no code implementations • 30 Nov 2023 • Hongjian Gu, Yaochen Hu, Yingxue Zhang
Graph-based collaborative filtering methods have prevailing performance for recommender systems since they can capture high-order information between users and items, in which the graphs are constructed from the observed user-item interactions that might miss links or contain spurious positive interactions in industrial scenarios.
no code implementations • 6 Nov 2023 • Fuyuan Lyu, Yaochen Hu, Xing Tang, Yingxue Zhang, Ruiming Tang, Xue Liu
Hence, we propose a hypothesis that the negative sampler should align with the capacity of the recommendation models as well as the statistics of the datasets to achieve optimal performance.
1 code implementation • 4 Mar 2023 • Wei Guo, Chang Meng, Enming Yuan, ZhiCheng He, Huifeng Guo, Yingxue Zhang, Bo Chen, Yaochen Hu, Ruiming Tang, Xiu Li, Rui Zhang
However, it is challenging to explore multi-behavior data due to the unbalanced data distribution and sparse target behavior, which lead to the inadequate modeling of high-order relations when treating multi-behavior data ''as features'' and gradient conflict in multitask learning when treating multi-behavior data ''as labels''.
no code implementations • 22 Feb 2023 • ZhiCheng He, Weiwen Liu, Wei Guo, Jiarui Qin, Yingxue Zhang, Yaochen Hu, Ruiming Tang
Besides, we elaborate on the industrial practices of UBM methods with the hope of providing insights into the application value of existing UBM solutions.
no code implementations • 1 Jan 2021 • Yaochen Hu, Amit Levi, Ishaan Kumar, Yingxue Zhang, Mark Coates
In recent years deep learning has become an important framework for supervised learning.
1 code implementation • Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2020 • Jianing Sun, Wei Guo, Dengcheng Zhang, Yingxue Zhang, Florence Regol, Yaochen Hu, Huifeng Guo, Ruiming Tang, Han Yuan, Xiuqiang He, Mark Coates
Because of the multitude of relationships existing in recommender systems, Graph Neural Networks (GNNs) based approaches have been proposed to better characterize the various relationships between a user and items while modeling a user's preferences.
no code implementations • 17 Jul 2019 • Yaochen Hu, Peng Liu, Linglong Kong, Di Niu
Distributed machine learning has been widely studied in order to handle exploding amount of data.
no code implementations • 16 Dec 2018 • Yaochen Hu, Di Niu, Jianming Yang, Shengping Zhou
Distributed machine learning has been widely studied in the literature to scale up machine learning model training in the presence of an ever-increasing amount of data.