Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Bi, Shuchao"'
Autor:
Wang, Jianling, Lu, Haokai, Liu, Yifan, Ma, He, Wang, Yueqi, Gu, Yang, Zhang, Shuzhou, Han, Ningren, Bi, Shuchao, Baugher, Lexi, Chi, Ed, Chen, Minmin
Traditional recommendation systems are subject to a strong feedback loop by learning from and reinforcing past user-item interactions, which in turn limits the discovery of novel user interests. To address this, we introduce a hybrid hierarchical fra
Externí odkaz:
http://arxiv.org/abs/2405.16363
Autor:
Liu, Zichang, Liu, Qingyun, Li, Yuening, Liu, Liang, Shrivastava, Anshumali, Bi, Shuchao, Hong, Lichan, Chi, Ed H., Zhao, Zhe
Recent advancements in foundation models have yielded impressive performance across a wide range of tasks. Meanwhile, for specific applications, practitioners have been developing specialized application models. To enjoy the benefits of both kinds of
Externí odkaz:
http://arxiv.org/abs/2402.14035
Autor:
Roh, Yuji, Liu, Qingyun, Gui, Huan, Yuan, Zhe, Tang, Yujin, Whang, Steven Euijong, Liu, Liang, Bi, Shuchao, Hong, Lichan, Chi, Ed H., Zhao, Zhe
Fine-tuning is becoming widely used for leveraging the power of pre-trained foundation models in new downstream tasks. While there are many successes of fine-tuning on various tasks, recent studies have observed challenges in the generalization of fi
Externí odkaz:
http://arxiv.org/abs/2402.04644
Autor:
Wang, Yueqi, Halpern, Yoni, Chang, Shuo, Feng, Jingchen, Le, Elaine Ya, Li, Longfei, Liang, Xujian, Huang, Min-Cheng, Li, Shane, Beutel, Alex, Zhang, Yaping, Bi, Shuchao
Sequential recommenders have been widely used in industry due to their strength in modeling user preferences. While these models excel at learning a user's positive interests, less attention has been paid to learning from negative user feedback. Nega
Externí odkaz:
http://arxiv.org/abs/2308.12256
Autor:
Su, Yi, Wang, Xiangyu, Le, Elaine Ya, Liu, Liang, Li, Yuening, Lu, Haokai, Lipshitz, Benjamin, Badam, Sriraj, Heldt, Lukasz, Bi, Shuchao, Chi, Ed, Goodrow, Cristos, Wu, Su-Lin, Baugher, Lexi, Chen, Minmin
Effective exploration is believed to positively influence the long-term user experience on recommendation platforms. Determining its exact benefits, however, has been challenging. Regular A/B tests on exploration often measure neutral or even negativ
Externí odkaz:
http://arxiv.org/abs/2305.07764
Autor:
Su, Yi, Wang, Xiangyu, Le, Elaine Ya, Liu, Liang, Li, Yuening, Lu, Haokai, Lipshitz, Benjamin, Badam, Sriraj, Heldt, Lukasz, Bi, Shuchao, Chi, Ed, Goodrow, Cristos, Wu, Su-Lin, Baugher, Lexi, Chen, Minmin
Effective exploration is believed to positively influence the long-term user experience on recommendation platforms. Determining its exact benefits, however, has been challenging. Regular A/B tests on exploration often measure neutral or even negativ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::68274848405cc463c5fb19b4eb852125