research-article

Global heterogeneous graph enhanced category-aware attention network for session-based recommendation

Authors:

Bang WangAuthors Info & Claims

Volume 243, Issue C

https://doi.org/10.1016/j.eswa.2023.122907

Published: 25 June 2024 Publication History

Abstract

Session-based recommendation (SBR) aims at predicting the next item based on an ongoing recorded session of user’s behaviors. Most of existing approaches mainly focus on capturing various item relations such as transitions, co-occurrences, via item identifiers (IDs). In such model, item attributes, such as category, are simply ignored. A few approaches try to explore items’ category information to design SBR model. However, due to the dynamically changing preferences of users, it is challenging to exactly pinpoint which category of items is currently of interest to the user. We argue that since a single session faces a serious problem of category information sparsity, the category context of other similar sessions can help to model user category interests more accurately. To model user intent more accurately, the intra-category information is vital beside the inter-category user interest modeling.

In this paper, we propose a Heterogeneous Graph Enhanced Category-aware Attention Network (HGCAN) as a solution model to accomplish both two tasks of estimating user’s category intent and predicting items for recommendation. The architecture of HGCAN consists of a weighted heterogeneous graph attention network (WHGAT) on global graph for item and category embeddings learning and a category-aware attention network for session representations learning. Experiments on three public datasets validate our HGCAN outperforms these state-of-the-art models. Source code and data are available at https://github.com/resistzzz/HGCAN.

Highlights

•

Exploit category information of items for session-based recommendation.

•

Propose a Heterogeneous Graph Enhanced Category-aware Attention Network.

•

Experiments validate the superiority of HGCAN over competitors.

References

[1]

Cai, R., Wu, J., San, A., Wang, C., & Wang, H. (2021). Category-aware collaborative sequential recommendation. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval (pp. 388–397).

[2]

Chen L., Cao J., Liang W., Wu J., Ye Q., Keywords-enhanced deep reinforcement learning model for travel recommendation, ACM Transactions on the Web (2022) Just Accepted.

[3]

Chen Q., Li G., Zhou Q., Shi S., Zou D., Double attention convolutional neural network for sequential recommendation, ACM Transactions on the Web 16 (4) (2022).

[4]

Chen R., Ma P., Zhu Y., Chen Q., Category-aware graph neural network for session-based recommendation, in: 2022 IEEE 28th international conference on parallel and distributed systems, IEEE, 2023, pp. 891–899.

[5]

Chen Y., Qian W., Liu D., Su Y., Zhou Y., Han J., Li R., Contrastive learning for session-based recommendation, in: International conference on artificial neural networks, Springer, 2022, pp. 358–369.

[6]

Chen, T., & Wong, R. C.-W. (2020). Handling Information Loss of Graph Neural Networks for Session-based Recommendation. In Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 1172–1180).

[7]

Cho, J., Kang, S., Hyun, D., & Yu, H. (2021). Unsupervised Proxy Selection for Session-based Recommender Systems. In Proceedings of the 44th international ACM SIGIR conference on research and development in information retrieval (pp. 327–336).

[8]

Cui C., Shen Q., Zhu S., Pang Y., Zhang Y., Gao H., Wei Z., Intention adaptive graph neural network for category-aware session-based recommendation, in: International conference on database systems for advanced applications, Springer, 2022, pp. 150–165.

[9]

Guo, J., Yang, Y., Song, X., Zhang, Y., Wang, Y., Bai, J., & Zhang, Y. (2022). Learning Multi-granularity Consecutive User Intent Unit for Session-based Recommendation. In Proceedings of the fifteenth ACM international conference on web search and data mining (pp. 343–352).

[10]

Hidasi, B., Karatzoglou, A., Baltrunas, L., & Tikk, D. (2016). Session-based recommendations with recurrent neural networks. In Proceedings of the 4th international conference on learning representations.

[11]

Huang C., Chen J., Xia L., Xu Y., Dai P., Chen Y., Bo L., Zhao J., Huang J.X., Graph-enhanced multi-task learning of multi-level transition dynamics for session-based recommendation, in: AAAI conference on artificial intelligence, 2021.

[12]

Kang W.C., McAuley J., Self-attentive sequential recommendation, in: 2018 IEEE international conference on data mining, IEEE, 2018, pp. 197–206.

[13]

Li A., Cheng Z., Liu F., Gao Z., Guan W., Peng Y., Disentangled graph neural networks for session-based recommendation, 2022, arXiv preprint arXiv:2201.03482.

[14]

Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., & Ma, J. (2017). Neural attentive session-based recommendation. In Proceedings of the 2017 ACM on conference on information and knowledge management (pp. 1419–1428).

