ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest
This work addresses the need for efficient and effective product embeddings in web-scale e-commerce recommendation systems, offering a domain-specific solution that is incremental in combining modalities and multi-task learning.
The paper tackles the problem of providing relevant shopping recommendations across multiple use cases at Pinterest by introducing ItemSage, a transformer-based architecture that aggregates text and image modalities, resulting in up to +7% gross merchandise value per user and +11% click volume.
Learned embeddings for products are an important building block for web-scale e-commerce recommendation systems. At Pinterest, we build a single set of product embeddings called ItemSage to provide relevant recommendations in all shopping use cases including user, image and search based recommendations. This approach has led to significant improvements in engagement and conversion metrics, while reducing both infrastructure and maintenance cost. While most prior work focuses on building product embeddings from features coming from a single modality, we introduce a transformer-based architecture capable of aggregating information from both text and image modalities and show that it significantly outperforms single modality baselines. We also utilize multi-task learning to make ItemSage optimized for several engagement types, leading to a candidate generation system that is efficient for all of the engagement objectives of the end-to-end recommendation system. Extensive offline experiments are conducted to illustrate the effectiveness of our approach and results from online A/B experiments show substantial gains in key business metrics (up to +7% gross merchandise value/user and +11% click volume).