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

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.