Pricing the Nearly Known - When Semantic Similarity is Just not Enough

Helping sellers price their listings is an important and challenging task at E-commerce marketplaces, as the information provided by sellers is often partially structured and lacking. To help the seller gain trust in the recommended price, a collection of supporting similar listings are retrieved and provided along with their prices. We address the problem of retrieval-based price recommendation using a novel approach, which enables a trade-off adjustment between semantic similarity and price accuracy. Balancing the two required since, based on our study, retrieval of semantically similar listings does not guarantee pricing accuracy. In contrast, a price-accuracy driven approach may produce less semantically supporting listings. We also suggest a third method - training a Multi-Task network which learns in parallel both semantic similarity and a pricing-based objective. Framing the solution as a Multi-Task network unfolds the ability to control the balance between explainability and accuracy, thus providing a powerful tool to precisely tailor the correct pricing solution to different real world business use cases.