=False)

print(sorted_similarity_df)

                                similarity_score
Title                                           
The Two Towers                          0.422488
Dune                                    0.363540
The Magicians Nephew                    0.316075
...                                     ...

一起來練習吧！

用 Python 建立推薦引擎