Transformer-Based Multi-Behavior Sequential Recommendation Method
DOI:
https://doi.org/10.54097/06t11748Keywords:
Multi-Behavior Sequential Recommendation, Transformer, Sequential Recommendation, Auxiliary BehaviorAbstract
Multi-Behavior Sequential Recommendation (MBSR) is an important research direction in sequential recommendation. It aims to exploit auxiliary behaviors in users’ historical interactions to improve item prediction under a target behavior. In recent years, Transformer architectures have provided an effective foundation for MBSR because self-attention can model long-range sequential dependencies and interactions among heterogeneous behaviors. This paper reviews recent advances in Transformer-based MBSR. We first introduce the task formulation, research background, and representative application scenarios. Representative methods are then reviewed in terms of behavior representation, relation modeling, temporal modeling, and interest modeling, with emphasis on how auxiliary behaviors are used to improve target-behavior item prediction and characterize evolving user interests. Finally, we summarize the main challenges, with particular attention to noisy auxiliary behaviors, efficient long-sequence modeling, and coordination among multiple target behaviors, and discuss promising directions for future research.
Downloads
References
[1] Kang, W. C., & McAuley, J. (2018). Self-attentive sequential recommendation. In 2018 IEEE International Conference on Data Mining (ICDM) (pp. 197–206).
[2] Sun, F., Liu, J., Wu, J., et al. (2019). BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. In Proceedings of the 28th ACM International Conference on Information and Knowledge Management (pp. 1441–1450).
[3] Li, J., Wang, Y., & McAuley, J. J. (2020). Time interval aware self-attention for sequential recommendation. In Proceedings of the 13th ACM International Conference on Web Search and Data Mining (pp. 322–330).
[4] Zhou, K., Wang, H., Zhao, W. X., et al. (2020). S3-Rec: Self-supervised learning for sequential recommendation with mutual information maximization. In Proceedings of the 29th ACM International Conference on Information and Knowledge Management (pp. 1893–1902).
[5] Xie, X., Sun, F., Liu, Z., et al. (2022). Contrastive learning for sequential recommendation. In 2022 IEEE 38th International Conference on Data Engineering (ICDE) (pp. 1259–1273).
[6] Qiu, R., Huang, Z., Yin, H., et al. (2022). Contrastive learning for representation degeneration problem in sequential recommendation. In Proceedings of the 15th ACM International Conference on Web Search and Data Mining (pp. 813–823).
[7] Chen, X., Li, Z., Pan, W., et al. (2026). A survey on multi-behavior sequential recommendation. Science China Information Sciences, 69(3), 131101.
[8] Kim, K., Kim, S., Lee, G., et al. (2026). A comprehensive survey on multi-behavior recommender systems: Extended taxonomy and recent advances. International Journal of Data Science and Analytics, 22(1), 40.
[9] Zhou, M., Ding, Z., Tang, J., et al. (2018). Micro behaviors: A new perspective in e-commerce recommender systems. In Proceedings of the 11th ACM International Conference on Web Search and Data Mining (pp. 727–735).
[10] Li, Z., Zhao, H., Liu, Q., et al. (2018). Learning from history and present: Next-item recommendation via discriminatively exploiting user behaviors. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1734–1743).
[11] He, M., Pan, W., & Ming, Z. (2022). BAR: Behavior-aware recommendation for sequential heterogeneous one-class collaborative filtering. Information Sciences, 608, 881–899.
[12] Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (pp. 5998–6008).
[13] Yuan, E., Guo, W., He, Z., et al. (2022). Multi-behavior sequential transformer recommender. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1642–1652).
[14] Yang, Y., Huang, C., Xia, L., et al. (2022). Multi-behavior hypergraph-enhanced transformer for sequential recommendation. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 2263–2274).
[15] Xia, L., Huang, C., Xu, Y., et al. (2023). Multi-behavior sequential recommendation with temporal graph transformer. IEEE Transactions on Knowledge and Data Engineering, 35(6), 6099–6112.
[16] Su, J., Chen, C., Lin, Z., et al. (2023). Personalized behavior-aware transformer for multi-behavior sequential recommendation. In Proceedings of the 31st ACM International Conference on Multimedia (pp. 6321–6331).
