@inproceedings{ubo_mods_00191230,
  author = {Ma, Yuan and Kleemann, Timm and Ziegler, Jürgen},
  editor = {},
  title = {Psychological User Characteristics and Meta-Intents in a Conversational Product Advisor},
  booktitle = {Proceedings of the 9th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems},
  series = {CEUR Workshop Proceedings},
  year = {2022},
  publisher = {},
  address = {},
  volume = {3222},
  pages = {18–32},
  keywords = {conversational UI design, interactive behavior analysis, decision making, influence of psychological factors on interaction},
  abstract = {We present a study investigating psychological characteristics of users of a GUI-style conversational recommender system in a real-world application case. We collected data of 496 customers of an online shop using a conversational product advisor (CPA), using questionnaire responses concerning decision- making style and a set of meta-intents, a concept we propose to represent high-level user preferences related to the decision process in a CPA. We also analyzed anonymized data on users’ interactions in the CPA. Concerning general decision-making style, we could identify two clusters of users who differ in their scores on scales measuring rational and intuitive decision-making. We found evidence that rationality and intuitiveness scores are differently correlated with the proposed meta-intents such as efficiency orientation, interest in detail, and openness for guidance. Relations with interaction data could be observed between rationality/intuitiveness scores and overall time spent in the CPA. Trying to classify users’ decision style from their interactions, however did not yield positive results. Despite the limitation that only a single CPA was studied in a single domain, our results provide evidence that the proposed meta-intents are linked to the general decision-making style of a user and can thus be instrumental in translating general decision-making factors into more concrete design guidance for CPA and their potential personalization.},
  issn = {1613-0073},
  url = {http://ceur-ws.org/Vol-3222/paper2.pdf},
  language = {en}
}


@inproceedings{ubo_mods_00185435,
  author = {Kleemann, Timm and Loepp, Benedikt and Ziegler, Jürgen},
  publisher = {ACM},
  address = {New York, NY, USA},
  title = {Towards Multi-Method Support for Product Search and Recommending},
  booktitle = {Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’22)},
  year = {2022},
  pages = {74–79},
  keywords = {Decision aids; Faceted filtering; Conversational recommender systems; Product advisors; Chatbots; Recommender systems; Search interfaces},
  isbn = {978-1-4503-9232-7},
  doi = {10.1145/3511047},
  url = {https://dl.acm.org/doi/10.1145/3511047.3536408?cid=87958660357},
  abstract = {Today, online shops offer a variety of components to support users in finding suitable items, ranging from filters and recommendations to conversational advisors and natural language chatbots. All these methods differ in terms of cognitive load and interaction effort, and, in particular, in their suitability for the specific user. However, it is often difficult for users to determine which method to use to reach their goal. Moreover, as the settings are not propagated between the methods, there is a lack of support for switching components. In this paper, we study the reasons for using the different components in more detail and present an initial proposal for a multi-method approach that provides a more seamless experience, allowing users to freely and flexibly choose from all available methods at any time.}
}


@inproceedings{ubo_mods_00167803,
  author = {Ma, Yuan and Kleemann, Timm and Ziegler, Jürgen},
  title = {Mixed-Modality Interaction in Conversational Recommender Systems},
  booktitle = {Proceedings of the 8th Joint Workshop on Interfaces and Human Decision Making for Recommender Systems},
  series = {CEUR Workshop Proceedings},
  year = {2021},
  publisher = {},
  address = {},
  volume = {2948},
  pages = {21–37},
  keywords = {Conversational Recommender Systems; User Interface; Preference Elicitation; Critique-based Recommendations},
  abstract = {Recent advances in natural language processing have made modern chatbots and Conversational Recommender Systems (CRS) increasingly intelligent, enabling them to handle more complex user inputs. Still, the interaction with a CRS is often tedious and error-prone. Especially when using written text as the form of conversation, the interaction is often less efficient in comparison to conventional GUI- style interaction. To keep the flexibility and mixed-initiative style of language-based conversation while leveraging the efficiency and simplicity of interacting through graphical widgets, we investigate the de- sign space of integrating GUI elements into text-based conversations. While simple response buttons have already been used in chatbots, the full range of such mixed-modality interactions has not yet been investigated in existing research. We propose two design dimensions along which integrations can be defined and analyze their applicability for preference elicitation and for critiquing the CRS’s responses at different levels. We report a user study in which we investigated user preferences and perceived usability of different techniques based on video prototypes.},
  note = {OA platinum},
  issn = {1613-0073},
  url = {http://ceur-ws.org/Vol-2948/paper2.pdf},
  language = {en}
}


@inproceedings{ubo_mods_00167689,
  author = {Kleemann, Timm and Wagner, Magdalena and Loepp, Benedikt and Ziegler, Jürgen},
  title = {Modeling User Interaction at the Convergence of Filtering Mechanisms, Recommender Algorithms and Advisory Components},
  booktitle = {Mensch Und Computer 2021 – Tagungsband},
  year = {2021},
  publisher = {ACM},
  address = {New York, NY, USA},
  pages = {531–543},
  keywords = {Human factors; User experience; User modeling; Search interfaces; Recommender systems},
  isbn = {978-1-4503-8645-6},
  doi = {10.1145/3473856},
  url = {https://dl.acm.org/doi/10.1145/3473856.3473859?cid=87958660357},
  language = {en},
  abstract = {A variety of methods is used nowadays to reduce the complexity of product search on e-commerce platforms, allowing users, for example, to specify exactly the features a product should have, but also, just to follow the recommendations automatically generated by the system. While such decision aids are popular with system providers, research to date has mostly focused on individual methods rather than their combination. To close this gap, we propose to support users in choosing the right method for the current situation. As a first step, we report in this paper a user study with a fictitious online shop in which users were able to flexibly use filter mechanisms, rely on recommendations, or follow the guidance of a dialog-based product advisor. We show that from the analysis of the interaction behavior, a model can be derived that allows predicting which of these decision aids is most useful depending on the user’s situation, and how this is affected by demographics and personality.}
}


