TITLE:
Personalized Recommendation Algorithm Based on Rating System and User Interest Association Network
AUTHORS:
Jiaquan Huang, Zhen Jia
KEYWORDS:
Recommender Systems, Association Network, Similarity, Bipartite Network, Collaborative Filtering
JOURNAL NAME:
Journal of Applied Mathematics and Physics,
Vol.10 No.12,
December
7,
2022
ABSTRACT: In most available recommendation algorithms, especially for rating systems, almost all the high rating information is utilized on the recommender system without using any low-rating information, which may include more user information and lead to the accuracy of recommender system being reduced. The paper proposes a algorithm of personalized recommendation (UNP algorithm) for rating system to fully explore the similarity of interests among users in utilizing all the information of rating data. In UNP algorithm, the similarity information of users is used to construct a user interest association network, and a recommendation list is established for the target user with combining the user interest association network information and the idea of collaborative filtering. Finally, the UNP algorithm is compared with several typical recommendation algorithms (CF algorithm, NBI algorithm and GRM algorithm), and the experimental results on Movielens and Netflix datasets show that the UNP algorithm has higher recommendation accuracy.