In this paper, we propose a novel framework called Self-Supervised Graph Neural Network (SelfGNN) for sequential recommendation. The SelfGNN framework encodes short-term graphs based on time intervals ...
Abstract: Sequential recommendation (SR) aims to predict users’ next-item preferences by analyzing their historical interaction sequences. Most existing SR models primarily focus on user preferences ...
Abstract: Sequential-social recommendation systems are essential for understanding users’ evolving interests and predicting their future behaviors. While existing methods employing bidirectional graph ...
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