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cs.IR2026
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…
cs.IR2026
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…
cs.IR2026
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…