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Time-aware sequence model for next-item recommendation

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Abstract

The sequences of users’ behaviors generally indicate their preferences, and they can be used to improve next-item prediction in sequential recommendation. Unfortunately, users’ behaviors may change over time, making it difficult to capture users’ dynamic preferences directly from recent sequences of behaviors. Traditional methods such as Markov Chains (MC), Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks only consider the relative order of items in a sequence and ignore important time information such as the time interval and duration in the sequence. In this paper, we propose a novel sequential recommendation model, named Interval- and Duration-aware LSTM with Embedding layer and Coupled input and forget gate (IDLSTM-EC), which leverages time interval and duration information to accurately capture users’ long-term and short-term preferences. In particular, the model incorporates global context information about sequences in the input layer to make better use of long-term memory. Furthermore, the model introduces the coupled input and forget gate and embedding layer to further improve efficiency and effectiveness. Experiments on real-world datasets show that the proposed approaches outperform the state-of-the-art baselines and can handle the problem of data sparsity effectively.

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Notes

  1. http://ocelma.net/MusicRecommendationDataset/lastfm-1K.html

  2. https://github.com/vallzey/IDLSTM

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Authors and Affiliations

  1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China

    Dongjing Wang, Dengwei Xu & Dongjin Yu

  2. Advanced Analytics Institute, University of Technology, Sydney, Australia

    Guandong Xu

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  1. Dongjing Wang
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  2. Dengwei Xu
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  3. Dongjin Yu
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  4. Guandong Xu
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Correspondence to Dongjin Yu.

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This research was supported by Zhejiang Provincial Natural Science Foundation of China under No. LQ20F020015, and the Fundamental Research Funds for the Provincial University of Zhejiang by Hangzhou Dianzi University under No. GK199900299012-017.

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Cite this article

Wang, D., Xu, D., Yu, D. et al. Time-aware sequence model for next-item recommendation. Appl Intell 51, 906–920 (2021). https://doi.org/10.1007/s10489-020-01820-2

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