arXiv AI
Explainable Recommendations at Scale: LLM Rationales for YouTube Music Artist Discovery
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arXiv AI
Sep 15
Safety as a Constraint: Fine-Tuning a LLM Recommender to Explain Itself
The paper presents a method for fine‑tuning a large language model (LLM) recommender to generate personalized, non‑harmful explanations for its recommendations. By training two LLM‑judge reward models and using constrained GRPO, the authors achieve a significant increase in the PASS rate for all three criteria, from 0.649 to 0.956 on their own judges and from 0.677 to 0.931 on an independent judge. The fine‑tuned model maintains its original recommendation performance, demonstrating that LLM‑based recommenders can be adapted to complex tasks without loss of effectiveness.
arXiv AI
Jul 14
An LLM-powered Agentic Recommendation System for Connected TV Content Discovery
arXiv:2607. 09988v1 Announce Type: cross Abstract: Recommendation systems, from traditional multi-stage to recent unified generative architectures, face challenges in incorporating diverse contextual signals, such as trending topics, breaking news, cultural events, and cross-surface user activities, into their ranking pipelines.
Hugging Face Trending Papers
Jul 10
An LLM-powered Agentic Recommendation System for Connected TV Content Discovery
Recommendation systems, from traditional multi-stage to recent unified generative architectures, face challenges in incorporating diverse contextual signals, such as trending topics, breaking news, cultural events, and cross-surface user activities, into their ranking pipelines. These systems are designed to consume structured behavioral signals with consistent schemas, and lack the reasoning capability to naturally process unstructured or heterogeneously formatted contextual information.
arXiv AI
Aug 24
Profiling What Matters: Context-Aware Item Profiles from Large-Scale Metadata for LLM Recommenders
arXiv:2608.20801v1 Announce Type: cross Abstract: While Large Language Models (LLMs) have significantly advanced reranking in recommendation, effectively leveraging item-side information remains chal...
arXiv AI
Jun 24
Impatient Bandits: Optimizing for the Long-Term Without Delay
arXiv:2501. 07761v2 Announce Type: replace-cross Abstract: Increasingly, recommender systems are tasked with improving users' long-term satisfaction.
Hugging Face Trending Papers
Sep 17
Reproducing Transparent and Scrutable Recommendations: Exploring Open-Weight Models via Natural-Language User Profiles
The study reproduces a prior work on recommender systems that use generated natural‑language user profiles to enhance transparency and user control. It confirms that the User Profile Recommendation (UPR) model performs competitively and that altering these profiles uniformly shifts predicted ratings without changing ranking order. Additional experiments include context ablation, multi‑seed stability, and mechanistic interpretability analysis with the nnsight framework.