arXiv:2604. 14586v3 Announce Type: replace-cross Abstract: The rapid expansion of gaming industry requires advanced recommender systems tailored to its dynamic landscape.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2506. 16114v3 Announce Type: replace-cross Abstract: Generative recommendations (GR), which usually include item tokenizers and generative Large Language Models (LLMs), have demonstrated remarkable success across a wide range of scenarios.
Accurately modeling and understanding player experience is crucial for designing engaging puzzle games. To achieve this, a common approach involves collecting diverse user data to train predictive playtesting models that mimic player behavior.
arXiv:2607. 11501v1 Announce Type: new Abstract: Accurately modeling and understanding player experience is crucial for designing engaging puzzle games.
GMTRouter is a personalized large language model router that represents multi‑turn user‑LLM interactions as a heterogeneous graph with five node types—user, LLM, query, response, and turn—to preserve relational structure. Using a lightweight inductive graph learning framework and a user‑conditioned graph sampling mechanism, it captures user preferences from few‑shot data, enabling effective personalization without extensive fine‑tuning. Experiments show GMTRouter outperforms strong baselines, improving accuracy by up to 0.108 and AUC by 0.124, and adapts to new users with minimal data.
arXiv:2607. 20737v1 Announce Type: new Abstract: Graph Neural Networks trained on heterogenous bipartite graphs form a common basis in recommendation systems.
arXiv:2607. 25471v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) and Large Language Models (LLMs) have each advanced recommendation systems by modeling structural and semantic signals, respectively.