RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems (1803.03467v4)

Abstract: To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance. This paper considers the knowledge graph as the source of side information. To address the limitations of existing embedding-based and path-based methods for knowledge-graph-aware recommendation, we propose Ripple Network, an end-to-end framework that naturally incorporates the knowledge graph into recommender systems. Similar to actual ripples propagating on the surface of water, Ripple Network stimulates the propagation of user preferences over the set of knowledge entities by automatically and iteratively extending a user's potential interests along links in the knowledge graph. The multiple "ripples" activated by a user's historically clicked items are thus superposed to form the preference distribution of the user with respect to a candidate item, which could be used for predicting the final clicking probability. Through extensive experiments on real-world datasets, we demonstrate that Ripple Network achieves substantial gains in a variety of scenarios, including movie, book and news recommendation, over several state-of-the-art baselines.

• The paper introduces RippleNet, a unified framework that combines embedding-based and path-based methods to integrate knowledge graphs into recommender systems.
• It presents a novel preference propagation mechanism that iteratively extends user interests along hierarchical KG links to capture deeper semantic relationships.
• Empirical evaluations on movies, books, and news datasets demonstrate significant gains in AUC, precision, and recall, effectively addressing cold start and data sparsity issues.

RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems

The paper "RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems" presents RippleNet, an innovative framework that integrates knowledge graphs (KGs) into recommender systems to overcome limitations associated with collaborative filtering (CF), such as data sparsity and cold start problems. RippleNet combines the strengths of embedding-based and path-based KG-aware recommendation methods, providing a unified solution that addresses key deficiencies in these approaches.

Key Contributions

The paper outlines the following significant contributions:

1. Unified Framework: RippleNet is the first approach to combine embedding-based and path-based methods for KG-aware recommendation. It incorporates the KG end-to-end into the recommendation process.
2. Preference Propagation: RippleNet introduces the concept of preference propagation. This mechanism automatically extends users' interests in a hierarchical manner along the KG links, which helps in capturing deeper semantic relationships.
3. Empirical Validation: Extensive experiments across multiple real-world scenarios (movies, books, and news) validate the efficacy of RippleNet. The framework demonstrated substantial gains over several state-of-the-art baselines.

Problem Description

The authors address a critical issue in CF methods, which generally suffer from insufficient user-item interactions, especially when facing new users or items. To mitigate this, previous works have incorporated side information such as social networks and item attributes. RippleNet takes the novel approach of using KGs as a rich source of factual and relational data about items.

Core Mechanism

RippleNet’s central mechanism, preference propagation, is inspired by the physical phenomenon of ripples on water caused by raindrops. Analogously, RippleNet propagates user preferences over a KG, iteratively extending a user’s potential interests by stimulating KG links. The core steps include:

• Ripple Set Construction: Creating ripple sets for each user by recursively extending their interacted items along the KG links up to a certain number of hops.
• Preference Propagation: Using attention mechanisms to propagate preferences from the user's historical interactions to candidate items. This step involves relevance probability calculation and weighted sum aggregation of entity embeddings.

Empirical Evaluation

The authors validate RippleNet across three datasets—MovieLens-1M, Book-Crossing, and Bing-News—demonstrating its efficacy in various recommendation scenarios. RippleNet outperformed notable baselines such as CKE, SHINE, DKN, PER, LibFM, and Wide${content}$Deep in terms of both AUC and accuracy for click-through rate (CTR) prediction, and Precision@K, Recall@K, and F1@K for top-K recommendations. Notably, RippleNet showed substantial gains, with AUC improvements ranging from 2.0% to 40.6% in movie recommendations, 2.5% to 17.4% in book recommendations, and 2.6% to 22.4% in news recommendations.

Practical and Theoretical Implications

Practical Implications:

• Enhanced Recommendation Accuracy: RippleNet can substantially improve the accuracy of recommendations, which is crucial for user satisfaction and engagement.
• Explainability: The framework allows for better explainability by tracking preference paths in the KG, thus enhancing user trust and acceptance of recommendations.

Theoretical Implications:

• Model Hybridization: RippleNet sets a precedent for successfully combining multiple recommendation techniques, offering avenues for further development of hybrid models.
• Attention Mechanism: The multi-level attention mechanism in RippleNet could inspire more granular and sophisticated models in various domains.

Considerations for Future Work

The paper opens several avenues for future research:

• Enhanced Entity-Relation Interaction: Exploring richer representations and interaction methods between entities and relations could further enhance model performance.
• Optimized Ripple Set Sampling: Developing non-uniform samplers during preference propagation might better capture user interests and optimize computational resources.

Conclusion

RippleNet introduces a robust framework that leverages KGs to enhance recommender systems. By combining embedding-based and path-based methods and introducing preference propagation, RippleNet effectively addresses key CF limitations. Rigorous empirical validation underscores its superiority over contemporary methods, making it a substantial contribution to the field. Future research can potentially build on this work to refine recommender systems further, ultimately enhancing user experience with more accurate, explainable, and efficient recommendations.