Improving Diversity of User-Based Two-Step Recommendation Algorithm with Popularity Normalization

文献类型： 外文期刊

第一作者： Zhao, Xiangyu

作者： Zhao, Xiangyu;Liu, Zhongqiang;Chen, Wei;Zhao, Xiangyu;Yang, Feng;Chen, Wei;Yang, Feng;Liu, Zhongqiang

作者机构：

关键词： Recommender system;Collaborative filtering;Diversity;Two-step recommendation;Popularity normalization

期刊名称：DATABASE SYSTEMS FOR ADVANCED APPLICATIONS, DASFAA 2016 （ 影响因子：0.302； ）

ISSN： 0302-9743

年卷期： 2016 年 9645 卷

页码：

收录情况： SCI

摘要： Recommender systems become increasingly significant in solving the information overload problem. Beyond conventional rating prediction and ranking prediction recommendation technologies, two-step recommendation algorithms have been demonstrated that they have outstanding accuracy performance in top-N recommendation tasks. However, their recommendation lists are biased towards popular items. In this paper, we propose a popularity normalization method to improve the diversity of user-based two-step recommendation algorithms. Experiment results show that our proposed approach improves the diversity performance significantly while maintaining the advantage of two-step recommendation approaches on accuracy metrics.

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