Abstract：This paper firstly constructs the multi-interest feature library from readers’ interest lexicon and index with update algorithms combining gradual forgetting strategy and sliding window, then calculates the similarity measures of readers’ interest lexicon and index with books, and adds the two similarity with linear superposition to propose an operable and extensible hybrid recommendation algorithm. This algorithm synthetically uses the index types of books in Chinese Library Classification, and effectively solves the problem of data sparseness. Finally, the paper achieves a personalized recommendation system of the library books, and correlative experimental results are introduced in details.
马健, 杜泽宇, 李树青. 基于多兴趣特征分析的图书馆个性化图书推荐方法[J]. 现代图书情报技术, 2012, 28(6): 1-8.
Ma Jian, Du Zeyu, Li Shuqing. Personalized Book Recommendation Algorithm Based on Multi-interest Analysis in Library. New Technology of Library and Information Service, 2012, 28(6): 1-8.
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