用户画像推荐

用 Python 构建推荐引擎

Rob O'Callaghan

Director of Data

基于物品的推荐

展示基于相似项进行推荐的图片。

用户画像

tfidf_summary_df：

书籍	冒险	奇幻	悲剧	社会评论
The Hobbit	1	1	0	0
Macbeth	0	0	1	0
...	...	...	...	...

用户画像：

用户画像	冒险	奇幻	悲剧	社会评论
User_001	???	???	???	???

提取用户数据

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    ...

构建用户画像

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, ...]

为用户查找推荐

# 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"])

获取最高分推荐

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
...                                     ...

¡Vamos a practicar!

用 Python 构建推荐引擎