@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_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.} } @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_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_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} } @inproceedings{ubo:73248, author = {Loepp, Benedikt and Barbu, Catalin-Mihai and Ziegler, Jürgen}, chapter = {}, title = {Interactive Recommending: Framework, State of Research and Future Challenges}, year = {2016}, pages = {3–13}, keywords = {Survey}, abstract = {In this paper, we present a framework describing the various aspects of recommender systems that can serve for empowering users by giving them more interactive control and transparency in the recommendation process. While conventional recommenders mostly operate like black boxes that cannot be influenced by the user, we identify four aspects properly connected with the recommendation algorithm—namely input data, user model, external con-text model and presentation—as essential points in which a system may be enhanced by additional interaction possibilities. In light of this framework, we take a closer look at prior and present solutions to integrate recommender systems with more interactivity and describe future research challenges. Regarding these challenges, we especially focus on experiences gained in our own work and outline future research we have planned in the area of interactive recommending.}, url = {http://ceur-ws.org/Vol-1705/02-paper.pdf}, booktitle = {Proceedings of the 1st Workshop on Engineering Computer-Human Interaction in Recommender Systems (EnCHIReS ’16)} } @inproceedings{ubo:72486, author = {Barbu, Catalin-Mihai}, chapter = {}, title = {Increasing the Trustworthiness of Recommendations by Exploiting Social Media Sources}, year = {2016}, address = {New York, NY, USA}, publisher = {ACM}, pages = {447–450}, keywords = {recommender systems}, abstract = {Current recommender systems mostly do not take into account as well as they might the wealth of information available in social media, thus preventing the user from obtaining a broad and reliable overview of different opinions and ratings on a product. Furthermore, there is a lack of user control over the recommendation process–which is mostly fully automated and does not allow the user to influence the sources and mechanisms by which recommendations are produced–as well as over the presentation of recommended items. Consequently, recommendations are often not transparent to the user, are considered to be less trustworthy, or do not meet the user’s situational needs. This work will investigate the theoretical foundations for user-controllable, interactive methods of recommending, will develop techniques that exploit social media data in conjunction with other sources, and will validate the research empirically in the area of e-commerce product recommendations. The methods developed are intended to be applicable in a wide range of recommending and decision support scenarios.}, isbn = {978-1-4503-4035-9}, doi = {10.1145/2959100.2959104}, url = {https://dl.acm.org/citation.cfm?id=2959104}, booktitle = {Proceedings of the 10th ACM Conference on Recommender Systems} }