@inproceedings{ubo_mods_00170072, author = {Aker, Ahmet and Sliwa, Alfred and Ma, Yuan and Liu, Ruishen and Borad, Niravkumar and Ziyaei, Seyedeh Fatemeh and Ghbadi, Mina}, title = {What works and what does not: Classifier and feature analysis for argument mining}, booktitle = {Proceedings of the 4th Workshop on Argument Mining}, year = {2017}, publisher = {Association for Computational Linguistics (ACL)}, address = {Stroudsburg}, pages = {91–96}, isbn = {978-1-945626-84-5}, language = {en} } @inproceedings{ubo_mods_00149808, author = {van de Sand, Laura and Dogangün, Aysegül}, editor = {Eibl, Maximillian and Gaedke, Martin}, title = {Visualisierung von Datensicherheitsaspekten in Healthmonitoring-Apps}, booktitle = {Informatik 2017 - Bände I-III: Tagung vom 25.-29. September 2017 in Chemnitz}, series = {GI-Edition Proceedings}, year = {2017}, publisher = {Gesellschaft fur Informatik (GI)}, address = {Bonn}, volume = {275}, pages = {697–700}, isbn = {978-3-88579-669-5}, issn = {1617-5468}, doi = {10.18420/in2017_66}, url = {https://dl.gi.de/handle/20.500.12116/4087} } @inproceedings{10.1007/978-3-319-53676-7_2, author = {Jannach, Dietmar and Naveed, Sidra and Jugovac, Michael}, editor = {Bridge, Derek and Stuckenschmidt, Heiner}, title = {User Control in Recommender Systems: Overview and Interaction Challenges}, booktitle = {E-Commerce and Web Technologies}, year = {2017}, publisher = {Springer International Publishing}, pages = {21–33}, abstract = {Recommender systems have shown to be valuable tools that help users find items of interest in situations of information overload. These systems usually predict the relevance of each item for the individual user based on their past preferences and their observed behavior. If the system’s assumption about the users’ preferences are however incorrect or outdated, mechanisms should be provided that put the user into control of the recommendations, e.g., by letting them specify their preferences explicitly or by allowing them to give feedback on the recommendations. In this paper we review and classify the different approaches from the research literature of putting the users into active control of what is recommended. We highlight the challenges related to the design of the corresponding user interaction mechanisms and finally present the results of a survey-based study in which we gathered user feedback on the implemented user control features on Amazon.} } @inproceedings{ubo_mods_00111873, author = {Stegemann, Timo and Ziegler, Jürgen}, editor = {Nikitina, Nadeschda and Song, Dezhao}, title = {Pattern-based analysis of SPARQL queries from the LSQ dataset}, booktitle = {Proceedings of the ISWC 2017 Posters & Demonstrations and Industry Tracks}, series = {CEUR Workshop Proceedings}, year = {2017}, publisher = {CEUR-WS}, address = {Aachen}, volume = {1963}, issn = {1613-0073}, url = {http://ceur-ws.org/Vol-1963/paper542.pdf} } @inproceedings{ubo_mods_00111846, author = {Herrmanny, Katja and Dogangün, Aysegül}, editor = {Elsweiler, David and Hors-Fraile, Santiago}, title = {The impact of prediction uncertainty in recommendations for health-related behavior}, booktitle = {Proceedings of the 2nd International Workshop on Health Recommender Systems}, series = {CEUR Workshop Proceedings}, year = {2017}, volume = {1953}, pages = {14–17}, keywords = {Uncertainty}, issn = {1613-0073} } @inproceedings{ubo_mods_00108514, author = {Dogangün, Aysegül and Schwarz, Michael and Kloppenborg, Katharina and Le, Robert}, title = {An approach to improve physical activity by generating individual implementation intentions}, booktitle = {Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization}, year = {2017}, publisher = {ACM}, address = {New York}, pages = {370–375}, keywords = {User modeling}, isbn = {9781450350679}, doi = {10.1145/3099023.3099101} } @inproceedings{ubo_mods_00108262, author = {Schäfer, Hanna and Hors-Fraile, Santiago and Karumur, Pavan Raghav and Valdez, Calero André and Said, Alan and Torkamaan, Helma and Ulmer, Tom and Trattner, Christoph}, title = {Towards Health (Aware) Recommender Systems}, booktitle = {Proceedings of the 2017 International