A comparison of explicit and implicit proactive dialogue strategies for conversational recommendation

Recommendation systems aim at facilitating information retrieval for users by taking into account their preferences. Based on previous user behaviour, such a system suggests items or provides information that a user might like or find useful. Nonetheless, how to provide suggestions is still an open question. Depending on the way a recommendation is communicated influences the user’s perception of the system. This paper presents an empirical study on the effects of proactive dialogue strategies on user acceptance. Therefore, an explicit strategy based on user preferences provided directly by the user, and an implicit proactive strategy, using autonomously gathered information, are compared. The results show that proactive dialogue systems significantly affect the perception of human-computer interaction. Although no significant differences are found between implicit and explicit strategies, proactivity significantly influences the user experience compared to reactive system behaviour. The study contributes new insights to the human-agent interaction and the voice user interface design. Furthermore, interesting tendencies are discovered that motivate future work

Proactive behavior in voice assistants: A systematic review and conceptual model

Voice assistants (VAs) are increasingly integrated into everyday activities and tasks, raising novel challenges for users and researchers. One emergent research direction concerns proactive VAs, who can initiate interaction without direct user input, offering unique benefits including efficiency and natural interaction. Yet, there is a lack of review studies synthesizing the current knowledge on how proactive behavior has been implemented in VAs and under what conditions proactivity has been found more or less suitable. To this end, we conducted a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. We searched for articles in the ACM Digital Library, IEEExplore, and PubMed, and included primary research studies reporting user evaluations of proactive VAs, resulting in 21 studies included for analysis. First, to characterize proactive behavior in VAs we developed a novel conceptual model encompassing context, initiation, and action components: Activity/status emerged as the primary contextual element, direct initiation was more common than indirect initiation, and suggestions were the primary action observed. Second, proactive behavior in VAs was predominantly explored in domestic and in-vehicle contexts, with only safety-critical and emergency situations demonstrating clear benefits for proactivity, compared to mixed findings for other scenarios. The paper concludes with a summary of the prevailing knowledge gaps and potential research avenues