%0 Conference Paper %1 Beel_2021 %A Beel, Joeran %A Dixon, Haley %B Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization %D 2021 %I ACM %K interactive-recommender recommender umap2021 %P 22-28 %R 10.1145/3450614.3461682 %T The `Unreasonable' Effectiveness of Graphical User Interfaces for Recommender Systems %U https://doi.org/10.1145%2F3450614.3461682 %X The impact of Graphical User Interfaces (GUI) for recommender systems is a little explored area. Therefore, we conduct an empirical study in which we create, deploy, and evaluate seven different GUI variations. We use these variations to display 68.260 related-blog-post recommendations to 10.595 unique visitors of our blog. The study shows that the GUIs have a strong effect on the recommender systems’ performance, measured in click-through rate (CTR). The best performing GUI achieved a 66% higher CTR than the worst performing GUI (statist. significant with p<0.05). In other words, with a few days of work to develop different GUIs, a recommender-system operator could increase CTR notably – maybe even more than by tuning the recommendation algorithm. In analogy to the ‘unreasonable effectiveness of data’ discussion by Google and others, we conclude that the effectiveness of graphical user interfaces for recommender systems is equally ‘unreasonable’. Hence, the recommender system community should spend more time on researching GUIs for recommender systems. In addition, we conduct a survey and find that the ACM Recommender Systems Conference has a strong focus on algorithms – 81% of all short and full papers published in 2019 and 2020 relate to algorithm development, and none to GUIs for recommender systems. We also surveyed the recommender systems of 50 blogs. While most displayed a thumbnail (86%) and had a mouseover interaction (62%) other design elements were rare. Only few highlighted top recommendations (8%), displayed rankings or relevance scores (6%), or offered a ‘view more’ option (4%).

@inproceedings{Beel_2021, abstract = {The impact of Graphical User Interfaces (GUI) for recommender systems is a little explored area. Therefore, we conduct an empirical study in which we create, deploy, and evaluate seven different GUI variations. We use these variations to display 68.260 related-blog-post recommendations to 10.595 unique visitors of our blog. The study shows that the GUIs have a strong effect on the recommender systems’ performance, measured in click-through rate (CTR). The best performing GUI achieved a 66% higher CTR than the worst performing GUI (statist. significant with p<0.05). In other words, with a few days of work to develop different GUIs, a recommender-system operator could increase CTR notably – maybe even more than by tuning the recommendation algorithm. In analogy to the ‘unreasonable effectiveness of data’ discussion by Google and others, we conclude that the effectiveness of graphical user interfaces for recommender systems is equally ‘unreasonable’. Hence, the recommender system community should spend more time on researching GUIs for recommender systems. In addition, we conduct a survey and find that the ACM Recommender Systems Conference has a strong focus on algorithms – 81% of all short and full papers published in 2019 and 2020 relate to algorithm development, and none to GUIs for recommender systems. We also surveyed the recommender systems of 50 blogs. While most displayed a thumbnail (86%) and had a mouseover interaction (62%) other design elements were rare. Only few highlighted top recommendations (8%), displayed rankings or relevance scores (6%), or offered a ‘view more’ option (4%). }, added-at = {2021-07-13T18:16:38.000+0200}, author = {Beel, Joeran and Dixon, Haley}, biburl = {https://www.bibsonomy.org/bibtex/2f50fb9c1880e80eba09275e8dcae094d/brusilovsky}, booktitle = {Adjunct Proceedings of the 29th {ACM} Conference on User Modeling, Adaptation and Personalization}, description = {The ‘Unreasonable’ Effectiveness of Graphical User Interfaces for Recommender Systems | Adjunct Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization}, doi = {10.1145/3450614.3461682}, interhash = {c9e3ea2e6671c9545853ab2a2117cf96}, intrahash = {f50fb9c1880e80eba09275e8dcae094d}, keywords = {interactive-recommender recommender umap2021}, month = jun, pages = {22-28}, publisher = {{ACM}}, timestamp = {2021-07-13T18:16:38.000+0200}, title = {The `Unreasonable' Effectiveness of Graphical User Interfaces for Recommender Systems}, url = {https://doi.org/10.1145%2F3450614.3461682}, year = 2021 }