UTSP: User-Based Two-Step Recommendation With Popularity Normalization Towards Diversity and Novelty
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 recomme...
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| Veröffentlicht in: | IEEE access 2019, Vol.7, p.145426-145434 |
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| container_title | IEEE access |
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| creator | Niu, Ke Zhao, Xiangyu Li, Fangfang Li, Ning Peng, Xueping Chen, Wei |
| description | 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. |
| doi_str_mv | 10.1109/ACCESS.2019.2939945 |
| format | Article |
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| source | DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek; IEEE Xplore Open Access Journals |
| subjects | Aggregates Agriculture Algorithms collaborative filtering Information technology Measurement Overloading popularity normalization Prediction algorithms Predictive models Recommender systems Top-N recommendation two-step recommendation algorithm |
| title | UTSP: User-Based Two-Step Recommendation With Popularity Normalization Towards Diversity and Novelty |
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