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AutoLossGen: Automatic Loss Function Generation for Recommender Systems.

, , , and . SIGIR, page 1304-1315. ACM, (2022)

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CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation., , , , , , , , , and 1 other author(s). CIKM, page 1645-1654. ACM, (2020)Counterfactual Evaluation for Explainable AI., , , , , , , , and . CoRR, (2021)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)UP5: Unbiased Foundation Model for Fairness-aware Recommendation., , , , , and . EACL (1), page 1899-1912. Association for Computational Linguistics, (2024)Recommendation as Language Processing (RLP): A Unified Pretrain, Personalized Prompt & Predict Paradigm (P5)., , , , and . RecSys, page 299-315. ACM, (2022)Causal Collaborative Filtering., , , , , and . ICTIR, page 235-245. ACM, (2023)Counterfactual Collaborative Reasoning., , , , , , , and . WSDM, page 249-257. ACM, (2023)Counterfactual Explainable Recommendation., , , , , and . CIKM, page 1784-1793. ACM, (2021)Towards Personalized Fairness based on Causal Notion., , , , and . SIGIR, page 1054-1063. ACM, (2021)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)