Initiative transfer in conversational recommender systems

Ma, Y., & Ziegler, J. (2023). In J. Zhang & L. Chen (Eds.), Proceedings of the 17th ACM Conference on Recommender Systems, RecSys 2023 (pp. 978–984). Association for Computing Machinery, Inc.

Abstract

Conversational recommender systems (CRS) are increasingly designed to offer mixed-initiative dialogs in which the user and the system can take turns in starting a communicative exchange, for example, by asking questions or stating preferences. However, whether and when users make use of the mixed-initiative capabilities in a CRS and which factors influence their behavior is as yet not well understood. We report an online study investigating user interaction behavior, especially the transfer of initiative between user and system in a real-time online CRS. We assessed the impact of dialog initiative at system start as well as of several psychological user characteristics that may influence their preference for either initiative mode. To collect interaction data, we implemented a chatbot in the domain of smartphones. Two groups of participants on Prolific (total n=143) used the system which started either with a system-initiated or user-initiated dialog. In addition to interaction data, we measured several psychological factors as well as users’ subjective assessment of the system through questionnaires. We found that: 1. Most users tended to take over the initiative from the system or stay in user-initiated mode when this mode was offered initially. 2. Starting the dialog in user-initiated mode CRS led to fewer interactions needed for selecting a product than in system-initiated mode. 3. The user’s initiative transfer was mainly affected by their personal interaction preferences (especially initiative preference). 4. The initial mode of the mixed-initiative CRS did not affect the user experience, but the occurrence of initiative transfers in the dialog negatively affected the degree of user interest and excitement. The results can inform the design and potential personalization of CRS.

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