Social-label-based method for optimizing personalized recommendation system

Abstract

The invention discloses a social-label-based method for optimizing a personalized recommendation system. In the method, social label similarity and score similarity are adopted and applied to calculation of a user-and-project oriented K-nearest neighbor model, and then a user and a project of a K-nearest neighbor are used for calculating a prediction score of the project by the user at the same time. Because the label similarity and the score similarity are adopted in the method at the same time, so that the K-nearest neighbor calculation of the user and the project is more accurate, the recommendation accuracy is obviously higher than that obtained by singly adopting the score similarity, and a cold-start problem based on a label similarity model can be solved. Therefore, a data sparsityproblem can be solved by using a user-and-project oriented recommendation model, and the recommendation accuracy is also obviously higher than that of a conventional user-oriented recommendation model and a project-oriented recommendation model.

Description

technical field [0001] The present invention relates to a personalized recommendation system applicable to e-commerce information, more particularly, to a user- and project-oriented personalized recommendation system optimization method based on social tags. Background technique [0002] In the process of rapid development of e-commerce, personalized recommendation technology is indispensable. Personalized recommendation is based on user's preferences and interests. Personalized recommendation system recommends items that he may be interested in to the user, thereby promoting the increase of sales. Most of the personalized recommendation systems use the collaborative filtering method to provide personalized recommendation information. In the collaborative filtering recommendation system, the user's interest in the item is quantified as the user's rating on the item. In a given user set U={u 1 , u 2 ,...,u c ,...u m} and itemset I = {i 1 , i 2 ,...,i a ,... i n}, the ...

AI Technical Summary

Problems solved by technology

[0004] In the research field of personalized recommendation system, the K-nearest neighbor model is the most convenient, simple and mature method in the collaborative filtering recommendation system. However, the recommendation performance of a single K-nearest neighbor model is defective. Research on optimization is very active, including the combination of item-oriented and user-oriented recommendation models through similarity fusion proposed by Jun Wan et al., matrix extension of social tags and user-item matrices proposed by Karen H.L. Form an item-oriented and user-oriented model matrix with social tags, then calculate the similarity to make recommendations, etc.

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