[15]

Li Y., Tarlow D., Brockschmidt M., Zemel R., Gated graph sequence neural networks, 2015, arXiv preprint arXiv:1511.05493.

[16]

Li X., Wang X., Zhang H., Zhang J., Session-based recommendation with temporal graph neural network and contrastive learning, in: 2023 3rd International conference on neural networks, information and communication engineering, IEEE, 2023, pp. 10–14.

[17]

Liu L., Wang L., Lian T., CaSe4SR: Using category sequence graph to augment session-based recommendation, Knowledge-Based Systems 212 (2021).

[18]

Liu, Q., Zeng, Y., Mokhosi, R., & Zhang, H. (2018). STAMP: short-term attention/memory priority model for session-based recommendation. In Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 1831–1839).

[19]

Liu B., Zhang H., Kong L., Niu D., Factorizing historical user actions for next-day purchase prediction, ACM Transactions on the Web 16 (1) (2021).

[20]

Luo, A., Zhao, P., Liu, Y., Zhuang, F., Wang, D., Xu, J., Fang, J., & Sheng, V. S. (2020). Collaborative Self-Attention Network for Session-based Recommendation. In IJCAI (pp. 2591–2597).

[21]

Meng, W., Yang, D., & Xiao, Y. (2020). Incorporating user micro-behaviors and item knowledge into multi-task learning for session-based recommendation. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval (pp. 1091–1100).

[22]

Pan, Z., Cai, F., Chen, W., Chen, H., & de Rijke, M. (2020). Star Graph Neural Networks for Session-based Recommendation. In Proceedings of the 29th ACM international conference on information & knowledge management (pp. 1195–1204).

[23]

Pan, Z., Cai, F., Ling, Y., & de Rijke, M. (2020). An Intent-guided Collaborative Machine for Session-based Recommendation. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval (pp. 1833–1836).

[24]

Pan, Z., Cai, F., Ling, Y., & de Rijke, M. (2020). Rethinking Item Importance in Session-based Recommendation. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval (pp. 1837–1840).

[25]

Qian X., Rossi R.A., Du F., Kim S., Koh E., Malik S., Lee T.Y., Ahmed N.K., Personalized visualization recommendation, ACM Transactions on the Web 16 (3) (2022).

[26]

Qiu, R., Li, J., Huang, Z., & Yin, H. (2019). Rethinking the item order in session-based recommendation with graph neural networks. In Proceedings of the 28th ACM international conference on information and knowledge management (pp. 579–588).

[27]

Quadrana, M., Karatzoglou, A., Hidasi, B., & Cremonesi, P. (2017). Personalizing session-based recommendations with hierarchical recurrent neural networks. In Proceedings of the eleventh ACM conference on recommender systems (pp. 130–137).

[28]

Ren, P., Chen, Z., Li, J., Ren, Z., Ma, J., & de Rijke, M. (2019). RepeatNet: A repeat aware neural recommendation machine for session-based recommendation. In Proceedings of the AAAI conference on artificial intelligence, vol. 33 (pp. 4806–4813).

[29]

Ren, R., Liu, Z., Li, Y., Zhao, W. X., Wang, H., Ding, B., & Wen, J. R. (2020). Sequential recommendation with self-attentive multi-adversarial network. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval (pp. 89–98).

[30]

Rendle, S., Freudenthaler, C., & Schmidt-Thieme, L. (2010). Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 19th international conference on world wide web (pp. 811–820).

[31]

Shani G., Heckerman D., Brafman R.I., An MDP-based recommender system, Journal of Machine Learning Research 6 (Sep) (2005) 1265–1295.

[32]

Tai W., Lan T., Wu Z., Wang P., Wang Y., Zhou F., Improving session-based recommendation with contrastive learning, User Modeling and User-Adapted Interaction 33 (1) (2023) 1–42.

[33]

Tan, Y. K., Xu, X., & Liu, Y. (2016). Improved recurrent neural networks for session-based recommendations. In Proceedings of the 1st workshop on deep learning for recommender systems (pp. 17–22).

[34]

Wang S., Cao L., Wang Y., Sheng Q.Z., Orgun M.A., Lian D., A survey on session-based recommender systems, ACM Computing Surveys 54 (7) (2021) 1–38.

[35]

Wang F., Lu X., Lyu L., CGSNet: Contrastive graph self-attention network for session-based recommendation, Knowledge-Based Systems 251 (2022).

[36]

Wang, M., Ren, P., Mei, L., Chen, Z., Ma, J., & de Rijke, M. (2019). A collaborative session-based recommendation approach with parallel memory modules. In Proceedings of the 42nd international ACM SIGIR conference on research and development in information retrieval (pp. 345–354).