[17] Elsayed, S., Rashed, A., & Schmidt-Thieme, L. (2024). Multi-behavioral sequential recommendation. In Proceedings of the 18th ACM Conference on Recommender Systems (pp. 902–906).
[18] Jiang, J., Zhang, K., Kang, J., et al. (2025). GEAR: Generalized alternating regressor for multi-behavior sequential recommendation. In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 2870–2874).
[19] Li, Y., Cheng, M., Luo, Y., et al. (2026). BLADE: A behavior-level data augmentation framework with dual fusion modeling for multi-behavior sequential recommendation. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 40, No. 18, pp. 15189–15197).
[20] Chen, X., Luo, Y., Xu, Z., Pan, W., & Ming, Z. (2026). Behavior-aware attribute-infused sequential encoders for next-item recommendation. ACM Transactions on Information Systems, 44(5), 107, 1–24.
[21] Chen, W., He, M., Ni, Y., et al. (2022). Global and personalized graphs for heterogeneous sequential recommendation by learning behavior transitions and user intentions. In Proceedings of the 16th ACM Conference on Recommender Systems (pp. 268–277).
[22] He, C., Liu, Y., Li, Q., et al. (2025). Multi-grained preference enhanced transformer for multi-behavior sequential recommendation. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 872–883).
[23] Li, Q., Ma, H., Jin, W., et al. (2024). Hypergraph-enhanced multi-interest learning for multi-behavior sequential recommendation. Expert Systems with Applications, 255, 124497.
[24] Chen, R., Fan, J., Wu, M., Cheng, R., & Chai, M. (2026). GXNSRec: Multi-behavior sequential recommender based on graph cross networks. Expert Systems with Applications, 297, 129387.
[25] Jin, H., Yang, G., Chan, Z., et al. (2026). Multi-behavior sequential modeling with transition-aware graph attention network for e-commerce recommendation. In Proceedings of the ACM Web Conference 2026 (pp. 8681–8684).
[26] Zhuang, B., Lan, Y., & Zhang, M. (2025). CAMBSRec: A context-aware multi-behavior sequential recommendation model. Informatics, 12(3), 79.
[27] Liu, H., Ding, J., Zhu, Y., et al. (2023). Modeling multi-aspect preferences and intents for multi-behavioral sequential recommendation. Knowledge-Based Systems, 280, 111013.
[28] Luo, J., He, M., Lin, X., et al. (2022). Dual-task learning for multi-behavior sequential recommendation. In Proceedings of the 31st ACM International Conference on Information and Knowledge Management (pp. 1379–1388).
[29] Li, X., Chen, H., Yu, J., et al. (2024). Global heterogeneous graph and target interest denoising for multi-behavior sequential recommendation. In Proceedings of the 17th ACM International Conference on Web Search and Data Mining (pp. 387–395).
[30] Han, Y., Wang, H., Wang, K., et al. (2024). Efficient noise-decoupling for multi-behavior sequential recommendation. In Proceedings of the ACM Web Conference 2024 (pp. 3297–3306).
[31] Xiao, Q., Xiao, J., Pan, W., et al. (2026). MBASR: A generic framework for multi-behavior data augmentation in sequential recommendation. ACM Transactions on Recommender Systems, 4(3), 36, 1–28.
[32] Chen, Y., Cao, Q., Huang, X., et al. (2024). Multi-behavior collaborative contrastive learning for sequential recommendation. Complex & Intelligent Systems, 10(4), 5033–5048.
[33] Liu, Z., Hou, Y., & McAuley, J. J. (2024). Multi-behavior generative recommendation. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (pp. 1575–1585).
[34] Cui, X., Lu, W., Tong, Y., et al. (2025). Multi-modal multi-behavior sequential recommendation with conditional diffusion-based feature denoising. In Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 1593–1602).
[35] Zhao, L., Tian, B., Wang, T., et al. (2025). Enhancing multi-behavior sequential recommendation via transformer-based cross-layer contrastive learning. In Web Information Systems Engineering – WISE 2025 (pp. 17–27).
[36] Yang, R., Li, X., Sheng, J., et al. (2026). From agnostic to specific: Latent preference diffusion for multi-behavior sequential recommendation. In Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Vol. 1, pp. 1775–1786).
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Journal of Computer Science and Artificial Intelligence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
