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  • Shi C, Yan S, Zhang S, Wang H and Lin K. (2025). Knowledge-Guided Semantically Consistent Contrastive Learning for sequential recommendation. Neural Networks. 10.1016/j.neunet.2025.107191. 185. (107191). Online publication date: 1-May-2025.

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  • Cheng Y, Zheng J, Wu B and Ma Q. (2025). Sequential recommendation via agent-based irrelevancy skipping. Neural Networks. 10.1016/j.neunet.2025.107134. 185. (107134). Online publication date: 1-May-2025.

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  • Hua Q, Zhou J, Zhang F, Dong C and Xu D. Attention-Enhanced and Knowledge-Fused Dual Item Representations Network for Recommendation. Tsinghua Science and Technology. 10.26599/TST.2023.9010143. 30:2. (585-599).

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  • Wang W, Lin Y, Ren P, Chen Z, Mine T, Zhao J, Zhao Q, Zhang M, Ben X and Li Y. (2025). Privacy-Preserving Sequential Recommendation with Collaborative Confusion. ACM Transactions on Information Systems. 43:2. (1-25). Online publication date: 31-Mar-2025.

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  • Zhang L, Zhou X, Zeng Z and Shen Z. (2024). Multimodal Pre-training for Sequential Recommendation via Contrastive Learning. ACM Transactions on Recommender Systems. 3:1. (1-23). Online publication date: 31-Mar-2025.

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  • Liu M, Zhang S and Long C. Facet-Aware Multi-Head Mixture-of-Experts Model for Sequential Recommendation. Proceedings of the Eighteenth ACM International Conference on Web Search and Data Mining. (127-135).

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  • Zhao Z, Wang P, Wang X, Wen H, Xie X, Zhou Z, Zhang Q and Wang Y. Delayed Bottlenecking: Alleviating Forgetting in Pre-trained Graph Neural Networks. IEEE Transactions on Knowledge and Data Engineering. 10.1109/TKDE.2024.3516192. 37:3. (1140-1153).

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  • Liu T, Zhou H, Li C and Zhao Z. (2024). Self-supervised progressive graph neural network for enhanced multi-behavior recommendation. International Journal of Machine Learning and Cybernetics. 10.1007/s13042-024-02353-7. 16:3. (1573-1588). Online publication date: 1-Mar-2025.

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  • Zhang J, Li C and Zhao Z. (2025). Lightweight yet Efficient: An External Attentive Graph Convolutional Network with Positional Prompts for Sequential Recommendation. ACM Transactions on Information Systems. 10.1145/3719343.

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  • Li C, Xie T, Yu C, Hu B, Li Z, Cheng L, Kong B and Niu D. (2025). DGT: Unbiased sequential recommendation via Disentangled Graph Transformer. Knowledge-Based Systems. 10.1016/j.knosys.2024.112946. 310. (112946). Online publication date: 1-Feb-2025.

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  • Zhang Y, Chen W, Cai F, Shi Z and Qi F. (2025). DMR: disentangled and denoised learning for multi-behavior recommendation. Complex & Intelligent Systems. 10.1007/s40747-024-01778-5. 11:2. Online publication date: 1-Feb-2025.

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  • Wu S, Xin X, Ren P, Chen Z, Ma J, de Rijke M and Ren Z. (2024). Learning Robust Sequential Recommenders through Confident Soft Labels. ACM Transactions on Information Systems. 43:1. (1-27). Online publication date: 31-Jan-2025.

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  • Tang H, Wu S, Sun X, Zeng J, Xu G and Li Q. (2024). TCGC: Temporal Collaboration-Aware Graph Co-Evolution Learning for Dynamic Recommendation. ACM Transactions on Information Systems. 43:1. (1-27). Online publication date: 31-Jan-2025.

