@inproceedings{ubo_mods_00140449,
  author = {Torkamaan, Helma and Barbu, Catalin-Mihai and Ziegler, Jürgen},
  editor = {Bogers, Toine and Said, Alan},
  title = {How Can They Know That? A Study of Factors Affecting the Creepiness of Recommendations},
  booktitle = {Proceedings of the 13th ACM Conference on Recommender Systems},
  year = {2019},
  publisher = {ACM},
  address = {New York, NY},
  pages = {423–427},
  keywords = {Trust},
  isbn = {978-1-4503-6243-6},
  doi = {10.1145/3298689.3346982},
  abstract = {Recommender systems (RS) often use implicit user preferences extracted from behavioral and contextual data, in addition to traditional rating-based preference elicitation, to increase the quality and accuracy of personalized recommendations. However, these approaches may harm user experience by causing mixed emotions, such as fear, anxiety, surprise, discomfort, or creepiness. RS should consider users’ feelings, expectations, and reactions that result from being shown personalized recommendations. This paper investigates the creepiness of recommendations using an online experiment in three domains: movies, hotels, and health. We define the feeling of creepiness caused by recommendations and find out that it is already known to users of RS. We further find out that the perception of creepiness varies across domains and depends on recommendation features, like causal ambiguity and accuracy. By uncovering possible consequences of creepy recommendations, we also learn that creepiness can have a negative influence on brand and platform attitudes, purchase or consumption intention, user experience, and users’ expectations of—and their trust in—RS.}
}


@inproceedings{ubo_mods_00136811,
  author = {Kunkel, Johannes and Donkers, Tim and Michael, Lisa and Barbu, Catalin-Mihai and Ziegler, Jürgen},
  title = {Let Me Explain: Impact of Personal and Impersonal Explanations on Trust in Recommender Systems},
  booktitle = {Proceedings of the 37th International Conference on Human Factors in Computing Systems (CHI ’19)},
  year = {2019},
  publisher = {ACM},
  address = {New York},
  pages = {487:1–487:12},
  isbn = {978-1-4503-5970-2},
  doi = {10.1145/3290605.3300717},
  url = {https://doi.org/10.1145/3290605.3300717},
  abstract = {Trust in a Recommender System (RS) is crucial for its overall success. However, it remains underexplored whether users trust personal recommendation sources (i.e. other humans) more than impersonal sources (i.e. conventional RS), and, if they do, whether the perceived quality of explanation provided account for the difference. We conducted an empirical study in which we compared these two sources of recommendations and explanations. Human advisors were asked to explain movies they recommended in short texts while the RS created explanations based on item similarity. Our experiment comprised two rounds of recommending. Over both rounds the quality of explanations provided by users was assessed higher than the quality of the system’s explanations. Moreover, explanation quality significantly influenced perceived recommendation quality as well as trust in the recommendation source. Consequently, we suggest that RS should provide richer explanations in order to increase their perceived recommendation quality and trustworthiness.}
}


@inproceedings{ubo_mods_00132857,
  author = {Barbu, Catalin-Mihai and Carbonell, Guillermo and Ziegler, Jürgen},
  title = {The Influence of Trust Cues on the Trustworthiness of Online Reviews for Recommendations},
  booktitle = {Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing},
  year = {2019},
  publisher = {ACM Press},
  address = {New York},
  pages = {1687–1689},
  keywords = {User study},
  isbn = {978-1-4503-5933-7},
  doi = {10.1145/3297280.3297603},
  abstract = {In recent years, recommender systems have started to exploit user-generated content, in particular online reviews, as an additional means of personalizing and explaining their predictions. However, reviews that are poorly written or perceived as fake may have a detrimental effect on the users’ trust in the recommendations. Embedding so-called "trust cues" in the user interface is a technique that can help users judge the trustworthiness of presented information. We report preliminary results from an online user study that investigated the impact of trust cues—in the form of helpfulness votes—on the trustworthiness of online reviews for recommendations.}
}


@article{ubo_mods_00127145,
  author = {Carbonell, Guillermo and Barbu, Catalin-Mihai and Vorgerd, Laura and Brand, Matthias},
  title = {The impact of emotionality and trust cues on the perceived trustworthiness of online reviews},
  journal = {Cogent Business and Management},
  year = {2019},
  volume = {6},
  number = {1},
  pages = {1586062},
  keywords = {trust cues},
  abstract = {Online reviews and trust cues are two core aspects of e-commerce. Based on these features, users can make informed decisions about the products and services they buy online. Although prior studies have investigated on various review characteristics, the writing style has been examined less frequently. This empirical study simulated an e-commerce platform, in which participants (N =?124) were confronted with the reviews and helpfulness votes of other users while searching for one certain product (i.e. a laptop). The task was to rate how trustworthy or fake the reviews are, and the purchase intention after reading each review. Our results show that a factual writing style is considered more trustworthy, less fake, and entails a higher purchase intention when compared to emotional reviews. The trust cues were only relevant in interaction with variables that measure trust in the Internet as a safe environment for making monetary transactions. Furthermore, we found that trustworthiness influenced purchase intention, but the fakeness perception of the review does not yield such effects. We suggest future studies to understand this result and highlight implications for platform design.},
  issn = {2331-1975},
  doi = {10.1080/23311975.2019.1586062}
}


@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}
}