Graph Neural Networks for Social Context Modeling and User Preference Prediction in Conversational Agents
Keywords:
graph neural networks, conversational agents, social context modeling, preference prediction, sociotechnical systems, fairness, governanceAbstract
Conversational agents increasingly operate within dense social environments where user preferences are not isolated preferences but are shaped by interpersonal influence, group memberships, historical interactions, and shifting contextual expectations. Modeling these dependencies requires representational frameworks capable of capturing relational structure beyond sequential dialogue content. Graph neural networks have emerged as a suitable class of architectures because they learn from irregular relational topologies, propagate contextual signals across connected entities, and support heterogeneous interaction graphs. This paper presents a system-level examination of graph neural network-based social context modeling and user preference prediction in conversational agents. It addresses the structural design of graph-based user and interaction representations, the architectural trade-offs among message passing, attention, temporal modeling, and pairwise preference prediction, and the requirements of production-scale data infrastructure. The discussion further considers deployment constraints such as latency, incremental graph updates, model drift, and integration with existing dialogue management stacks. Attention is given to fairness, robustness, privacy, explainability, and the governance mechanisms needed to prevent socially learned preferences from amplifying bias or undermining user autonomy. Rather than focusing narrowly on model accuracy, the paper emphasizes infrastructure sustainability, organizational accountability, and policy alignment. The analysis integrates perspectives from graph representation learning, recommender systems, conversational AI, sociotechnical design, and operational machine learning to provide a multidimensional research agenda for building preference-aware conversational systems that are both technically effective and institutionally responsible.
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