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

arXiv: https://arxiv.org/abs/2003.07160 · HTML: https://mlsanity.com/paper/2003.07160/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2020.

## Built on by (most cited first, 4 of 4)

- [Contrastive language and vision learning of general fashion concepts](https://mlsanity.com/paper/2204.03972/) (2022)
- [Query2Prod2Vec Grounded Word Embeddings for eCommerce](https://mlsanity.com/paper/2104.02061/) (2021)
- [Language in a (Search) Box: Grounding Language Learning in Real-World Human-Machine Interaction](https://mlsanity.com/paper/2104.08874/) (2021)
- [(Vector) Space is Not the Final Frontier: Product Search as Program Synthesis](https://mlsanity.com/paper/2304.11473/) (2023)

## Abstract (arXiv)

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.