Fine-grained auxiliary learning for real-world product recommendation

2025-10-06

AI Summary

This paper addresses the critical challenge of insufficient automated coverage in real-world recommender systems. We propose the Auxiliary Learning with Coverage (ALC) framework, which enhances model discrimination against hard negative samples via fine-grained auxiliary tasks, introduces a dual-objective optimization mechanism based on batch-wise hardest negatives, and incorporates a threshold-consistent margin loss to align similarity ranking with binary classification decisions. By integrating principles from extreme multi-label classification, ALC is evaluated on two large-scale benchmarks—LF-AmazonTitles-131K and Tech and Durables—demonstrating significant improvements in recommendation accuracy and stability under high-coverage scenarios. Experimental results show that ALC achieves state-of-the-art performance while maintaining high automated coverage, effectively meeting the practical deployment requirements of industrial-scale recommender systems.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Data Mining & Knowledge Management: Recommender Systems
• Search and Optimization: Learning to Search

Application Category

• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems

Abstract

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.

Problem

• Improving automated recommendation coverage in real-world systems
• Enhancing fine-grained product embeddings for better discrimination
• Addressing hard negatives through auxiliary learning objectives

Innovation

• Auxiliary learning strategy for fine-grained embeddings
• Hardest negatives used for discriminative training signals
• Combination with threshold-consistent margin loss