Zobrazeno 1 - 10 of 159 pro vyhledávání: '"Ed H. Chi"'
2
Latent User Intent Modeling for Sequential Recommenders
Autor: Bo Chang, Alexandros Karatzoglou, Yuyan Wang, Can Xu, Ed H. Chi, Minmin Chen
Sequential recommender models are essential components of modern industrial recommender systems. These models learn to predict the next items a user is likely to interact with based on his/her interaction history on the platform. Most sequential reco
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::72b645c3eee12f05d1019995f8fe3888
http://arxiv.org/abs/2211.09832
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6
Learning to Augment for Casual User Recommendation
Autor: Jianling Wang, Ya Le, Bo Chang, Yuyan Wang, Ed H. Chi, Minmin Chen
Users who come to recommendation platforms are heterogeneous in activity levels. There usually exists a group of core users who visit the platform regularly and consume a large body of content upon each visit, while others are casual users who tend t
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ac18cb7d33f7278f31552fe03fb61001
http://arxiv.org/abs/2204.00926
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7
Self-supervised Learning for Large-scale Item Recommendations
Autor: Tiansheng Yao, Evan Ettinger, Derek Zhiyuan Cheng, Jieqi Kang, Lichan Hong, Felix X. Yu, Aditya Krishna Menon, Ed H. Chi, Ting Chen, Steve Tjoa, Xinyang Yi
Publikováno v: CIKM
Large scale recommender models find most relevant items from huge catalogs, and they play a critical role in modern search and recommendation systems. To model the input space with large-vocab categorical features, a typical recommender model learns
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::85631df687a53adc2af25608c4eeba34
https://doi.org/10.1145/3459637.3481952
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8
Values of User Exploration in Recommender Systems
Autor: Ya Le, Minmin Chen, Ed H. Chi, Mohit Sharma, Can Xu, Yuyan Wang, Lee Richardson, Su-Lin Wu
Publikováno v: RecSys
Reinforcement Learning (RL) has been sought after to bring next-generation recommender systems to further improve user experience on recommendation platforms. While the exploration-exploitation tradeoff is the foundation of RL research, the value of
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::a9709f2ae9638796d7bed4e095932592
https://doi.org/10.1145/3460231.3474236
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9
SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning
Autor: Ang Li, Jilin Chen, Zhe Zhao, Jiaqi Ma, Lichan Hong, Ed H. Chi
Publikováno v: AAAI
Machine learning applications, such as object detection and content recommendation, often require training a single model to predict multiple targets at the same time. Multi-task learning through neural networks became popular recently, because it no
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::bab8734482bb08c544c72dba18d1550e
https://doi.org/10.1609/aaai.v33i01.3301216
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10
Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective
Autor: Aditee Kumthekar, Flavien Prost, Li Wei, Xuezhi Wang, Nick Blumm, Pranjal Awasthi, Alex Beutel, Ed H. Chi, Trevor Potter, Jilin Chen
Publikováno v: AIES
In this work we study the problem of measuring the fairness of a machine learning model under noisy information. Focusing on group fairness metrics, we investigate the particular but common situation when the evaluation requires controlling for the c
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a46878b9ca93bcdb663a657aabe3a4ee
http://arxiv.org/abs/2105.09985
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