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  • Niu J, Zhou W, Luo F, Zhang Y, Zeng J and Wen J. Intent-guided Bilateral Long and Short-Term Information Mining with Contrastive Learning for Sequential Recommendation. IEEE Transactions on Services Computing. 10.1109/TSC.2024.3520868. (1-15).

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  • Liang S, Kong Q, Lei Y and Li C. (2025). Graphical contrastive learning for multi-interest sequential recommendation. Expert Systems with Applications. 10.1016/j.eswa.2024.125285. 259. (125285). Online publication date: 1-Jan-2025.

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  • Chen S, Liu Y, Che C, Wei Z and Zhong Z. (2024). DualCFGL: dual-channel fusion global and local features for sequential recommendation. Complex & Intelligent Systems. 10.1007/s40747-024-01734-3. 11:1. Online publication date: 1-Jan-2025.

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  • Fan X, Ji Y and Hui B. (2024). A dynamic preference recommendation model based on spatiotemporal knowledge graphs. Complex & Intelligent Systems. 10.1007/s40747-024-01658-y. 11:1. Online publication date: 1-Jan-2025.

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  • Zhao R, Zhang Y, Ju S, Peng J and Yang Y. (2025). Adaptive user multi-level and multi-interest preferences for sequential recommendation. World Wide Web. 10.1007/s11280-025-01332-4. 28:1. Online publication date: 1-Jan-2025.

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  • Feng Y, Wen W, Hao Z and Cai R. (2024). Time-aware tensor factorization for temporal recommendation. Applied Intelligence. 10.1007/s10489-024-05851-x. 55:1. Online publication date: 1-Jan-2025.

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  • Wu T, Qu J, Wang D, Cui Z, Liu G and Zhao P. (2025). Contrasting Transformer and Hypergraph Network for Cooperative Sequential Recommendation. Database Systems for Advanced Applications. 10.1007/978-981-97-5555-4_6. (83-98).

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  • Yan M, Huang H, Liu Y, Zhao J, Gao X, Xu C, Guan Z and Zhao W. (2025). TruthSR: Trustworthy Sequential Recommender Systems via User-generated Multimodal Content. Database Systems for Advanced Applications. 10.1007/978-981-97-5555-4_12. (180-195).

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  • Chen L, Zhang J, Zhang Y, Yu S and Li B. (2025). Cross-Domain Sequential Recommendation with Temporal Encoding and Projection-Based Learning. Web Information Systems Engineering – WISE 2024. 10.1007/978-981-96-0570-5_6. (75-90).

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  • Wang T, Tian B, Zhang W, Yuan L and Jiang M. (2025). The Research of Sequence Recommendation Method Based on Heterogeneous Enhanced Transformer with Multi-behavior Data. Web Information Systems Engineering – WISE 2024. 10.1007/978-981-96-0570-5_11. (148-163).

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  • Zhang W, Tian B, Wang T, Yuan L and Jiang M. (2025). Research on Micro-videos Recommendation Method Integrating Multimodal Data and User Multi-behavior. Web Information Systems Engineering – WISE 2024. 10.1007/978-981-96-0570-5_1. (3-16).

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  • Zhu H, Li S and Chu L. (2025). Multifaceted Anchor Nodes Attack on Graph Neural Networks: A Budget-Efficient Approach. Pattern Recognition. 10.1007/978-3-031-78122-3_24. (372-390).

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  • Alexandra Martins C, Friedrich Dorneles C and Antonio Winckler M. (2024). A Comprehensive Review of User Interaction for Recommendation Systems. iSys - Brazilian Journal of Information Systems. 10.5753/isys.2024.4064. 17:1.

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  • Wang Z, Zhu Y, Wang C, Zhao X, Li B, Yu J and Tang F. Graph Diffusion-Based Representation Learning for Sequential Recommendation. IEEE Transactions on Knowledge and Data Engineering. 10.1109/TKDE.2024.3477621. 36:12. (8395-8407).

