@inproceedings{ubo_mods_00199546,
  author = {Ma, Yuan and Donkers, Tim and Kleemann, Timm and Ziegler, Jürgen},
  editor = {Gwizdka, Jacek and Rieh, Soo Young},
  title = {An Instrument for measuring users’ meta-intents},
  booktitle = {CHIIR ’23: Proceedings of the 2023 Conference on Human Information Interaction and Retrieval},
  year = {2023},
  publisher = {ACM},
  address = {Washington},
  pages = {290–302},
  abstract = {We propose the concept of meta-intents which represent high-level user preferences related to the interaction and decision-making in conversational recommender systems (CRS) and present a questionnaire instrument for measuring meta-intents. We conducted a two-stage user study, an exploratory study with 212 participants on Prolific, and a confirmatory study with 394 participants on Prolific. We obtained a reliable and stable meta-intents questionnaire with 22 question items, corresponding to seven latent factors (concepts). These seven factors cover important interaction preferences and are closely related to users’ decision-making process. For example, the factor dialog-initiative reflects whether users prefer to follow the system’s guidance or ask their own questions in a CRS. We conducted statistical analyses of meta-intents in two domains (smartphones and hotels), and a general chatbot scenario. We also investigated the influence of additional factors (demography, decision-making style) on meta-intents through Structural Equation Modeling (SEM). Our results provide preliminary evidence that the proposed meta-intents are domain and demography (gender, age) independent. They can be linked to the general decision-making style and can thus be instrumental in translating general decision-making factors into more concrete design guidance for CRS and their potential personalization. Meta-intents also provide a basis for future analyses of interaction behavior in CRS and the development of a cognitively founded theoretical framework.},
  isbn = {979-8-4007-0035-4},
  doi = {10.1145/3576840},
  url = {https://doi.org/10.1145/3576840.3578317},
  language = {en}
}


@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_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}
}