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Session-based recommendation with an importance extraction module

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Abstract

The goal of session-based recommendation is to make item predictions at the next timestamp based on the anonymous ongoing session. Previous work mainly models user’s preference by exploring the transition pattern between the interacted items in the session. However, they generally fail to pay enough attention to the item importance in terms of the relevance of the items to user’s main purpose. This paper proposes a Session-based Recommendation approach with an Importance Extraction Module, i.e., SR-IEM\(_{{{\text{improved}}}}\), which can simultaneously consider user’s long-term interactions and recent behavior in current session. Specifically, we modify the self-attention mechanism to avoid introducing bias for estimating the importance of each item in the session. Then, the item importances are utilized to produce user’s long-term preference, and the sequential signals are incorporated in the long-term interest modeling. Next, the long-term preference and user’s current interest which is conveyed by the last interacted item in the session are combined to obtain user’s final preference representation. Finally, item predictions are generated using the user preference, where a normalization layer is adopted to solve the long-tail problem. Extensive experiments are conducted on three public benchmark datasets, i.e., Yoochoose 1/64, Yoochoose 1/4 and Diginetica. The experimental results show that SR-IEM\(_{{{\text{improved}}}}\) can outperform the start-of-the-art baselines in terms of Recall and MRR for session-based recommendation. In addition, compared to the state-of-the-art neural methods, SR-IEM\(_{{{\text{improved}}}}\) can obviously reduce the computational complexity.

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Notes

  1. In [32], it has been proved that the last item can represent user’s instant interest and contribute much to capturing user’s main intent.

  2. http://2015.recsyschallenge.com/challege.html.

  3. http://cikm2016.cs.iupui.edu/cikm-cup.

  4. https://github.com/wmeirui/CSRM_SIGIR2019.

  5. https://github.com/CRIPAC-DIG/SR-GNN.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China under No. 61702526 and the Postgraduate Scientific Research Innovation Project of Hunan Province under No. CX20200055. All content represents the opinion of the authors, which is not necessarily shared or endorsed by their respective employers and/or sponsors.

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  1. Zhiqiang Pan and Wanyu Chen have contributed equally to this work.

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  1. Science and Technology on Information Systems Engineering Labratory, National University of Defense Technology, Changsha, 410000, China

    Zhiqiang Pan, Fei Cai, Wanyu Chen & Honghui Chen

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A preliminary version of this paper has appeared in the proceedings of SIGIR 2020 [19]. Compared to the conference version, we (1) propose a new model SR-IEM\(_{{{\text{improved}}}}\) which extends our previous work on accurately obtaining user’s preference by utilizing the layer normalization to solve the long-tail problem and adding the position embeddings to incorporate the sequential signal; (2) include a large scale dataset Yoochoose 1/4 to conduct additional experiments for verifying the effectiveness of our model; (3) conduct an ablation study for validating the utility of each module in the recommendation approach; (4) give a case study to show how the IEM module works on distinguishing the items according to their corresponding importances; and (5) include more related work and present the analysis of our proposal as well as the experimental results in detail.

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Pan, Z., Cai, F., Chen, W. et al. Session-based recommendation with an importance extraction module. Neural Comput & Applic 34, 9813–9829 (2022). https://doi.org/10.1007/s00521-022-06966-3

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