61 citations · 61 across the 3 of their papers we have counts for
3 papers
cs.IR2026
GenPage: Towards End-to-End Generative Homepage Construction at Netflix
Lequn Wang, Jiangwei Pan, Linas Baltrunas
We present GenPage, an end-to-end generative approach to Netflix homepage construction that replaces the traditional multi-stage recommender stack with a single transformer. GenPag…
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.IR2015★ 61 cited
Frappe: Understanding the Usage and Perception of Mobile App Recommendations In-The-Wild
Linas Baltrunas, Karen Church, Alexandros Karatzoglou +1
This paper describes a real world deployment of a context-aware mobile app recommender system (RS) called Frappe. Utilizing a hybrid-approach, we conducted a large-scale app market…