A Personalized Recommendation Method Combining Trust and Influence Based on Deep Learning

Abstract

The invention discloses a deep learning-based personalized recommendation method combining trust and influence, which specifically includes: constructing a user-item rating matrix; extracting features from data; calculating the user-item rated matrix to obtain the user initial Direct trust, use the Pearson correlation coefficient to calculate the user similarity, and combine the user interaction frequency to get the user's preference for the item; in the successful or failed user interaction, assign different items to different items according to the user's preference for the item Weight, get the final direct trust degree, and form a user-user trust matrix; use the topological structure information in the trust network to mine influential user nodes in the social network; build and train the model; predict users through the trained model For the ratings of unknown items, the higher rated items are selected to generate the recommendation set. The present invention uses deep learning to obtain more accurate feature vectors, thereby improving the accuracy of recommendation.

Description

technical field [0001] The present invention relates to the field of recommendation system and social network, in particular, to a personalized recommendation method based on deep learning combining trust and influence. 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 information filtering technology that helps us filter useful data from massive data is becoming more and more important. The recommendation system is an ideal method to find data of interest to users from large-scale data according to user preferences. 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. Improving the accuracy and performance of the system has become a...

AI Technical Summary

Problems solved by technology

However, although the collaborative filtering algorithm can improve the recommendation accuracy to a certain extent, it faces the problems of "data sparsity" and "cold start" in practical applications.

The "data sparse" problem refers to the problem that there are too many empty elements in the user-item matrix and too few valuable elements, resulting in too little available data; matter of preference

Images