Linear-Time Graph Neural Networks for Scalable Recommendations

arXiv:2402.13973v1 Announce Type: cross
Abstract: In an era of information explosion, recommender systems are vital tools to deliver personalized recommendations for users. The key of recommender systems is to forecast users' future behaviors based on previous user-item interactions. Due to their strong expressive power of capturing high-order connectivities in user-item interaction data, recent years have witnessed a rising interest in leveraging Graph Neural Networks (GNNs) to boost the prediction performance of recommender systems. Nonetheless, classic Matrix Factorization (MF) and Deep …

abstract arxiv cs.ir cs.lg data forecast future graph graph neural networks information interactions key linear networks neural networks personalized personalized recommendations power recommendations recommender systems scalable systems the key tools type vital