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  • Ma X, Zhou Q and Li Y. (2024). Multi-interest sequential recommendation with contrastive learning and temporal analysis. Knowledge-Based Systems. 10.1016/j.knosys.2024.112657. 305. (112657). Online publication date: 1-Dec-2024.

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  • Li Q, Ma H, Jin W, Ji Y and Li Z. (2024). Hypergraph-enhanced multi-interest learning for multi-behavior sequential recommendation. Expert Systems with Applications. 10.1016/j.eswa.2024.124497. 255. (124497). Online publication date: 1-Dec-2024.

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  • Luo T, Liu Y and Pan S. (2024). Collaborative Sequential Recommendations via Multi-view GNN-transformers. ACM Transactions on Information Systems. 42:6. (1-27). Online publication date: 30-Nov-2024.

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  • Wang C, Wang H, Wang J and Feng G. AutoSR: Automatic Sequential Recommendation System Design. IEEE Transactions on Knowledge and Data Engineering. 10.1109/TKDE.2024.3400031. 36:11. (5647-5660).

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  • Wang H and Yuan Y. (2024). Cluster-Enhanced Federated Graph Neural Network for Recommendation 2024 China Automation Congress (CAC). 10.1109/CAC63892.2024.10865650. 979-8-3503-6860-4. (4636-4641).

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  • Zhang Z, Yang B, Chen X and Li Q. (2024). A global contextual enhanced structural-aware transformer for sequential recommendation. Knowledge-Based Systems. 10.1016/j.knosys.2024.112515. 304. (112515). Online publication date: 1-Nov-2024.

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  • Lv Z, He S, Zhan T, Zhang S, Zhang W, Chen J, Zhao Z and Wu F. Semantic Codebook Learning for Dynamic Recommendation Models. Proceedings of the 32nd ACM International Conference on Multimedia. (9611-9620).

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  • Zhang H and Li W. (2024). LSMRec: Leveraging Hash-Enhanced Semantic Mapping for Superior Sequential Recommendations 2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI). 10.1109/ICTAI62512.2024.00032. 979-8-3315-2723-5. (166-173).

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  • Ma J, Xiao Z, Yang L, Xue H, Liu X, Jiang W, Ning W and Zhang G. Modeling User Intent Beyond Trigger: Incorporating Uncertainty for Trigger-Induced Recommendation. Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. (4743-4751).

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  • Zhang R, Zhang H, Luo Y, Liu Z, Cheng M, Liu Q and Chen E. Learning the Dynamics in Sequential Recommendation by Exploiting Real-time Information. Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. (4288-4292).

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  • Chung H, Kim J, Jo H and Choi H. Exploiting Preferences in Loss Functions for Sequential Recommendation via Weak Transitivity. Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. (3704-3708).

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  • Chen J, Du X, Pan Y and Tang J. PTSR: Prefix-Target Graph-based Sequential Recommendation. Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. (239-248).

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  • Liu Y, Wang Y and Feng C. UniRec: A Dual Enhancement of Uniformity and Frequency in Sequential Recommendations. Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. (1483-1492).

    https://doi.org/10.1145/3627673.3679689

  • Zhang K, Shi T, Dai S, Zhang X, Li Y, Lu J, Zang X, Song Y and Xu J. SAQRec: Aligning Recommender Systems to User Satisfaction via Questionnaire Feedback. Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. (3165-3175).

    https://doi.org/10.1145/3627673.3679643

  • Xie X and Chen R. (2024). Contrastive Learning of Sequential Recommendation with Graph Attention Mechanisms 2024 5th International Conference on Computer Engineering and Intelligent Control (ICCEIC). 10.1109/ICCEIC64099.2024.10775770. 979-8-3315-0799-2. (152-156).

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  • Jiang Y, Li C, Chen G, Li P, Zhang Q, Lin J, Jiang P, Sun F and Zhang W. MMGCL: Meta Knowledge-Enhanced Multi-view Graph Contrastive Learning for Recommendations. Proceedings of the 18th ACM Conference on Recommender Systems. (538-548).

