%0 Conference Paper %1 Schaffer:2018:EPS:3209219.3209222 %A Schaffer, James %A O'Donovan, John %A Höllerer, Tobias %B Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization %C New York, NY, USA %D 2018 %I ACM %K evaluation recommender %P 177--185 %R 10.1145/3209219.3209222 %T Easy to Please: Separating User Experience from Choice Satisfaction %U http://doi.acm.org/10.1145/3209219.3209222 %X Recommender systems are evaluated based on both their ability to create a satisfying user experience and their ability to help a user make better choices. Despite this, quantitative evidence from previous research in recommender systems indicate very high correlations between user experience attitudes and choice satisfaction. This might imply invalidity in the measurement methodologies of these constructs, whereas they may not be measuring what researchers think they are measuring. To remedy this, we present a new methodology for the measurement of choice satisfaction. Part of our approach is to measure a user's "ease of satisfaction," or that user's natural propensity to be satisfied, which is measured using three different approaches. An (N=526) observational study is conducted wherein users browse a movie catalog. A factor analysis is done to assess the discriminant validity of our proposed choice satisfaction apparatus from user experience. A statistical analysis suggests that accounting for ease-of-satisfaction allows for a model of choice satisfaction that is not only discriminant, but independent, from user experience. This enables researchers to more objectively identify recommender system factors that lead users to good choices. %@ 978-1-4503-5589-6

@inproceedings{Schaffer:2018:EPS:3209219.3209222, abstract = {Recommender systems are evaluated based on both their ability to create a satisfying user experience and their ability to help a user make better choices. Despite this, quantitative evidence from previous research in recommender systems indicate very high correlations between user experience attitudes and choice satisfaction. This might imply invalidity in the measurement methodologies of these constructs, whereas they may not be measuring what researchers think they are measuring. To remedy this, we present a new methodology for the measurement of choice satisfaction. Part of our approach is to measure a user's "ease of satisfaction," or that user's natural propensity to be satisfied, which is measured using three different approaches. An (N=526) observational study is conducted wherein users browse a movie catalog. A factor analysis is done to assess the discriminant validity of our proposed choice satisfaction apparatus from user experience. A statistical analysis suggests that accounting for ease-of-satisfaction allows for a model of choice satisfaction that is not only discriminant, but independent, from user experience. This enables researchers to more objectively identify recommender system factors that lead users to good choices.}, acmid = {3209222}, added-at = {2019-01-29T15:56:01.000+0100}, address = {New York, NY, USA}, author = {Schaffer, James and O'Donovan, John and H\"{o}llerer, Tobias}, biburl = {https://www.bibsonomy.org/bibtex/28fff5e5183668dadafc10e41f216f52a/brusilovsky}, booktitle = {Proceedings of the 26th Conference on User Modeling, Adaptation and Personalization}, description = {Easy to Please}, doi = {10.1145/3209219.3209222}, interhash = {1dd44d39b36c75ccda8fe6192eef9744}, intrahash = {8fff5e5183668dadafc10e41f216f52a}, isbn = {978-1-4503-5589-6}, keywords = {evaluation recommender}, location = {Singapore, Singapore}, numpages = {9}, pages = {177--185}, publisher = {ACM}, series = {UMAP '18}, timestamp = {2019-01-29T15:56:01.000+0100}, title = {Easy to Please: Separating User Experience from Choice Satisfaction}, url = {http://doi.acm.org/10.1145/3209219.3209222}, year = 2018 }