Choice of Implicit Signal Matters: Accounting for User Aspirations in Podcast Recommendations
Zahra Nazari, Praveen Chandar, Ghazal Fazelnia, Catherine M. Edwards, Benjamin Carterette, Mounia Lalmas
Abstract
Recommender systems are modulating what billions of people are exposed to on a daily basis. Typically, these systems are optimized for user engagement signals such as clicks, streams, likes, or a weighted combination of such sets. Despite the pervasiveness of this practice, little research has been done to explore the downstream impacts of optimization choice on users, creators and the ecosystem they are offered in. We used a platform that caters recommendations to millions of people and show in practice what you optimize for can have a large impact on the content users are exposed to, as well as what they end up consuming.
Topics & Concepts
Computer scienceRecommender systemDownstream (manufacturing)SIGNAL (programming language)User engagementInformation retrievalWorld Wide WebBusinessMarketingProgramming languageRecommender Systems and TechniquesAdvanced Bandit Algorithms ResearchMobile Crowdsensing and Crowdsourcing