Unified Graph-Temporal Recommendation Model Based on Preference Drift and Prediction

Modern recommender systems operate in highly dynamic environments where user preferences evolve over time. This phenomenon, commonly referred to as preference drift, leads to performance degradation when models assume stationary behavior. In this work, we propose a drift-aware recommendation framework that models and predicts user preference evolution using graph neural networks combined with temporal sequence modeling. We represent user–item interactions as a sequence of time-dependent bipartite graphs and learn user embeddings via GraphSAGE. Temporal dynamics are captured using a recurrent neural network that predicts future user embeddings. We further incorporate Monte Carlo dropout to estimate predictive uncertainty and quantify drift magnitude as geometric displacement in latent space. Experiments on MovieLens and Yambda datasets demonstrate that the proposed method improves embedding prediction accuracy, enhances recommendation robustness under high-drift conditions, and enables reliable detection of anomalous behavioral changes. The results show that drift-aware modeling significantly mitigates performance degradation in non-stationary environments while providing actionable uncertainty signals.

Recommender Systems; Graph Neural Networks; Preference Drift; Temporal Modeling; Uncertainty Estimation; Dynamic Graphs.

1. Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks. ICLR. [Google Scholar] [Crossref]

2. Hamilton, W., Ying, Z., & Leskovec, J. (2017). Inductive representation learning on large graphs. NeurIPS. [Google Scholar] [Crossref]

3. Hidasi, B., Karatzoglou, A., Baltrunas, L., & Tikk, D. (2015). Session-based recommendations with recurrent neural networks. ICLR. [Google Scholar] [Crossref]

4. Kang, W. C., & McAuley, J. (2018). Self-attentive sequential recommendation. ICDM. [Google Scholar] [Crossref]

5. He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., & Wang, M. (2020). LightGCN: Simplifying and powering graph convolution network for recommendation. SIGIR. [Google Scholar] [Crossref]

6. Rossi, E., Chamberlain, B., Frasca, F., Eynard, D., Monti, F., & Bronstein, M. (2020). Temporal graph networks for deep learning on dynamic graphs. ICML Workshop. [Google Scholar] [Crossref]

7. Liu, Y., et al. (2024). SelfGNN: Self-supervised graph neural networks for sequential recommendation. arXiv preprint. [Google Scholar] [Crossref]

8. Bifet, A., & Gavalda, R. (2007). Learning from time-changing data with adaptive windowing. SDM. [Google Scholar] [Crossref]

9. Korobov, M., et al. (2016). Dynamic embeddings for user profiling in Twitter. KDD. [Google Scholar] [Crossref]

10. Ding, H. (2025). Capturing dynamic user preferences with temporal drift modeling. MDPI Information. [Google Scholar] [Crossref]

11. Koren, Y., Bell, R., & Volinsky, C. (2009). Matrix factorization techniques for recommender systems. Computer, 42(8), 30–37. [Google Scholar] [Crossref]

12. Koren, Y. (2010). Collaborative filtering with temporal dynamics. Communications of the ACM, 53(4), 89–97. [Google Scholar] [Crossref]

13. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T.-S. (2017). Neural collaborative filtering. WWW. [Google Scholar] [Crossref]

14. Sun, F., Liu, J., Wu, J., Pei, C., Lin, X., Ou, W., & Jiang, P. (2019). BERT4Rec: Sequential recommendation with bidirectional encoder representations from transformer. CIKM. [Google Scholar] [Crossref]

15. Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2025). Graph foundation models for recommendation: A survey and future directions. ACM Computing Surveys, 57(2), 1–38. [Google Scholar] [Crossref]

16. Gal, Y., & Ghahramani, Z. (2016). Dropout as a Bayesian approximation: Representing model uncertainty in deep learning. ICML. [Google Scholar] [Crossref]

17. Liang, D., Krishnan, R. G., Hoffman, M. D., & Jebara, T. (2018). Variational autoencoders for collaborative filtering. WWW. [Google Scholar] [Crossref]

18. Wang, C., et al. (2023). Calibrated uncertainty estimation for recommender systems. WSDM. [Google Scholar] [Crossref]

19. Zhang, Y., et al. (2025). Preference drift modeling with graph recurrent networks. Data Mining and Knowledge Discovery, 39(1), 1–28. [Google Scholar] [Crossref]

20. Chen, L., Zhang, H., & Li, T. (2023). Hierarchical adaptive temporal tree for drift detection in recommender systems. IEEE Transactions on Knowledge and Data Engineering, 35(6), 6123–6137. [Google Scholar] [Crossref]

21. Zhao, P., Xie, X., Zhou, X., & Liu, Y. (2024). TDTL: A drift-aware temporal recommender system based on trust decay. Neurocomputing, 599, 128–145. [Google Scholar] [Crossref]

22. Xiao, Y., Wang, H., Zhang, Y., & Wang, J. (2024). SILSSRec: A sequential recommender system with long and short-term preferences based on contrastive learning. Electronics, 13(18), 3710. [Google Scholar] [Crossref]

23. Fan, W., et al. (2022). Incorporating dynamic user interests into sequential recommendation via hypergraph attention networks. IEEE Transactions on Knowledge and Data Engineering. [Google Scholar] [Crossref]

24. Liu, Q., et al. (2024). A comprehensive survey on graph neural networks for dynamic graphs. IEEE Transactions on Neural Networks and Learning Systems. [Google Scholar] [Crossref]

25. Li, Y., et al. (2025). Uncertainty-aware session-based recommendation with Monte Carlo dropout. Information Sciences, 680, 121–135. [Google Scholar] [Crossref]

26. Zhou, K., et al. (2020). S3-Rec: Self-supervised learning for sequential recommendation with mutual information maximization. CIKM. [Google Scholar] [Crossref]

27. Xu, Y., et al. (2024). Temporal knowledge graph reasoning for sequential recommendation. The Web Conference. [Google Scholar] [Crossref]

28. Zhang, J., et al. (2022). Multi-interest evolution modeling for sequential recommendation. KDD. [Google Scholar] [Crossref]

29. Chen, L., & Pu, P. (2024). Trust-aware recommendation with uncertainty quantification. User Modeling and User-Adapted Interaction, 34(2), 215–248. [Google Scholar] [Crossref]

30. Liang, D., et al. (2016). Bernoulli matrix factorization for implicit feedback. WWW. [Google Scholar] [Crossref]

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