Abstract

Recommender systems have become pervasive on the web, shaping the way users see information and thus the decisions they make. As these systems get more complex, there is a growing need for transparency. In this paper, we study the problem of generating and visualizing personalized explanations for hybrid recommender systems, which incorporate many different data sources. We build upon a hybrid probabilistic graphical model and develop an approach to generate real-time recommendations along with personalized explanations. To study 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. We experiment with 1) different explanation styles (e.g., user-based, item-based), 2) manipulating the number of explanation styles presented …

Date

March 17, 2019

Authors

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

Conference

Proceedings of the 24th International Conference on Intelligent User Interfaces

Pages

379-390

Publisher

ACM