Recurrent neural graph collaborative filtering

Abstract

Collaborative Filtering (CF) is a prevalent technique in recommender systems. Substantial research focuses on learning the embedding of users and items via exploiting past user-item interactions. Recent years have witnessed the boom of Graph Convolutional Networks (GCNs) on CF. Performing graph convolution iteratively, GCN-based models concatenate/average/sum all outputs from different graph convolution layers to generate the embeddings of users and items. Although the previous methods have been proven effective, the pooling operations in the previous methods fail to consider the outputs from different graph convolution layers have different weights and the weights are related to sequential dependencies from precursor nodes. To resolve the aforementioned problems, in this work, we present a new model, Recurrent Neural Graph Collaborative Filtering (RNGCF), which proposes a sequential dependency construction module to adaptively generate the embeddings. Specifically, the module applies a gated recurrent unit (GRU) to learn the sequential dependencies from precursor nodes and an adaptive gated unit (AGU) to adaptively construct the embeddings based on the sequential dependencies. Extensive experiments on three benchmark datasets show that our model outperforms state-of-the-art models consistently. Our implementation is available in PyTorch.