Graph Process Neurons for Sequential Recommendation

Graph neural network (GNN)-based methods have achieved remarkable performance on sequential recommendation tasks. They compress collaborative relations and item transitions into compact node representations and propagate them to neighboring nodes, thereby learning user interests from collaborative signals. However, such representations are static summaries of historical interactions. User preference is inherently time-varying, and this compression overlooks how interest rises and fades between observed interactions. To address this limitation, we propose a Graph Process Neuron (GPrN) for sequential recommendation. It represents the user's history as a continuous preference process over real time and lets each candidate induce its own reading of it. Specifically, we first organize the visible interactions into a query-specific preference process along an observable temporal coordinate, which characterizes how user preference evolves over time. Then, each candidate induces branch-specific scalar reading kernels over complementary temporal views of this process, allowing candidate-dependent evidence to contribute at different scales. A factorized implementation over a finite cosine basis supports matrix-based full ranking. The final score augments structural and sequential preferences with a candidate-specific process residual controlled by a bounded query-adaptive gate. Extensive experiments on three real-world datasets show that GPrN outperforms nine state-of-the-art baselines on sequential recommendation tasks. The code and datasets are available at https://anonymous.4open.science/r/GPrN.