Jointly Learning Explainable Rules for Recommendation with Knowledge Graph (1903.03714v1)

Abstract: Explainability and effectiveness are two key aspects for building recommender systems. Prior efforts mostly focus on incorporating side information to achieve better recommendation performance. However, these methods have some weaknesses: (1) prediction of neural network-based embedding methods are hard to explain and debug; (2) symbolic, graph-based approaches (e.g., meta path-based models) require manual efforts and domain knowledge to define patterns and rules, and ignore the item association types (e.g. substitutable and complementary). In this paper, we propose a novel joint learning framework to integrate \textit{induction of explainable rules from knowledge graph} with \textit{construction of a rule-guided neural recommendation model}. The framework encourages two modules to complement each other in generating effective and explainable recommendation: 1) inductive rules, mined from item-centric knowledge graphs, summarize common multi-hop relational patterns for inferring different item associations and provide human-readable explanation for model prediction; 2) recommendation module can be augmented by induced rules and thus have better generalization ability dealing with the cold-start issue. Extensive experiments\footnote{Code and data can be found at: \url{https://github.com/THUIR/RuleRec}} show that our proposed method has achieved significant improvements in item recommendation over baselines on real-world datasets. Our model demonstrates robust performance over "noisy" item knowledge graphs, generated by linking item names to related entities.

• The paper introduces a joint framework that combines rule induction from knowledge graphs with neural recommendation models.
• The rule learning module derives multi-hop relational patterns, offering human-readable explanations for item associations.
• Experimental results demonstrate improved recommendation accuracy and robustness under data sparsity and noisy conditions.

Insights into Jointly Learning Explainable Rules for Recommendation with Knowledge Graph

The paper "Jointly Learning Explainable Rules for Recommendation with Knowledge Graph" presents a framework aimed at enhancing both the explainability and effectiveness of recommender systems through the integration of knowledge graphs. The paper is centered on addressing key limitations observed in traditional recommendation approaches, such as the opaque nature of neural network-based methods and the manual effort required in symbolic graph-based approaches.

Key Contributions

The primary contribution of this work lies in the development of a joint learning framework that bridges the process of rule induction from knowledge graphs with the construction of a rule-infused neural recommendation model. The framework is comprised of two interconnected modules:

1. Rule Learning Module: This module is designed to inductively derive explainable rules from knowledge graphs that encapsulate item relationships. The mined rules summarize multi-hop relational patterns and provide insights into item associations like substitutability and complementarity, offering human-readable explanations for recommendation decisions.
2. Recommendation Module: Augmented by the induced rules, this module aims to enhance the generalization capabilities of recommendation systems, particularly under cold-start conditions. By incorporating these rules, the recommendation process becomes more robust and interpretable, addressing the limitations of conventional methods.

Experimental Evaluation

The framework was subjected to extensive experimentation using real-world datasets, demonstrating significant improvements in recommendation performance when compared to baseline methods. The framework's efficacy is underscored by its consistent performance even with "noisy" item knowledge graphs, thereby validating its robustness in practical scenarios. The experimental results highlight the framework's potential to effectively handle data sparsity and enhance user satisfaction through improved explainability.

Implications and Future Directions

The integration of explainable rules as proposed in this paper holds substantial implications for both the practical and theoretical aspects of AI-driven recommendation systems:

• Practical Implications: By enhancing the transparency of recommendation processes, the framework can potentially boost user trust and engagement. The ability to articulate the rationale behind recommendations can lead to improved user experience and acceptance of the system's suggestions.
• Theoretical Implications: From a theoretical perspective, this research contributes to the ongoing discourse on explainable AI by providing a structured approach to incorporate knowledge graphs into recommendation systems. This not only aids in explainability but also enriches the underlying models with additional semantic context.

Future research could explore the refinement of rule induction techniques to further optimize their accuracy and applicability across diverse domains. Additionally, expanding the scope of knowledge graphs to integrate more comprehensive data sources could yield deeper insights into user preferences and behavior patterns.

In conclusion, the proposed framework represents a significant step toward creating recommendation systems that are both effective and transparent, aligning with broader efforts to develop AI systems that are interpretable and understandable by users and stakeholders alike.