Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Yao, Tiansheng"'
Autor:
Zhang, Yin, Wang, Ruoxi, Yao, Tiansheng, Yi, Xinyang, Hong, Lichan, Caverlee, James, Chi, Ed H., Cheng, Derek Zhiyuan
Industry recommender systems usually suffer from highly-skewed long-tail item distributions where a small fraction of the items receives most of the user feedback. This skew hurts recommender quality especially for the item slices without much user f
Externí odkaz:
http://arxiv.org/abs/2210.14309
Multi-Task Learning (MTL) is a powerful learning paradigm to improve generalization performance via knowledge sharing. However, existing studies find that MTL could sometimes hurt generalization, especially when two tasks are less correlated. One pos
Externí odkaz:
http://arxiv.org/abs/2205.09797
Highly skewed long-tail item distribution is very common in recommendation systems. It significantly hurts model performance on tail items. To improve tail-item recommendation, we conduct research to transfer knowledge from head items to tail items,
Externí odkaz:
http://arxiv.org/abs/2010.15982
Autor:
Kang, Wang-Cheng, Cheng, Derek Zhiyuan, Yao, Tiansheng, Yi, Xinyang, Chen, Ting, Hong, Lichan, Chi, Ed H.
Embedding learning of categorical features (e.g. user/item IDs) is at the core of various recommendation models including matrix factorization and neural collaborative filtering. The standard approach creates an embedding table where each row represe
Externí odkaz:
http://arxiv.org/abs/2010.10784
Autor:
Yao, Tiansheng, Yi, Xinyang, Cheng, Derek Zhiyuan, Yu, Felix, Chen, Ting, Menon, Aditya, Hong, Lichan, Chi, Ed H., Tjoa, Steve, Kang, Jieqi, Ettinger, Evan
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:
http://arxiv.org/abs/2007.12865
Publikováno v:
In Artificial Intelligence March 2017 244:239-257
Publikováno v:
Computer Vision - ECCV 2012; 2012, p126-139, 14p