Recsperts - Recommender Systems Experts

In episode 33 of Recsperts, I speak with Joseph A. Konstan, Distinguished McKnight University Professor and Distinguished University Teaching Professor at the University of Minnesota, co-founder of GroupLens, and the very first General Chair of the ACM RecSys conference back in 2007. This year he returns in that role as General Co-Chair of the 20th RecSys in Minneapolis. We talk about what actually makes a recommendation useful, why the field is more than a machine learning application, and how the community around RecSys came into being.

We start with what Joe cares about most: usefulness. He recalls the supermarket thought experiment of printing "buy bananas and bread" on every shopping cart and explains why the early systems were valuable because they were wrong a lot. A recommender that took ten people like you and often got it wrong was also often surprising when it got it right, whereas today's systems are wrong far less and useless far more. As Joe puts it, "I don't care about prediction at all. I care about changing people's behavior." We discuss why optimizing for click-through in news reliably produces clickbait, why leave-one-out evaluation only makes sense if you assume the user already knew the right answer and had simply forgotten it, and why usefulness can never be read off a single metric but depends on the task, the context and the breadth of the user's intent. Sometimes the most useful thing is not the recommendation itself, but the stars, the reviews or the comparison table you put around it.

From there we turn to the community itself. Joe traces it back to the spring of 1996 and the Berkeley Collaborative Filtering Workshop organized by Hal Varian and Paul Resnick, through a decade of scattered workshops at SIGIR, CSCW and CHI, to the first RecSys in 2007: a room on the Minnesota campus, over 120 people, and a substantial delegation from industry including Amazon. We discuss why the conference has always been heavily international and always at the intersection of research and practice, what surprised him most in 20 years (that we are still here and thriving), and the vision he pushed against the pull of becoming just another application of machine learning: a highly constrained, highly contextualized, multi-measure and often multi-stakeholder problem spanning algorithms, interfaces, data and business. This year the 20th RecSys comes home to Minneapolis, with Joe as General Co-Chair alongside George Karypis and Gediminas Adomavicius.

We close on where things are heading. Joe's advice for newcomers is to immerse yourself in an application and find the real problems and opportunities there, rather than arriving enamored with a tool and treating every nail as something to hit. He picks up Karl Higley's point from the POPROX team that the research community keeps focusing on the model when the real object is the system: your algorithm is useless if it is not embedded in something that can deliver its results in a useful way. And he names the work he is most excited about — human decision-making and consumer psychology in the context of choice, the business school perspective that brings marketing and pricing into the picture, multi-sided marketplaces with their often invisible market maker, and the ethics of these systems as a practical lens on the long-term value our metrics still fail to capture.

Enjoy this enriching episode of RECSPERTS – Recommender Systems Experts.
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