Abstract
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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
