Personalized Recommendation Algorithm for books and its implementation. Issue 1 (January 2021)

• Record Type: Journal Article
• Authors: Chun-mei, Li; yi-han, Ma; Wei, Pi; Yan, Qi; Jie-teng, Jiang; Shuo, Dong
• Is Part Of: Journal of physics. Volume 1738:Issue 1(2021)
• Publication Date: 2021-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/1738/1/012053
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: In recent years, with the completion of the new library of Qinghai University, the collection of books in the library has greatly increased. The library has a total collection of 880, 000 volumes, covering a dozen disciplines such as science, engineering, agriculture, literature, history, economics, philosophy, law, education, management and medicine. It is difficult for users to find the books they are interested in among the numerous materials. Based on the actual situation of the library of Qinghai University, the differences of different professional users and their personal interests, this paper chooses the item-based collaborative filtering algorithm to realize personalized recommendation. First of all, in the calculation of book similarity, the traditional user score data is not chosen to calculate the similarity, but to calculate the similarity between books and books according to the feature vector of book name. Secondly, in order to avoid the problem of cold start, the system recommends the users who have no borrowing record, but the most borrowed books in their department. The combination of the two realized the personalized recommendation of books. By comparing with other traditional recommendation algorithms, it is found that the algorithm adopted in this paper has better recommendation effect.