Welcome to our podcast, where we delve into cutting-edge AI in e-commerce! Today, we're exploring ItemSage, Pinterest's innovative product embedding system for shopping recommendations. Developed by engineers at Pinterest, ItemSage revolutionises how users discover products across Home, Closeup, and Search surfaces.

A key novelty is its transformer-based architecture, combining both text and image modalities to create rich product representations, significantly outperforming single-modality approaches. ItemSage also leverages multi-task learning to optimise for diverse engagement objectives, including purchases and add-to-cart actions, making the recommendation funnel more efficient, particularly for sparse labels. This unified embedding system, compatible with existing PinSage and SearchSage embeddings, drastically reduces infrastructure and maintenance costs by three times across different recommendation verticals.

While ItemSage has delivered substantial gains—up to +7% Gross Merchandise Value per user and +11% click volume in online A/B experiments—future work aims to enhance text feature modeling with pre-trained Transformers. Join us to understand this powerful system transforming shopping at Pinterest!

Paper link: https://arxiv.org/pdf/2205.11728