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pro vyhledávání: '"Lu, Haokai"'
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
The reasoning and generalization capabilities of LLMs can help us better understand user preferences and item characteristics, offering exciting prospects to enhance recommendation systems. Though effective while user-item interactions are abundant,
Externí odkaz:
http://arxiv.org/abs/2402.11724
Autor:
Wang, Jianling, Lu, Haokai, zhang, Sai, Locanthi, Bart, Wang, Haoting, Greaves, Dylan, Lipshitz, Benjamin, Badam, Sriraj, Chi, Ed H., Goodrow, Cristos, Wu, Su-Lin, Baugher, Lexi, Chen, Minmin
Recommendation system serves as a conduit connecting users to an incredibly large, diverse and ever growing collection of contents. In practice, missing information on fresh (and tail) contents needs to be filled in order for them to be exposed and d
Externí odkaz:
http://arxiv.org/abs/2306.01720
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
Publikováno v:
In Neurocomputing 15 November 2012 97:317-331
Publikováno v:
2016 IEEE/ACM International Conference on Advances in Social Networks Analysis & Mining (ASONAM); 2016, p279-286, 8p
Akademický článek
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