%0 Journal Article %1 Starke_2023 %A Starke, Alain D. %A Asotic, Edis %A Trattner, Christoph %A Loo, Ellen J. Van %D 2023 %I Association for Computing Machinery (ACM) %J ACM Transactions on Recommender Systems %K carousel food-recommender recommender %R 10.1145/3581930 %T Examining the User Evaluation of Multi-list Recommender Interfaces in the Context of Healthy Recipe Choices %U https://doi.org/10.1145%2F3581930 %X Multi-list recommender systems have become widespread in entertainment and e-commerce applications. Yet, extensive user evaluation research is missing. Since most content is optimized towards a user’s current preferences, this may be problematic in recommender domains that involve behavioral change, such as food recommender systems for healthier food intake. We investigate the merits of multi-list recommendation in the context of internet-sourced recipes. We compile lists that adhere to varying food goals in a multi-list interface, examining whether multi-list interfaces and personalized explanations support healthier food choices. We examine the user evaluation (i.e., diversity, understandability, choice difficulty and satisfaction) of a multi-list recommender interface, linking choice behavior to evaluation aspects through the user experience framework. We present two studies, based on 1) similar-item retrieval and 2) knowledge-based recommendation. Study 1 (N = 366) compared single-list (5 recipes) and multi-list recommenders (25 recipes; presented with or without explanations). Study 2 (N = 164) compared single-list and multi-list food recommenders with similar set sizes and varied whether presented explanations were personalized. Multi-list interfaces were perceived as more diverse and understandable than single-list interfaces, while results for choice difficulty and satisfaction were mixed. Moreover, multi-list interfaces triggered changes in food choices, which tended to be unhealthier, but also more goal based.

@article{Starke_2023, abstract = {Multi-list recommender systems have become widespread in entertainment and e-commerce applications. Yet, extensive user evaluation research is missing. Since most content is optimized towards a user’s current preferences, this may be problematic in recommender domains that involve behavioral change, such as food recommender systems for healthier food intake. We investigate the merits of multi-list recommendation in the context of internet-sourced recipes. We compile lists that adhere to varying food goals in a multi-list interface, examining whether multi-list interfaces and personalized explanations support healthier food choices. We examine the user evaluation (i.e., diversity, understandability, choice difficulty and satisfaction) of a multi-list recommender interface, linking choice behavior to evaluation aspects through the user experience framework. We present two studies, based on 1) similar-item retrieval and 2) knowledge-based recommendation. Study 1 (N = 366) compared single-list (5 recipes) and multi-list recommenders (25 recipes; presented with or without explanations). Study 2 (N = 164) compared single-list and multi-list food recommenders with similar set sizes and varied whether presented explanations were personalized. Multi-list interfaces were perceived as more diverse and understandable than single-list interfaces, while results for choice difficulty and satisfaction were mixed. Moreover, multi-list interfaces triggered changes in food choices, which tended to be unhealthier, but also more goal based.}, added-at = {2023-03-20T16:48:12.000+0100}, author = {Starke, Alain D. and Asotic, Edis and Trattner, Christoph and Loo, Ellen J. Van}, biburl = {https://www.bibsonomy.org/bibtex/266f18c4044bd78998363bb5ef75aa4a7/brusilovsky}, description = {Examining the User Evaluation of Multi-list Recommender Interfaces in the Context of Healthy Recipe Choices | ACM Transactions on Recommender Systems}, doi = {10.1145/3581930}, interhash = {7a88acbf483eac218d12968d47c0d8f1}, intrahash = {66f18c4044bd78998363bb5ef75aa4a7}, journal = {{ACM} Transactions on Recommender Systems}, keywords = {carousel food-recommender recommender}, month = feb, publisher = {Association for Computing Machinery ({ACM})}, timestamp = {2023-03-20T16:48:12.000+0100}, title = {Examining the User Evaluation of Multi-list Recommender Interfaces in the Context of Healthy Recipe Choices}, url = {https://doi.org/10.1145%2F3581930}, year = 2023 }