RippleNet Propagating User Preferences on the Knowledge Graph for Recommender Systems

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introduction

CF-->sparsity, the cold start-->side information-->knowledge graph

KG's strength

  • KG introduces semantic relatedness among items, which can help find their latent connections and improve the precision of recommended items;
  • KG consists of relations with various types, which is helpful for extending a user’s interests reasonably and increasing the diversity of recommended items;
  • KG connects a user’s historical records and the recommended ones, thereby bringing explainability to recommender systems.

KG's categories:

  1. Embedding-based methods: DKN, CKE, SHINE------Embedding-based methods show high flexibility in utilizing KG to assist recommender systems, but the adopted KGE algorithms in these methods are usually more suitable for in-graph applications such as link prediction than for recommendation, thus the learned entity embeddings are less intuitive and effective to characterize inter-item relations.
  2. Path-based methods: explore the various patterns of connections among items in KG to provide additional guidance for recommendations. PER, Meta-Graph Based Recommendation.------Path-based methods make use of KG in a more natural and intuitive way, but they rely heavily on manually designed meta-paths, which is hard to optimize in practice. Another concern is that it is impossible to design hand-crafted meta-paths in certain scenarios (e.g., news recommendation) where entities and relations are not within one domain.

RippleNet(CTR prediction)

The major difference between RippleNet and existing literature is that RippleNet combines the advantages of the above mentioned two types of methods: (1) RippleNet incorporates the KGE methods into recommendation naturally by preference propagation; (2) RippleNet can automatically discover possible paths from an item in a user’s history to a candidate item, without any sort of hand-crafted design.

contribution

  • To the best of our knowledge, this is the first work to combine embedding-based and path-based methods in KG-aware recommendation.
  • We propose RippleNet, an end-to-end framework utilizing KG to assist recommender systems. RippleNet automatically discovers users’ hierarchical potential interests by iteratively propagating users’ preferences in the KG.
  • We conduct experiments on three real-world recommendation scenarios, and the results prove the efficacy