In today’s digital landscape, recommender systems have become ubiquitous, curating user experiences across a wide array of online platforms, including Vinted - Europe’s largest online second-hand fashion marketplace. In this blog post, we outline our journey of adopting the Vespa search engine to serve personalized homepage listing recommendations, helping our members find deals they will enjoy. We are excited to share our story as we have found Vespa to be a great solution combining the now trendy vector search with more traditional sparse search techniques, as well as offering a great engineering experience.
At Vinted, we’ve implemented a 3-stage recommender system that leverages both explicit and implicit user preferences to offer users a curated list of items presented on the homepage. Explicit preferences are inputted by users on the app, allowing them to specify details such as the clothing sizes they are interested in. Meanwhile, implicit preferences are extracted from historical user interactions on the platform, including clicks and purchases, via the use of machine learning models. This system distills a tailored selection from millions of available listings, presenting users with options most aligned with their tastes and behaviors.Continue reading →