Collaborative filtering recommendation method based on emotions and trust

Abstract

The invention relates to the field of a recommendation system and discloses a collaborative filtering recommendation method based on emotions and trust. The collaborative filtering recommendation method comprises a step 1 of obtaining an explicit degree of satisfaction by normalizing an item scored matrix of users; calculating the similarity between scored items and items that are not scored according to a vector cosine method, obtaining an implicit degree of satisfaction by using the explicit degree of satisfaction and the similarity, and constituting an extended satisfaction matrix by the explicit degree of satisfaction and the implicit degree of satisfaction; a step 2 of calculating the score similarity and the preference similarity according to the extended satisfaction matrix, and obtaining an objective degree of trust generated by the users on the opinion similarity of the items by using the score similarity, the preference similarity and weights set by a supervised learning algorithm; and a step 3 of abstracting a user social network according to the user satisfaction interaction frequency, establishing a weighted directed graph based on the six-degree partition theory, and calculating a subjective degree of trust generated by familiarity among the users. The method of the invention realizes the collaborative filtering.

Description

technical field [0001] The invention relates to the field of recommendation systems, in particular to a collaborative filtering recommendation method based on emotion and trust. Background technique [0002] With the development of the information age, the increasingly large data flow on the Internet makes it more and more difficult for people to obtain the information they need, and information overload has become an urgent problem to be solved. The recommendation system has attracted the attention of academia and industry because of the personalized service it provides and the sorting and filtering of information. But in the increasingly complex social network environment, the sparsity of user-item rating matrix and the weak transfer of trust still affect the recommendation accuracy. The mainstream recommendation algorithm represented by collaborative filtering is also vulnerable to trolling attacks due to the preference of neighbors for recommendations. Improving the ac...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to provide a collaborative filtering recommendation method based on emotion and trust, which overcomes the problems of low recommendation accuracy and easy entrusted attacks in the prior art, and realizes collaborative filtering

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