Context-aware Sequential Recommendation

TL;DR

This paper introduces CA-RNN, a novel context-aware sequential recommendation model that adaptively incorporates external contextual information and time intervals, significantly improving recommendation accuracy over existing methods.

Contribution

The paper proposes CA-RNN, which uses adaptive context-specific matrices to better model external factors and temporal dynamics in sequential recommendations.

Findings

CA-RNN outperforms state-of-the-art methods on Taobao and Movielens-1M datasets.

Incorporating context improves recommendation accuracy.

Adaptive matrices effectively model external and temporal influences.

Abstract

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 real-world 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 context-specific 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 CA-RNN model yields significant improvements over state-of-the-art sequential recommendation methods and context-aware recommendation methods on two public datasets, i.e., the Taobao dataset and the Movielens-1M dataset.