Conference on Digital Health}, year = {2017}, publisher = {ACM}, address = {New York}, pages = {157–161}, keywords = {Patient modeling}, isbn = {978-1-4503-5249-9}, doi = {10.1145/3079452.3079499} } @inproceedings{ubo_mods_00106644, author = {Álvarez Márquez, Jesús Omar and Ziegler, Jürgen}, editor = {}, title = {Improving the Shopping Experience with an Augmented Reality-Enhanced Shelf}, booktitle = {Mensch und Computer 2017 - Workshopband}, year = {2017}, pages = {629–632}, keywords = {augmented reality; enhanced retailing; human-computer interaction}, doi = {10.18420/muc2017-demo-0351}, language = {en} } @article{ubo_mods_00106902, author = {Troncy, Raphaël and Rizzo, Giuseppe and Jameson, Anthony and Corcho, Oscar and Plu, Julien and Palumbo, Enrico and Ballesteros Hermida, Carlos Juan and Spirescu, Adrian and Kuhn, Kai-Dominik and Barbu, Catalin-Mihai and Rossi, Matteo and Celino, Irene and Agarwal, Rachit and Scanu, Christian and Valla, Massimo and Haaker, Timber}, title = {3cixty: Building comprehensive knowledge bases for city exploration}, journal = {Journal of Web Semantics}, year = {2017}, publisher = {Elsevier B.V.}, address = {Amsterdam [u.a.]}, volume = {46-47}, pages = {2–13}, keywords = {Smart city}, issn = {1570-8268}, doi = {10.1016/j.websem.2017.07.002} } @inproceedings{ubo_mods_00081254, author = {Bakhtiyari, Kaveh and Ziegler, Jürgen and Husain, Hafizah}, chapter = {}, title = {The Effect of Presentation in Online Advertising on Perceived Intrusiveness and Annoyance in Different Emotional States}, series = {Lecture Notes in Computer Science}, year = {2017}, publisher = {Springer}, volume = {10191}, pages = {140–149}, isbn = {978-3-319-54472-4}, doi = {10.1007/978-3-319-54472-4}, url = {https://link.springer.com/chapter/10.1007%2F978-3-319-54472-4_14}, booktitle = {Proceedings of the 9th Asian Conference on Intelligent Information and Database Systems (ACIIDS ’17)} } @inproceedings{ubo_mods_00102542, author = {Bakhtiyari, Kaveh and Ziegler, Jürgen and Husain, Hafizah}, chapter = {}, title = {KinRes: depth sensor noise reduction in contactless respiratory monitoring}, year = {2017}, publisher = {ACM}, address = {New York, NY, USA}, pages = {472–475}, isbn = {978-1-4503-6363-1}, doi = {10.1145/3154862.3154896}, booktitle = {Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare} } @article{ubo_mods_00084836, author = {Bakhtiyari, Kaveh and Beckmann, Nils and Ziegler, Jürgen}, title = {Contactless heart rate variability measurement by IR and 3D depth sensors with respiratory sinus arrhythmia}, journal = {Procedia Computer Science}, year = {2017}, volume = {109}, pages = {498–505}, abstract = {Heart rate variability (HRV) is known to be correlated with emotional arousal, cognitive depletion, and health status. Despite the accurate HRV detection by various body-attached sensors, a contactless method is desirable for the HCI purposes. In this research, we propose a non-invasive contactless HRV measurement by Microsoft Kinect 2 sensor with Respiratory Sinus Arrhythmia (RSA) correction. The Infrared and RGB cameras are used to measure the heart rate signal, and its 3D Depth sensor is employed to capture the human respiratory signal to correct the initially calculated HRV with RSA. The correlation analysis among the calculated HRVs by different methods and devices showed a significant improvement in reliable HRV measurements. This study enlightens the researchers and developers to choose a proper method for HRV calculations based on their required accuracy and application.