@inproceedings{loepp2023how,
  author = {Loepp, Benedikt and Ziegler, Jürgen},
  booktitle = {RecSys ’23: Proceedings of the 17th ACM Conference on Recommender Systems},
  title = {How Users Ride the Carousel: Exploring the Design of Multi-List Recommender Interfaces From a User Perspective},
  year = {2023},
  address = {New York, NY, USA},
  publisher = {ACM},
  isbn = {9798400702419},
  url = {https://doi.org/10.1145/3604915.3610638},
  doi = {10.1145/3604915.3610638},
  abstract = {Multi-list interfaces are widely used in recommender systems, especially in industry, showing collections of recommendations, one below the other, with items that have certain commonalities. The composition and order of these "carousels" are usually optimized by simulating user interaction based on probabilistic models learned from item click data. Research that actually involves users is rare, with only few studies investigating general user experience in comparison to conventional recommendation lists. Hence, it is largely unknown how specific design aspects such as carousel type and length influence the individual perception and usage of carousel-based interfaces. This paper seeks to fill this gap through an exploratory user study. The results confirm previous assumptions about user behavior and provide first insights into the differences in decision making in the presence of multiple recommendation carousels.}
}


@inproceedings{ubo_mods_00191514,
  author = {Loepp, Benedikt},
  title = {Recommender Systems Alone Are Not Everything: Towards a Broader Perspective in the Evaluation of Recommender Systems},
  booktitle = {PERSPECTIVES ’22: Proceedings of the 2nd Workshop on Perspectives on the Evaluation of Recommender Systems},
  year = {2022},
  abstract = {Thus far, in most of the user experiments conducted in the area of recommender systems, the respective system is considered as an isolated component, i.e., participants can only interact with the recommender that is under investigation. This fails to recognize the situation of users in real-world settings, where the recommender usually represents only one part of a greater system, with many other options for users to find suitable items than using the mechanisms that are part of the recommender, e.g., liking, rating, or critiquing. For example, in current web applications, users can often choose from a wide range of decision aids, from text-based search over faceted filtering to intelligent conversational agents. This variety of methods, which may equally support users in their decision making, raises the question of whether the current practice in recommender evaluation is sufficient to fully capture the user experience. In this position paper, we discuss the need to take a broader perspective in future evaluations of recommender systems, and raise awareness for evaluation methods which we think may help to achieve this goal, but have not yet gained the attention they deserve.},
  url = {http://ceur-ws.org/Vol-3228/paper5.pdf}
}


@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_00154785,
  author = {Naveed, Sidra and Loepp, Benedikt and Ziegler, Jürgen},
  title = {On the Use of Feature-based Collaborative Explanations: An Empirical Comparison of Explanation Styles},
  booktitle = {ExUM ’20: Proceedings of the International Workshop on Transparent Personalization Methods based on Heterogeneous Personal Data},
  year = {2020},
  publisher = {ACM},
  address = {New York},
  pages = {226–232},
  keywords = {User Experience},
  doi = {10.1145/3386392.3399303},
  url = {https://dl.acm.org/doi/10.1145/3386392.3399303?cid=87958660357},
  abstract = {Current attempts to explain recommendations mostly exploit a single type of data, i.e. usually either ratings provided by users for items in collaborative filtering systems, or item features in content-based systems. While this might be sufficient in straightforward recommendation scenarios, the complexity of other situations could require the use of multiple datasources, for instance, depending on the product domain. Even though hybrid systems have a long and successful history in recommender research, the connections between user ratings and item features have only rarely been used for offering more informative and transparent explanations. In previous work, we presented a prototype system based on a feature-weighting mechanism that constitutes an exception, allowing to recommend both items and features based on ratings while offering advanced explanations based on content data. In this paper, we empirically evaluate this prototype in terms of user-oriented aspects and user experience against to widely accepted baselines. Two user studies show that our novel approach outperforms conventional collaborative filtering, while a pure content-based system was perceived in a similarly positive light. Overall, the results draw a promising picture, which becomes particularly apparent from a user perspective when participants were specifically asked to use the explanations: they indicated in their qualitative feedback that they understood them and highly appreciated their availability.}
}


@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.}
}