2 citations · 2 across the 4 of their papers we have counts for
6 papers
LoopCTR: Unlocking the Loop Scaling Power for Click-Through Rate Prediction
Jiakai Tang, Runfeng Zhang, Weiqiu Wang +7
Scaling Transformer-based click-through rate (CTR) models by stacking more parameters brings growing computational and storage overhead, creating a widening gap between scaling amb…
MOON Embedding: Multimodal Representation Learning for E-commerce Search Advertising
Chenghan Fu, Daoze Zhang, Yukang Lin +8
We introduce MOON, our comprehensive set of sustainable iterative practices for multimodal representation learning for e-commerce applications. MOON has already been fully deployed…
Think before Recommendation: Autonomous Reasoning-enhanced Recommender
Xiaoyu Kong, Junguang Jiang, Bin Liu +6
The core task of recommender systems is to learn user preferences from historical user-item interactions. With the rapid development of large language models (LLMs), recent researc…
Reinforced Preference Optimization for Recommendation
Junfei Tan, Yuxin Chen, An Zhang +7
Recent breakthroughs in large language models (LLMs) have fundamentally shifted recommender systems from discriminative to generative paradigms, where user behavior modeling is ach…
See Beyond a Single View: Multi-Attribution Learning Leads to Better Conversion Rate Prediction
Sishuo Chen, Zhangming Chan, Xiang-Rong Sheng +6
Conversion rate (CVR) prediction is a core component of online advertising systems, where the attribution mechanisms-rules for allocating conversion credit across user touchpoints-…
MOON: Generative MLLM-based Multimodal Representation Learning for E-commerce Product Understanding
Daoze Zhang, Chenghan Fu, Zhanheng Nie +7
With the rapid advancement of e-commerce, exploring general representations rather than task-specific ones has attracted increasing research attention. For product understanding, a…