Where and What: Reasoning Dynamic and Implicit Preferences in Situated Conversational Recommendation
Summary: arXiv:2604.20749v1 Announce Type: new
Abstract: Situated conversational recommendation (SCR) utilizes visual scenes grounded in specific environments and natural language dialogue to deliver contextually appropriate recommendations. This research direction has emerged as a promising avenue due to its close alignment with real-world scenarios. Unlike traditional recommendation systems, SCR requires a deeper understanding of dynamic and implicit user preferences, as the surrounding scene often influences users’ underlying interests, which may evolve across conversations. This complexity significantly impacts the timing and relevance of recommendations.
To address these challenges, we propose situated preference reasoning (SiPeR), a novel framework that integrates two core mechanisms:
- Scene transition estimation: This mechanism estimates whether the current scene satisfies user needs and guides the user toward a more suitable scene when necessary.
- Bayesian inverse inference: This mechanism leverages the likelihood of multimodal large language models (MLLMs) to predict user preferences regarding candidate items within the scene.
Extensive experiments conducted on two representative benchmarks demonstrate SiPeR’s superiority in both recommendation accuracy and response generation quality. The findings suggest that SiPeR effectively captures the dynamic nature of user preferences in situated conversations, paving the way for more personalized and relevant recommendations.
The significance of this research lies not only in its theoretical contributions but also in its practical applications. By improving the accuracy of recommendations in real-world scenarios, SCR has the potential to enhance user experiences across various domains, including e-commerce, hospitality, and entertainment.
In addition to its innovative framework, SiPeR’s implementation is accessible for further research and development. The code and data are available at https://github.com/DongdingLin/SiPeR, enabling other researchers to build upon this work and explore new avenues in situated conversational recommendation.
As the field of artificial intelligence continues to evolve, the integration of visual and textual information in conversational systems represents a significant step forward. The ability to account for dynamic user preferences not only enhances the relevance of recommendations but also aligns with the growing demand for personalized interactions in technology. The implications of this research extend beyond mere recommendation systems; they invite a rethinking of how we understand user engagement and interaction in digital environments.
In conclusion, the situated preference reasoning framework introduced in this study marks a pivotal development in the landscape of conversational AI. By addressing the complexities of dynamic and implicit preferences, SiPeR lays the groundwork for future innovations that promise to redefine user experiences in situated contexts.