【摘要】 【目的】解决传统数字文献资源内容服务推荐中,无法充分挖掘用户潜在信息需求以及评分矩阵稀疏问题。【方法】利用关联语义链和协同过滤算法提出数字文献资源内容服务推荐算法。【结果】实验结果证明,该算法可以克服单一推荐算法中存在的无法挖掘用户潜在信息需求以及评分矩阵稀疏问题。【局限】缺少对数字资源的大规模采集,实验案例较少。【结论】该算法充分挖掘用户信息需求并产生数字资源推荐信息,为数字资源服务提供商提高用户感知的能力,增强资源服务推荐的准确性和针对性提供了一种新途径。更多还原

【Abstract】 [Objective] Service recommendation of the content of traditional digital literature resources is unable to fully exploit the user potential information demand and the ratings matrixes are always sparse. This paper provides an algorithm using collaborative filtering algorithm and association semantic link. [Methods] A recommendation algorithm for the content of digital literature resources is proposed by using the association semantic link and collaborative filtering algorithm. [Results] The experimental result shows that the algorithm can overcome the problems of the potential information needs of the users and the sparsity of the matrix. [Limitations] Lack of large-scale collection of digital resources, and the experimental cases are few. [Conclusions] The algorithm can fully exploit the users’ information demand and generate the literature recommendation information. Finally, the validity and practicability of the proposed algorithm are verified by experiments.更多还原