290 citations · 555 across the 8 of their papers we have counts for
7 papers · 1 filter
Are ID Embeddings Necessary? Whitening Pre-trained Text Embeddings for Effective Sequential Recommendation
Lingzi Zhang, Xin Zhou, Zhiwei Zeng +1
Recent sequential recommendation models have combined pre-trained text embeddings of items with item ID embeddings to achieve superior recommendation performance. Despite their eff…
Capturing Popularity Trends: A Simplistic Non-Personalized Approach for Enhanced Item Recommendation
Jiazheng Jing, Yinan Zhang, Xin Zhou +1
Recommender systems have been gaining increasing research attention over the years. Most existing recommendation methods focus on capturing users' personalized preferences through…
A Comprehensive Survey on Multimodal Recommender Systems: Taxonomy, Evaluation, and Future Directions
Hongyu Zhou, Xin Zhou, Zhiwei Zeng +2
Recommendation systems have become popular and effective tools to help users discover their interesting items by modeling the user preference and item property based on implicit in…
Enhancing Dyadic Relations with Homogeneous Graphs for Multimodal Recommendation
Hongyu Zhou, Xin Zhou, Lingzi Zhang +1
User interaction data in recommender systems is a form of dyadic relation that reflects the preferences of users with items. Learning the representations of these two discrete sets…
A Tale of Two Graphs: Freezing and Denoising Graph Structures for Multimodal Recommendation
Xin Zhou, Zhiqi Shen
Multimodal recommender systems utilizing multimodal features (e.g., images and textual descriptions) typically show better recommendation accuracy than general recommendation model…
Layer-refined Graph Convolutional Networks for Recommendation
Xin Zhou, Donghui Lin, Yong Liu +1
Recommendation models utilizing Graph Convolutional Networks (GCNs) have achieved state-of-the-art performance, as they can integrate both the node information and the topological…
Bootstrap Latent Representations for Multi-modal Recommendation
Xin Zhou, Hongyu Zhou, Yong Liu +5
This paper studies the multi-modal recommendation problem, where the item multi-modality information (e.g., images and textual descriptions) is exploited to improve the recommendat…