9 citations · 13 across the 4 of their papers we have counts for
5 papers
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…
Dual Graph Multitask Framework for Imbalanced Delivery Time Estimation
Lei Zhang, Mingliang Wang, Xin Zhou +5
Delivery Time Estimation (DTE) is a crucial component of the e-commerce supply chain that predicts delivery time based on merchant information, sending address, receiving address,…
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…