@phdthesis{ubo_mods_00228809, author = {Ma, Yuan}, title = {Meta-Intents: Interaction Preferences in Conversational Recommender Systems}, year = {2025}, address = {Duisburg, Essen}, keywords = {Meta Intents, Conversational Recommender Systems, HCI, User Modeling, Interaction Behavior Analysis}, abstract = {With advancements in natural language processing, dialogue systems have become increasingly proficient in comprehending users’ intricate utterances. This convergence, when integrated with the application contexts of recommendation systems, has given rise to a burgeoning research domain known as conversational recommender systems (CRS). CRS can obtain user preferences through text conversations with users and recommends appropriate products. Different from traditional RS, CRS has a higher degree of interaction freedom, which makes us expect that CRS can interact with customers like a real-life salesperson. For example, it can not only ask users for features requirements, but also remind users to compare the current product with other candidates. More personalized interactive functions can emerge in CRS scenarios and make CRS more efficient and user-friendly, at the same time, it also puts forward new requirements for the user model. Different users and contexts necessitate distinct interactive functionalities, highlighting the importance of understanding and describing users’ interactive preferences—a relatively underexplored area of research. In this work, we propose the concept of meta-intents (MI) which represent high-level user preferences related to the interaction styles and decision-making support in conversational recommender systems (CRS). We developed a stable instrument to measure MI via a two-stage factor analysis, revealing that MI can be linked to users’ general decision-making styles. This connection allows for the translation of broad psychological user characteristics into more concrete design guidance for CRS. After proposing MI, we conducted a series of user studies to analyze how they impact various users’ interaction behaviors in a real instance of CRS. We analyzed the observed phenomena and extracted some helpful suggestions for interaction design. These suggestions can help us understand the role of MI in actual scenarios and build a CRS interaction strategy that is more in line with user habits.}, note = {Dissertation, Universität Duisburg-Essen, 2025 (kumulative Dissertation)}, doi = {10.17185/duepublico/83003}, url = {https://doi.org/10.17185/duepublico/83003}, language = {en} } @article{ubo_mods_00225128, author = {Ma, Yuan and Ziegler, Jürgen}, title = {Investigating meta-intents: user interaction preferences in conversational recommender systems}, journal = {User Modeling and User-Adapted Interaction}, year = {2024}, publisher = {Springer}, pages = {in press}, keywords = {Conversational recommender systems; Conversational UI design; Interaction behavior analysis; User model}, note = {in press}, issn = {1573-1391}, doi = {10.1007/s11257-024-09411-3}, url = {https://doi.org/10.1007/s11257-024-09411-3}, language = {en} } @inproceedings{ubo_mods_00222970, author = {Ma, Yuan and Ziegler, Jürgen}, editor = {The Association for Computing Machinery (ACM)}, title = {The Effect of Proactive Cues on the Use of Decision Aids in Conversational Recommender Systems}, booktitle = {Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization (UMAP Adjunct ’24)}, year = {2024}, publisher = {ACM}, address = {New York}, pages = {305–315}, keywords = {Conversational recommender systems; Conversational UI design; Interaction behavior analysis; Meta-intents; Proactive interaction scheme}, isbn = {979-8-4007-0466-6}, doi = {10.1145/3631700.3665186}, url = {https://doi.org/10.1145/3631700.3665186}, language = {en} } @inproceedings{ubo_mods_00222336, author = {Ma, Yuan and Ziegler, Jürgen}, title = {The Effect of Proactive Cues on the Use of Decision Aids in Conversational Recommender Systems}, booktitle = {Adjunct Proceedings of the 32nd ACM Conference on User Modeling, Adaptation and Personalization}, year = {2024}, publisher = {ACM}, address = {New York}, isbn = {979-8-4007-0466-6}, doi = {10.1145/3631700.3665186}, url = {https://doi.org/10.1145/3631700}, language = {en} } @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_00233627, author = {Ma, Yuan and Donkers, Tim and Kleemann, Timm and Ziegler, Jürgen}, editor = {Anelli, Vito and Basile, Pierpaolo and De Melo, Gerard}, title = {Meta-Intents in Conversational Recommender Systems}, booktitle = {Proceedings of the Fourth Knowledge-aware and Conversational Recommender Systems Workshop co-located with 16th ACM Conference on Recommender Systems (RecSys 2022)}, series = {CEUR Workshop Proceedings}, year = {2022}, publisher = {RWTH Aachen}, address = {Aachen}, volume = {3294}, pages = {81–90}, issn = {1613-0073}, url = {https://ceur-ws.org/Vol-3294/long6.pdf}, 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{ma2022kars, year = {2022}, title = {Meta-Intents in Conversational Recommender Systems}, abstract = {We present a study investigating the psychological characteristics of users and their conversation-related preferences in a conversational recommender system (CRS). We collected data from 260 participants on Prolific, using questionnaire responses concerning decision-making style, conversation-related feature preferences in the smartphone domain, and a set of meta- intents, a concept we propose to represent high-level user preferences related to the interaction and decision-making in CRS. We investigated the relationship between users’ decision-making style, meta-intents and feature preferences through Structural Equation Modeling. We find that decision-making style has a significant influence on meta-intents as well as on feature preferences, however, meta-intents do not have a mediating effect between these two factors, indicating that meta-intents are independent of item feature preferences and may thus be generalizable, domain-independent concepts. 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 CRS and their potential personalization. As meta-intents seem to be domain-independent factors, we assume meta-intents do not affect users’ various interests in concrete product features and mainly reflect users’ general decision-support needs and interaction preferences in CRS.}, booktitle = {Proceedings of the 4th Edition of Knowledge-aware and Conversational Recommender Systems (KaRS) Workshop @ RecSys 2022}, author = {Yuan, Ma and Donkers, Tim and Kleemann, Timm and Jürgen, Ziegler} } @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_00170072, author = {Aker, Ahmet and Sliwa, Alfred and Ma, Yuan and Liu, Ruishen and Borad, Niravkumar and Ziyaei, Seyedeh Fatemeh and Ghbadi, Mina}, title = {What works and what does not: Classifier and feature analysis for argument mining}, booktitle = {Proceedings of the 4th Workshop on Argument Mining}, year = {2017}, publisher = {Association for Computational Linguistics (ACL)}, address = {Stroudsburg}, pages = {91–96}, isbn = {978-1-945626-84-5}, language = {en} }