Abstract
To investigate and address the problem of context-aware sequential prediction, this chapter introduces a sequential prediction model, named context-aware recurrent neural networks (CA-RNNs). Instead of using the constant input matrix and transition matrix in conventional RNN models, CA-RNN uses a context-aware recurrent structure and employs context-specific input matrices and context-specific transition matrices. The context-specific input matrix captures the situations that user behaviors happen, such as time, location, weather, while the context-specific transition matrix captures the time intervals between adjacent behaviors in historical sequences.
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Wu, S., Liu, Q., Wang, L., Tan, T. (2017). Context-Aware Recurrent Structure. In: Context-Aware Collaborative Prediction. SpringerBriefs in Computer Science. Springer, Singapore. https://doi.org/10.1007/978-981-10-5373-3_5
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DOI: https://doi.org/10.1007/978-981-10-5373-3_5
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