5 papers
Reasoning Reranking with Semantic IDs as the Sole Item Representation
Zhenwei Tang, Jintao Liu, Di Bai +2
Reasoning rerankers write a rationale before ranking a candidate slate, and existing systems read item metadata to do so, even though generative recommenders increasingly represent…
Dream-RSI: Recursive Self-Improvement through Evolving Worlds
Tong Zheng, Xidong Wu, Zheng Zhang +14
Recursive self-improvement is becoming essential for autonomous AI agents, whose progress depends on discovering high-value solutions across complex domains. Effective exploration…
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…