%0 Conference Paper %1 Kouki:2019:PEH:3301275.3302306 %A Kouki, Pigi %A Schaffer, James %A Pujara, Jay %A O'Donovan, John %A Getoor, Lise %B Proceedings of the 24th International Conference on Intelligent User Interfaces %C New York, NY, USA %D 2019 %I ACM %K explanation iui2019 recommender %P 379--390 %R 10.1145/3301275.3302306 %T Personalized Explanations for Hybrid Recommender Systems %U http://doi.acm.org/10.1145/3301275.3302306 %X 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, and 3) manipulating the presentation format (e.g., textual vs. visual). We apply a mixed model statistical analysis to consider user personality traits as a control variable and demonstrate the usefulness of our approach in creating personalized hybrid explanations with different style, number, and format. %@ 978-1-4503-6272-6

@inproceedings{Kouki:2019:PEH:3301275.3302306, 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, and 3) manipulating the presentation format (e.g., textual vs. visual). We apply a mixed model statistical analysis to consider user personality traits as a control variable and demonstrate the usefulness of our approach in creating personalized hybrid explanations with different style, number, and format.}, acmid = {3302306}, added-at = {2019-03-07T18:01:10.000+0100}, address = {New York, NY, USA}, author = {Kouki, Pigi and Schaffer, James and Pujara, Jay and O'Donovan, John and Getoor, Lise}, biburl = {https://www.bibsonomy.org/bibtex/293e19cc9b222e0e4feece180371ed4eb/brusilovsky}, booktitle = {Proceedings of the 24th International Conference on Intelligent User Interfaces}, description = {Personalized explanations for hybrid recommender systems}, doi = {10.1145/3301275.3302306}, interhash = {8acef653220e23b75eed3a8379fd9d79}, intrahash = {93e19cc9b222e0e4feece180371ed4eb}, isbn = {978-1-4503-6272-6}, keywords = {explanation iui2019 recommender}, location = {Marina del Ray, California}, numpages = {12}, pages = {379--390}, publisher = {ACM}, series = {IUI '19}, timestamp = {2019-06-09T09:31:20.000+0200}, title = {Personalized Explanations for Hybrid Recommender Systems}, url = {http://doi.acm.org/10.1145/3301275.3302306}, year = 2019 }