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Recommender System Combining Popularity and Novelty Based on One-Mode Projection of Weighted Bipartite Network

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摘要 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.
出处 《Computers, Materials & Continua》 SCIE EI 2020年第4期489-507,共19页 计算机、材料和连续体(英文)
基金 Project funded by the National Science Foundation of China under Grant(Nos.61462091,61672020,U1803263,61866039,61662085) by the Data Driven Software Engineering innovation team of Yunnan province(No.2017HC012) by Scientific Research Foundation Project of Yunnan Education Department(No.2019J0008,2019J0010) by China Postdoctoral Science Foundation(Nos.2013M542560,2015T81129) A Project of Shandong Province Higher Educational Science and Technology Program(No.J16LN61).
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