我有 4 个带有模式的表(app、text_id、title、text)。现在我想计算所有可能的文本对(标题和文本连接)之间的余弦相似度，并最终将它们存储在包含字段(app1、app2、text_id1、text1、text_id2、text2、cosine_similarity)的 csv 文件中。
由于有很多可能的组合，它应该会非常有效地运行。这里最常见的方法是什么？如有任何指点，我将不胜感激。
编辑: 尽管提供的引用资料可能会触及我的问题，但我仍然不知道如何解决这个问题。有人可以提供有关完成此任务的策略的更多详细信息吗？除了计算余弦相似度之外，我还需要相应的文本对作为输出。
请您参考如下方法:
以下是计算一组文档之间的成对余弦相似度的最小示例(假设您已成功从数据库中检索到标题和文本)。
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
# Assume thats the data we have (4 short documents)
data = [
'I like beer and pizza',
'I love pizza and pasta',
'I prefer wine over beer',
'Thou shalt not pass'
]
# Vectorise the data
vec = TfidfVectorizer()
X = vec.fit_transform(data) # `X` will now be a TF-IDF representation of the data, the first row of `X` corresponds to the first sentence in `data`
# Calculate the pairwise cosine similarities (depending on the amount of data that you are going to have this could take a while)
S = cosine_similarity(X)
'''
S looks as follows:
array([[ 1. , 0.4078538 , 0.19297924, 0. ],
[ 0.4078538 , 1. , 0. , 0. ],
[ 0.19297924, 0. , 1. , 0. ],
[ 0. , 0. , 0. , 1. ]])
The first row of `S` contains the cosine similarities to every other element in `X`.
For example the cosine similarity of the first sentence to the third sentence is ~0.193.
Obviously the similarity of every sentence/document to itself is 1 (hence the diagonal of the sim matrix will be all ones).
Given that all indices are consistent it is straightforward to extract the corresponding sentences to the similarities.
'''
