An Analysis of Learned Product Embeddings in an E-Commerce Context

Recommender systems often represent products with learnable embeddings.Yet, we seldom examine the structure of the embedding space, and what implications it has for the recommendation task at hand.In contrast, embeddings in natural language processing are well-understood and offer intuitive properties through word analogies (e.g."queen -king = woman -man").In this work, we present a corresponding approach that reveals latent knowledge in the structure of product embeddings.We prove their relevance in evaluating several embeddings learned from different data modalities in a home-furnishing context.Our findings evince distinct embedding strengths: visual embeddings capture explicit attributes like colour and shape; textual embeddings encode abstract concepts like style and functionality; while behavioural embeddings offer versatile representations driven by user interactions.We also highlight trade-offs, and link our evaluations to practical considerations in embedding development within the e-commerce domain.