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We address the problem of personalizing query completion in a digitalcommerce setting, in which the bounce rate is typically high and recurringusers are rare. We focus on in-session personalization and improve a standardnoisy channel model by injecting dense vectors computed from product images atquery time. We argue that image-based personalization displays severaladvantages over alternative proposals (from data availability to businessscalability), and provide quantitative evidence and qualitative support on theeffectiveness of the proposed methods. Finally, we show how a shared vectorspace between similar shops can be used to improve the experience of usersbrowsing across sites, opening up the possibility of applying zero-shotunsupervised personalization to increase conversions. This will prove to beparticularly relevant to retail groups that manage multiple brands and/orwebsites and to multi-tenant SaaS providers that serve multiple clients in thesame space.

"An Image is Worth a Thousand Features": Scalable Product Representations for In-Session Type-Ahead Personalization