Yong Yu¹, Yongjun Luo¹, Tong Li², Shudong Li^{3, *}, Xiaobo Wu⁴, Jinzhuo Liu^{1, *}, Yu Jiang^{3, *}

CMC-Computers, Materials & Continua, Vol.63, No.1, pp. 489-507, 2020, DOI:10.32604/cmc.2020.07616

Abstract 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… More >

Gongshen Liu¹, Kui Meng^1,*, Jiachen Ding¹, Jan P. Nees¹, Hongyi Guo¹, Xuewen Zhang¹

CMC-Computers, Materials & Continua, Vol.58, No.1, pp. 101-120, 2019, DOI:10.32604/cmc.2019.03898

Abstract Collaborative filtering is the most popular approach when building recommender systems, but the large scale and sparse data of the user-item matrix seriously affect the recommendation results. Recent research shows the user’s social relations information can improve the quality of recommendation. However, most of the current social recommendation algorithms only consider the user's direct social relations, while ignoring potential users’ interest preference and group clustering information. Moreover, project attribute is also important in item rating. We propose a recommendation algorithm which using matrix factorization technology to fuse user information and project information together. We first detect the community structure using… More >