基于文本的相似度

用 Python 构建推荐引擎

Rob O'Callaghan

Director of Data

缺少明确属性时的处理方法

来自亚马逊的商品描述示例。

用 Python 构建推荐引擎

词频-逆文档频率（TF-IDF）

$$ \Large{\text{TF-IDF} = \frac{\frac{\text{Count of word occurrences}}{\text{Total words in document}}}{\log({\frac{\text{Number of docs word is in}}{\text{Total number of docs}}})}} $$

用 Python 构建推荐引擎

我们的数据

book_summary_df：

Book Description
The Hobbit "Bilbo Baggins lives a simple life with his fellow hobbits in the shire..."
The Great Gatsby "Set in Jazz Age New York, the novel tells the tragic story of Jay ..."
A Game of Thrones "15 years have passed since Robert's rebellion, with a nine-year-long ..."
Macbeth "A brave Scottish general receives a prophecy from a trio of witches ..."
... ...
用 Python 构建推荐引擎

实例化向量器

from sklearn.feature_extraction.text import TfidfVectorizer

tfidfvec = TfidfVectorizer(        ,           )
用 Python 构建推荐引擎

过滤数据

from sklearn.feature_extraction.text import TfidfVectorizer

tfidfvec = TfidfVectorizer(min_df=2,           )
用 Python 构建推荐引擎

过滤数据

from sklearn.feature_extraction.text import TfidfVectorizer

tfidfvec = TfidfVectorizer(min_df=2, max_df=0.7)
用 Python 构建推荐引擎

向量化数据

vectorized_data = tfidfvec.fit_transform(book_summary_df['Descriptions'])

print(tfidfvec.get_feature_names)

['age', 'ancient', 'angry', 'brave', 'battle', 'fellow', 'game', 'general', ...]

print(vectorized_data.to_array())

[[0.21,      0.53,    0.41,    0.64,     0.01,     0.02,     ...
 [0.31,      0.00,    0.42,    0.03,     0.00,     0.73,     ...
 [...,        ...,     ...,     ...,      ...,      ...,     ...
用 Python 构建推荐引擎

格式化数据

tfidf_df = pd.DataFrame(vectorized_data.toarray(),
                        columns=tfidfvec.get_feature_names())

tfidf_df.index = book_summary_df['Book']

print(tfidf_df)

                   | 'age'| 'ancient'| 'angry'| 'brave'| 'battle'| 'fellow'|...
|------------------|------|----------|--------|--------|---------|---------|...
| The Hobbit       |  0.21|      0.53|    0.41|    0.64|     0.01|     0.02|...
| The Great Gatsby |  0.31|      0.00|    0.42|    0.03|     0.00|     0.73|...
| A Game of Thrones|  0.61|      0.42|    0.77|    0.31|     0.83|     0.03|...
|               ...|   ...|       ...|     ...|     ...|      ...|      ...|...
用 Python 构建推荐引擎

余弦相似度

余弦距离： $$cos(\theta)=\frac{A.B }{||A||\cdot||B||}$$

用 Python 构建推荐引擎

余弦相似度

from sklearn.metrics.pairwise import cosine_similarity

# Find similarity between all items
cosine_similarity_array = cosine_similarity(tfidf_summary_df)

# Find similarity between two items
cosine_similarity(tfidf_df.loc['The Hobbit'].values.reshape(1, -1),
                  tfidf_df.loc['Macbeth'].values.reshape(1, -1))
用 Python 构建推荐引擎

开始练习吧！

用 Python 构建推荐引擎

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