Using Meta-Knowledge Mined from Identifiers to Improve Intent Recognition in Conversational Systems

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 metaintents, 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.