42 citations · 140 across the 11 of their papers we have counts for
11 papers
Intent-aware Recommendation via Disentangled Graph Contrastive Learning
Yuling Wang, Xiao Wang, Xiangzhou Huang +5
Graph neural network (GNN) based recommender systems have become one of the mainstream trends due to the powerful learning ability from user behavior data. Understanding the user i…
M2GNN: Metapath and Multi-interest Aggregated Graph Neural Network for Tag-based Cross-domain Recommendation
Zepeng Huai, Yuji Yang, Mengdi Zhang +3
Cross-domain recommendation (CDR) is an effective way to alleviate the data sparsity problem. Content-based CDR is one of the most promising branches since most kinds of products c…
When do homomorphism counts help in query algorithms?
Balder ten Cate, Víctor Dalmau, Phokion G. Kolaitis +1
A query algorithm based on homomorphism counts is a procedure for determining whether a given instance satisfies a property by counting homomorphisms between the given instance and…
Discovering A Variety of Objects in Spatio-Temporal Human-Object Interactions
Yong-Lu Li, Hongwei Fan, Zuoyu Qiu +8
Spatio-temporal Human-Object Interaction (ST-HOI) detection aims at detecting HOIs from videos, which is crucial for activity understanding. In daily HOIs, humans often interact wi…
Mask and Reason: Pre-Training Knowledge Graph Transformers for Complex Logical Queries
Xiao Liu, Shiyu Zhao, Kai Su +6
Knowledge graph (KG) embeddings have been a mainstream approach for reasoning over incomplete KGs. However, limited by their inherently shallow and static architectures, they can h…
Long Short-Term Preference Modeling for Continuous-Time Sequential Recommendation
Huixuan Chi, Hao Xu, Hao Fu +5
Modeling the evolution of user preference is essential in recommender systems. Recently, dynamic graph-based methods have been studied and achieved SOTA for recommendation, majorit…