Abstract

Recommender systems are ubiquitous and shape the way users access information and make decisions. As these systems become more complex, there is a growing need for transparency and interpretability. In this article, we study the problem of generating and visualizing personalized explanations for recommender systems that incorporate signals from many different data sources. We use a flexible, extendable probabilistic programming approach and show how we can generate real-time personalized recommendations. We then turn these personalized recommendations into explanations. We perform an extensive user study to evaluate the benefits of explanations for hybrid recommender systems. We conduct a crowd-sourced user study where our system generates personalized recommendations and explanations for real users of the last.fm music platform. First, we evaluate the performance of the …

Date

November 8, 2020

Authors

Pigi Kouki, James Schaffer, Jay Pujara, John O’Donovan, Lise Getoor

Journal

ACM Transactions on Interactive Intelligent Systems (TiiS)

Volume

10

Issue

4

Pages

1-40

Publisher

ACM