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A Dynamic Recurrent Model for Next Basket Recommendation
Yu, Feng; Liu, Qiang; Wu, Shu; Wang, Liang; Tan, Tieniu
2016
Conference NameInternational Conference on Research on Development in Information Retrieval (SIGIR)
Source PublicationIn Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2016
Conference DateJuly 17-21
Conference Placepisa
AbstractNext basket recommendation becomes an increasing concern. Most conventional models explore either sequential transaction features or general interests of users. Further, some works treat users’ general interests and sequential behaviors as two totally divided matters, and then combine them in some way for next basket recommendation. Moreover, the state-of-the-art models are based on the assumption of Markov Chains (MC), which only capture local sequential features between two adjacent baskets. In this work, we propose a novel model, Dynamic REcurrent bAsket Model (DREAM), based on Recurrent Neural Network (RNN). DREAM not only learns a dynamic representation of a user but also captures global sequential features among baskets. The dynamic representation of a specific user can reveal user’s dynamic interests at different time, and the global sequential features reflect interactions of all baskets of the user over time. Experiment results on two public datasets indicate that DREAM is more effective than the state-of-the-art models for next basket recommendation.
KeywordNext Basket Recommendation Recurrent Neural Network
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12348
Collection智能感知与计算研究中心
Corresponding AuthorWu, Shu
AffiliationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yu, Feng,Liu, Qiang,Wu, Shu,et al. A Dynamic Recurrent Model for Next Basket Recommendation[C],2016.
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