1 citations · 2 across the 5 of their papers we have counts for
4 papers · 1 filter
Decomposing Staleness in Recommender Systems: A Dual-Filter Framework for Supersession and Decay
Di Bai, Feng Han, Zhenwei Tang +3
Stale recommendations are a pervasive challenge and a leading source of user complaints on large-scale content platforms. Items lose relevance through two primary mechanisms: super…
Heterogeneous Ranking in Industrial-Scale Recommender Systems: A Case Study
Di Bai, Jintao Liu, Zhenwei Tang +3
Heterogeneous recommendation feeds present complex challenges that extend beyond those found in highly homogeneous environments (e.g., music-only or video-only closed-ecosystem pla…
AgenticRecTune: Multi-Agent with Self-Evolving Skillhub for Recommendation System Optimization
Xidong Wu, Yue Zhuan, Ruoqiao Wei +7
Modern large-scale recommendation systems are typically constructed as multi-stage pipelines, encompassing pre-ranking, ranking, and re-ranking phases. While traditional recommenda…
Context-aware Tree-based Deep Model for Recommender Systems
Daqing Chang, Jintao Liu, Ziru Xu +3
How to predict precise user preference and how to make efficient retrieval from a big corpus are two major challenges of large-scale industrial recommender systems. In tree-based m…