18 citations · 60 across the 8 of their papers we have counts for
8 papers
OVO-Bench: How Far is Your Video-LLMs from Real-World Online Video Understanding?
Yifei Li, Junbo Niu, Ziyang Miao +12
Temporal Awareness, the ability to reason dynamically based on the timestamp when a question is raised, is the key distinction between offline and online video LLMs. Unlike offline…
ECAT: A Entire space Continual and Adaptive Transfer Learning Framework for Cross-Domain Recommendation
Chaoqun Hou, Yuanhang Zhou, Yi Cao +1
In industrial recommendation systems, there are several mini-apps designed to meet the diverse interests and needs of users. The sample space of them is merely a small subset of th…
JiuZhang 2.0: A Unified Chinese Pre-trained Language Model for Multi-task Mathematical Problem Solving
Wayne Xin Zhao, Kun Zhou, Beichen Zhang +8
Although pre-trained language models~(PLMs) have recently advanced the research progress in mathematical reasoning, they are not specially designed as a capable multi-task solver,…
JiuZhang: A Chinese Pre-trained Language Model for Mathematical Problem Understanding
Wayne Xin Zhao, Kun Zhou, Zheng Gong +7
This paper aims to advance the mathematical intelligence of machines by presenting the first Chinese mathematical pre-trained language model~(PLM) for effectively understanding and…
C2-CRS: Coarse-to-Fine Contrastive Learning for Conversational Recommender System
Yuanhang Zhou, Kun Zhou, Wayne Xin Zhao +3
Conversational recommender systems (CRS) aim to recommend suitable items to users through natural language conversations. For developing effective CRSs, a major technical issue is…
CRSLab: An Open-Source Toolkit for Building Conversational Recommender System
Kun Zhou, Xiaolei Wang, Yuanhang Zhou +5
In recent years, conversational recommender system (CRS) has received much attention in the research community. However, existing studies on CRS vary in scenarios, goals and techni…
Towards Topic-Guided Conversational Recommender System
Kun Zhou, Yuanhang Zhou, Wayne Xin Zhao +2
Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. To develop an effective CRS, the support of high-quality da…
Improving Conversational Recommender Systems via Knowledge Graph based Semantic Fusion
Kun Zhou, Wayne Xin Zhao, Shuqing Bian +3
Conversational recommender systems (CRS) aim to recommend high-quality items to users through interactive conversations. Although several efforts have been made for CRS, two major…