UTSP: User-Based Two-Step Recommendation With Popularity Normalization Towards Diversity and Novelty
文献类型： 外文期刊
作者： Niu, Ke 1 ; Zhao, Xiangyu 3 ; Li, Fangfang 5 ; Li, Ning 1 ; Peng, Xueping 2 ; Chen, Wei 6 ;
作者机构： 1.Beijing Informat Sci & Technol Univ, Comp Sch, Beijing 100101, Peoples R China
2.Univ Technol Sydney, Fac Engn & Informat Technol, Sch Comp Sci, CAI, Ultimo, NSW 2007, Australia
3.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
4.Beijing Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
5.oOh Media Ltd, Sydney, NSW 2060, Australia
6.Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100081, Peoples R China
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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