Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Hu, Yaochen"'
Autor:
Wang, Yuening, Chen, Man, Hu, Yaochen, Guo, Wei, Zhang, Yingxue, Guo, Huifeng, Liu, Yong, Coates, Mark
Publikováno v:
CIKM (2024) 2462-2471
Many platforms, such as e-commerce websites, offer both search and recommendation services simultaneously to better meet users' diverse needs. Recommendation services suggest items based on user preferences, while search services allow users to searc
Externí odkaz:
http://arxiv.org/abs/2410.21487
Autor:
Hu, Yaochen, Zeng, Mai, Zhang, Ge, Rumiantsev, Pavel, Ma, Liheng, Zhang, Yingxue, Coates, Mark
Graph Neural Networks (GNN) exhibit superior performance in graph representation learning, but their inference cost can be high, due to an aggregation operation that can require a memory fetch for a very large number of nodes. This inference cost is
Externí odkaz:
http://arxiv.org/abs/2410.19723
Autor:
Zhou, Jiaming, Ghaddar, Abbas, Zhang, Ge, Ma, Liheng, Hu, Yaochen, Pal, Soumyasundar, Coates, Mark, Wang, Bin, Zhang, Yingxue, Hao, Jianye
Despite recent advances in training and prompting strategies for Large Language Models (LLMs), these models continue to face challenges with complex logical reasoning tasks that involve long reasoning chains. In this work, we explore the potential an
Externí odkaz:
http://arxiv.org/abs/2409.12437
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 m
Externí odkaz:
http://arxiv.org/abs/2312.11486
Negative sampling methods are vital in implicit recommendation models as they allow us to obtain negative instances from massive unlabeled data. Most existing approaches focus on sampling hard negative samples in various ways. These studies are ortho
Externí odkaz:
http://arxiv.org/abs/2311.03526
Autor:
Guo, Wei, Meng, Chang, Yuan, Enming, He, Zhicheng, Guo, Huifeng, Zhang, Yingxue, Chen, Bo, Hu, Yaochen, Tang, Ruiming, Li, Xiu, Zhang, Rui
Multi-types of user behavior data (e.g., clicking, adding to cart, and purchasing) are recorded in most real-world recommendation scenarios, which can help to learn users' multi-faceted preferences. However, it is challenging to explore multi-behavio
Externí odkaz:
http://arxiv.org/abs/2303.02418
User Behavior Modeling (UBM) plays a critical role in user interest learning, which has been extensively used in recommender systems. Crucial interactive patterns between users and items have been exploited, which brings compelling improvements in ma
Externí odkaz:
http://arxiv.org/abs/2302.11087
Distributed machine learning has been widely studied in order to handle exploding amount of data. In this paper, we study an important yet less visited distributed learning problem where features are inherently distributed or vertically partitioned a
Externí odkaz:
http://arxiv.org/abs/1907.07735
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. We study distributed machine learning from another perspective, where the informa
Externí odkaz:
http://arxiv.org/abs/1812.06415
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.