%0 Conference Paper %1 Chatti_2022 %A Chatti, Mohamed Amine %A Guesmi, Mouadh %A Vorgerd, Laura %A Ngo, Thao %A Joarder, Shoeb %A Ain, Qurat Ul %A Muslim, Arham %B Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization %D 2022 %I ACM %K differences explanation individual interest-model recommender transparency twitter umap2022 %P 254-264 %R 10.1145/3503252.3531304 %T Is More Always Better? The Effects of Personal Characteristics and Level of Detail on the Perception of Explanations in a Recommender System %U https://doi.org/10.1145%2F3503252.3531304 %X Despite the acknowledgment that the perception of explanations may vary considerably between end-users, explainable recommender systems (RS) have traditionally followed a one-size-fits-all model, whereby the same explanation level of detail is provided to each user, without taking into consideration individual user’s context, i.e., goals and personal characteristics. To fill this research gap, we aim in this paper at a shift from a one-size-fits-all to a personalized approach to explainable recommendation by giving users agency in deciding which explanation they would like to see. We developed a transparent Recommendation and Interest Modeling Application (RIMA) that provides on-demand personalized explanations of the recommendations, with three levels of detail (basic, intermediate, advanced) to meet the demands of different types of end-users. We conducted a within-subject study (N=31) to investigate the relationship between user’s personal characteristics and the explanation level of detail, and the effects of these two variables on the perception of the explainable RS with regard to different explanation goals. Our results show that the perception of explainable RS with different levels of detail is affected to different degrees by the explanation goal and user type. Consequently, we suggested some theoretical and design guidelines to support the systematic design of explanatory interfaces in RS tailored to the user’s context.

@inproceedings{Chatti_2022, abstract = {Despite the acknowledgment that the perception of explanations may vary considerably between end-users, explainable recommender systems (RS) have traditionally followed a one-size-fits-all model, whereby the same explanation level of detail is provided to each user, without taking into consideration individual user’s context, i.e., goals and personal characteristics. To fill this research gap, we aim in this paper at a shift from a one-size-fits-all to a personalized approach to explainable recommendation by giving users agency in deciding which explanation they would like to see. We developed a transparent Recommendation and Interest Modeling Application (RIMA) that provides on-demand personalized explanations of the recommendations, with three levels of detail (basic, intermediate, advanced) to meet the demands of different types of end-users. We conducted a within-subject study (N=31) to investigate the relationship between user’s personal characteristics and the explanation level of detail, and the effects of these two variables on the perception of the explainable RS with regard to different explanation goals. Our results show that the perception of explainable RS with different levels of detail is affected to different degrees by the explanation goal and user type. Consequently, we suggested some theoretical and design guidelines to support the systematic design of explanatory interfaces in RS tailored to the user’s context.}, added-at = {2022-07-07T16:25:41.000+0200}, author = {Chatti, Mohamed Amine and Guesmi, Mouadh and Vorgerd, Laura and Ngo, Thao and Joarder, Shoeb and Ain, Qurat Ul and Muslim, Arham}, biburl = {https://www.bibsonomy.org/bibtex/20a565acdb8cb2ff94d46ec5e6898a931/brusilovsky}, booktitle = {Proceedings of the 30th {ACM} Conference on User Modeling, Adaptation and Personalization}, description = {Is More Always Better? The Effects of Personal Characteristics and Level of Detail on the Perception of Explanations in a Recommender System | Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization}, doi = {10.1145/3503252.3531304}, interhash = {e7072066b696a024f6a6190c77e4c7b8}, intrahash = {0a565acdb8cb2ff94d46ec5e6898a931}, keywords = {differences explanation individual interest-model recommender transparency twitter umap2022}, month = jul, note = {Several detail-levels of explanations and their match to individual differences}, pages = {254-264}, publisher = {{ACM}}, timestamp = {2022-07-07T16:25:41.000+0200}, title = {Is More Always Better? The Effects of Personal Characteristics and Level of Detail on the Perception of Explanations in a Recommender System}, url = {https://doi.org/10.1145%2F3503252.3531304}, year = 2022 }