Learning over Knowledge-Base Embeddings for Recommendation

TL;DR

This paper introduces a method that leverages knowledge base embeddings to enhance personalized recommendation systems by integrating structured and unstructured data, leading to improved performance.

Contribution

It proposes a novel approach to embed heterogeneous entities from knowledge bases for recommendation, effectively combining structured knowledge with collaborative filtering.

Findings

Outperforms state-of-the-art baselines on real-world datasets.

Effectively integrates structured knowledge with unstructured data.

Demonstrates superior recommendation accuracy.

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.