A personalized Recommendation Algorithm for College Books based on User Interest
Jiabei Li, Tian Wei Xu, Juxiang Zhou · 2020
Aiming at the problems of cold start, data sparseness, and similarity calculation bias in collaborative filtering algorithms, this paper proposes an interest-based university book recommendation algorithm. This algorithm solves the problem of the lack of scoring and the inability to use the collaborative filtering algorithm. At the same time, the combination of popularity and inverse popularity with similarity is considered to be closer to readers' behavioral characteristics. Experiments show that the algorithm is better than the traditional collaborative filtering recommendation algorithm, and has certain recommendation effect and practical value in the application of university libraries.