Collaborative filtering recommendation method of theme based on viewpoint enhancement

Abstract

The invention discloses a collaborative filtering recommendation method of a theme based on viewpoint enhancement. The method includes the steps that first, comment text attribute words are extractedbased on an LDA theme model; then, the similar attribute words are classified as one category by utilizing a word2vec model to calculate relations among the attribute words based on the attribute words extracted by the LDA; the attribute words with the same meanings are collected as an attribute surface; the sentiment polarity of comment texts is obtained through the analysis of attribute surfaceviewpoint enhancement; next, a scoring matrix is constructed according to the polarity of sentiment words and attribute words, and the similarity among users is calculated according to a scoring method and through a collaborative filtering algorithm; finally, goods with high scores in the matrix are recommended to other users according to a k-nearest neighbor algorithm. Experimental results show that the collaborative filtering recommendation method is excellent in F value extracted by attribute words and mean absolute error of recommendation.

Description

technical field [0001] The invention belongs to the field of information technology and relates to an information recommendation method, in particular to a new comment text-based personalized recommendation method which combines the advantages of collaborative filtering algorithm and content-based recommendation. Background technique [0002] The development of the Internet sharing information platform enables people all over the world to share their emotions or opinions with others through the Internet. However, the explosive growth of information makes it difficult for users to obtain information closely related to themselves. The recommendation system can provide users with the most needed information by utilizing the potential relationship between users and products, thus solving this problem better. The recommendation system is different from the earlier information system tools or technologies such as databases and search engines. It is a relatively new field. There ...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to transform the relationship between the user and the product to the relationship between the user and the product attribute surface by improving the granularity and accuracy of the product recommendation effect, so that the user's scoring matrix on the product attribute surface has strong robustness , to overcome the problem that the user's preference distribution for products is too scattered and the recommendation effect is not good

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