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Securing long-term success is the ultimate aim of recommender systems, demanding strategies capable of foreseeing and shaping the impact of decisions on future user satisfaction.
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Wide & Deep Learning for Recommender Systems
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A Pareto-efficient Algorithm for Multiple Objective Optimization in E-commerce Recommendation. In Proceedings of the 13th ACM Conference on recommender systems (RecSys ’19) . 20–28
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BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer
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Keeping Dataset Biases out of the Simulation: A Debiased Simulator for Reinforcement Learning Based Recommender Systems. In RecSys ’20 . 190–199
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Multi-Task Learning with User Preferences: Gradient Descent with Controlled Ascent in Pareto Optimization. In Proceedings of the 37th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 119) , Hal Daumé III and Aarti Singh (Eds.). PMLR, 6597–6607
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A Large-Scale Rich Context Query and Recommendation Dataset in Online Knowledge-Sharing
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Offline Reinforcement Learning as One Big Sequence Modeling Problem. In Advances in Neural Information Processing Systems
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Personalized Approximate Pareto-Efficient Recommendation. In WWW ’21: The Web Conference 2021, Virtual Event / Ljubljana, Slovenia, April 19-23, 2021 . ACM / IW3C2, 3839–3849
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Causal Decision Transformer for Recommender Systems via Offline Reinforcement Learning
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User Retention-oriented Recommendation with Decision Transformer. In Proceedings of the ACM Web Conference 2023 (Austin, TX, USA) (WWW ’23) . Association for Computing Machinery, New York, NY, USA, 1141–1149
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Toward Pareto Efficient Fairness-Utility Trade-off in Recommendation through Reinforcement Learning. In Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining (Virtual Event, AZ, USA) (WSDM ’22) . 316–324
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State Encoders in Reinforcement Learning for Recommendation: A Reproducibility Study. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (Madrid, Spain) (SIGIR ’22) . 2738–2748
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Multi-Objective Recommendation: Overview and Challenges. In Proceedings of the 2nd Workshop on Multi-Objective Recommender Systems co-located with 16th ACM Conference on Recommender Systems (RecSys 2022), Seattle, WA, USA, 18th-23rd September 2022 (CEUR Workshop Proceedings, Vol. 3268) . CEUR-WS.org
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Return Augmentation gives Supervised RL Temporal Compositionality. In Deep Reinforcement Learning Workshop NeurIPS 2022
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Surrogate for Long-Term User Experience in Recommender Systems. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (Washington DC, USA) (KDD ’22) . 4100–4109
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Two-Stage Constrained Actor-Critic for Short Video Recommendation. In Proceedings of the ACM Web Conference 2023, WWW 2023, Austin, TX, USA, 30 April 2023 - 4 May 2023 . ACM, 865–875
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Controllable Multi-Objective Re-ranking with Policy Hypernetworks. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023, Long Beach, CA, USA, August 6-10, 2023 . ACM, 3855–3864
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Interpretable User Retention Modeling in Recommendation. In Proceedings of the 17th ACM Conference on Recommender Systems (Singapore, Singapore) (RecSys ’23) . Association for Computing Machinery, New York, NY, USA, 702–708
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Free from Bellman Completeness: Trajectory Stitching via Model-based Return-conditioned Supervised Learning. In NeurIPS 2023 Foundation Models for Decision Making Workshop
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When Should We Prefer Decision Transformers for Offline Reinforcement Learning?
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Chongming Gao, Ruijun Chen, Shuai Yuan, Kexin Huang, Yuanqing Yu, and Xiangnan He. 2024 · 2024
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Conversational Topic Recommendation in Counseling and Psychotherapy with Decision Transformer and Large Language Models. In Proceedings of the 6th Clinical Natural Language Processing Workshop , Tristan Naumann, Asma Ben Abacha, Steven Bethard, Kirk Roberts, and Danielle Bitterman (Eds.). Association for Computational Linguistics, Mexico City, Mexico, 196–201
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Sequential Recommendation for Optimizing Both Immediate Feedback and Long-term Retention. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (Washington DC, USA) (SIGIR ’24) . Association for Computing Machinery, New York, NY, USA, 1872–1882
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Aligning Large Language Models for Controllable Recommendations. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (ACL ’24) , Lun-Wei Ku, Andre Martins, and Vivek Srikumar (Eds.). Association for Computational Linguistics, Bangkok, Thailand, 8159–8172
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A survey of controllable learning: Methods and applications in information retrieval
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Sparks of Surprise: Multi-objective Recommendations with Hierarchical Decision Transformers for Diversity, Novelty, and Serendipity. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (Boise, ID, USA) (CIKM ’24) . Association for Computing Machinery, New York, NY, USA, 2358–2368
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A Survey on Large Language Models for Recommendation
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EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender Systems. In Proceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval (Washington DC, USA) (SIGIR ’24) . Association for Computing Machinery, New York, NY, USA, 977–987
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