Vol.63, No.1, 2020, pp.489-507, doi:10.32604/cmc.2020.07616
Recommender System Combining Popularity and Novelty Based on One-Mode Projection of Weighted Bipartite Network
  • Yong Yu1, Yongjun Luo1, Tong Li2, Shudong Li3, *, Xiaobo Wu4, Jinzhuo Liu1, *, Yu Jiang3, *
1 School of Software, Key Laboratory in Software Engineering of Yunnan Province, Yunnan University, Kunming, 650091, China.
2 School of Big Data, Yunnan Agricultural University, Kunming, 650201, China.
3 Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou, 510006, China.
4 School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, 510006, China.
* Corresponding Author: Jinzhuo Liu. Email: jinzhuo.liu@hotmail.com; lishudong@gzhu.edu.cn.
Received 12 June 2019; Accepted 16 July 2019; Issue published 30 March 2020
Personalized recommendation algorithms, which are effective means to solve information overload, are popular topics in current research. In this paper, a recommender system combining popularity and novelty (RSCPN) based on one-mode projection of weighted bipartite network is proposed. The edge between a user and item is weighted with the item’s rating, and we consider the difference in the ratings of different users for an item to obtain a reasonable method of measuring the similarity between users. RSCPN can be used in the same model for popularity and novelty recommendation by setting different parameter values and analyzing how a change in parameters affects the popularity and novelty of the recommender system. We verify and compare the accuracy, diversity and novelty of the proposed model with those of other models, and results show that RSCPN is feasible.
Personalized recommendation, one-mode projection, weighted bipartite network, novelty recommendation, diversity.
Cite This Article
Yu, Y., Luo, Y., Li, T., Li, S., Wu, X. et al. (2020). Recommender System Combining Popularity and Novelty Based on One-Mode Projection of Weighted Bipartite Network. CMC-Computers, Materials & Continua, 63(1), 489–507.
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