Learning over Knowledge-Base Embeddings for Recommendation

Abstract

State-of-the-art recommendation algorithms -- especially the 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 neglected recently due to the availability of vast amount of data, and the learning power of many complex models. However, structured knowledge bases 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. A great challenge for using knowledge bases for recommendation is how to integrated large-scale structured and unstructured data, while taking advantage of collaborative filtering for highly accurate performance. Recent achievements on knowledge base embedding 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. In this work, we propose to reason over knowledge base embeddings for personalized recommendation. Specifically, we propose a knowledge base representation learning approach to embed heterogeneous entities for recommendation. Experimental results on real-world dataset verified the superior performance of our approach compared with state-of-the-art baselines.

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Cited by in corpus (57)

• KGAT: Knowledge Graph Attention Network for Recommendation
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• Reinforced Negative Sampling over Knowledge Graph for Recommendation
• MetaKG: Meta-learning on Knowledge Graph for Cold-start Recommendation
• CAFE: Coarse-to-Fine Neural Symbolic Reasoning for Explainable Recommendation
• Neural Collaborative Reasoning
• PRINCE: Provider-side Interpretability with Counterfactual Explanations in Recommender Systems
• Explainable Fairness in Recommendation
• Explaining Latent Factor Models for Recommendation with Influence Functions
• Post Processing Recommender Systems with Knowledge Graphs for Recency, Popularity, and Diversity of Explanations
• Attention Is Not the Only Choice: Counterfactual Reasoning for Path-Based Explainable Recommendation
• VRKG4Rec: Virtual Relational Knowledge Graphs for Recommendation
• RecBole: Towards a Unified, Comprehensive and Efficient Framework for Recommendation Algorithms
• Generate Natural Language Explanations for Recommendation
• Comprehensible Artificial Intelligence on Knowledge Graphs: A survey
• Knowledge-refined Denoising Network for Robust Recommendation
• Efficient Non-Sampling Knowledge Graph Embedding
• Finding Paths for Explainable MOOC Recommendation: A Learner Perspective
• Causal Collaborative Filtering
• Recent Advances in Heterogeneous Relation Learning for Recommendation
• A Survey on Knowledge Graph-Based Recommender Systems
• Explanation as a Defense of Recommendation
• Counterfactual Collaborative Reasoning
• Automatic Meta-Path Discovery for Effective Graph-Based Recommendation
• Relational Collaborative Filtering:Modeling Multiple Item Relations for Recommendation
• Comparative Explanations of Recommendations
• Fairness-Aware Explainable Recommendation over Knowledge Graphs
• Recommending on graphs: a comprehensive review from a data perspective
• Explainable Recommendation with Personalized Review Retrieval and Aspect Learning
• AliCoCo: Alibaba E-commerce Cognitive Concept Net
• Unify Local and Global Information for Top-$N$ Recommendation
• From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems
• Neural-Symbolic Reasoning over Knowledge Graph for Multi-stage Explainable Recommendation
• Learning Post-Hoc Causal Explanations for Recommendation
• Learning to Deceive Knowledge Graph Augmented Models via Targeted Perturbation
• Fast Matrix Factorization with Non-Uniform Weights on Missing Data
• Personalized Transformer for Explainable Recommendation
• Dual Preference Distribution Learning for Item Recommendation
• KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?
• Metapath- and Entity-aware Graph Neural Network for Recommendation
• Improving Location Recommendation with Urban Knowledge Graph
• Understanding the Effectiveness of Reviews in E-commerce Top-N Recommendation
• Explainable Product Search with a Dynamic Relation Embedding Model
• Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph
• Brain-inspired Search Engine Assistant based on Knowledge Graph
• Faithfully Explainable Recommendation via Neural Logic Reasoning
• On Explainability in AI-Solutions: A Cross-Domain Survey
• Explainable Recommendation with Simulated Human Feedback
• Augmenting the User-Item Graph with Textual Similarity Models
• Conceptualize and Infer User Needs in E-commerce
• UGRec: Modeling Directed and Undirected Relations for Recommendation
• Beyond Next Item Recommendation: Recommending and Evaluating List of Sequences
• Mining Implicit Entity Preference from User-Item Interaction Data for Knowledge Graph Completion via Adversarial Learning
• Thematic recommendations on knowledge graphs using multilayer networks
• Utilizing FastText for Venue Recommendation