UTSP: User-Based Two-Step Recommendation With Popularity Normalization Towards Diversity and Novelty

文献类型： 外文期刊

第一作者： Niu, Ke

作者： Niu, Ke;Li, Ning;Niu, Ke;Peng, Xueping;Zhao, Xiangyu;Zhao, Xiangyu;Li, Fangfang;Chen, Wei

作者机构：

关键词： Top-N recommendation; collaborative filtering; popularity normalization; two-step recommendation algorithm

期刊名称：IEEE ACCESS （ 影响因子：3.367； 五年影响因子：3.671 ）

ISSN： 2169-3536

年卷期： 2019 年 7 卷

页码：

收录情况： SCI

摘要： Information technologies such as e-commerce and e-news bring overloaded information as well as convenience to users, cooperatives and companies. Recommender system is a significant technology in solving this information overload problem. Due to the outstanding accuracy performance in top-N recommendation tasks, two-step recommendation algorithms are suitable to generate recommendations. However, their recommendation lists are biased towards popular items. In this paper, we propose a user based two-step recommendation algorithm with popularity normalization to improve recommendation diversity and novelty, as well as two evaluation metrics to measure diverse and novel performance. Experimental results demonstrate that our proposed approach significantly improves the diversity and novelty performance while still inheriting the advantage of two-step recommendation approaches on accuracy metrics.

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[1]UTSP: User-Based Two-Step Recommendation With Popularity Normalization Towards Diversity and Novelty. Niu, Ke,Li, Ning,Niu, Ke,Peng, Xueping,Zhao, Xiangyu,Zhao, Xiangyu,Li, Fangfang,Chen, Wei. 2019

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