Are words enough?: a study on text-based representations and retrieval models for linking pins to online shops

Abstract

User-generated content offers opportunities to learn about people's interests and hobbies. We can leverage this information to help users find interesting shops and businesses find interested users. However this content is highly noisy and unstructured as posted on social media sites and blogs. In this work we evaluate different textual representations and retrieval models that aim to make sense of social media data for retail applications. Our task is to link the text of pins (from Pinterest.com) to online shops (formed by clustering Amazon.com's products). Our results show that document representations that combine latent concepts with single words yield the best performance.