23 citations · 48 across the 19 of their papers we have counts for
13 papers · 1 filter
Agents as Knowledge Integrator and Utilizer in Multimodal Recommendation
Jinfeng Xu, Zheyu Chen, Shuo Yang +9
Online platforms increasingly rely on multimodal recommender systems to rank products, media, and other Web content. Existing methods usually inject visual and textual features int…
Neural Tree Collaborative Filtering: Rethinking Graph Collaborative Filtering as Tree Collaborative Filtering with Curvature-Aware Propagation Depth
Jinfeng Xu, Zheyu Chen, Ziyue Peng +5
Graph Collaborative Filtering (GCF) has become the dominant paradigm in modern recommender systems by modeling user-item interactions as a bipartite graph and propagating embedding…
One Graph, Multiple Gains: Single High-Quality Item-Item Graph for Multimodal Recommendation
Jinfeng Xu, Zheyu Chen, Ziyue Peng +6
Multimodal recommendation leverages item multimodal features alongside collaborative signals to capture user preferences. While item-item graphs have become a key building block in…
Well Begun is Half Done: Training-Free and Model-Agnostic Semantically Guaranteed User Representation Initialization for Multimodal Recommendation
Jinfeng Xu, Zheyu Chen, Shuo Yang +6
Recent advancements in multimodal recommendations, which leverage diverse modality information to mitigate data sparsity and improve recommendation accuracy, have gained significan…
CAMMSR: Category-Guided Attentive Mixture of Experts for Multimodal Sequential Recommendation
Jinfeng Xu, Zheyu Chen, Shuo Yang +6
The explosion of multimedia data in information-rich environments has intensified the challenges of personalized content discovery, positioning recommendation systems as an essenti…
VI-MMRec: Similarity-Aware Training Cost-free Virtual User-Item Interactions for Multimodal Recommendation
Jinfeng Xu, Zheyu Chen, Shuo Yang +6
Although existing multimodal recommendation models have shown promising performance, their effectiveness continues to be limited by the pervasive data sparsity problem. This proble…
The Best is Yet to Come: Graph Convolution in the Testing Phase for Multimodal Recommendation
Jinfeng Xu, Zheyu Chen, Shuo Yang +2
The efficiency and scalability of graph convolution networks (GCNs) in training recommender systems remain critical challenges, hindering their practical deployment in real-world s…
NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation
Jinfeng Xu, Zheyu Chen, Shuo Yang +5
Graph Neural Networks (GNNs) are widely used in collaborative filtering to capture high-order user-item relationships. To address the data sparsity problem in recommendation system…
MDVT: Enhancing Multimodal Recommendation with Model-Agnostic Multimodal-Driven Virtual Triplets
Jinfeng Xu, Zheyu Chen, Jinze Li +6
The data sparsity problem significantly hinders the performance of recommender systems, as traditional models rely on limited historical interactions to learn user preferences and…
A Survey on Multimodal Recommender Systems: Recent Advances and Future Directions
Jinfeng Xu, Zheyu Chen, Shuo Yang +5
Acquiring valuable data from the rapidly expanding information on the internet has become a significant concern, and recommender systems have emerged as a widely used and effective…
AlignGroup: Learning and Aligning Group Consensus with Member Preferences for Group Recommendation
Jinfeng Xu, Zheyu Chen, Jinze Li +3
Group activities are important behaviors in human society, providing personalized recommendations for groups is referred to as the group recommendation task. Existing methods can u…
Enhancing Graph Collaborative Filtering with FourierKAN Feature Transformation
Jinfeng Xu, Zheyu Chen, Jinze Li +4
Graph Collaborative Filtering (GCF) has emerged as a dominant paradigm in modern recommendation systems, excelling at modeling complex user-item interactions and capturing high-ord…
MENTOR: Multi-level Self-supervised Learning for Multimodal Recommendation
Jinfeng Xu, Zheyu Chen, Shuo Yang +3
With the increasing multimedia information, multimodal recommendation has received extensive attention. It utilizes multimodal information to alleviate the data sparsity problem in…