Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation (1805.03352v2)

Abstract: Providing model-generated explanations in recommender systems is important to user experience. State-of-the-art recommendation algorithms - especially collaborative filtering (CF)-based approaches with shallow or deep models - usually work with various unstructured information sources for recommendation, such as textual reviews, visual images, and various implicit or explicit feedbacks. Though structured knowledge bases were considered in content-based approaches, they have been largely ignored recently due to the research focus on CF approaches. However, structured knowledge exhibit unique advantages in personalized recommendation systems. When the explicit knowledge about users and items is considered for recommendation, the system could provide highly customized recommendations based on users' historical behaviors and the knowledge is helpful for providing informed explanations regarding the recommended items. A great challenge for using knowledge bases for recommendation is how to integrate large-scale structured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements in knowledge-base embedding (KBE) sheds light on this problem, which makes it possible to learn user and item representations while preserving the structure of their relationship with external knowledge for explanation. In this work, we propose to explain knowledge-base embeddings for explainable recommendation. Specifically, we propose a knowledge-base representation learning framework to embed heterogeneous entities for recommendation, and based on the embedded knowledge base, a soft matching algorithm is proposed to generate personalized explanations for the recommended items. Experimental results on real-world e-commerce datasets verified the superior recommendation performance and the explainability power of our approach compared with state-of-the-art baselines.

Explainable Recommendation through Heterogeneous Knowledge Base Embeddings

The paper, "Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendation," addresses the challenges and potential of integrating structured knowledge bases with collaborative filtering (CF) for enhanced recommender systems. Traditional CF-based recommendation algorithms heavily rely on unstructured data, such as user reviews and implicit feedback, which can lack transparency and explainability. However, structured knowledge bases provide explicit relationships between users and items that can be leveraged to create more personalized and explainable recommendations. This paper proposes an innovative approach to utilize knowledge-base embeddings (KBEs) as a means to overcome the challenges associated with integrating structured and unstructured data in recommender systems.

Key Contributions

1. User-Item Knowledge Graph: The paper introduces a novel user-item knowledge graph that consolidates various data types into a singular, structured framework. This graph encodes user behaviors and item properties through relational graph structures, capturing comprehensive user preferences by embedding heterogeneous entities.
2. Knowledge Base Embedding Model: The authors propose an embedding-based framework that projects multi-type user behaviors and item properties into a unified low-dimensional space, preserving inherent relations. This framework extends CF methodologies to operate efficiently over structured knowledge graphs, a capability not fully realized in previous CF-centric approaches.
3. Soft Matching for Explainability: The integration of a soft matching algorithm allows for constructing explanations by exploring paths within the knowledge graph embedding space. This facilitates the generation of personalized and intelligible explanations for recommendations, a critical component for improving user trust and transparency in recommender systems.

Empirical Evaluation

The proposed model was empirically validated using real-world e-commerce datasets, demonstrating substantial improvements over several state-of-the-art baselines, including both traditional matrix factorization techniques and advanced neural models. For instance, noticeable improvements in NDCG (3.44%) and Precision (5.13%) were achieved on the CDs and Vinyl dataset when compared to the JRL baseline. These findings highlight the efficacy of incorporating structured product knowledge into CF frameworks, significantly enhancing recommendation accuracy and explainability.

Implications and Future Directions

The integration of KBEs into recommendation systems holds significant theoretical and practical implications. Theoretically, it bridges the gap between collaborative filtering and content-based recommendation strategies, offering a holistic approach to user preference modeling. Practically, this fusion enables the development of more user-friendly and accountable recommender systems.

Future research could explore several avenues:

• Complex Relationship Modeling: Further studies could investigate more sophisticated methods to capture and exploit complex relational structures between diverse entity types within the knowledge graph.
• Scalability and Efficiency: Addressing scalability issues and improving computational efficiency of the embedding process, especially in large-scale recommender systems, would be a critical area of subsequent inquiry.
• Real-world Deployments: Implementing and testing these explainable recommendation frameworks in different domains outside e-commerce, such as media streaming or healthcare, could offer valuable insights into the generalizability of the approach.

Overall, this paper advances the field by proposing a robust framework capable of leveraging structured knowledge bases to enhance the performance and transparency of recommendation systems. The paper underscores the potential of merging structured and unstructured data, paving the way for more explainable AI-driven applications in personalized user experiences.