Name
Title | ||
|---|---|---|
A Recommendation Algorithm Based on Belief Propagation and Probability Matrix Factorization |
Abstract | ||
|---|---|---|
Collaborative filtering recommendation algorithm is the most important recommended application methods at present. The selection of users (items) similarity measurement methods in this method may affect the final recommendation effect. Probability matrix factorization algorithm is one of the more and more methods applied in collaborative filtering algorithms. This paper proposes to convert the users (items) consumption network into a bipartite graph and use belief propagation algorithm to obtain fuzzy nearest neighbor set, improving users (items) similarity measure and incorporating it into probability matrix factorization. Experimental tests on the data set showed that the proposed method performance was good. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00076 | 2020 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) |
Keywords | DocType | ISBN |
component,bipartite graph,belief propagation algorithm,probability matrix factorization,nearest neighbors | Conference | 978-1-7281-6610-0 |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Juan Li | 1 | 17 | 15.21 |
| Xiaofeng Wang | 2 | 3 | 4.45 |
| Wanjing Feng | 3 | 0 | 0.34 |