A Dynamic Recurrent Model for Next Basket Recommendation

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	A Dynamic Recurrent Model for Next Basket Recommendation
	Yu, Feng; Liu, Qiang; Wu, Shu; Wang, Liang; Tan, Tieniu
	2016
会议日期	July 17-21
会议地点	pisa
关键词	Next Basket Recommendation Recurrent Neural Network
英文摘要	Next 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.
会议录	In Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), 2016
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/12348]
专题	自动化研究所_智能感知与计算研究中心
通讯作者	Wu, Shu
作者单位	Institute of Automation, Chinese Academy of Sciences
推荐引用方式 GB/T 7714	Yu, Feng,Liu, Qiang,Wu, Shu,et al. A Dynamic Recurrent Model for Next Basket Recommendation[C]. 见:. pisa. July 17-21.