Improving Diversity of User-Based Two-Step Recommendation Algorithm with Popularity Normalization
文献类型: 会议论文
第一作者: Xiangyu Zhao
作者: Xiangyu Zhao 1 ; Wei Chen 2 ; Feng Yang 1 ; Zhongqiang Liu 3 ;
作者机构: 1.National Engineering Research Center for Information Technology in Agriculture
2.Agricultural Information Institute, Chinese Academy of Agricultural Sciences
3.Beijing Engineering Research Center of Agricultural Internet of Things
关键词: Recommender system;Collaborative filtering;Diversity;Two-step recommendation;Popularity normalization
会议名称: International Conference on Database Systems for Advanced Applications
主办单位:
页码: 15-26
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.
分类号: TP311.13-53
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