34 citations · 77 across the 11 of their papers we have counts for
5 papers · 1 filter
PMG : Personalized Multimodal Generation with Large Language Models
Xiaoteng Shen, Rui Zhang, Xiaoyan Zhao +2
The emergence of large language models (LLMs) has revolutionized the capabilities of text comprehension and generation. Multi-modal generation attracts great attention from both th…
Understanding the Ranking Loss for Recommendation with Sparse User Feedback
Zhutian Lin, Junwei Pan, Shangyu Zhang +5
Click-through rate (CTR) prediction is a crucial area of research in online advertising. While binary cross entropy (BCE) has been widely used as the optimization objective for tre…
Beyond Two-Tower Matching: Learning Sparse Retrievable Cross-Interactions for Recommendation
Liangcai Su, Fan Yan, Jieming Zhu +5
Two-tower models are a prevalent matching framework for recommendation, which have been widely deployed in industrial applications. The success of two-tower matching attributes to…
UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendation
Kelong Mao, Jieming Zhu, Xi Xiao +3
With the recent success of graph convolutional networks (GCNs), they have been widely applied for recommendation, and achieved impressive performance gains. The core of GCNs lies i…
SimpleX: A Simple and Strong Baseline for Collaborative Filtering
Kelong Mao, Jieming Zhu, Jinpeng Wang +4
Collaborative filtering (CF) is a widely studied research topic in recommender systems. The learning of a CF model generally depends on three major components, namely interaction e…