Graph Neural Network-Based Online Collaborative Filtering Using Transductive Node Embeddings

The field of recommendation systems is a hot topic thanks to the increasing number of available digital products and services. In connection with this topic, the research of Graph Neural Network solutions has played a significant role in recent years. Research and development of an online recommendation system that also manages the challenges of a rapidly changing environment are important from a practical point of view as well. Our aim was to develop an approach that possesses scalable inference and adaptation and uses latent features. The main contribution of this paper is the development of a candidate generation process for online collaborative filtering on implicit feedback data that can scale to large user and item bases. We proposed multiple ways how embeddings can be obtained in a fast and scalable way, namely Lookup, Inductive neighbor aggregation, Neighbor aggregation with importance scores, and GraphSAGE-based Graph Neural Network (GraphSAGE+) method with continuous representation update for online learning. By combining these inductive and transductive methods for the embeddings, we developed a novel online Collaborative Filtering approach. We evaluated our approach on two e-commerce datasets and found that it outperformed traditional recommendation algorithms such as Matrix Factorization.