NEUMF–LSTM: Improving Target Behavior Prediction in Multi-Behavior Recommender Systems
Abstract
This study proposes a NeuMF–LSTM model for multi-behavior recommender systems in e-commerce, integrating the nonlinear interaction learning capability of NeuMF with the sequential modeling strength of LSTM. Experiments on the Tianchi and Tmall datasets show that the proposed model improves purchase prediction accuracy compared to baseline methods, confirming its effectiveness.
Tài liệu tham khảo
[1] S. Hochreiter and J. Schmidhuber. “Long Short-Term Memory.” in Neural Computa-tion. vol. 9. no. 8. pp. 1735-1780. 15 Nov. 1997. doi: 10.1162/neco.1997.9.8.1735.
[2] Cheng. Heng-Tze. et al. "Wide & deep learning for recommender sys-tems." Proceedings of the 1st workshop on deep learning for recommender systems. 2016
[3] Guo. Huifeng. et al. "DeepFM: a factorization-machine based neural network for CTR prediction." arXiv preprint arXiv:1703.04247 (2017).
[4] He. Xiangnan. et al. "Neural collaborative filtering." Proceedings of the 26th interna-tional conference on world wide web. 2017.
[5] Vaswani. Ashish. et al. "Attention is all you need." Advances in neural information processing systems 30 (2017).
[6] Xia. Lianghao. et al. "Knowledge-enhanced hierarchical graph transformer network for multi-behavior recommendation." Proceedings of the AAAI conference on artificial in-telligence. Vol. 35. No. 5. 2021.
[7] Xu. Jingcao. et al. "Multi-behavior self-supervised learning for recommenda-tion." Proceedings of the 46th international ACM SIGIR conference on research and devel-opment in information retrieval. 2023.
[8] Liu. Zihan. Yupeng Hou. and Julian McAuley. "Multi-behavior generative recom-mendation." Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. 2024.
[9] Yan. Mingshi. et al. "Behavior-contextualized item preference modeling for multi-behavior recommendation." Proceedings of the 47th international ACM SIGIR conference on research and development in information retrieval. 2024.
[10] Cheng. Zhiyong. et al. "Disentangled cascaded graph convolution networks for mul-ti-behavior recommendation." ACM Transactions on Recommender Systems 2.4 (2024): 1-27.
[11] F. Li. Y. Liu. L. Xu. Z. Qiao and Y. Li. “Convolutional Concatenation Fusion Imag-ing Method for ERT/UTT Dual-Modality Tomography.” in IEEE Access. vol. 12. pp. 118099-118108. 2024. doi: 10.1109/ACCESS.2024.3447477.
[12] Liu. Zihan. Yupeng Hou. and Julian McAuley. "Multi-behavior generative recom-mendation." Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. 2024.
[13] Yuan. Y.. Zhou. Y.. Chen. X.. Xiong. Q.. & Okere. H. C. "Enhancing Recommenda-tion Diversity and Novelty with Bi-LSTM and Mean Shift Clustering. ". Electronics. 2024.
[14] Kim. Doyeon et al. “Accurate multi-behavior sequence-aware recommendation via graph convolution networks.” PloS one vol. 20.1 e0314282. 7 Jan. 2025. doi:10.1371/journal.pone.0314282.
[15] Du. Y.. Yu. Z. & Wang. H. Multi-behavior aware recommendation with joint con-trastive learning and reinforced negative sampling. Complex Intell. Syst. 11. 352 (2025). https://doi.org/10.1007/s40747-025-01970-1.
[16] Zhu. Kaiyao et al. “Hierarchical fine-grained multi-behavior recommendation with behavior-aware contrastive learning.” Neural networks : the official journal of the Interna-tional Neural Network Society. vol. 192 107912. 28 Jul. 2025. doi:10.1016/j.neunet.2025.107912
[17] Khan. Hikmat Ullah et al. “A transformer-based architecture for collaborative filter-ing modeling in personalized recommender systems.” Scientific reports vol. 15.1 24503. 8 Jul. 2025. doi:10.1038/s41598-025-08931-1.
[18] Kim. Kyungho. et al. "Multi-Behavior Recommender Systems: A Survey." Pacific-Asia Conference on Knowledge Discovery and Data Mining. Singapore: Springer Nature Singapore. 2025.