Zhenwen Dai, Praveen Chandar, Ghazal Fazelnia, Benjamin Carterette, Mounia Lalmas
Query Understanding for Surfacing Under-served Music Content
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.
Casper Hansen, Christian Hansen, Lucas Maystre, Rishabh Mehrotra, Brian Brost, Federico Tomasi, Mounia Lalmas
Inferring the Causal Impact of New Track Releases on Music Recommendation Platforms through Counterfactual Predictions
Rishabh Mehrotra, Prasanta Bhattacharya, Mounia Lalmas