}, issn = {1877-0509}, doi = {10.1016/j.procs.2017.05.319}, note = {The 8th International Conference on Ambient Systems, Networks and Technologies, ANT-2017 and the 7th International Conference on Sustainable Energy Information Technology, SEIT 2017, 16-19 May 2017, Madeira, Portugal} } @inproceedings{ubo_mods_00096389, author = {Torkamaan, Helma and Ziegler, Jürgen}, booktitle = {2017 Seventh International Conference on Affective Computing and Intelligent Interaction (ACII), San Antonio, Texas, October 23-26}, year = {2017}, title = {A Taxonomy of Mood Research and Its Applications in Computer Science}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, address = {Piscataway}, pages = {421–426}, isbn = {978-1-5386-0563-9}, doi = {10.1109/ACII.2017.8273634} } @inproceedings{Stegemann2017, author = {Stegemann, Timo and Ziegler, Jürgen}, editor = {d’Amato, Claudia and Fernandez, Miriam and Tamma, Valentina and Lecue, Freddy and Cudré-Mauroux, Philippe and Sequeda, Juan and Lange, Christoph and Heflin, Jeff}, title = {Investigating Learnability, User Performance, and Preferences of the Path Query Language SemwidgQL Compared to SPARQL}, booktitle = {The Semantic Web – ISWC 2017: 16th International Semantic Web Conference, Vienna, Austria, October 21–25, 2017, Proceedings, Part I}, year = {2017}, publisher = {Springer International Publishing}, address = {Cham}, pages = {611–627}, abstract = {In this paper, we present an empirical comparison of user performance and perceived usability for Sparql versus SemwidgQL, a path-oriented Rdf query language. We developed SemwidgQL to facilitate the formulation of Rdf queries and to enable non-specialist developers and web authors to integrate Linked Data and other semantic data sources into standard web applications. We performed a user study in which participants wrote a set of queries in both languages. We measured both objective performance as well as subjective responses to a set of questionnaire items. Results indicate that SemwidgQL is easier to learn, more efficient, and preferred by learners. To assess the applicability of SemwidgQL in real applications, we analyzed its expressiveness based on a large corpus of observed Sparql queries, showing that the language covers more than 90% of the typical queries performed on Linked Data.}, isbn = {978-3-319-68288-4}, doi = {10.1007/978-3-319-68288-4_36}, url = {https://doi.org/10.1007/978-3-319-68288-4_36} } @inproceedings{ubo_mods_00093591, author = {Beckmann, Nils and Viga, Reinhard and Dogangün, Aysegül and Grabmaier, Anton}, chapter = {}, title = {Measuring local pulse transit time for affective computing applications}, year = {2017}, publisher = {Hochschule Ruhr West}, address = {Mülheim an der Ruhr}, pages = {106–107}, isbn = {978-3-9814801-9-1}, url = {https://www.hochschule-ruhr-west.de/fileadmin/user_upload/02_Forschung/Fachbereich_4/Institut_Mess-_und_Sensortechnik/IEEE_Workshop/IEEE_2017/Abstractbook_Online_Version_2017_06_12.pdf}, booktitle = {Industrial and medical measurement and sensor technology, vehicle sensor technnology: IEEE Workshop 2017 : abstractbook} } @inproceedings{ubo_mods_00094630, author = {Barbu, Catalin-Mihai and Ziegler, Jürgen}, editor = {Boratto, Ludovico and Carta, Salvatore and Fenu, Gianni}, chapter = {}, title = {Towards a Design Space for Personalizing the Presentation of Recommendations}, series = {CEUR workshop proceedings}, year = {2017}, volume = {1945}, pages = {10–17}, keywords = {Interactive control}, url = {http://ceur-ws.org/Vol-1945/paper_3.pdf}, abstract = {Although personalization plays a major role in the development of recommender systems, the presentation of recommendations and especially the way in which it can be adapted to suit the user’s needs has received relatively little attention from the research community. We introduce a design space for personalizing the presentation of recommendations and propose several dimensions that should be a part of it. Moreover, we present our initial insights about possible interactive mechanisms as well as potential evaluation criteria. Our goal is to provide a systematic way of designing personalized recommendation content, which should prove benecial for other researchers working on this topic. In the longer term, we are interested to investigate whether such personalized presentation implementations influence the perceived trustworthiness of the recommendations.