"An Image is Worth a Thousand Features": Scalable Product Representations for In-Session Type-Ahead Personalization
This addresses high bounce rates and rare recurring users in e-commerce, offering scalable personalization for retail groups and SaaS providers.
The paper tackles personalizing query completion in digital commerce by enhancing a noisy channel model with dense vectors from product images, showing effectiveness through quantitative evidence and enabling cross-site personalization.
We address the problem of personalizing query completion in a digital commerce setting, in which the bounce rate is typically high and recurring users are rare. We focus on in-session personalization and improve a standard noisy channel model by injecting dense vectors computed from product images at query time. We argue that image-based personalization displays several advantages over alternative proposals (from data availability to business scalability), and provide quantitative evidence and qualitative support on the effectiveness of the proposed methods. Finally, we show how a shared vector space between similar shops can be used to improve the experience of users browsing across sites, opening up the possibility of applying zero-shot unsupervised personalization to increase conversions. This will prove to be particularly relevant to retail groups that manage multiple brands and/or websites and to multi-tenant SaaS providers that serve multiple clients in the same space.