19 citations · 55 across the 20 of their papers we have counts for
7 papers · 1 filter
Macro Graph of Experts for Billion-Scale Multi-Task Recommendation
Hongyu Yao, Zijin Hong, Hao Chen +6
Graph-based multi-task learning at billion-scale presents a significant challenge, as different tasks correspond to distinct billion-scale graphs. Traditional multi-task learning m…
AliBoost: Ecological Boosting Framework in Alibaba Platform
Qijie Shen, Yuanchen Bei, Zihong Huang +8
Maintaining a healthy ecosystem in billion-scale online platforms is challenging, as users naturally gravitate toward popular items, leaving cold and less-explored items behind. Th…
CheatAgent: Attacking LLM-Empowered Recommender Systems via LLM Agent
Liang-bo Ning, Shijie Wang, Wenqi Fan +4
Recently, Large Language Model (LLM)-empowered recommender systems (RecSys) have brought significant advances in personalized user experience and have attracted considerable attent…
FilterLLM: Text-To-Distribution LLM for Billion-Scale Cold-Start Recommendation
Ruochen Liu, Hao Chen, Yuanchen Bei +6
Large Language Model (LLM)-based cold-start recommendation systems continue to face significant computational challenges in billion-scale scenarios, as they follow a "Text-to-Judgm…
Knapsack Optimization-based Schema Linking for LLM-based Text-to-SQL Generation
Zheng Yuan, Hao Chen, Zijin Hong +4
Generating SQLs from user queries is a long-standing challenge, where the accuracy of initial schema linking significantly impacts subsequent SQL generation performance. However, c…
Cold-Start Recommendation towards the Era of Large Language Models (LLMs): A Comprehensive Survey and Roadmap
Weizhi Zhang, Yuanchen Bei, Liangwei Yang +15
Cold-start problem is one of the long-standing challenges in recommender systems, focusing on accurately modeling new or interaction-limited users or items to provide better recomm…
Benchmarking LLMs' Mathematical Reasoning with Unseen Random Variables Questions
Zijin Hong, Hao Wu, Su Dong +8
Recent studies have raised significant concerns regarding the reliability of current mathematics benchmarks, highlighting issues such as simplistic design and potential data contam…