}, booktitle = {EnCHIReS 2017: Engineering Computer-Human Interaction in Recommender Systems : Proceedings of the Second Workshop on Engineering Computer-Human Interaction in Recommender Systems co-located with the 9th ACM SIGCHI Symposium on Engineering Interactive Computing Systems (EICS 2017)} } @inproceedings{ubo_mods_00093590, author = {Beckmann, Nils and Viga, Reinhard and Dogangün, Aysegül and Grabmaier, Anton}, chapter = {}, title = {Measuring local pulse transit time for affective computing applications}, year = {2017}, publisher = {Hochschule Ruhr West}, address = {Mülheim an der Ruhr}, pages = {30–31}, url = {http://yra-medtech.de/daten_medtech/dokumente2017/YRA_Booklet_2017_LO.pdf}, booktitle = {2nd YRA MedTech Symposium, Young Researchers Academy: booklet : jointly held with the IEEE Workshop & SENSORICA 2017 Hochschule Ruhr West, June 8-9, Mülheim a. d. Ruhr, Germany, 2017} } @inproceedings{ubo_mods_00093653, author = {Beckmann, Nils and Viga, Reinhard and Dogangün, Aysegül and Grabmaier, Anton}, chapter = {}, title = {Reproducibility of photoplethysmography-based local pulse transit time measurement}, year = {2017}, publisher = {IEEE}, address = {Piscataway, NJ}, pages = {246–249}, keywords = {correlation;correlation coefficient;feature extraction;pulse measurements;sensors;signal-to-noise ratio (SNR);time measurement}, doi = {10.1109/EMBC.2017.8036808}, booktitle = {2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)} } @inproceedings{ubo_mods_00092173, author = {Biefang, Kai and Kunkel, Johannes and Loepp, Benedikt and Ziegler, Jürgen}, chapter = {}, title = {Eine Sandbox zur physisch-virtuellen Exploration von Ausgrabungsstätten}, year = {2017}, publisher = {Gesellschaft für Informatik}, keywords = {Archäologie}, doi = {10.18420/muc2017-demo-0300}, url = {https://dl.gi.de/handle/20.500.12116/3248}, abstract = {In diesem Beitrag stellen wir die Archäologische Sandbox vor: Ein Tangible User Interface (TUI) mit dem archäologische Ausgrabungsstätten und dort gefundene Artefakte exploriert werden können. Das System zielt auf den Einsatz in Museen ab, die ihren Besuchern den Zusammenhang von ausgestellten Exponaten und der Ausgrabungsstätte näherbringen möchten, an der diese gefunden wurden. Den Kern des TUIs bildet eine mit Sand gefüllte Box, auf dessen Oberfläche eine geografische Karte projiziert wird. Durch das Graben im Sand an der richtigen Stelle werden Informationen zu an diesem Ort gefundenen Ausstellungsstücken abgerufen. Eine durchgeführte qualitative Interviewstudie bestätigt die intuitive Bedienbarkeit und die intrinsisch motivierenden Interaktionsmöglichkeiten des Systems.}, booktitle = {Mensch und Computer 2017 – Workshopband} } @inproceedings{ubo_mods_00090488, author = {Donkers, Tim and Loepp, Benedikt and Ziegler, Jürgen}, chapter = {}, title = {Sequential User-based Recurrent Neural Network Recommendations}, year = {2017}, publisher = {ACM}, address = {New York, NY, USA}, pages = {152–160}, keywords = {Sequential Recommendations}, doi = {10.1145/3109859.3109877}, url = {https://dl.acm.org/doi/10.1145/3109859.3109877?cid=87958660357}, abstract = {Recurrent Neural Networks are powerful tools for modeling sequences. They are flexibly extensible and can incorporate various kinds of information including temporal order. These properties make them well suited for generating sequential recommendations. In this paper, we extend Recurrent Neural Networks by considering unique characteristics of the Recommender Systems domain. One of these characteristics is the explicit notion of the user recommendations are specifically generated for. We show how individual users can be represented in addition to sequences of consumed items in a new type of Gated Recurrent Unit to effectively produce personalized next item recommendations. Offline experiments on two real-world datasets indicate that our extensions clearly improve objective performance when compared to state-of-the-art recommender algorithms and to a conventional Recurrent Neural Network.