Study on Personalized Book Recommendation Service for New College Readers

• Published in: Nongye tushu qingbao xuekan Vol. 31; no. 5; p. 50
• Main Author: WANG Shengbin
• Format: Journal Article
• Language: Chinese
• Published: Beijing Agricultural Information Institute of Chinese Academy of Agricultural Sciences 01.01.2019
• ISSN: 1002-1248
• DOI: 10.13998/j.cnki.issn1002-1248.2019.05.19-0154

[Purpose/Significance]Aimed at the user cold start problem that prevent the university smart library from accurately recommending books to new readers, a personalized recommendation method for new college readers is proposed to provide practical solutions for carrying out personalized recommendation service and increasing new readers' borrowing rate.[Method/Process]Through data mining on borrowing circulating big data in Beihua university library, the conclusion is reached that new readers and existing readers with similar attributes have similar borrowing and reading preferences;Then, the singular value decomposition is used to solve the data sparse problem and the Euclidean distance and ant colony algorithm are used to cluster the new readers and existing readers, which building a bridge between new readers and existing readers. Finally, the Top-N algorithm is adopted to recommend the books borrowed by existing readers to new readers. [Result/Conclusion]Take the readers from grade 2017 as experiment subject