[37]

Wang, Z., Wei, W., Cong, G., Li, X. L., Mao, X. L., & Qiu, M. (2020). Global context enhanced graph neural networks for session-based recommendation. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval (pp. 169–178).

[38]

Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., & Tan, T. (2019). Session-based recommendation with graph neural networks. In Proceedings of the AAAI conference on artificial intelligence, vol. 33 (pp. 346–353).

[39]

Xia, X., Yin, H., Yu, J., Shao, Y., & Cui, L. (2021). Self-Supervised Graph Co-Training for Session-based Recommendation. In Proceedings of the 30th ACM international conference on information & knowledge management (pp. 2180–2190).

[40]

Xia X., Yin H., Yu J., Wang Q., Cui L., Zhang X., Self-supervised hypergraph convolutional networks for session-based recommendation, in: Proceedings of the AAAI Conference on Artificial Intelligence, 35, 2021, pp. 4503–4511.

[41]

Xu H., Yang B., Liu X., Fan W., Li Q., Category-aware multi-relation heterogeneous graph neural networks for session-based recommendation, Knowledge-Based Systems 251 (2022).

[42]

Xu, C., Zhao, P., Liu, Y., Sheng, V. S., Xu, J., Zhuang, F., Fang, J., & Zhou, X. (2019). Graph Contextualized Self-Attention Network for Session-based Recommendation. In IJCAI, vol. 19 (pp. 3940–3946).

[43]

Yi Z., Song R., Li J., Xu H., Neighbor-enhanced graph transition network for session-based recommendation, International Journal of Machine Learning and Cybernetics 14 (4) (2023) 1317–1331.

[44]

Yu, F., Zhu, Y., Liu, Q., Wu, S., Wang, L., & Tan, T. (2020). TAGNN: Target attentive graph neural networks for session-based recommendation. In Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval (pp. 1921–1924).

[45]

Yuan, F., Karatzoglou, A., Arapakis, I., Jose, J. M., & He, X. (2019). A simple convolutional generative network for next item recommendation. In Proceedings of the twelfth ACM international conference on web search and data mining (pp. 582–590).

[46]

Yuan, J., Song, Z., Sun, M., Wang, X., & Zhao, W. X. (2021). Dual Sparse Attention Network For Session-based Recommendation. In Proceedings of the AAAI conference on artificial intelligence, vol. 35 (5), (pp. 4635–4643).

[47]

Zhang Z., Wang B., Learning sequential and general interests via a joint neural model for session-based recommendation, Neurocomputing 415 (2020) 165–173.

[48]

Zhang Z., Wang B., Fusion of latent categorical prediction and sequential prediction for session-based recommendation, Information Sciences 569 (2021) 125–137.

[49]

Zhang Z., Wang B., Graph neighborhood routing and random walk for session-based recommendation, in: 2021 IEEE international conference on data mining, IEEE, 2021, pp. 1517–1522.

[50]

Zhang Z., Wang B., Graph spring network and informative anchor selection for session-based recommendation, 2022, arXiv preprint arXiv:2202.09502.

[51]

Zhang X., Xu B., Yang L., Li C., Ma F., Liu H., Lin H., Price DOES matter! modeling price and interest preferences in session-based recommendation, 2022, arXiv preprint arXiv:2205.04181.

[52]

Zhang S., Yao L., Sun A., Tay Y., Deep learning based recommender system: A survey and new perspectives, ACM Computing Surveys 52 (1) (2019) 1–38.

Digital Library

[53]

Zhang C., Zheng W., Liu Q., Nie J., Zhang H., SEDGN: Sequence enhanced denoising graph neural network for session-based recommendation, Expert Systems with Applications 203 (2022).

[54]

Zhao, Q., Chen, J., Chen, M., Jain, S., Beutel, A., Belletti, F., & Chi, E. H. (2018). Categorical-attributes-based item classification for recommender systems. In Proceedings of the 12th ACM conference on recommender systems (pp. 320–328).

[55]

Zheng, Y., Liu, S., Li, Z., & Wu, S. (2020). DGTN: Dual-channel Graph Transition Network for Session-based Recommendation. In ICDMW.

[56]

Zheng X., Wu R., Han Z., Chen C., Chen L., Han B., Heterogeneous information crossing on graphs for session-based recommender systems, ACM Transactions on the Web (2022).

[57]

Zhou, F., Wen, Z., Zhang, K., Trajcevski, G., & Zhong, T. (2019). Variational session-based recommendation using normalizing flows. In The world wide web conference (pp. 3476–3475).

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Published In

cover image Expert Systems with Applications: An International Journal

Expert Systems with Applications: An International Journal Volume 243, Issue C

Jun 2024

1588 pages

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Elsevier Ltd.

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Pergamon Press, Inc.

United States

Publication History

Published: 25 June 2024

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