Beyond Ranking Accuracy: Evaluating LLM-Cited Feature Rationales for Next Basket Repurchase Recommendation
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
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arXiv:2606. 04387v1 Announce Type: cross Abstract: Sales lead conversion in high-stakes domains (e.
arXiv:2609.40241v1 Announce Type: cross Abstract: Large language models (LLMs) have shown promise for recommendation reranking, but their use introduces an important tradeoff between recommendation q...
arXiv:2610.19065v1 Announce Type: cross Abstract: Generative retrieval (GR) has emerged as a strong candidate generator for sequential recommendation as language models and semantic-item identifiers...
FORTE is a model‑agnostic production framework that discovers, validates, and removes features from industrial recommender systems. It uses efficient perturbation and grouped ablation to evaluate candidate feature sets daily, and lineage tracing to identify all downstream models affected. In two large‑scale systems, FORTE removed up to 43.3% of features without online regressions, cutting trainable parameters by 29.9% and improving serving throughput by 19.8‑25.5%.
arXiv:2606. 02004v1 Announce Type: cross Abstract: Consumer-price measurement increasingly draws on alternative data sources -- scanner, web-scraped, and transaction/receipt data.
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.