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  • Cui Y, Liu F, Wang P, Wang B, Tang H, Wan Y, Wang J and Chen J. Distillation Matters: Empowering Sequential Recommenders to Match the Performance of Large Language Models. Proceedings of the 18th ACM Conference on Recommender Systems. (507-517).

    https://doi.org/10.1145/3640457.3688118

  • Qu Z, Xie R, Xiao C, Kang Z and Sun X. The Elephant in the Room: Rethinking the Usage of Pre-trained Language Model in Sequential Recommendation. Proceedings of the 18th ACM Conference on Recommender Systems. (53-62).

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  • Yang K, Yu R, Guo B and Li J. Interaction Subgraph Sequential Topology-Aware Network for Transferable Recommendation. IEEE Transactions on Knowledge and Data Engineering. 10.1109/TKDE.2024.3384965. 36:10. (5221-5233).

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  • Chen J, Wang L, Liang Y, Yu Y, Feng J, Zhao J and Ding X. Order Dispatching Via GNN-Based Optimization Algorithm for On-Demand Food Delivery. IEEE Transactions on Intelligent Transportation Systems. 10.1109/TITS.2024.3389090. 25:10. (13147-13162).

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  • Yan S, Shi C, Wang H, Chen L, Jiang L, Guo R and Lin K. (2024). Teach and Explore: A Multiplex Information-guided Effective and Efficient Reinforcement Learning for Sequential Recommendation. ACM Transactions on Information Systems. 42:5. (1-26). Online publication date: 30-Sep-2024.

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  • Zhang R, Wang H and He J. (2024). HyperCLR: A Personalized Sequential Recommendation Algorithm Based on Hypergraph and Contrastive Learning. Mathematics. 10.3390/math12182887. 12:18. (2887).

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  • Yang M, Liu Z, Wu Y and Dong W. (2024). Salient object detection via multi-grained refinement polygon topology positive feedback. Expert Systems with Applications. 10.1016/j.eswa.2024.123903. 250. (123903). Online publication date: 1-Sep-2024.

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  • Luo Z, Sheng Z and Zhang T. (2024). Dual perspective denoising model for session-based recommendation. Expert Systems with Applications: An International Journal. 249:PC. Online publication date: 1-Sep-2024.

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  • Ji S, Liu M, Sun L, Liu C and Zhu T. MemMap: An Adaptive and Latent Memory Structure for Dynamic Graph Learning. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. (1257-1268).

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  • Du Y, Wang Z, Sun Z, Ma Y, Liu H and Zhang J. Disentangled Multi-interest Representation Learning for Sequential Recommendation. Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. (677-688).

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  • Gao C, Zheng Y, Wang W, Feng F, He X and Li Y. (2024). Causal Inference in Recommender Systems: A Survey and Future Directions. ACM Transactions on Information Systems. 42:4. (1-32). Online publication date: 31-Jul-2024.

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  • Razgallah H, Vlachos M, Ajalloeian A, Liu N, Schneider J and Steinmann A. (2024). Using Neural and Graph Neural Recommender Systems to Overcome Choice Overload: Evidence From a Music Education Platform. ACM Transactions on Information Systems. 42:4. (1-26). Online publication date: 31-Jul-2024.

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  • Zhou P, Huang Y, Xie Y, Gao J, Wang S, Kim J and Kim S. Is Contrastive Learning Necessary? A Study of Data Augmentation vs Contrastive Learning in Sequential Recommendation. Proceedings of the ACM Web Conference 2024. (3854-3863).

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  • Wu C, Shi S, Wang C, Liu Z, Peng W, Wu W, Kong D, Li H and Gai K. Enhancing Recommendation Accuracy and Diversity with Box Embedding: A Universal Framework. Proceedings of the ACM Web Conference 2024. (3756-3766).

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