高校图书馆的个性化图书推荐系统应用研究

igital libraries have become the majority of colleges and universities to promote the information technology projects, in this project, through the recommendation system to provide users with personalized service is the hot issue in the field of research. Aiming at the personalized recommendation system still exist in the relationship modeling, data sparse research and other issues. Proposed for the university library books, personalized recommendation system framework for users, books homogeneity relationship between the user and book modeling the relationship between heterogeneity and thus possible 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 and books simultaneously to users of cluster analysis, thus improving the accuracy of clustering. Through comparative analysis of historical data, the proposed recommendation algorithm with the accuracy of the more obvious increase.