Fine-grained auxiliary learning for real-world product recommendation
This work addresses the need for high automation in production recommendation systems, offering an incremental improvement to boost coverage rates.
The paper tackles the problem of improving coverage in real-world product recommendation systems by proposing ALC, an auxiliary learning strategy that uses fine-grained embeddings and hardest negatives to enhance discriminative training. The method achieves state-of-the-art coverage rates when combined with a threshold-consistent margin loss on datasets like LF-AmazonTitles-131K and a proprietary one.
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.