| 英文标题 | Recommendation algorithm based on filling method and multi-weight similarity |
| 作者英文名 | Zou Yang, Wu Hecheng, Jiang Yunzhi, Zhao Yingding |
| 机构英文名 | 1.College of Economic & Management,Nanjing University of Aeronautics & Astronautics,Nanjing 211106,China;2.School of Mathematics & Systems Science,Guangdong Polytechnic Normal University,Guangzhou 510540,China;3.College of Software,Jiangxi Agricultural University,Nanchang 330045,China |
| 英文摘要 | In order to solve the problem of data sparsity in traditional recommendation algorithms, many researchers at home and abroad have proposed corresponding recommendation algorithms. However, most of these algorithms have not achieved good recommendation results in personalized recommendation. Therefore, this paper proposed a recommendation algorithm based on improved filling method and multi-weight similarity. Firstly, the algorithm filled missing values and reduced data dimension by improved filling method, then calculated user trust degree and user association degree of bipartite graph respectively, and finally used multi-weight factor to combine the two similarities. Based on this, this algorithm obtained neighbor users according to similarity and made recommendation to target users. The experimental results show that the MAE of proposed algorithm is superior to other recommendation methods in the case of sparse data and personalized recommendation. |
| 英文关键词 | recommendation algorithm; bi-graph correlation; social network similarity; personalized recommendation |