@inproceedings{ubo_mods_00139552,
  author = {Kleemann, Timm and Ziegler, Jürgen},
  title = {Integration of Dialog-based Product Advisors into Filter Systems},
  booktitle = {Proceedings of the Conference on Mensch und Computer},
  series = {ACM International Conference Proceeding Series},
  year = {2019},
  publisher = {ACM Press},
  address = {New York},
  pages = {67–77},
  keywords = {Dialogbasierte Produktberater, Filtersysteme},
  isbn = {978-1-4503-7198-8},
  doi = {10.1145/3340764.3340786},
  abstract = { Different techniques such as search functions or recommendation components are used today to support the often complex product search on the Internet. Faceted filter systems that successively limit the result set according to the set filter settings have proven to be quite successful. However, this method requires clear objectives and domain knowledge on the part of the users. As an alternative, conversational product advisors who select suitable products on the basis of a sequence of questions have gained more importance in recent times, whereby the questions are based more on the tasks and application scenarios of the users than on the technical properties of the products. However, there is currently a lack of approaches that integrate filter systems and conversational advisors in a meaningful and closely coupled way. In this paper an integrated approach is presented, where users can switch between filter systems and advisory dialogues, whereby selection actions in one component have a consistent and transparent effect on the other component and can be further adjusted there. The aim is to better support users with different levels of knowledge of the product type concerned. We describe the requirements for such integrated systems resulting from our approach and report on a user study in which the user behavior and the subjective evaluation were examined in a prototypical implementation.}
}


@article{ubo_mods_00109856,
  author = {Loepp, Benedikt and Donkers, Tim and Kleemann, Timm and Ziegler, Jürgen},
  volume = {121},
  pages = {21–41},
  title = {Interactive Recommending with Tag-Enhanced Matrix Factorization (TagMF)},
  journal = {International Journal of Human Computer Studies},
  year = {2019},
  keywords = {Collaborative Filtering, Empirical studies, Human factors, Interactive recommending, Matrix Factorization, Recommender Systems, Tags, User experience, User interfaces, User profiles},
  doi = {10.1016/j.ijhcs.2018.05.002},
  url = {https://doi.org/10.1016/j.ijhcs.2018.05.002},
  abstract = {We introduce TagMF, a model-based Collaborative Filtering method that aims at increasing transparency and offering richer interaction possibilities in current Recommender Systems. Model-based Collaborative Filtering is currently the most popular method that predominantly uses Matrix Factorization: This technique achieves high accuracy in recommending interesting items to individual users by learning latent factors from implicit feedback or ratings the community of users provided for the items. However, the model learned and the resulting recommendations can neither be explained, nor can users be enabled to influence the recommendation process except by rating (more) items. In TagMF, we enhance a latent factor model with additional content information, specifically tags users provided for the items. The main contributions of our method are to use this integrated model to elucidate the hidden semantics of the latent factors and to let users interactively control recommendations by changing the influence of the factors through easily comprehensible tags: Users can express their interests, interactively manipulate results, and critique recommended items—at cold-start when no historical data is yet available for a new user, as well as in case a long-term profile representing the current user’s preferences already exists.
 To validate our method, we performed offline experiments and conducted two empirical user studies where we compared a recommender that employs TagMF against two established baselines, standard Matrix Factorization based on ratings, and a purely tag-based interactive approach. This user-centric evaluation confirmed that enhancing a model-based method with additional information positively affects perceived recommendation quality. Moreover, recommendations were considered more transparent and users were more satisfied with their final choice. Overall, learning an integrated model and implementing the interactive features that become possible as an extension to contemporary systems with TagMF appears beneficial for the subjective assessment of several system aspects, the level of control users are able to exert over the recommendation process, as well as user experience in general.}
}


@inproceedings{ubo_mods_00116566,
  author = {Loepp, Benedikt and Donkers, Tim and Kleemann, Timm and Ziegler, Jürgen},
  title = {Impact of Item Consumption on Assessment of Recommendations in User Studies},
  booktitle = {Proceedings of the 12th ACM Conference on Recommender Systems (RecSys ’18)},
  year = {2018},
  publisher = {ACM},
  address = {New York, NY, USA},
  pages = {49–53},
  keywords = {User Studies},
  isbn = {978-1-4503-5901-6},
  doi = {10.1145/3240323.3240375},
  url = {https://dl.acm.org/doi/10.1145/3240323.3240375?cid=87958660357},
  abstract = {In user studies of recommender systems, participants typically cannot consume the recommended items. Still, they are asked to assess recommendation quality and other aspects related to user experience by means of questionnaires. Without having listened to recommended songs or watched suggested movies, however, this might be an error-prone task, possibly limiting validity of results obtained in these studies. In this paper, we investigate the effect of actually consuming the recommended items. We present two user studies conducted in different domains showing that in some cases, differences in the assessment of recommendations and in questionnaire results occur. Apparently, it is not always possible to adequately measure user experience without allowing users to consume items. On the other hand, depending on domain and provided information, participants sometimes seem to approximate the actual value of recommendations reasonably well.}
}