# Fine-grained auxiliary learning for real-world product recommendation

arXiv: https://arxiv.org/abs/2510.04551 · HTML: https://mlsanity.com/paper/2510.04551/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 28 references that are in the map.
- First posted on arXiv 6 Oct 2025.

## Builds on (most cited first, 19 of 19)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://mlsanity.com/paper/2007.00808/) (2020)
- [Fine-Tuning LLaMA for Multi-Stage Text Retrieval](https://mlsanity.com/paper/2310.08319/) (2023)
- [RAFT: Adapting Language Model to Domain Specific RAG](https://mlsanity.com/paper/2403.10131/) (2024)
- [Proxy Anchor Loss for Deep Metric Learning](https://mlsanity.com/paper/2003.13911/) (2020)
- [InstructRAG: Instructing Retrieval-Augmented Generation via Self-Synthesized Rationales](https://mlsanity.com/paper/2406.13629/) (2024)
- [Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification](https://mlsanity.com/paper/2110.00685/) (2021)
- [Optimal strategies for reject option classifiers](https://mlsanity.com/paper/2101.12523/) (2021)
- [Information Retrieval: Recent Advances and Beyond](https://mlsanity.com/paper/2301.08801/) (2023)
- [NGAME: Negative Mining-aware Mini-batching for Extreme Classification](https://mlsanity.com/paper/2207.04452/) (2022)
- [Supervised Contrastive Learning for Product Matching](https://mlsanity.com/paper/2202.02098/) (2022)
- [LLMEmb: Large Language Model Can Be a Good Embedding Generator for Sequential Recommendation](https://mlsanity.com/paper/2409.19925/) (2024)
- [Exploring Query Understanding for Amazon Product Search](https://mlsanity.com/paper/2408.02215/) (2024)
- [Improving Calibration in Deep Metric Learning With Cross-Example Softmax](https://mlsanity.com/paper/2011.08824/) (2020)
- [Block-SCL: Blocking Matters for Supervised Contrastive Learning in Product Matching](https://mlsanity.com/paper/2207.02008/) (2022)
- [Prototypical Extreme Multi-label Classification with a Dynamic Margin Loss](https://mlsanity.com/paper/2410.20401/) (2024)

## Abstract (arXiv)

Product recommendation is the task of recovering the closest items to a given query within a large product corpora. Generally, one can determine if top-ranked products are related to the query by applying a similarity threshold; exceeding it deems the product relevant, otherwise manual revision is required. Despite being a well-known problem, the integration of these models in real-world systems is often overlooked. In particular, production systems have strong coverage requirements, i.e., a high proportion of recommendations must be automated. In this paper we propose ALC , an Auxiliary Learning strategy that boosts Coverage through learning fine-grained embeddings. Concretely, we introduce two training objectives that leverage the hardest negatives in the batch to build discriminative training signals between positives and negatives. We validate ALC using three extreme multi-label classification approaches in two product recommendation datasets; LF-AmazonTitles-131K and Tech and Durables (proprietary), demonstrating state-of-the-art coverage rates when combined with a recent threshold-consistent margin loss.