Co-Displayed Items Aware List Recommendation
| Autor: | Zhenpeng Li, Jinze Bai, Jun Gao, Jian Li, Junshuai Song, Chang Zhou, Zhao Li |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Measure (data warehouse)
reinforcement learning Information retrieval Dependency (UML) General Computer Science Computer science General Engineering list recommendation 02 engineering and technology 010501 environmental sciences Recommender system 01 natural sciences Preference Task (project management) 020204 information systems Co-displayed items 0202 electrical engineering electronic engineering information engineering Reinforcement learning General Materials Science lcsh:Electrical engineering. Electronics. Nuclear engineering lcsh:TK1-9971 0105 earth and related environmental sciences |
| Zdroj: | IEEE Access, Vol 8, Pp 64591-64602 (2020) |
| ISSN: | 2169-3536 |
| Popis: | Existing recommender systems usually generate personalized recommendation lists based on the estimation of the preference scores over user-item pairs, while ignoring the impacts of the entire display list that plays a central part in the decision making process of a user. This leaves us an opportunity to generate better recommendation results by considering the impacts of all offered choices. However, such an extension cannot be handled efficiently by traditional top- $k$ list recommendation methods, due to the entire list dependency issue which means a complete list of items is needed before we can precisely measure any item preference among the list. In this paper, we propose a Co-displayed Items Aware (CDIA) list generation approach, which is based on the reinforcement learning architecture, and can efficiently generate high-utility lists. Specifically, we propose CDIA-Sim to predict users’ preferences, which considers the impacts of the co-displayed items. Then, to overcome the entire list dependency issue in the list recommendation task, we utilize the reinforcement learning technique and design CDIA-RL to generate high-utility lists. Experimental results show that CDIA-Sim achieves significant improvements in modeling user-item preferences, and CDIA-RL can yield lists efficiently and effectively, illustrating better performance than other competitors. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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