@inproceedings{ubo_mods_00116350,
  author = {Barbu, Catalin-Mihai and Ziegler, Jürgen},
  editor = {Neidhardt, Julia and Wörndl, Wolfgang and Kuflik, Tsvi and Zanker, Markus},
  title = {Designing Interactive Visualizations of Personalized Review Data for a Hotel Recommender System},
  booktitle = {RecTour 2018: 3rd Workshop on Recommenders in Tourism co-located with the 12th ACM Conference on Recommender Systems (RecSys 2018)},
  series = {CEUR Workshop Proceedings},
  year = {2018},
  publisher = {RWTH},
  address = {Aachen},
  volume = {2222},
  pages = {7–12},
  keywords = {Tourism},
  abstract = {Online reviews extracted from social media are being used increasingly in recommender systems, typically to enhance prediction accuracy. A somewhat less studied avenue of research aims to investigate the underlying relationships that arise between users, items, and the topics mentioned in reviews. Identifying these–often implicit–relationships could be beneficial for at least a couple of reasons. First, they would allow recommender systems to personalize reviews based on a combination of both topic and user similarity. Second, they can facilitate the development of novel interactive visualizations that complement and help explain recommendations even further. In this paper, we report on our ongoing work to personalize user reviews and visualize them in an interactive manner, using hotel recommending as an example domain. We also discuss several possible interactive mechanisms and consider their potential benefits towards increasing users’ satisfaction.},
  issn = {1613-0073},
  url = {http://ceur-ws.org/Vol-2222/paper2.pdf}
}


@inproceedings{ubo_mods_00106122,
  author = {Kunkel, Johannes and Donkers, Tim and Barbu, Catalin-Mihai and Ziegler, Jürgen},
  booktitle = {2nd Workshop on Theory-Informed User Modeling for Tailoring and Personalizing Interfaces (HUMANIZE)},
  title = {Trust-Related Effects of Expertise and Similarity Cues in Human-Generated Recommendations},
  year = {2018},
  keywords = {Structural Equation Modeling},
  url = {http://ceur-ws.org/Vol-2068/humanize5.pdf},
  abstract = {A user’s trust in recommendations plays a central role in the acceptance or rejection of a recommendation. One factor that influences  trust  is  the  source  of  the  recommendations. In this paper we describe an empirical study that investigates the trust-related influence of social presence arising in two scenarios: human-generated recommendations and automated recommending. We further compare visual cues indicating the expertise of a human recommendation source and its similarity with the target user, and evaluate their influence on trust. Our analysis indicates that even subtle visual cues can signal expertise and similarity effectively, thus influencing a user’s trust in recommendations. These findings suggest that automated recommender systems could benefit from the inclusion of social components–especially when conveying characteristics of the recommendation source. Thus, more informative and persuasive recommendation interfaces may be designed using such a mixed approach.}
}


@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)}
}