@article {BAI2023119916,
	title = {Sequence recommendation using multi-level self-attention network with gated spiking neural P systems},
	journal = {Information Sciences},
	year = {2023},
	pages = {119916},
	publisher = {Elsevier},
	address = {Amsterdam (The Netherlands)},
	abstract = {Sequence recommendation is used to predict the user{\textquoteright}s next potentially interesting items and behaviors. It not only focuses on the user{\textquoteright}s independent interaction behavior, but also considers the user{\textquoteright}s historical behavior sequence. However, sequence recommendation still faces some challenges: the existing models still have shortcomings in addressing long-term dependencies and fully utilizing contextual information in sequence recommendation. To address these challenges, we propose a four-channel model based on a multi-level self-attention network with gated spiking neural P (GSNP) systems, termed SR-MAG model. The four channels are divided into two groups, and each group is composed of an attention channel and an GSNP attention channel. Moreover, they process long-term sequences and short-term sequences respectively to obtain long-term or short-term attention channel features. These features are then passed through a self-attention network to effectively extract user context information. The proposed SR-MAG model is tested on three real datasets and compared with 10 baseline methods. Experimental results demonstrate the effectiveness of the proposed SR-MAG model in sequence recommendation tasks.},
	keywords = {Gated spiking neural P systems, Nonlinear spiking neural P systems, Self-attention mechanism, Sequence recommendation},
	issn = {0020-0255},
	doi = {https://doi.org/10.1016/j.ins.2023.119916},
	url = {https://www.sciencedirect.com/science/article/pii/S0020025523015013},
	author = {Xinzhu Bai and Yanping Huang and Hong Peng and Jun Wang and Qian Yang and David Orellana-Mart{\'\i}n and Ram{\'\i}rez-de-Arellano, Antonio and Mario J. P{\'e}rez-Jim{\'e}nez}
}
