Exploring Goal-oriented Podcast Recommendations

Exploring Goal-oriented Podcast Recommendations

Recommender systems typically look to users’ past consumption to predict what they may want next. In practice, this approach tends to work best when what the user wants is similar to what they have consumed recently, and when it is relatively easy for that person to evaluate new items.

Latest Publications

We publish research papers and present our work in a wide range of venues.

A Survey on Multi-objective Recommender Systems

Dietmar Jannach and Himan Abdollahpouri

Estimating categorical counterfactuals via deep twin networks

Athanasios Vlontzos, Bernhard Kainz, Ciarán M. Gilligan-Lee

Enabling Goal-Focused Exploration of Podcasts in Interactive Recommender Systems

Yu Liang, Aditya Ponnada, Paul Lamere, Nediyana Daskalova

Research Areas

How do we create more personalized experiences? What can we learn about listeners based on how they use written language? How do we optimize testing methodologies? Explore all our research areas below.

We are looking for pioneers to join us in all research areas

We’re expanding knowledge of audio technology every day, sharing open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.