Open AccessPersonalized Resource Recommendation Based on Collaborative Filtering Algorithm
Author(s) -
Dongmin Mo,
Xuegang Chen,
Sheng Duan,
Luda Wang,
Qian Wu,
Meiling Zhang,
Lanqing Xie
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1302/2/022025
Subject(s) - collaborative filtering , computer science , recommender system , similarity (geometry) , resource (disambiguation) , information retrieval , object (grammar) , algorithm , data mining , image (mathematics) , artificial intelligence , computer network
Digital libraries can satisfy people’s demand for books and real-time information resources, but because digital libraries have huge resources, users cannot find the resources they need in time, at this time, personalized recommendation methods become an effective way to solve the problem. This paper takes collaborative filtering algorithm based on user recommendation as the research object, analyses the principle of the algorithm and the problem of sparse scoring data, an improved collaborative filtering algorithm is proposed, the algorithm sets scoring by borrowing time, and takes the sum of multiple user scoring as the scoring among users, then, according to the similarity of book content, the user’s scoring of non-scored books is predicted. The proposed algorithm is applied to the digital book recommendation system to obtain better recommendation performance, compared with traditional methods, and the recommendation system based on this algorithm can get more recommendation results close to the needs of readers.