5 citations · 5 across the 5 of their papers we have counts for
2 papers
cs.AI2026
From Logs to Language: Learning Optimal Verbalization for LLM-Based Recommendation at Industry Scale
Yucheng Shi, Ying Li, Yu Wang +8
Large language models (LLMs) are promising backbones for generative recommender systems, yet a key challenge remains underexplored: verbalization, i.e., converting structured user…
cs.IR2024★ 5 cited
Sliding Window Training -- Utilizing Historical Recommender Systems Data for Foundation Models
Swanand Joshi, Yesu Feng, Ko-Jen Hsiao +2
Long-lived recommender systems (RecSys) often encounter lengthy user-item interaction histories that span many years. To effectively learn long term user preferences, Large RecSys…