针对单一角度描述用户兴趣存在片面性的问题,该文提出一种融合用户主题兴趣和用户行为的文档推荐方法。一方面从主题兴趣的角度,构建反映用户主题兴趣的主题向量用户模型;另一方面从用户行为的角度,构建反映用户行为兴趣的打分矩阵用户模型。然后,基于上述用户模型提出了两种文档推荐方法,并采用线性加权的方式融合这两种方法,从而实现对用户主题兴趣与用户行为的融合。实验结果表明,该方法的推荐结果好于协同过滤推荐方法和基于内容的推荐方法。
Abstract
This paper proposes a method by combining the topic and the behavior to describe the user interest. On the one hand, from the perspective of the topics, a topic vector model is constructed to reflect the users interest in topic. On the other hand, from the perspective of behavior, a score matrix model is constructed to reflect the users interest in behavior. Then, based on two user models, two document recommendation methods are constructed, and then combined by the linear weighted method. Experimental results show that the proposed method is better than the collaborative filtering recommendation method and the content-based recommendation method.
关键词
用户模型 /
主题兴趣 /
用户行为 /
文档推荐
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Key words
user model /
topic interest /
user behavior /
document recommendation
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参考文献
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脚注
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基金
国家科技支撑计划(2015BAH20F01);国防科研基础项目(A0520131003)
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