Yi Q, Wu L, Tang J, Zeng Y and Song Z.
(2025). Hybrid contrastive multi-scenario learning for multi-task sequential-dependence recommendation. Neural Networks. 10.1016/j.neunet.2024.106953. 183. (106953). Online publication date: 1-Mar-2025.
Lin S, Yuan H, Liu G, Xian X, Cui Z and Zhao P.
(2025). Feature-Adaptive Meets Domain-Specific Networks for Multi-domain Recommendation. Web Information Systems Engineering – WISE 2024. 10.1007/978-981-96-0570-5_3. (32-47).
Zhang Y, Zhang Z, Wu Y, Sun Y, Zhuang F, Yu W, Hu L, Li H, Gai K, An Z and Xu Y. Tag Tree-Guided Multi-grained Alignment for Multi-Domain Short Video Recommendation. Proceedings of the 32nd ACM International Conference on Multimedia. (5683-5691).
Zhao W, Wu Z, Yang Y, Hua L and Xiong H. ECRT: Flexible Sequence Enhancement Framework for Cross-Domain Information Reuse in Recommendation. Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. (5094-5101).
Wang Y, Wang Y, Fu Z, Li X, Wang W, Ye Y, Zhao X, Guo H and Tang R. LLM4MSR: An LLM-Enhanced Paradigm for Multi-Scenario Recommendation. Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. (2472-2481).
Yuan G, Yang J, Li S, Zhong M, Li A, Ding K, He Y, Yang M, Zhang L, Zhang X and Mo L. MMLRec: A Unified Multi-Task and Multi-Scenario Learning Benchmark for Recommendation. Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. (3063-3072).
Gao J, Chen B, Zhu M, Zhao X, Li X, Wang Y, Wang Y, Guo H and Tang R. HierRec: Scenario-Aware Hierarchical Modeling for Multi-scenario Recommendations. Proceedings of the 33rd ACM International Conference on Information and Knowledge Management. (653-662).
Zhu X, Jin M, Zhang H, Meng C, Zhang D and Li X. Modeling Domains as Distributions with Uncertainty for Cross-Domain Recommendation. Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. (2517-2521).
Zhang M, Tang Y, Hu J and Zhang Y. Scenario-Adaptive Fine-Grained Personalization Network: Tailoring User Behavior Representation to the Scenario Context. Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. (1557-1566).
Zhang Z, Liu S, Yu J, Cai Q, Zhao X, Zhang C, Liu Z, Liu Q, Zhao H, Hu L, Jiang P and Gai K. M3oE: Multi-Domain Multi-Task Mixture-of Experts Recommendation Framework. Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval. (893-902).
Zhu J, Wang Y, Lin J, Qin J, Tang R, Zhang W and Yu Y. M-scan: A Multi-Scenario Causal-driven Adaptive Network for Recommendation. Proceedings of the ACM Web Conference 2024. (3844-3853).
Zhang K, Wang Y, Li X, Tang R and Zhang R. IncMSR: An Incremental Learning Approach for Multi-Scenario Recommendation. Proceedings of the 17th ACM International Conference on Web Search and Data Mining. (939-948).
Menglin K, Wang J, Pan Y, Zhang H and Hou M. C²DR: Robust Cross-Domain Recommendation based on Causal Disentanglement. Proceedings of the 17th ACM International Conference on Web Search and Data Mining. (341-349).
He M, Wen H, Hu X and An B.
(2024). ERMPD: causal intervention for popularity debiasing in recommendation via empirical risk minimization. CCF Transactions on Pervasive Computing and Interaction. 10.1007/s42486-024-00149-w. 6:1. (36-51). Online publication date: 1-Mar-2024.
Jia P, Wang Y, Lin S, Li X, Zhao X, Guo H and Tang R. D3. Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence and Thirty-Sixth Conference on Innovative Applications of Artificial Intelligence and Fourteenth Symposium on Educational Advances in Artificial Intelligence. (8553-8561).
Li X, Yan F, Zhao X, Wang Y, Chen B, Guo H and Tang R. HAMUR: Hyper Adapter for Multi-Domain Recommendation. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. (1268-1277).
Mu S, Wei P, Zhao W, Liu S, Wang L and Zheng B. Hybrid Contrastive Constraints for Multi-Scenario Ad Ranking. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. (1857-1866).
Fan S, Gou J, Li Y, Bai J, Lin C, Guan W, Li X, Deng H, Xu J and Zheng B. BOMGraph: Boosting Multi-scenario E-commerce Search with a Unified Graph Neural Network. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management. (514-523).
Tang X, Qiao Y, Fu Y, Lyu F, Liu D and He X. OptMSM: Optimizing Multi-Scenario Modeling for Click-Through Rate Prediction. Machine Learning and Knowledge Discovery in Databases: Applied Data Science and Demo Track. (567-584).
Min E, Luo D, Lin K, Huang C and Liu Y. Scenario-Adaptive Feature Interaction for Click-Through Rate Prediction. Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. (4661-4672).
Wang Y, Zhao X, Chen B, Liu Q, Guo H, Liu H, Wang Y, Zhang R and Tang R. PLATE: A Prompt-Enhanced Paradigm for Multi-Scenario Recommendations. Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. (1498-1507).
Zhang S, Jiang Z, Yao J, Feng F, Kuang K, Zhao Z, Li S, Yang H, Chua T and Wu F. Causal Distillation for Alleviating Performance Heterogeneity in Recommender Systems. IEEE Transactions on Knowledge and Data Engineering. 10.1109/TKDE.2023.3290545. (1-16).