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Context-aware Sequential Recommendation
Liu, Qiang1,2; Wu, Shu1,2; Wang, Diyi3; Li, Zhaokang4; Wang, Liang1,2
Conference NameIEEE International Conference on Data Mining (ICDM)
Source PublicationIn Proceedings of the IEEE International Conference on Data Mining (ICDM)
Conference Date2016-12
Conference PlaceBarcelona
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.
KeywordSequential Recommendation Contextual Information Recurrent Neural Networks
Document Type会议论文
Corresponding AuthorWu, Shu
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
3.Northeastern University
4.Rice University
Recommended Citation
GB/T 7714
Liu, Qiang,Wu, Shu,Wang, Diyi,et al. Context-aware Sequential Recommendation[C],2016.
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