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REASONER: An Explainable Recommendation Dataset with Multi-aspect Real User Labeled Ground Truths Towards more Measurable Explainable Recommendation.

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Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax Entropy., , , , , , and . CoRR, (2023)Recommendation with User Active Disclosing Willingness., , , and . CoRR, (2022)SimpleX: A Simple and Strong Baseline for Collaborative Filtering., , , , , , and . CIKM, page 1243-1252. ACM, (2021)Adversarially Regularized Graph Attention Networks for Inductive Learning on Partially Labeled Graphs., , , , and . CoRR, (2021)Debiased recommendation with neural stratification., , and . AI Open, (January 2022)Benchmarking News Recommendation in the Era of Green AI., , , and . CoRR, (2024)An Attention-based Model for Conversion Rate Prediction with Delayed Feedback via Post-click Calibration., , , , , , , , , and . IJCAI, page 3522-3528. ijcai.org, (2020)Scheduled for July 2020, Yokohama, Japan, postponed due to the Corona pandemic..FINAL: Factorized Interaction Layer for CTR Prediction., , , , , , , and . SIGIR, page 2006-2010. ACM, (2023)Boosting Deep CTR Prediction with a Plug-and-Play Pre-trainer for News Recommendation., , , and . COLING, page 2823-2833. International Committee on Computational Linguistics, (2022)Domain Adaptive Graph Infomax via Conditional Adversarial Networks., , , , , and . IEEE Trans. Netw. Sci. Eng., 10 (1): 35-52 (2023)