Neural graph personalized ranking for Top-N Recommendation

Abstract

Personalized recommendation has been widely applied to many real-world services. Many of recent studies focus on collaborative filtering (CF) by deep neural networks, which pursue to predict users’ preference on items based on the past user–item interactions (e.g., a user rates an item). A general CF approach consists of two key modules, embedding representation learning and interaction modeling. In most existing methods, the embedding module is followed by the interaction modeling module, and the user–item interaction information is only emploited in interaction modeling directly. Existing methods, however, defectively overlook the correlation between users and items, as well as the inherent connection between embedding learning and the interaction information. To fill this gap, we propose neural graph personalized ranking (NGPR) which directly makes use of the user–item interaction information in embedding learning by incorporating the user–item interaction graph in embedding learning. Specifically, we construct the user–item interaction graph using de facto interaction between a user and an item. Correlation between users and items can also be reserved by concatenating representations of users and items in the entire procedure of embedding learning. Moreover, more complicated structures like multilayer perceptron (MLP) can be used in interaction modeling to make the most use of the representations, rather than simple linear transformation. We conduct extensive experiments on three public benchmarks and demonstrate the superior performance of the proposed NGPR model on personalized ranking task. In addition, our ablation studies verify that our novel design to incorporate the user–item interaction graph in embedding learning is effective.