Monday 19 May 2025
Researchers have been exploring how conversational styles can influence user interactions and outcomes in recommender systems, which are designed to suggest personalized products or services based on individual preferences.
The study focused on two distinct conversational styles: high involvement (fast-paced, direct, and proactive) and high considerateness (polite, accommodating, and prioritizing clarity). Participants were asked to interact with a virtual assistant that used one of these styles, while performing tasks such as searching for academic papers or recommending music.
The results showed that the conversational style significantly impacted user behavior. When users interacted with the high-involvement style, they were more likely to engage in longer conversations and explore more options, resulting in a higher number of preferences elicited. In contrast, the high-considerateness style led to shorter conversations and fewer preferences elicited, but participants reported higher satisfaction scores.
The study also found that the effectiveness of each conversational style depended on the user’s level of domain familiarity. For users with low familiarity, the high-considerateness style was more effective in guiding them towards relevant recommendations. In contrast, users with high familiarity preferred the high-involvement style, which allowed them to quickly explore and identify relevant options.
The findings suggest that conversational styles can be used to tailor interactions to individual user needs and preferences, leading to improved outcomes. The study highlights the importance of considering both the style of interaction and the user’s level of domain knowledge when designing recommender systems.
In this context, virtual assistants could adapt their conversational style based on user feedback or domain-specific information, allowing them to better serve users with varying levels of familiarity. This approach could lead to more effective and personalized recommendations, ultimately improving the overall user experience.
The research also has implications for human-computer interaction beyond recommender systems. As conversational interfaces become increasingly prevalent in various applications, understanding how different styles can impact user behavior and satisfaction is crucial for designing effective and engaging interactions.
Overall, this study demonstrates the importance of considering the role of conversational style in shaping user experiences, and its findings have significant implications for the development of more personalized and effective human-computer interfaces.
Cite this article: “The Impact of Conversational Style on User Interactions and Outcomes”, The Science Archive, 2025.
Recommender Systems, Conversational Styles, User Behavior, Virtual Assistants, Domain Familiarity, Personalized Recommendations, Human-Computer Interaction, Conversational Interfaces, User Experience, Satisfaction Scores
