SPEX: A Generic Framework for Enhancing Neural Social Recommendation

Abstract

Social Recommender Systems (SRS) have attracted considerable attention since its accompanying service, social networks, helps increase user satisfaction and provides auxiliary information to improve recommendations. However, most existing SRS focus on social influence and ignore another essential social phenomenon, i.e., social homophily. Social homophily, which is the premise of social influence, indicates that people tend to build social relations with similar people and form influence propagation paths. In this article, we propose a generic framework Social PathExplorer (SPEX) to enhance neural SRS. SPEX treats the neural recommendation model as a black box and improves the quality of recommendations by modeling the social recommendation task, the formation of social homophily, and their mutual effect in the manner of multi-task learning. We design a Graph Neural Network based component for influence propagation path prediction to help SPEX capture the rich information conveyed by the formation of social homophily. We further propose an uncertainty based task balancing method to set appropriate task weights for the recommendation task and the path prediction task during the joint optimization. Extensive experiments have validated that SPEX can be easily plugged into various state-of-the-art neural recommendation models and help improve their performance. The source code of our work is available at: https://github.com/XMUDM/SPEX.

Graph embedding and geometric deep learning relevance to network biology and structural chemistry

“…This is accomplished by identifying the eigenvectors of the graph's Laplacian matrix. Differentiable Pooling: This technique enables for the handling of variable-size graph-structured data by dynamically adjusting the number of nodes in a graph during the training phase (references in Ying et al, 2018 ; Li et al, 2021a ). Spectral Graph Convolution is a method for handling graph-structured data that are based on the eigenvectors of the graph Laplacian.…”