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【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。
【Objective】 In order to solve the problem of traditional digital document resource content service recommendation, the potential information needs of users and sparseness of scoring matrix can not be fully tapped. 【Method】 The proposed algorithm of digital document resource content service recommendation was proposed by using relational semantic chain and collaborative filtering algorithm. 【Result】 The experimental results show that the proposed algorithm can overcome the problem that the single recommendation algorithm can not mine the potential information needs of users and the scoring matrix sparseness problem. [Limitations] Lack of large-scale acquisition of digital resources, experimental cases less. 【Conclusion】 This algorithm fully exploits the user information needs and generates digital resource recommendation information, which provides a new way for digital resource service providers to improve user perception and enhance the accuracy and pertinence of resource service recommendation.