@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{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)}
}