Abstract: User profile analysis aims to delve into users’ preferences in internet applications,which holds significant importance for various practical applications like recommendation systems and personalized advertising.Recent research trends consider users and their interactions as nodes in a graph structure,transforming user profile construction into a node classification task and utilizing deep graph neural network technology for user feature extraction.However,these studies often fail to fully consider the differences in interaction types among different users and their temporal relationships,thereby limiting the accuracy of user profile analysis.In light of this,this paper proposes a graph neural network method based on selected state space for user profile analysis to simultaneously capture context information such as multi-user comparisons and temporal patterns implied by graph structure relationships.To effectively model the long-range dependency relationships in user operation sequences,we introduce a state space model into the graph neural network and combine it with a node prioritization strategy based on attention mechanisms to enhance context-aware reasoning,thereby improving the predictive performance of explicit user attributes such as gender and age.Experimental validation on two real internet application datasets confirms the effectiveness of our proposed method.

Key words: User profile, Graph neural network, Selection state space, Attention mechanism, Timing analysis