Graph convolutional collaborative filtering recommendation method based on temporal information during node aggregation process

Abstract

Graph Convolutional Networks (GCN) are prevalent techniques in collaborative filtering recommendations. However, current GCN-based approaches for collaborative filtering recommendation have limitations in effectively embedding neighboring nodes during node and neighbor information aggregation. Furthermore, weight allocation for the user (or item) representations after convolution of each layer is too uniform. To resolve these limitations, we propose a new Graph Convolutional Collaborative Filtering recommendation method based on temporal information during the node aggregation process (TA-GCCF). The method aggregates and propagates information using Gated Recurrent Units, while dynamically updating features based on the timing and sequence of interactions between nodes and their neighbors. Concurrently, we have developed a convolution attention coefficient to ascertain the significance of embedding at distinct layers. Experiments on three benchmark datasets show that our method significantly outperforms the comparison methods in the accuracy of prediction.