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高校图书馆个性化图书推荐系统应用研究

发表时间:2015-04-30  浏览量:1666  下载量:643
全部作者: 于海涛,闫相斌
作者单位: 哈尔滨工业大学图书馆
摘 要: 针对目前个性化推荐系统中仍然存在的关系建模、数据稀疏等问题开展研究。提出面向高校图书馆的个性化图书推荐系统框架,对用户之间、图书之间的同质性关系,用户和图书之间的异质关系进行建模,尽可能解决数据的稀疏性问题。推荐系统采用正交非负矩阵三分解实现联合聚类算法,确保解的唯一性和可解释性。联合聚类算法同时对用户和图书进行聚类分析,进而提高聚类的准确性。通过历史数据的对比分析发现,提出的推荐算法在准确性方面有了较为明显的提高。
关 键 词: 图书馆学;数字图书馆;个性化推荐;联合聚类;数据稀疏
Title: Application research on personalized book recommendation system of university library
Author: YU Haitao, YAN Xiangbin
Organization: Harbin Institute of Technology Library
Abstract: Aiming at the personalized recommendation system still existing in the relationship modeling, data sparse research and other issues, we proposed the university library personalized recommendation system framework for users, books homogeneity relationship between the user and book modeling, the relationship between heterogeneity, possibly to solve the problem of data sparsity. Recommended system uses three orthogonal non-negative matrix decomposition algorithm for joint clustering to ensure the uniqueness of solution and interpretability. Joint clustering algorithm analyzes books and users simultaneously to improve the accuracy of clustering. Through comparative analysis of historical data, The accuracy of the proposed recommendation algorithm is significantly increased.
Key words: library science; digital library; personalized recommendations; joint clustering; sparse data
发表期数: 2015年4月第8期
引用格式: 于海涛,闫相斌. 高校图书馆个性化图书推荐系统应用研究[J]. 中国科技论文在线精品论文,2015,8(8):758-762.
 
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