Query Understanding for Surfacing Under-served Music Content

Abstract

Platform ecosystems have witnessed an explosive growth by facilitating interactions between consumers and suppliers. Search systems powering such platforms play an important role in surfacing content in front of users. To maintain a healthy, sustainable platform, systems designers often need to explicitly consider exposing under-served content to users, content which might otherwise remain undiscovered. In this work, we consider the question when we might surface under-served content in search results, and investigate ways to provide exposure to certain content groups. We propose a framework to develop query understanding techniques to identify potential non-focused search queries on a music streaming platform, where users’ information needs are non-specific enough to expose under-served content without severely impacting user satisfaction. We present insights from a search ranker deployed at scale and present results from live A/B test targeting a random sample of 72 million users and 593 million sessions, to compare performance of different methods considered to identify non-focused queries for surfacing under-served content.

Related

October 2021 | CSCW

Let Me Ask You This: How Can a Voice Assistant Elicit Explicit User Feedback?

Ziang Xiao, Sarah Mennicken, Bernd Huber, Adam Shonkoff, Jennifer Thom

September 2021 | ECML-PKDD

Gaussian Process Encoders: VAEs with Reliable Latent-Space Uncertainty

Judith Bütepage, Lucas Maystre, Mounia Lalmas

August 2021 | KDD

Neural Instant Search for Music and Podcast

Helia Hashemi, Aasish Pappu, Mi Tian, Praveen Ravichandran, Mounia Lalmas, Ben Carterette