Investigating meta-intents: user interaction preferences in conversational recommender systems

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). For determining meta-intent factors, we conduct an exploratory study with 212 participants, and a confirmatory study with 394 participants, from this, we obtain a reliable and stable MI questionnaire with 22 items corresponding to seven concepts. These seven factors cover important interaction preferences. We find that MI can be linked to users' general decision-making style and can thus be instrumental in translating general psychological user characteristics into more concrete design guidance for CRS. We further explore the correlations between MI and user interactions in real CRS scenarios. For this purpose, we propose a CRS framework and implement a chatbot in the smartphone domain to collect real interaction data. We conduct an online study with 99 participants and an interview study in the laboratory with 19 participants. Regarding the impact of MI on interaction behavior, we observe that dialog-initiation, efficiency-orientation and interest in details have a significant and direct impact on interaction behavior. Based on the findings, we provide some heuristic suggestions for leveraging MI in the design and adaptation of CRS. Our studies show the usefulness of the meta-intents concept for bridging the gap between general user characteristics and the concrete design of CRS and indicate their potential for personalizing the interaction in real-time conversations.