Explaining Synergistic Effects in Social Recommendations

2026-01-26

AI Summary

This work addresses the challenge of interpreting the nonlinear and black-box nature of synergistic effects arising from multi-source social networks in social recommendation systems. To this end, the authors propose SemExplainer, a novel framework that extends information gain theory to graph-structured data. By quantifying graph information gain, SemExplainer identifies subgraphs that embody such synergistic effects and incorporates a conditional entropy–based optimization mechanism to select critical subgraphs from multi-view social networks. These subgraphs are then leveraged to uncover user-to-item paths for generating interpretable explanations. Extensive experiments on three real-world datasets demonstrate that SemExplainer significantly outperforms baseline methods in both accurately identifying and effectively explaining synergistic interactions, thereby enhancing the transparency and trustworthiness of recommendation systems.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Machine Learning: Graph-based Machine Learning

Application Category

• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

Abstract

In social recommenders, the inherent nonlinearity and opacity of synergistic effects across multiple social networks hinders users from understanding how diverse information is leveraged for recommendations, consequently diminishing explainability. However, existing explainers can only identify the topological information in social networks that significantly influences recommendations, failing to further explain the synergistic effects among this information. Inspired by existing findings that synergistic effects enhance mutual information between inputs and predictions to generate information gain, we extend this discovery to graph data. We quantify graph information gain to identify subgraphs embodying synergistic effects. Based on the theoretical insights, we propose SemExplainer, which explains synergistic effects by identifying subgraphs that embody them. SemExplainer first extracts explanatory subgraphs from multi-view social networks to generate preliminary importance explanations for recommendations. A conditional entropy optimization strategy to maximize information gain is developed, thereby further identifying subgraphs that embody synergistic effects from explanatory subgraphs. Finally, SemExplainer searches for paths from users to recommended items within the synergistic subgraphs to generate explanations for the recommendations. Extensive experiments on three datasets demonstrate the superiority of SemExplainer over baseline methods, providing superior explanations of synergistic effects.

Problem

• synergistic effects
• social recommendations
• explainability
• graph data
• information gain

Innovation

• synergistic effects
• graph information gain
• explainable recommendation
• conditional entropy optimization
• multi-view social networks