Geometric deep learning based recommender system and an interpretable decision support system

Abstract

Recommender systems (RS) are ubiquitous in digital space. This paper develops a deep learning-based approach to address three practical challenges in RS: complex structures of high-dimensional data, noise in relational information, and the black-box nature of machine learning algorithms. Our method—Multi-GraphGraph Attention Network (MG-GAT)—learns latent user and business representations by aggregating a diverse set of information from neighbors of each user (business) on a neighbor importance graph. MG-GAT out-performs state-of-the-art deep learning models in the recommendation task using two large-scale datasets collected from Yelp and four other standard datasets in RS. The improved performance highlights MG-GAT’s advantage in incorporating multi-modal features in a principled manner. The features importance, neighbor importance graph, and latent representations reveal business insights on predictive features and explainable characteristics of business and users. Moreover, the learned neighbor importance graph can be used in a variety of management applications, such as targeting customers, promoting new businesses, and designing information acquisition strategies. Our paper presents a quintessential big data application of deep learning models in management while providing interpretability essential for real-world decision-making.

Keywords: Recommender systems, Heterogeneous information, Matrix completion, Geometric deep learning, Representation learning, Interpretable machine learning