552 citations · 617 across the 5 of their papers we have counts for
5 papers
GenPage: Towards End-to-End Generative Homepage Construction at Netflix
Lequn Wang, Jiangwei Pan, Linas Baltrunas +1
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…
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…
Session-based Recommendations with Recurrent Neural Networks
Balázs Hidasi, Alexandros Karatzoglou, Linas Baltrunas +1
We apply recurrent neural networks (RNN) on a new domain, namely recommender systems. Real-life recommender systems often face the problem of having to base recommendations only on…
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…
GAPfm: Optimal Top-N Recommendations for Graded Relevance Domains
Yue Shi, Alexandros Karatzoglou, Linas Baltrunas +2
Recommender systems are frequently used in domains in which users express their preferences in the form of graded judgments, such as ratings. If accurate top-N recommendation lists…