1 citations · 2 across the 16 of their papers we have counts for
4 papers · 1 filter
Paragon: Parameter Generation for Controllable Multi-Task Recommendation
Chenglei Shen, Jiahao Zhao, Xiao Zhang +3
Commercial recommender systems face the challenge that task requirements from platforms or users often change dynamically (e.g., varying preferences for accuracy or diversity). Ide…
MoRE: A Mixture of Reflectors Framework for Large Language Model-Based Sequential Recommendation
Weicong Qin, Yi Xu, Weijie Yu +5
Large language models (LLMs) have emerged as a cutting-edge approach in sequential recommendation, leveraging historical interactions to model dynamic user preferences. Current met…
MobileExperts: A Dynamic Tool-Enabled Agent Team in Mobile Devices
Jiayi Zhang, Chuang Zhao, Yihan Zhao +3
The attainment of autonomous operations in mobile computing devices has consistently been a goal of human pursuit. With the development of Large Language Models (LLMs) and Visual L…
A Survey of Controllable Learning: Methods and Applications in Information Retrieval
Chenglei Shen, Xiao Zhang, Teng Shi +3
Controllability has become a crucial aspect of trustworthy machine learning, enabling learners to meet predefined targets and adapt dynamically at test time without requiring retra…