What is Media Diversity and Do Recommender Systems Have It?

Priyanjana Bengani is a Senior Research Fellow at the Tow Center for Digital Journalism at Columbia University. Jonathan Stray is a Senior Scientist at The Center for Human-Compatible Artificial Intelligence (CHAI), Berkeley. Luke Thorburn is a doctoral researcher in safe and trusted AI at King’s College London. One of the classic criticisms of recommender systems arises when they show you a narrow range of content, be it popular items or items similar to whatever you’ve already clicked on. This can be a failing of more elementary approaches to recommendation systems, but production recommender systems typically include diversification algorithms for myriad reasons. We’ve previously discussed the mechanics of filter bubbles, echo chambers, and other types of information-limiting environments. Here we examine the many meanings of “diversity” in media, why society in general and platforms in particular might want it, whether existing platforms have it (and what that even means), and how recommender systems can help achieve it. There are many different definitions of diversity used by communications scholars, as well as a variety of metrics and diversification algorithms used in recommenders that cover much of the same ground from different perspectives. We might want each user to see items from a variety of sources, across diverse viewpoints, on multiple topics, or including multiple media formats. We could also think of diversity in terms of novelty and serendipity, thereby ensuring users don’t see posts covering the same topics every time they log in, which can lead to boredom and engagement dropping off in the longer…What is Media Diversity and Do Recommender Systems Have It?