@inproceedings{kleemann2023,
  abstract = {Today’s e-commerce websites often provide many different components, such as filters and conversational product advisors, to help users find relevant items. However, filters and advisors are often presented separately and treated as independent entities so that the previous input is discarded when users switch between them. This leads to memory loads and disruptions during the search process. In addition, the reasoning behind the advisors’ results is often not transparent. To overcome these limitations, we propose a novel approach that exploits a graph structure to create an integrated system that allows a seamless coupling between filters and advisors. The integrated system utilizes the graph to suggest appropriate filter values and items based on the user’s answers in the advisor. Moreover, it determines follow-up questions based on the filter values set by the user. The interface visualizes and explains the relationship between a given answer and its relevant features to achieve increased transparency in the guidance process. We report the results of an empirical user study with 120 participants that compares the integrated system to a system in which the filtering and advisory mechanisms operate separately. The findings indicate that displaying recommendations and explanations directly in the filter component can increase acceptance and trust in the system. Similarly, combining the advisor with the filters along with the displayed explanations leads to significantly higher levels of knowledge about the relevant product features.},
  address = {Cham},
  author = {Kleemann, Timm and Ziegler, Jürgen},
  series = {Lecture Notes in Computer Science},
  booktitle = {Human-Computer Interaction – INTERACT 2023 : 19th IFIP TC13 International Conference, York, UK, August 28 – September 1, 2023, Proceedings, Part III},
  editor = {Abdelnour Nocera, José and Kristı́n Lárusdóttir, Marta and Petrie, Helen and Piccinno, Antonio and Winckler, Marco},
  isbn = {9783031422850},
  issn = {0302-9743},
  volume = {14144},
  doi = {10.1007/978-3-031-42286-7_8},
  pages = {137–159},
  publisher = {Springer Nature Switzerland},
  title = {Blending Conversational Product Advisors and Faceted Filtering in a Graph-Based Approach},
  url = {https://doi.org/10.1007/978-3-031-42286-7_8},
  note = {10.1007/978-3-031-42286-7_8},
  language = {en},
  keywords = {Search interfaces; explanations; knowledge graph},
  year = {2023},
  month = {aug},
  day = {25},
  month_numeric = {8}
}


@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_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_00156892,
  author = {Kleemann, Timm and Ziegler, Jürgen},
  title = {Distribution sliders: Visualizing data distributions in range selection sliders},
  booktitle = {Conference on "Mensch und Computer"},
  series = {ACM International Conference Proceeding Series},
  year = {2020},
  publisher = {Association for Computing Machinery (ACM)},
  address = {New York},
  pages = {67–78},
  isbn = {9781450375405},
  doi = {10.1145/3404983.3405512},
  abstract = {Sliders are often used to enable users to easily enter preferences for continuous data. Although efforts have already been made to enrich and improve these interaction tools with additional information and visualizations, only rather basic variants of sliders are commonly used in online shops or databases. However, these sliders often provide users only with very limited information about underlying data.We describe and evaluate three different slider designs, which enrich the tools with information in various ways, enabling users to efficiently explore the space of available items and to choose items in an informed manner. In one of the described slider designs we propose a new approach that integrates item recommendations directly into the slider, enabling users to see suitable items within the selection tool. In two user studies we show that these enhancements, both visualizations and recommendations, are powerful methods to directly support users in their searches.}
}