Temporal aware Multi-Interest Graph Neural Network for Session-based Recommendation

Qi Shen, Shixuan Zhu, Yitong Pang, Yiming Zhang, Zhihua Wei
Proceedings of The 14th Asian Conference on Machine Learning, PMLR 189, 2023.

Abstract

Session-based recommendation (SBR) is a challenging task, which aims at recommending next items based on anonymous interaction sequences. Despite the superior performance of existing methods for SBR, there are still several limitations: (i) Almost all existing works concentrate on single interest extraction and fail to disentangle multiple interests of user, which easily results in suboptimal representations for SBR. (ii) Furthermore, previous methods also ignore the multi-form temporal information, which is significant signal to obtain current intention for SBR. To address the limitations mentioned above, we propose a novel method, called Temporal aware Multi-Interest Graph Neural Network (TMI-GNN) to disentangle multi-interest and yield refined intention representations with the injection of two level temporal information. Specifically, by appending multiple interest nodes, we construct a multi-interest graph for current session, and adopt the GNNs to model the item-item relation to capture adjacent item transitions, item-interest relation to disentangle the multi-interests, and interest-item relation to refine the item representation. Meanwhile, we incorporate item-level time interval signals to guide the item information propagation, and interest-level time distribution information to assist the scattering of interest information. Experiments on three benchmark datasets demonstrate that TMI-GNN outperforms other state-of-the-art methods consistently.

Cite this Paper

BibTeX
@InProceedings{pmlr-v189-shen23a, title = {Temporal aware Multi-Interest Graph Neural Network for Session-based Recommendation}, author = {Shen, Qi and Zhu, Shixuan and Pang, Yitong and Zhang, Yiming and Wei, Zhihua}, booktitle = {Proceedings of The 14th Asian Conference on Machine Learning}, year = {2023}, editor = {Khan, Emtiyaz and Gonen, Mehmet}, volume = {189}, series = {Proceedings of Machine Learning Research}, month = {12--14 Dec}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v189/shen23a/shen23a.pdf}, url = {https://proceedings.mlr.press/v189/shen23a.html}, abstract = {Session-based recommendation (SBR) is a challenging task, which aims at recommending next items based on anonymous interaction sequences. Despite the superior performance of existing methods for SBR, there are still several limitations: (i) Almost all existing works concentrate on single interest extraction and fail to disentangle multiple interests of user, which easily results in suboptimal representations for SBR. (ii) Furthermore, previous methods also ignore the multi-form temporal information, which is significant signal to obtain current intention for SBR. To address the limitations mentioned above, we propose a novel method, called Temporal aware Multi-Interest Graph Neural Network (TMI-GNN) to disentangle multi-interest and yield refined intention representations with the injection of two level temporal information. Specifically, by appending multiple interest nodes, we construct a multi-interest graph for current session, and adopt the GNNs to model the item-item relation to capture adjacent item transitions, item-interest relation to disentangle the multi-interests, and interest-item relation to refine the item representation. Meanwhile, we incorporate item-level time interval signals to guide the item information propagation, and interest-level time distribution information to assist the scattering of interest information. Experiments on three benchmark datasets demonstrate that TMI-GNN outperforms other state-of-the-art methods consistently.} }
Endnote
%0 Conference Paper %T Temporal aware Multi-Interest Graph Neural Network for Session-based Recommendation %A Qi Shen %A Shixuan Zhu %A Yitong Pang %A Yiming Zhang %A Zhihua Wei %B Proceedings of The 14th Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Emtiyaz Khan %E Mehmet Gonen %F pmlr-v189-shen23a %I PMLR %U https://proceedings.mlr.press/v189/shen23a.html %V 189 %X Session-based recommendation (SBR) is a challenging task, which aims at recommending next items based on anonymous interaction sequences. Despite the superior performance of existing methods for SBR, there are still several limitations: (i) Almost all existing works concentrate on single interest extraction and fail to disentangle multiple interests of user, which easily results in suboptimal representations for SBR. (ii) Furthermore, previous methods also ignore the multi-form temporal information, which is significant signal to obtain current intention for SBR. To address the limitations mentioned above, we propose a novel method, called Temporal aware Multi-Interest Graph Neural Network (TMI-GNN) to disentangle multi-interest and yield refined intention representations with the injection of two level temporal information. Specifically, by appending multiple interest nodes, we construct a multi-interest graph for current session, and adopt the GNNs to model the item-item relation to capture adjacent item transitions, item-interest relation to disentangle the multi-interests, and interest-item relation to refine the item representation. Meanwhile, we incorporate item-level time interval signals to guide the item information propagation, and interest-level time distribution information to assist the scattering of interest information. Experiments on three benchmark datasets demonstrate that TMI-GNN outperforms other state-of-the-art methods consistently.
APA
Shen, Q., Zhu, S., Pang, Y., Zhang, Y. & Wei, Z.. (2023). Temporal aware Multi-Interest Graph Neural Network for Session-based Recommendation. Proceedings of The 14th Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 189 Available from https://proceedings.mlr.press/v189/shen23a.html.

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