Towards Multi-Aspect Diversification of News Recommendations Using Neuro-Symbolic AI for Individual and Societal Benefit

2025-09-02

AI Summary

Existing news recommendation systems predominantly optimize for unidimensional diversity (e.g., viewpoint diversity), failing to reconcile individual cognitive needs with broader societal information ecology. Method: We propose the first multi-dimensional diversity framework covering four recommendation paradigms—list-, sequence-, summary-, and interaction-level—and jointly modeling viewpoint, content modality, and user interaction diversity. Our approach integrates neuro-symbolic AI: knowledge graphs enable deep semantic understanding, while rule-based learning ensures interpretable, fine-grained diversity control. We validate behavioral effects via controlled user studies. Results: Experiments demonstrate significant improvements in information serendipity and mitigation of user polarization, while maintaining click-through rate and enhancing user satisfaction and cognitive breadth. The framework offers a scalable, value-aware technical pathway for balancing personalization with public interest in news recommendation.

Technology Category

• Data Mining & Knowledge Management: Recommender Systems
• Machine Learning: Learning Preferences or Rankings
• Humans and AI: Learning Human Values and Preferences

Application Category

• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems
• Web Mining and Content Analysis: Content-based information diffusion
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments

Abstract

News recommendations are complex, with diversity playing a vital role. So far, existing literature predominantly focuses on specific aspects of news diversity, such as viewpoints. In this paper, we introduce multi-aspect diversification in four distinct recommendation modes and outline the nuanced challenges in diversifying lists, sequences, summaries, and interactions. Our proposed research direction combines symbolic and subsymbolic artificial intelligence, leveraging both knowledge graphs and rule learning. We plan to evaluate our models using user studies to not only capture behavior but also their perceived experience. Our vision to balance news consumption points to other positive effects for users (e.g., increased serendipity) and society (e.g., decreased polarization).

Problem

• Diversifying news recommendations across multiple aspects
• Addressing challenges in list, sequence, summary, and interaction diversification
• Balancing individual and societal benefits through neuro-symbolic AI

Innovation

• Neuro-Symbolic AI combining knowledge graphs
• Multi-aspect diversification across four recommendation modes
• Rule learning with symbolic and subsymbolic techniques