9 citations · 24 across the 6 of their papers we have counts for
6 papers
Heterogeneous Information Crossing on Graphs for Session-based Recommender Systems
Xiaolin Zheng, Rui Wu, Zhongxuan Han +3
Recommender systems are fundamental information filtering techniques to recommend content or items that meet users' personalities and potential needs. As a crucial solution to addr…
Cross-Architecture Self-supervised Video Representation Learning
Sheng Guo, Zihua Xiong, Yujie Zhong +4
In this paper, we present a new cross-architecture contrastive learning (CACL) framework for self-supervised video representation learning. CACL consists of a 3D CNN and a video tr…
Poincaré Heterogeneous Graph Neural Networks for Sequential Recommendation
Naicheng Guo, Xiaolei Liu, Shaoshuai Li +5
Sequential recommendation (SR) learns users' preferences by capturing the sequential patterns from users' behaviors evolution. As discussed in many works, user-item interactions of…
AdaMixer: A Fast-Converging Query-Based Object Detector
Ziteng Gao, Limin Wang, Bing Han +1
Traditional object detectors employ the dense paradigm of scanning over locations and scales in an image. The recent query-based object detectors break this convention by decoding…
Semi-Supervised Clustering with Contrastive Learning for Discovering New Intents
Feng Wei, Zhenbo Chen, Zhenghong Hao +4
Most dialogue systems in real world rely on predefined intents and answers for QA service, so discovering potential intents from large corpus previously is really important for bui…
HCGR: Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation
Naicheng Guo, Xiaolei Liu, Shaoshuai Li +6
Session-based recommendation (SBR) learns users' preferences by capturing the short-term and sequential patterns from the evolution of user behaviors. Among the studies in the SBR…