RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems (2018-03-09T00:00:00.000000Z)

TL;DR

This paper proposes RippleNet, an end-to-end framework that naturally incorporates the knowledge graph into recommender systems, and demonstrates that RippleNet achieves substantial gains in a variety of scenarios, including movie, book and news recommendation, over several state-of-the-art baselines.

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 RippleNet, an end-to-end framework that naturally incorporates the knowledge graph into recommender systems. Similar to actual ripples propagating on the water, RippleNet 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 RippleNet achieves substantial gains in a variety of scenarios, including movie, book and news recommendation, over several state-of-the-art baselines.

Authors

Fuzheng Zhang

4 papers

Xing Xie

6 papers

Wenjie Li

4 papers

TL;DR

Abstract

Authors

References48 items

Sequential Recommendation with User Memory Networks

DKN: Deep Knowledge-Aware Network for News Recommendation

SHINE: Signed Heterogeneous Information Network Embedding for Sentiment Link Prediction

Knowledge Graph Embedding: A Survey of Approaches and Applications

GraphGAN: Graph Representation Learning with Generative Adversarial Nets

Joint Topic-Semantic-aware Social Recommendation for Online Voting

Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction

End-to-End Adversarial Memory Network for Cross-domain Sentiment Classification

Collaborative Intent Prediction with Real-Time Contextual Data

Dynamic Attention Deep Model for Article Recommendation by Learning Human Editors' Demonstration

Aspect Based Recommendations: Recommending Items with the Most Valuable Aspects Based on User Reviews

Attentive Collaborative Filtering: Multimedia Recommendation with Item- and Component-Level Attention

Meta-Graph Based Recommendation Fusion over Heterogeneous Information Networks

Mention Recommendation for Twitter with End-to-end Memory Network

Combining Knowledge with Deep Convolutional Neural Networks for Short Text Classification

Deep Interest Network for Click-Through Rate Prediction

Analogical Inference for Multi-relational Embeddings

Collaborative Knowledge Base Embedding for Recommender Systems

Representation Learning of Knowledge Graphs with Hierarchical Types

Wide & Deep Learning for Recommender Systems

Complex Embeddings for Simple Link Prediction

Key-Value Memory Networks for Directly Reading Documents

Holographic Embeddings of Knowledge Graphs

Aligning Knowledge and Text Embeddings by Entity Descriptions

Knowledge Graph Embedding via Dynamic Mapping Matrix

Question Answering over Freebase with Multi-Column Convolutional Neural Networks

End-To-End Memory Networks

Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

Learning Entity and Relation Embeddings for Knowledge Graph Completion

Embedding Entities and Relations for Learning and Inference in Knowledge Bases

RC-NET: A General Framework for Incorporating Knowledge into Word Representations

Memory Networks

Neural Machine Translation by Jointly Learning to Align and Translate

Explicit factor models for explainable recommendation based on phrase-level sentiment analysis

Recurrent Models of Visual Attention

Knowledge Graph Embedding by Translating on Hyperplanes

Personalized entity recommendation: a heterogeneous information network approach

Translating Embeddings for Modeling Multi-relational Data

Distributed Representations of Words and Phrases and their Compositionality

Do social explanations work?: studying and modeling the effects of social explanations in recommender systems

Factorization Machines with libFM

A Three-Way Model for Collective Learning on Multi-Relational Data

A matrix factorization technique with trust propagation for recommendation in social networks

Matrix Factorization Techniques for Recommender Systems

Tagsplanations: explaining recommendations using tags

Factorization meets the neighborhood: a multifaceted collaborative filtering model

A Survey of Explanations in Recommender Systems

Injecting Logical Background Knowledge into Embeddings for Relation Extraction

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