Learned Ranking Function: From Short-term Behavior Predictions to Long-term User Satisfaction by Wu, Yi, Chang, Daryl, She, Jennifer, Zhao, Zhe, Li, Wei, Heldt, Lukasz

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Aligning Large Language Models with Recommendation Knowledge by Cao, Yuwei, Mehta, Nikhil, Yi, Xinyang, Keshavan, Raghunandan, Heldt, Lukasz, Hong, Lichan, Chi, Ed H, Maheswaran Sathiamoorthy

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Online Matching: A Real-time Bandit System for Large-scale Recommendations by Yi, Xinyang, Shao-Chuan, Wang, He, Ruining, Chandrasekaran, Hariharan, Wu, Charles, Heldt, Lukasz, Hong, Lichan, Chen, Minmin, Chi, Ed H

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Better Generalization with Semantic IDs: A case study in Ranking for Recommendations by Singh, Anima, Vu, Trung, Keshavan, Raghunandan, Mehta, Nikhil, Yi, Xinyang, Hong, Lichan, Heldt, Lukasz, Li, Wei, Chi, Ed, Maheswaran Sathiamoorthy

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Long-Term Value of Exploration: Measurements, Findings and Algorithms by Su, Yi, Wang, Xiangyu, Elaine Ya Le, Liu, Liang, Li, Yuening, Lu, Haokai, Lipshitz, Benjamin, Sriraj Badam, Heldt, Lukasz, Bi, Shuchao, Chi, Ed, Goodrow, Cristos, Su-Lin, Wu, Baugher, Lexi, Chen, Minmin

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Recommender Systems with Generative Retrieval by Rajput, Shashank, Mehta, Nikhil, Singh, Anima, Keshavan, Raghunandan H, Vu, Trung, Heldt, Lukasz, Hong, Lichan, Tay, Yi, Tran, Vinh Q, Samost, Jonah, Kula, Maciej, Chi, Ed H, Maheswaran Sathiamoorthy

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Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations by Prost, Flavien, Packer, Ben, Chen, Jilin, Li, Wei, Kremp, Pierre, Blumm, Nicholas, Wang, Susan, Doshi, Tulsee, Osadebe, Tonia, Heldt, Lukasz, Chi, Ed H, Beutel, Alex

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Fairness in Recommendation Ranking through Pairwise Comparisons by Beutel, Alex, Chen, Jilin, Doshi, Tulsee, Qian, Hai, Li, Wei, Wu, Yi, Heldt, Lukasz, Zhao, Zhe, Hong, Lichan, Chi, Ed H, Goodrow, Cristos

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