Gated graph spiking neural P network for session-based recommendation

Session-based recommendation (SBR) is a recommendation system application scenario that focuses on user interests and behaviors in sessions formed within a short period. However, existing SBR methods do not fully extract user contextual information, and gated graph neural networks(GNNs) still face challenges in handling long-term dependencies. The nonlinear spiking neural P system is a novel spiking neural-like computing model in which the nonlinear spiking mechanism is a typical feature. Based on this nonlinear spiking mechanism, we propose a new recurrent-like model called the extended gated spiking neural P model or the EGSNP model, which can effectively capture temporal relationships in their sequence and obtain the user's contextual information to alleviate information extraction problems in SBR. Based on this EGSNP model, we propose a new type of GNN called the extended gated graph spiking neural P network or termed the EGGSNP network. Finally, we use the EGSNP model and EGGSNP network to construct a new SBR model for the session recommendation task, termed the SR-EGGSNP model. This SR-EGGSNP model can effectively extract contextual information and flexibly capture the long-term evolution of interest while alleviating the long-distance dependency problem in GNNs. We conducted extensive comparative experiments on two public datasets, and the results demonstrated the effectiveness of the proposed model and accuracy of the recommendations.