<p class="jove_content">This paper proposes a novel model, gated-slot linear attention graph neural networks for session-based recommendation, which adopts gated-slot linear attention based on a dual-constructed session graph. It boosts recommendation accuracy and efficiency and outperforms SOTA on three public datasets.</p>

gated-slot-linear-attention-graph-neural-networks-for-session-based

Gated-Slot Linear Attention Graph Neural Networks for Session-based Recommendation

Session-based Recommendation (SBR) systems aim to predict the next item which a user is likely to interact with, based on behavior sequence within the ...

Institution - Jiangxi Science and Technology Normal University

Articles Published in JoVE