}, booktitle = {Proceedings of the 11th ACM Conference on Recommender Systems (RecSys ’17)} } @inproceedings{ubo_mods_00090298, author = {Barbu, Catalin-Mihai and Ziegler, Jürgen}, editor = {Neidhardt, Julia and Fesenmaier, Daniel and Kuflik, Tsvi and Wörndl, Wolfgang}, chapter = {}, title = {Co-Staying: a Social Network for Increasing the Trustworthiness of Hotel Recommendations}, series = {CEUR workshop proceedings}, year = {2017}, volume = {1906}, pages = {35–39}, keywords = {Trustworthiness}, abstract = {Recommender systems attempt to match users’ preferences with items. To achieve this, they typically store and process a large amount of user profiles, item attributes, as well as an ever-increasing volume of user-generated feedback about those items. By mining user-generated data, such as reviews, a complex network consisting of users, items, and item properties can be created. Exploiting this network could allow a recommender system to identify, with greater accuracy, items that users are likely to find attractive based on the attributes mentioned in their past reviews as well as in those left by similar users. At the same time, allowing users to visualize and explore the network could lead to novel ways of interacting with recommender systems and might play a role in increasing the trustworthiness of recommendations. We report on a conceptual model for a multimode network for hotel recommendations and discuss potential interactive mechanisms that might be employed for visualizing it.}, url = {http://ceur-ws.org/Vol-1906/paper6.pdf}, booktitle = {RecTour 2017: 2nd Workshop on Recommenders in Tourism : Proceedings of the 2nd Workshop on Recommenders in Tourism co-located with 11th ACM Conference on Recommender Systems (RecSys 2017) Como, Italy, August 27, 2017} } @inproceedings{ubo_mods_00090297, author = {Barbu, Catalin-Mihai and Ziegler, Jürgen}, editor = {Domonkos, Tikk and Pu, Pearl}, chapter = {}, title = {Users’ Choices About Hotel Booking: Cues for Personalizing the Presentation of Recommendations}, series = {CEUR workshop proceedings}, year = {2017}, volume = {1905}, pages = {44–45}, keywords = {Tourism}, abstract = {Personalization in recommender systems has typically been applied to the underlying algorithms. In contrast, the presentation of individual recommendations—specifically, the various ways in which it can be adapted to suit the user’s needs in a more effective manner—has received relatively little attention by comparison. We present the results of an exploratory survey about users’ choices regarding hotel recommendations and draw preliminary conclusions about whether these choices can influence the presentation of recommendations.}, url = {http://ceur-ws.org/Vol-1905/recsys2017_poster22.pdf}, booktitle = {Poster Proceeding of ACM Recsys 2017: Proceedings of the Poster Track of the 11th ACM Conference on Recommender Systems (RecSys 2017) Como, Italy, August 28, 2017} } @inproceedings{ubo_mods_00089204, author = {Barbu, Catalin-Mihai and Ziegler, Jürgen}, editor = {Brusilovsky, Peter and de Gemmis, Marco and Felfernig, Alexander and Lops, Pasquale and O’Donovan, John and Tintarev, Nava and Willemsen, C. Martijn}, chapter = {}, title = {User Model Dimensions for Personalizing the Presentation of Recommendations}, series = {CEUR workshop proceedings}, year = {2017}, volume = {1884}, pages = {20–23}, keywords = {User profile}, abstract = {Personalization in recommender systems has typically been applied to the underlying algorithms and to the predicted result sets. Meanwhile, the presentation of individual recommendations—specifically, the various ways in which it can be adapted to suit the user’s needs in a more effective manner—has received relatively little attention by comparison. A limiting factor for the design of such interactive and personalized presentations is the quality of the user data, such as elicited preferences, that is available to the recommender system. At the same time, many of the existing user models are not optimized sufficiently for this specific type of personalization. We present the results of an exploratory survey about users’ choices regarding the presentation of hotel recommendations. Based on our analysis, we propose several novel dimensions to the conventional user models exploited by recommender systems. We argue that augmenting user profiles with this range of information would facilitate the development of more interactive mechanisms for personalizing the presentation of recommendations. This, in turn, could lead to increased transparency and control over the recommendation process.