Reinforcement Knowledge Graph Reasoning for Explainable Recommendation (1906.05237v1)

Abstract: Recent advances in personalized recommendation have sparked great interest in the exploitation of rich structured information provided by knowledge graphs. Unlike most existing approaches that only focus on leveraging knowledge graphs for more accurate recommendation, we perform explicit reasoning with knowledge for decision making so that the recommendations are generated and supported by an interpretable causal inference procedure. To this end, we propose a method called Policy-Guided Path Reasoning (PGPR), which couples recommendation and interpretability by providing actual paths in a knowledge graph. Our contributions include four aspects. We first highlight the significance of incorporating knowledge graphs into recommendation to formally define and interpret the reasoning process. Second, we propose a reinforcement learning (RL) approach featuring an innovative soft reward strategy, user-conditional action pruning and a multi-hop scoring function. Third, we design a policy-guided graph search algorithm to efficiently and effectively sample reasoning paths for recommendation. Finally, we extensively evaluate our method on several large-scale real-world benchmark datasets, obtaining favorable results compared with state-of-the-art methods.

Reinforcement Knowledge Graph Reasoning for Explainable Recommendation

The paper focuses on enhancing the capability of recommender systems by integrating knowledge graphs (KGs) with reinforcement learning techniques to produce explainable recommendations. This research introduces a novel method named Policy-Guided Path Reasoning (PGPR), which addresses the need for renewed approaches that not only improve recommendation accuracy but also ensure that decisions made by the system are interpretable through clearer causal paths in knowledge graphs.

Key Contributions

1. Contextual Knowledge Incorporation: The authors emphasize the essentiality of leveraging rich structured information within KGs. They argue that this promotes enhanced recommendation performance and system interpretability by defining clear entity relationships, offering a formal reasoning process.
2. Reinforcement Learning Framework: PGPR employs a reinforcement learning (RL) paradigm to navigate KGs. Distinctly, it uses an innovative soft reward mechanism, which deals with uncertainties of user interactions with items. Actions are pruned conditionally based on the user, which reduces the computational burden yet maintains effectiveness.
3. Multi-hop Path Reasoning: The solution proposed utilizes a multi-hop scoring function within its RL approach. This function evaluates actions based on multiple levels of information embedded within KGs, encouraging the path navigation to be efficient and relevant to the user, subsequently improving recommendation quality.
4. Policy-Guided Exploration: An efficient beam search is implemented to sample diverse reasoning paths. This guarantees that the system identifies a variety of interpretable paths, preventing overfitting on a singular reasoning pattern while recommending items. This diversity also reinforces the robustness of the system by providing multiple explanations for recommendations.

Results and Implications

The evaluations conducted demonstrate that PGPR achieves significant improvements over state-of-the-art baselines across multiple large-scale datasets collected from Amazon. By exceeding other methods in terms of NDCG, Recall, and other metrics, PGPR validates the effectiveness of targeted multi-hop reasoning within KGs.

Crucially, PGPR's ability to generate interpretable paths opens opportunities for recommender systems to be deployed in environments where transparency is vital, such as in legal, medical, and financial domains. The reliance on explicit reasoning paths ensures that each recommendation is backed by causal evidence, potentially increasing user trust and satisfaction.

Future Directions

While the PGPR method has set a foundation for explainable KG-based recommendation, future research can explore broader implications and extensions of this work. Potential directions include adapting the method to time-evolving knowledge graphs that capture temporal dynamics, which would enhance its applicability in rapidly changing data contexts such as real-time personalization and market trend analysis. Additionally, incorporating further domains of heterogeneous data may advance the system's adaptability and robustness, enabling it to accommodate more complex user-item interaction patterns found in diverse application scenarios.

The paper not only presents a methodological advancement in leveraging KGs for explainable recommendations but also stimulates further inquiry into making AI-driven systems more interpretable and trustworthy through explicit reasoning mechanisms.