438 citations · 833 across the 19 of their papers we have counts for
10 papers · 1 filter
Graph Foundation Models for Recommendation: A Comprehensive Survey
Bin Wu, Yihang Wang, Yuanhao Zeng +7
Recommender systems (RS) serve as a fundamental tool for navigating the vast expanse of online information, with deep learning advancements playing an increasingly important role i…
GenCRF: Generative Clustering and Reformulation Framework for Enhanced Intent-Driven Information Retrieval
Wonduk Seo, Haojie Zhang, Yueyang Zhang +6
Query reformulation is a well-known problem in Information Retrieval (IR) aimed at enhancing single search successful completion rate by automatically modifying user's input query.…
Disentangled Contrastive Collaborative Filtering
Xubin Ren, Lianghao Xia, Jiashu Zhao +2
Recent studies show that graph neural networks (GNNs) are prevalent to model high-order relationships for collaborative filtering (CF). Towards this research line, graph contrastiv…
Whole Page Unbiased Learning to Rank
Haitao Mao, Lixin Zou, Yujia Zheng +5
The page presentation biases in the information retrieval system, especially on the click behavior, is a well-known challenge that hinders improving ranking models' performance wit…
Hypergraph Contrastive Collaborative Filtering
Lianghao Xia, Chao Huang, Yong Xu +3
Collaborative Filtering (CF) has emerged as fundamental paradigms for parameterizing users and items into latent representation space, with their correlative patterns from interact…
Sequential Recommendation with User Evolving Preference Decomposition
Weiqi Shao, Xu Chen, Long Xia +2
Modeling user sequential behaviors has recently attracted increasing attention in the recommendation domain. Existing methods mostly assume coherent preference in the same sequence…
Contrastive Meta Learning with Behavior Multiplicity for Recommendation
Wei Wei, Chao Huang, Lianghao Xia +3
A well-informed recommendation framework could not only help users identify their interested items, but also benefit the revenue of various online platforms (e.g., e-commerce, soci…
User behavior understanding in real world settings
Weiqi Shao, Xu Chen, Jiashu Zhao +2
How to extract meaningful information in user historical behavior plays a crucial role in recommendation. User behavior sequence often contains multiple conceptually distinct items…
Gumble Softmax For User Behavior Modeling
Weiqi Shao, Xu Chen, Jiashu Zhao +2
Recently, sequential recommendation systems are important in solving the information overload in many online services. Current methods in sequential recommendation focus on learnin…
Graph-Enhanced Multi-Task Learning of Multi-Level Transition Dynamics for Session-based Recommendation
Chao Huang, Jiahui Chen, Lianghao Xia +6
Session-based recommendation plays a central role in a wide spectrum of online applications, ranging from e-commerce to online advertising services. However, the majority of existi…