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Context-aware Sequential Recommendation
Liu, Qiang1,2; Wu, Shu1,2; Wang, Diyi3; Li, Zhaokang4; Wang, Liang1,2
2016
会议名称IEEE International Conference on Data Mining (ICDM)
会议录名称In Proceedings of the IEEE International Conference on Data Mining (ICDM)
会议日期2016-12
会议地点Barcelona
摘要
Since sequential information plays an important role in modeling user behaviors, various sequential recommendation methods have been proposed. Methods based on Markov assumption are widely-used, but independently combine several most recent components. Recently, Recurrent Neural Networks (RNN) based methods have been successfully applied in several sequential modeling tasks. However, for realworld applications, these methods have difficulty in modeling the contextual information, which has been proved to be very important for behavior modeling. In this paper, we propose
a novel model, named Context-Aware Recurrent Neural Networks (CA-RNN). Instead of using the constant input matrix and transition matrix in conventional RNN models, CA-RNN employs adaptive context-specific input matrices and adaptive context-specific transition matrices. The adaptive contextspecific input matrices capture external situations where user behaviors happen, such as time, location, weather and so on. And the adaptive context-specific transition matrices capture how lengths of time intervals between adjacent behaviors in historical sequences affect the transition of global sequential features. Experimental results show that the proposed CARNN model yields significant improvements over state-of-theart sequential recommendation methods and context-aware recommendation methods on two public datasets, i.e., the Taobao dataset and the Movielens-1M dataset.
关键词Sequential Recommendation Contextual Information Recurrent Neural Networks
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12321
专题智能感知与计算研究中心
通讯作者Wu, Shu
作者单位1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.Northeastern University
4.Rice University
推荐引用方式
GB/T 7714
Liu, Qiang,Wu, Shu,Wang, Diyi,et al. Context-aware Sequential Recommendation[C],2016.
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