arXiv AI
AURA: Agentic Diagnosis and Refinement for Production Recommender Systems at Scale
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AURA: Agentic Diagnosis and Refinement for Production Recommender Systems at Scale
How and why does a recommender system fail the users it serves? Oftentimes, practitioners are left to improve their algorithms based on a combination of feedback from stakeholder teams, domain experti...
arXiv AI
Jul 21
SR-Agent: An Experience-Driven Agentic Framework for Post-Ranking Strategies Refinement in E-Commerce Recommendation
arXiv:2607. 17719v1 Announce Type: new Abstract: User experience is a first-class objective in industrial e-commerce recommender systems (RS).
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SR-Agent: An Experience-Driven Agentic Framework for Post-Ranking Strategy Refinement in E-Commerce Recommendation
arXiv:2607. 17719v2 Announce Type: replace Abstract: User experience is a first-class objective in industrial e-commerce recommender systems (RS).
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User experience is a first-class objective in industrial e-commerce recommender systems (RS). Post-ranking strategies, which govern diversity, similarity, and exposure over a ranked list, are widely deployed in industrial RS for their simplicity and low serving cost.
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Shape Your Feed: An LLM-based Agentic System for Conversational Recommendation
arXiv:2608. 06632v1 Announce Type: new Abstract: Industrial recommendation systems predominantly adopt a passive ranking paradigm that infers user preferences from implicit behavioral signals (e.
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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.