5 citations · 7 across the 8 of their papers we have counts for
8 papers
MI-DPG: Decomposable Parameter Generation Network Based on Mutual Information for Multi-Scenario Recommendation
Wenzhuo Cheng, Ke Ding, Xin Dong +3
Conversion rate (CVR) prediction models play a vital role in recommendation and advertising systems. Recent research on multi-scenario recommendation shows that learning a unified…
A Learnable Fully Interacted Two-Tower Model for Pre-Ranking System
Chao Xiong, Xianwen Yu, Wei Xu +3
Pre-ranking plays a crucial role in large-scale recommender systems by significantly improving the efficiency and scalability within the constraints of providing high-quality candi…
MCPToolBench++: A Large Scale AI Agent Model Context Protocol MCP Tool Use Benchmark
Shiqing Fan, Xichen Ding, Liang Zhang +1
LLMs' capabilities are enhanced by using function calls to integrate various data sources or API results into the context window. Typical tools include search, web crawlers, maps,…
CTR-Sink: Attention Sink for Language Models in Click-Through Rate Prediction
Zixuan Li, Binzong Geng, Jing Xiong +11
Click-Through Rate (CTR) prediction, a core task in recommendation systems, estimates user click likelihood using historical behavioral data. Modeling user behavior sequences as te…
DOGR: Leveraging Document-Oriented Contrastive Learning in Generative Retrieval
Penghao Lu, Xin Dong, Yuansheng Zhou +3
Generative retrieval constitutes an innovative approach in information retrieval, leveraging generative language models (LM) to generate a ranked list of document identifiers (doci…
Breaking the Length Barrier: LLM-Enhanced CTR Prediction in Long Textual User Behaviors
Binzong Geng, Zhaoxin Huan, Xiaolu Zhang +5
With the rise of large language models (LLMs), recent works have leveraged LLMs to improve the performance of click-through rate (CTR) prediction. However, we argue that a critical…
AntMC: A Large Scale Dataset For Multi-Scenario Multi-Modal CTR Prediction
Zhaoxin Huan, Ke Ding, Ang Li +10
Click-through rate (CTR) prediction is a crucial issue in recommendation systems. There has been an emergence of various public CTR datasets. However, existing datasets primarily s…
GDOD: Effective Gradient Descent using Orthogonal Decomposition for Multi-Task Learning
Xin Dong, Ruize Wu, Chao Xiong +6
Multi-task learning (MTL) aims at solving multiple related tasks simultaneously and has experienced rapid growth in recent years. However, MTL models often suffer from performance…