}, url = {http://ceur-ws.org/Vol-1884/paper4.pdf}, booktitle = {IntRS 2017: Interfaces and Human Decision Making for Recommender Systems : Proceedings of the 4th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems co-located with ACM Conference on Recommender Systems (RecSys 2017)} } @inproceedings{ubo_mods_00089097, author = {Feuerbach, Jan and Loepp, Benedikt and Barbu, Catalin-Mihai and Ziegler, Jürgen}, title = {Enhancing an Interactive Recommendation System with Review-based Information Filtering}, booktitle = {Proceedings of the 4th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems (IntRS ’17)}, series = {CEUR workshop proceedings}, year = {2017}, volume = {1884}, pages = {2–9}, keywords = {User Reviews}, abstract = {Integrating interactive faceted filtering with intelligent recommendation techniques has shown to be a promising means for increasing user control in Recommender Systems. In this paper, we extend the concept of blended recommending by automatically extracting meaningful facets from social media by means of Natural Language Processing. Concretely, we allow users to influence the recommendations by selecting facet values and weighting them based on information other users provided in their reviews. We conducted a user study with an interactive recommender implemented in the hotel domain. This evaluation shows that users are consequently able to find items fitting interests that are typically difficult to take into account when only structured content data is available. For instance, the extracted facets representing the opinions of hotel visitors make it possible to effectively search for hotels with comfortable beds or that are located in quiet surroundings without having to read the user reviews.}, url = {http://ceur-ws.org/Vol-1884/paper1.pdf} } @incollection{ubo_mods_00084449, author = {Loepp, Benedikt and Ziegler, Jürgen}, editor = {Proff, Heike and Fojcik, Martin Thomas}, title = {Empirische Bedarfsanalyse zur intermodalen Navigation und dem Einsatz von Informationssystemen zur Förderung ihrer Attraktivität}, booktitle = {Innovative Produkte und Dienstleistungen in der Mobilität: Technische und betriebswirtschaftliche Aspekte}, year = {2017}, publisher = {Springer Gabler}, address = {Wiesbaden, Germany}, pages = {409–426}, keywords = {Intermodale Navigation}, isbn = {978-3-658-18613-5}, doi = {10.1007/978-3-658-18613-5_26}, url = {http://dx.doi.org/10.1007/978-3-658-18613-5_26}, abstract = {Eine vermehrt intermodale Fortbewegung ist in Zeiten, in denen angesichts überfüllter Innenstädte und Straßen die Verringerung des Verkehrsaufkommens und Verlagerung des motorisierten Individualverkehrs auf umweltfreundlichere Alternativen zunehmend relevanter werden, von großer Bedeutung. Heute gängige Navigationslösungen und Routenplaner sind bezüglich der Unterstützung komplexer, insbesondere intermodaler Mobilitätsketten jedoch oft nur unzureichend entwickelt: Es mangelt einerseits an einer einheitlichen Integration der Informationen unterschiedlicher Verkehrsmittel, andererseits vor allem an Personalisierungsmöglichkeiten und einer intelligenten Anpassung der Systeme an die momentane Situation des Nutzers. Oft werden beispielsweise Angebote wie Car- und Bikesharing noch außer Acht gelassen, und auch der Kontext als wichtige Determinante bei der Wahl einer Route – für einen beruflichen Termin bei schlechter Witterung kann ein Fahrrad etwa weniger geeignet sein als bei einem Familienausflug unter sonnigen Bedingungen – bleibt meist unberücksichtigt. In diesem Beitrag stellen wir die Ergebnisse einer im Rahmen des BMVI-geförderten Projekts colognE-mobil II durchgeführten Bedarfsanalyse bestehend aus zwei empirischen Untersuchungen vor: Sowohl online als auch vor Ort im Rhein-Ruhr-Gebiet wurden 318 bzw. 130 Personen befragt, um die aktuelle Nutzung unterschiedlicher Verkehrsträger zu untersuchen, sowie die Einschätzung ihrer bestehenden Probleme und künftigen Potenziale zu ermitteln. Spezieller Fokus lag auf den gerade für den städtischen Raum relevanten Alternativen, etwa Car- und Bikesharing, aber auch intermodaler Navigation im Allgemeinen. Während sich grundsätzlich eine hohe