A Personalized Recommendation Algorithm Based on Improve Belief Propagation
GONG An
SUN Yuhong
Abstract：The RWR-based method as a TOP-N recommendation solves most of the problems encountered by the traditional recommendation algorithm,but it only considers homophily of nodes and requires a great space cost in the matrix decomposition. It would not exist these problems if applied Belief Propagation algorithm to the personalized recommendation system. However,the time complexity is large when the traditional Belief Propagation algorithm calculates target node's confidence through the global nodes. Therefore,this paper optimizes the BP algorithm and applies it to the personalized recommendation algorithm. The users and the projects are two sets of nodes. Confidence of the target node is calculated by the nodes in the adaptive size region. Then the corre?sponding project is recommended to the target user according to the final node confidence. In the experiment,the corresponding pa?rameter setting under the optimal precision is obtained through the comparison with the traditional Belief Propagation algorithm,and the algorithm of this paper with the RWR-based method and the project-based collaborative filtering algorithm are compared,the results show that the proposed algorithm is superior to the above algorithm.
Keywords：personalized recommendationBelief Propagation algorithmnode's confidenceadaptive size region
Publication Date：2019-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 2137-2140,2195 )