7 citations · 10 across the 10 of their papers we have counts for
7 papers · 1 filter
Semantic Item Graph Enhancement for Multimodal Recommendation
Xiaoxiong Zhang, Xin Zhou, Zhiwei Zeng +2
Multimodal recommendation systems have attracted increasing attention for their improved performance by leveraging items' multimodal information. Prior methods often build modality…
CM: Calibrating Multimodal Recommendation
Xin Zhou, Yongjie Wang, Zhiqi Shen
Alignment and uniformity are fundamental principles within the domain of contrastive learning. In recommender systems, prior work has established that optimizing the Bayesian Perso…
EGRA:Toward Enhanced Behavior Graphs and Representation Alignment for Multimodal Recommendation
Xiaoxiong Zhang, Xin Zhou, Zhiwei Zeng +2
MultiModal Recommendation (MMR) systems have emerged as a promising solution for improving recommendation quality by leveraging rich item-side modality information, prompting a sur…
Learning Item Representations Directly from Multimodal Features for Effective Recommendation
Xin Zhou, Xiaoxiong Zhang, Dusit Niyato +1
Conventional multimodal recommender systems predominantly leverage Bayesian Personalized Ranking (BPR) optimization to learn item representations by amalgamating item identity (ID)…
Personalized Federated Knowledge Graph Embedding with Client-Wise Relation Graph
Xiaoxiong Zhang, Zhiwei Zeng, Xin Zhou +2
Federated Knowledge Graph Embedding (FKGE) has recently garnered considerable interest due to its capacity to extract expressive representations from distributed knowledge graphs,…
Multi-modal Food Recommendation using Clustering and Self-supervised Learning
Yixin Zhang, Xin Zhou, Qianwen Meng +4
Food recommendation systems serve as pivotal components in the realm of digital lifestyle services, designed to assist users in discovering recipes and food items that resonate wit…
Multimodal Pre-training Framework for Sequential Recommendation via Contrastive Learning
Lingzi Zhang, Xin Zhou, Zhiwei Zeng +1
Current multimodal sequential recommendation models are often unable to effectively explore and capture correlations among behavior sequences of users and items across different mo…