133 citations · 185 across the 12 of their papers we have counts for
12 papers
Training Large Recommendation Models via Graph-Language Token Alignment
Mingdai Yang, Zhiwei Liu, Liangwei Yang +4
Recommender systems (RS) have become essential tools for helping users efficiently navigate the overwhelming amount of information on e-commerce and social platforms. However, trad…
Instruction-based Hypergraph Pretraining
Mingdai Yang, Zhiwei Liu, Liangwei Yang +4
Pretraining has been widely explored to augment the adaptability of graph learning models to transfer knowledge from large datasets to a downstream task, such as link prediction or…
Multi-view Graph Convolution for Participant Recommendation
Xiaolong Liu, Liangwei Yang, Chen Wang +3
Social networks have become essential for people's lives. The proliferation of web services further expands social networks at an unprecedented scale, leading to immeasurable comme…
Group-Aware Interest Disentangled Dual-Training for Personalized Recommendation
Xiaolong Liu, Liangwei Yang, Zhiwei Liu +4
Personalized recommender systems aim to predict users' preferences for items. It has become an indispensable part of online services. Online social platforms enable users to form g…
Unified Pretraining for Recommendation via Task Hypergraphs
Mingdai Yang, Zhiwei Liu, Liangwei Yang +4
Although pretraining has garnered significant attention and popularity in recent years, its application in graph-based recommender systems is relatively limited. It is challenging…
Knowledge Graph Context-Enhanced Diversified Recommendation
Xiaolong Liu, Liangwei Yang, Zhiwei Liu +4
The field of Recommender Systems (RecSys) has been extensively studied to enhance accuracy by leveraging users' historical interactions. Nonetheless, this persistent pursuit of acc…
Collaborative Semantic Alignment in Recommendation Systems
Chen Wang, Liangwei Yang, Zhiwei Liu +4
Traditional recommender systems primarily leverage identity-based (ID) representations for users and items, while the advent of pre-trained language models (PLMs) has introduced ri…
Graph-based Alignment and Uniformity for Recommendation
Liangwei Yang, Zhiwei Liu, Chen Wang +4
Collaborative filtering-based recommender systems (RecSys) rely on learning representations for users and items to predict preferences accurately. Representation learning on the hy…
Group Identification via Transitional Hypergraph Convolution with Cross-view Self-supervised Learning
Mingdai Yang, Zhiwei Liu, Liangwei Yang +4
With the proliferation of social media, a growing number of users search for and join group activities in their daily life. This develops a need for the study on the group identifi…
Dimension Independent Mixup for Hard Negative Sample in Collaborative Filtering
Xi Wu, Liangwei Yang, Jibing Gong +4
Collaborative filtering (CF) is a widely employed technique that predicts user preferences based on past interactions. Negative sampling plays a vital role in training CF-based mod…
DGRec: Graph Neural Network for Recommendation with Diversified Embedding Generation
Liangwei Yang, Shengjie Wang, Yunzhe Tao +4
Graph Neural Network (GNN) based recommender systems have been attracting more and more attention in recent years due to their excellent performance in accuracy. Representing user-…
Ranking-based Group Identification via Factorized Attention on Social Tripartite Graph
Mingdai Yang, Zhiwei Liu, Liangwei Yang +4
Due to the proliferation of social media, a growing number of users search for and join group activities in their daily life. This develops a need for the study on the ranking-base…