TGP: Explainable Temporal Graph Neural Networks for Personalized Recommendation

Fan Zhang

Keywords: deep learning, graph neural networks, temporal graph, retrieval models, recommendation system

Abstract: The majority of item retrieval algorithms in typical "retrieval-rank-rerank" structured recommendation systems can be separated into three categories: deep latent, sequential and graph-based recommenders, which collect collaborative-filtering, sequential and homogeneous signals respectively. However, there is a conceptual overlap between sequential and graph recommenders on a user's past interacted items. It triggers an idea that the sequential, collaborative-filtering and homegeneous signals can be included in one temporal graph formatted data structure, and the sequential, latent and graph learning algorithms can be summarized as one temporal graph encoder. In this paper, Temporal Graph Plugin is proposed as a such explainable temporal graph encoder to supplement deep latent algorithms with aggregated $k$-hop temporal neighborhood message via a local attention module. We conduct extensive experiments on two public datasets Reddit and Wikipedia, where TGP exceeds SOTA sequential, latent, graph algorithms by $1.1\%$, $52.8\%$ and $98.9\%$ respectively, partially verifying the proposed hypothesis. Codes will be made public upon receival.

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