@inproceedings{ubo_mods_00167689, author = {Kleemann, Timm and Wagner, Magdalena and Loepp, Benedikt and Ziegler, Jürgen}, title = {Modeling User Interaction at the Convergence of Filtering Mechanisms, Recommender Algorithms and Advisory Components}, booktitle = {Mensch Und Computer 2021 – Tagungsband}, year = {2021}, publisher = {ACM}, address = {New York, NY, USA}, pages = {531–543}, keywords = {Human factors; User experience; User modeling; Search interfaces; Recommender systems}, isbn = {978-1-4503-8645-6}, doi = {10.1145/3473856}, url = {https://dl.acm.org/doi/10.1145/3473856.3473859?cid=87958660357}, language = {en}, abstract = {A variety of methods is used nowadays to reduce the complexity of product search on e-commerce platforms, allowing users, for example, to specify exactly the features a product should have, but also, just to follow the recommendations automatically generated by the system. While such decision aids are popular with system providers, research to date has mostly focused on individual methods rather than their combination. To close this gap, we propose to support users in choosing the right method for the current situation. As a first step, we report in this paper a user study with a fictitious online shop in which users were able to flexibly use filter mechanisms, rely on recommendations, or follow the guidance of a dialog-based product advisor. We show that from the analysis of the interaction behavior, a model can be derived that allows predicting which of these decision aids is most useful depending on the user’s situation, and how this is affected by demographics and personality.} } @inproceedings{ubo_mods_00167688, author = {Loepp, Benedikt}, title = {On the Convergence of Intelligent Decision Aids}, booktitle = {Mensch Und Computer 2021 – Workshopband}, year = {2021}, publisher = {Gesellschaft für Informatik e.V.}, address = {Bonn}, keywords = {Decision support; Human factors; Information filtering; Adaptive systems; Recommender systems; User experience; User modeling}, abstract = {On the one hand, users’ decision making in today’s web is supported in numerous ways, with mechanisms ranging from manual search over automated recommendation to intelligent advisors. The focus on algorithmic accuracy, however, is questioned more and more. On the other hand, although the boundaries between the mechanisms are blurred increasingly, research on user-related aspects is still conducted separately in each area. In this position paper, we present a research agenda for providing a more holistic solution, in which users are supported with the right decision aid at the right time depending on personal characteristics and situational needs.}, doi = {10.18420/muc2021-mci-ws02-371}, url = {https://doi.org/10.18420/muc2021-mci-ws02-371}, language = {en} } @inproceedings{ubo_mods_00144402, author = {Loepp, Benedikt and Ziegler, Jürgen}, title = {Measuring the Impact of Recommender Systems – A Position Paper on Item Consumption in User Studies}, booktitle = {Proceedings of the 1st Workshop on Impact of Recommender Systems (ImpactRS ’19)}, year = {2019}, keywords = {User Studies}, url = {https://impactrs19.github.io/papers/short4.pdf}, abstract = {While participants of recommender systems user studies usually cannot experience recommended items, it is common practice that researchers ask them to fill in questionnaires regarding the quality of systems and recommendations. While this has been shown to work well under certain circumstances, it sometimes seems not possible to assess user experience without enabling users to consume items, raising the question of whether the impact of recommender systems has always been measured adequately in past user studies. In this position paper, we aim at exploring this question by means of a literature review and at identifying aspects that need to be further investigated in terms of their influence on assessments in users studies, for instance, the difference between consumption of products or only of related information as well as the effect of domain, domain knowledge and other possibly confounding factors.} } @inproceedings{ubo_mods_00142455, author = {Loepp, Benedikt and Donkers, Tim and Kleemann, Timm and Ziegler, Jürgen}, title = {Impact of Consuming Suggested Items on the Assessment of Recommendations in User Studies on Recommender Systems}, booktitle = {Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI ’19)}, year = {2019}, publisher = {IJCAI Organization}, pages = {6201–6205}, keywords = {Recommender Systems}, doi = {10.24963/ijcai.2019/863}, url = {https://doi.org/10.24963/ijcai.2019/863}, abstract = {User studies are increasingly considered important in research on recommender systems. Although participants typically cannot consume any of the recommended items, they are often asked to assess the quality of recommendations and of other aspects related to user experience by means of questionnaires. Not being able to listen to recommended songs or to watch suggested movies, might however limit the validity of the obtained results. Consequently, we have investigated the effect of consuming suggested items. In two user studies conducted in different domains, we showed that consumption may lead to differences in the assessment of recommendations and in questionnaire answers. Apparently, adequately measuring user experience is in some cases not possible without allowing users to consume items. On the other hand, participants sometimes seem to approximate the actual value of recommendations reasonably well depending on domain and provided information.} } @inproceedings{ubo_mods_00116566, author = {Loepp, Benedikt and Donkers, Tim and Kleemann, Timm and Ziegler, Jürgen}, title = {Impact of Item Consumption on Assessment of Recommendations in User Studies}, booktitle = {Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18)}, year = {2018}, publisher = {ACM}, address = {New York, NY, USA}, pages = {49–53}, keywords = {User Studies}, isbn = {978-1-4503-5901-6}, doi = {10.1145/3240323.3240375}, url = {https://dl.acm.org/doi/10.1145/3240323.3240375?cid=87958660357}, abstract = {In user studies of recommender systems, participants typically cannot consume the recommended items. Still, they are asked to assess recommendation quality and other aspects related to user experience by means of questionnaires. Without having listened to recommended songs or watched suggested movies, however, this might be an error-prone task, possibly limiting validity of results obtained in these studies. In this paper, we investigate the effect of actually consuming the recommended items. We present two user studies conducted in different domains showing that in some cases, differences in the assessment of recommendations and in questionnaire results occur. Apparently, it is not always possible to adequately measure user experience without allowing users to consume items. On the other hand, depending on domain and provided information, participants sometimes seem to approximate the actual value of recommendations reasonably well.} }