Martijn Millecamp|Nyi Nyi Htun|Cristina Conati|Katrien Verbert
To explain or not to explain
KU Leuven|KU Leuven|University of British Columbia Hospital|KU Leuven
recommender systems increasingly used online services consume daily facebook netflix youtube spotify however systems often presented users black box ie rationale providing individual recommendations remains unexplained users recent years various attempts made address black box issue providing textual explanations interactive visualisations enable users explore provenance recommendations among things results demonstrated benefits terms precision user satisfaction previous research also indicated personal characteristics domain knowledge trust propensity persistence may also play important role perceived benefits yet date little known effects personal characteristics explaining recommendations address gap developed music recommender system explanations conducted online study using withinsubject design captured various personal characteristics participants administered qualitative quantitative evaluation methods results indicate personal characteristics significant influence interaction perception recommender systems influence changes adding explanations people low need cognition explained recommendations beneficial people high need cognition observed explanations could create lack confidence based results present design implications explaining recommendations