Nutzungsbereitschaft abzeichnete, leiden derartige Angebote aus Sicht der Befragten u. a. an Zugangsschwierigkeiten, mangelnder Verbreitung, oder waren schlicht zu unbekannt, als dass sie eine echte Alternative hätten darstellen können. Die Auswahl an Verkehrsmitteln wird unterdessen beständig größer, und beinhaltet zunehmend ökologischere Varianten als den Individualverkehr. Aufgrund steigender Kraftstoffpreise, CO2-Abgaben, Straßenmaut und überlasteten Innenstädten erhöht sich dementsprechend die Bedeutung der Unabhängigkeit vom privaten PKW, vor allem bei Jüngeren. Mit Hilfe von Mock-Ups eines Routenplaners untersuchten wir deshalb ebenfalls, ob sich Nutzer mittels geeigneter Hinweise – etwa auf erhöhte Umweltfreundlichkeit oder die aktuelle Verkehrslage – dazu animieren lassen, ihre typischerweise routinierte Strecken- und Verkehrsmittelwahl anzupassen. Die Ergebnisse zeigen u. a., dass zur Assistenz bei Fahrplanauskunft oder Routenplanung entwickelte Informationssysteme auf diese Weise die wahrgenommene Attraktivität intermodaler Wegeketten sowie von Car- und Bikesharing-Angeboten signifikant steigern können.} } @inproceedings{ubo:80746, author = {Loepp, Benedikt and Ziegler, Jürgen}, chapter = {}, title = {On User Awareness in Model-Based Collaborative Filtering Systems}, year = {2017}, keywords = {User Experience}, url = {https://iuiaware2017.files.wordpress.com/2016/11/on_user_awareness_in_model-based_collaborative_filtering_systems2.pdf}, abstract = {In this paper, we discuss several aspects that users are typically not fully aware of when using model-based Collaborative Filtering systems. For instance, the methods prevalently used in conventional recommenders infer abstract models that are opaque to users, making it difficult to understand the learned profile, and consequently, why certain items are recommended. Further, users are not able to keep an overview of the item space, and thus the alternatives that in principle could also be suggested. By summarizing our experiences on exploiting latent factor models for increasing control and transparency, we show that the respective techniques may also contribute to make users more aware of their preferences’ representation, the rationale behind the results, and further items of potential interest.}, booktitle = {Proceedings of the 1st Workshop on Awareness Interfaces and Interactions (AWARE ’17)} } @inproceedings{ubo:80745, author = {Kunkel, Johannes and Loepp, Benedikt and Ziegler, Jürgen}, chapter = {}, title = {A 3D Item Space Visualization for Presenting and Manipulating User Preferences in Collaborative Filtering}, year = {2017}, publisher = {ACM}, address = {New York, NY, USA}, pages = {3–15}, keywords = {3D Visualizations}, doi = {10.1145/3025171.3025189}, url = {https://dl.acm.org/doi/10.1145/3025171.3025189?cid=87958660357}, abstract = {While conventional Recommender Systems perform well in automatically generating personalized suggestions, it is often difficult for users to understand why certain items are recommended and which parts of the item space are covered by the recommendations. Also, the available means to influence the process of generating results are usually very limited. To alleviate these problems, we suggest a 3D map-based visualization of the entire item space in which we position and present sample items along with recommendations. The map is produced by mapping latent factors obtained from Collaborative Filtering data onto a 2D surface through Multidimensional Scaling. Then, areas that contain items relevant with respect to the current user’s preferences are shown as elevations on the map, areas of low interest as valleys. In addition to the presentation of his or her preferences, the user may interactively manipulate the underlying profile by raising or lowering parts of the landscape, also at cold-start. Each change may lead to an immediate update of the recommendations. Using a demonstrator, we conducted a user study that, among others, yielded promising results regarding the usefulness of our approach.}, booktitle = {Proceedings of the 22nd International Conference on Intelligent User Interfaces (IUI ’17)} }