Geodesic
Towards Understandable Personalized Recommendations: Hybrid Explanations
Computer Science and Information Systems, Tome 16 (2019) no. 1 Cet article a éte moissonné depuis la source Computer Science and Information Systems website

Voir la notice de l'article

Nowadays, personalized recommendations are widely used and popular. There are a lot of systems in various fields, which use recommendations for different purposes. One of the basic problems is the distrust of users of recommended systems. Users often consider the recommendations as an intrusion of their privacy. Therefore, it is important to make recommendations transparent and understandable to users. To address these problems, we propose a novel hybrid method of personalized explanation of recommendations. Our method is independent of recommendation technique and combines basic explanation styles to provide the appropriate type of personalized explanation to each user. We conducted several online experiments in the news domain. Obtained results clearly show that the proposed personalized hybrid explanation approach improves the users’ attitude towards the recommender, moreover, we have observed the increase of recommendation precision.
Keywords: recommendations explanation, eye-tracking, collaborative filtering, personalized recommendation 
@article{CSIS_2019_16_1_a9,
     author = {Martin Svrcek and Michal Kompan and Maria Bielikova},
     title = {Towards {Understandable} {Personalized} {Recommendations:} {Hybrid} {Explanations}},
     journal = {Computer Science and Information Systems},
     year = {2019},
     volume = {16},
     number = {1},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2019_16_1_a9/}
}
TY  - JOUR
AU  - Martin Svrcek
AU  - Michal Kompan
AU  - Maria Bielikova
TI  - Towards Understandable Personalized Recommendations: Hybrid Explanations
JO  - Computer Science and Information Systems
PY  - 2019
VL  - 16
IS  - 1
UR  - http://geodesic.mathdoc.fr/item/CSIS_2019_16_1_a9/
ID  - CSIS_2019_16_1_a9
ER  - 
%0 Journal Article
%A Martin Svrcek
%A Michal Kompan
%A Maria Bielikova
%T Towards Understandable Personalized Recommendations: Hybrid Explanations
%J Computer Science and Information Systems
%D 2019
%V 16
%N 1
%U http://geodesic.mathdoc.fr/item/CSIS_2019_16_1_a9/
%F CSIS_2019_16_1_a9
Martin Svrcek; Michal Kompan; Maria Bielikova. Towards Understandable Personalized Recommendations: Hybrid Explanations. Computer Science and Information Systems, Tome 16 (2019) no. 1. http://geodesic.mathdoc.fr/item/CSIS_2019_16_1_a9/