Abstract
In recent years, many self-attention models have achieved good sequence recommendation performance by, capture the sequential dependencies between users and items. However, user behavior data inevitably contains noise, and the embedding of location information may interfere with item embedding semantics, causing noise in the data to further increase. At the same time, these self-attention models ignore the impact of high-relevance user-item interactions on the next item. To address these problems, we propose a new sequential recommendation system (AMFRec). Specifically, we adopted a three-way information (sequence, cross-channel, cross-feature) adaptive fusion scheme enhanced by a filtering algorithm. The proposed system is completely based on the MLP architecture attenuates noise in the frequency domain to reduce its impact on the model, and is naturally sensitive to location information. Finally, we designed a squeeze incentive module suitable for recommendation systems to activate multiple highly relevant projects. Experiments were conducted on three widely used datasets to demonstrate the effectiveness and efficiency of the proposed method.
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References
Lu, J., Wu, D., Mao, M., Wang, W.: Recommender system application developments: a survey. Decis. Support. Syst. 74, 12–32 (2015)
Shuai, Z., Lina, Y., South, W., Aixin S., Yi, T.: Deep learning based recommender system: A survey and new perspectives. ACM Comput. Surv. 52, 1–38 (2019)
Balázs, H., Alexandros, K., Linas, B., Domonkos, T.: Session-based recommendations with recurrent neural networks. In: International Conference on Learning Representations, pp. 241–248 (2016)
Ashish, V., Noam, S., Niki, P., Jakob, U., Llion J., Aidan N., G., Łukasz, K., Illia, P.: Attention is all you need. In: Proceedings of the NeurIPS (2017)
Angela, F., Edouard G., Armand, J.: Reducing transformer depth on demand with structured dropout (2019). arXiv:1909.11556
Sachin, M., Marjan, G., Srinivasan, I., Luke, Z., Hannaneh, H.: Delight: Deep and light-weight transformer (2020). arXiv:2008.00623
Tolstikhin, I. O., Houlsby, N., Kolesnikov, A., Beyer, L., Zhai, X., Unterthiner, T., Jessica, Y., Andreas, S., Daniel, K., Jakob, U., Mario, L., Alexey, D.: MLP-mixer: An all-MLP architecture for vision. Adv. Neural Inf. Process. Syst. 34, 24261–24272 (2021)
Hanxiao, L., Zihang, D., David, S., Quoc, V.L.: Pay attention to MLPs. Adv. Neural Inf. Process. Syst. 34, 9204–9215 (2021)
Li, M., Zhao, X., Lyu, C., Zhao, M., Wu, R., Guo, R.: MLP4Rec: A pure MLP architecture for sequential recommendations (2022). arXiv:2204.11510
Gao, J., Zhao, X., Li, M., Zhao, M., Wu, R., Guo, R., Liu, Y., Yin, D.: SMLP4Rec: An Efficient all-MLP Architecture for Sequential Recommendations. ACM Trans. Inf. Syst. 42(3), 1–23 (2024)
Soliman, S.S., Srinath, M.D.: Continuous and discrete signals and systems. Prentice Hall (1990)
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)
Tang, J., Wang, K.: Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 565–573 (2018)
Hidasi, B., Quadrana, M., Karatzoglou, A., Tikk, D.: Parallel recurrent neural network architectures for feature-rich session-based recommendations. In: Proceedings of the 10th ACM Conference on Recommender Systems, pp. 241–248 (2016)
Li, C., Wang, Y., Liu, Q., Zhao, X., Wang, W., Wang, Y., Zou, L., Fan, W., Li, Q.: STRec: Sparse transformer for sequential recommendations. In: Proceedings of the RecSys, pp. 101–111 (2023)
Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., Ma, J.: Neural attentive session-based recommendation. In: In Proceedings of the CIKM, pp. 1419–1428 (2017)
Kang, W.C., McAuley, J.: Self-attentive sequential recommendation. In: Proceedings of the CIKM, pp. 197–206 (2018)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding (2018). arXiv:1810.04805
Oreshkin, B.N., Carpov, D., Chapados, N., Bengio, Y.: N-beats: Neural basis expansion analysis for interpretable time series forecasting (2019). arXiv:1905.10437
Mao, K., Zhu, J., Su, L., Cai, G., Li, Y., Dong, Z.: FinalMLP: an enhanced two-stream MLP model for CTR prediction. In: Proceedings of the AAAI, vol. 37, no. 4, pp. 4552–4560 (2023)
Challu, C., Olivares, K.G., Oreshkin, B.N., Ramirez, F.G., Canseco, M.M., Dubrawski, A.: Nhits: Neural hierarchical interpolation for time series forecasting. In: Proceedings of the AAAI, vol. 37, no. 6, pp. 6989–6997 (2022)
Lee, H., Hwang, D., Hong, S., Kim, C., Kim, S., Choo, J.: MOImixer: Improving MLP-mixer with multi order interactions in sequential recommendation (2021). arXiv:2108.07505v1
Jiang, J., Zhang, P., Luo, Y., et al.: AdaMCT: adaptive mixture of CNN-transformer for sequential recommendation. In: CIKM, pp. 976–986 (2023)
Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., Jiang, P.: 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 (2019)
Tan, Q., Zhang, J., Yao, J., Liu, N., Zhou, J., Yang, H., Hu, X.: Sparse-interest network for sequential recommendation. In: Proceedings of the 14th ACM International Conference on Web Search and Data Mining, pp. 598–606 (2021)
Ma, C., Kang, P., Liu, X.: Hierarchical gating networks for sequential recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 825–833 (2019)
Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web, pp. 811–820 (2010)
Acknowledgment
This work is supported by the Xinjiang Uygur Autonomo-us Region Natural Science Foundation General Project (No.2023D01C17), the National Natural Science Foundation of China (No.62262064, No.61862060) and the Xinjiang Uygur Autonomous Region Science and Technology Program (No. 2023D4012).
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Li, S., Yang, X., Shen, H., Yu, J., Wu, Y. (2025). Adaptive Multi-information Feature Fusion MLP with Filter Enhancement for Sequential Recommendation. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2024. Lecture Notes in Computer Science, vol 15031. Springer, Singapore. https://doi.org/10.1007/978-981-97-8487-5_21
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DOI: https://doi.org/10.1007/978-981-97-8487-5_21
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