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

TL;DR

RippleNet is an end-to-end recommendation framework that propagates user preferences through a knowledge graph, effectively addressing data sparsity and cold start issues by modeling interest diffusion along knowledge links.

Contribution

It introduces Ripple Network, a novel method that automatically propagates user preferences over knowledge graphs, outperforming existing embedding and path-based approaches.

Findings

Significant improvement over state-of-the-art baselines

Effective in movie, book, and news recommendation scenarios

Addresses cold start and data sparsity problems

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.