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User-Controllable Recommendation via Counterfactual Retrospective and Prospective Explanations., , , , , , and . ECAI, volume 372 of Frontiers in Artificial Intelligence and Applications, page 2307-2314. IOS Press, (2023)Towards Long-term Fairness in Recommendation., , , , , , , , , and 1 other author(s). WSDM, page 445-453. ACM, (2021)Understanding Echo Chambers in E-commerce Recommender Systems., , , , , , and . SIGIR, page 2261-2270. ACM, (2020)Towards LLM-RecSys Alignment with Textual ID Learning., , , , , and . CoRR, (2024)Fairness-aware Federated Matrix Factorization., , , , and . RecSys, page 168-178. ACM, (2022)Automated Data Denoising for Recommendation., , , , , and . CoRR, (2023)sharedCharging: Data-Driven Shared Charging for Large-Scale Heterogeneous Electric Vehicle Fleets., , , , , , , and . Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 3 (3): 108:1-108:25 (2019)Counterfactual Evaluation for Explainable AI., , , , , , , , and . CoRR, (2021)CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation., , , , , , , , , and 1 other author(s). CIKM, page 1645-1654. ACM, (2020)Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)., , , , and . RecSys, page 299-315. ACM, (2022)