Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Zhai, Shaodan"'
Most recommender systems are myopic, that is they optimize based on the immediate response of the user. This may be misaligned with the true objective, such as creating long term user satisfaction. In this work we focus on mobile push notifications,
Externí odkaz:
http://arxiv.org/abs/2202.08812
Autor:
Yue, Yuguang, Xie, Yuanpu, Wu, Huasen, Jia, Haofeng, Zhai, Shaodan, Shi, Wenzhe, Hunt, Jonathan J
Listwise ranking losses have been widely studied in recommender systems. However, new paradigms of content consumption present new challenges for ranking methods. In this work we contribute an analysis of learning to rank for personalized mobile push
Externí odkaz:
http://arxiv.org/abs/2201.07681
Margin infused relaxed algorithms (MIRAs) dominate model tuning in statistical machine translation in the case of large scale features, but also they are famous for the complexity in implementation. We introduce a new method, which regards an N-best
Externí odkaz:
http://arxiv.org/abs/1909.09491
List-wise based learning to rank methods are generally supposed to have better performance than point- and pair-wise based. However, in real-world applications, state-of-the-art systems are not from list-wise based camp. In this paper, we propose a n
Externí odkaz:
http://arxiv.org/abs/1909.06722
In learning to rank area, industry-level applications have been dominated by gradient boosting framework, which fits a tree using least square error principle. While in classification area, another tree fitting principle, weighted least square error,
Externí odkaz:
http://arxiv.org/abs/1909.05965
Autor:
Zhai, Shaodan
Boosting, as one of the state-of-the-art classification approaches, is widely used in the industry for a broad range of problems. The existing boosting methods often formulate classification tasks as a convex optimization problem by using surrogates
Externí odkaz:
http://rave.ohiolink.edu/etdc/view?acc_num=wright1453001665
Publikováno v:
2015 IEEE International Conference on Data Mining; 2015, p1093-1098, 6p
Autor:
Zhao, Chenyang, Zhai, Shaodan
Publikováno v:
2015 IEEE Global Conference on Signal & Information Processing (GlobalSIP); 1/1/2015, p1342-1346, 5p
Publikováno v:
2015 IEEE China Summit & International Conference on Signal & Information Processing (ChinaSIP); 2015, p797-801, 5p
Publikováno v:
Proceedings of the 20th ACM SIGKDD International Conference Knowledge Discovery & Data Mining; 8/24/2014, p273-282, 10p