RECORD
Context-aware Sequential Recommendation
Information Retrieval · Submitted 19 Sep 2016
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Since sequential information plays an important role in modeling user behaviors, various sequential recommendation methods have been proposed. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | Methods based on Markov assumption are widely-used, but independently combine several most recent components. | background 0.79 | 0.04 | 0.02 | asserted 1.00 |
| s2 | Recently, Recurrent Neural Networks (RNN) based methods have been successfully applied in several sequential modeling tasks. | background 1.00 | 0.05 | 0.02 | asserted 1.00 |
| s3 | 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. | gap 0.89 | 0.08 | 0.03 | asserted 0.99 |
| s4 | In this paper, we propose a novel model, named Context-Aware Recurrent Neural Networks (CA-RNN). | contribution 0.99 | 0.90 | 0.07 | asserted 0.99 |
| s5 | 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. | method 0.93 | 0.79 | 0.06 | asserted 1.00 |
| s6 | The adaptive context-specific input matrices capture external situations where user behaviors happen, such as time, location, weather and so on. | method 0.97 | 0.49 | 0.03 | asserted 1.00 |
| s7 | 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. | method 0.97 | 0.54 | 0.04 | asserted 1.00 |
| s8 | 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. | result 1.00 | 0.96 | 0.91 | asserted 1.00 |
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Original 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.
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References
How the literature uses it
2 arXiv papers cite this in 3 sentences
Papers citing it · 1 sentence without a confident label
Builds on it 1 paper
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[ref] extends recurrent neural networks in context-aware recommendation by introducing context-dependent network components.
A Sequential Embedding Approach for Item Recommendation with Heterogeneous Attributes · Related Work · 0.90
Cites it as background 1 paper
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It has been successfully applied in a variety of applications [ref] .
Modeling Dyadic Conversations for Personality Inference · Recurrent Neural Networks · 0.91
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