A Survey on Knowledge Graph-Based Recommender Systems

Abstract

To solve the information explosion problem and enhance user experience in various online applications, recommender systems have been developed to model users' preferences. Although numerous efforts have been made toward more personalized recommendations, recommender systems still suffer from several challenges, such as data sparsity and cold-start problems. In recent years, generating recommendations with the knowledge graph as side information has attracted considerable interest. Such an approach can not only alleviate the above mentioned issues for a more accurate recommendation, but also provide explanations for recommended items. In this paper, we conduct a systematical survey of knowledge graph-based recommender systems. We collect recently published papers in this field, and group them into three categories, i.e., embedding-based methods, connection-based methods, and propagation-based methods. Also, we further subdivide each category according to the characteristics of these approaches. Moreover, we investigate the proposed algorithms by focusing on how the papers utilize the knowledge graph for accurate and explainable recommendation. Finally, we propose several potential research directions in this field.

Knowledge Transfer via Pre-training for Recommendation: A Review and Prospect

“…Without sufficient data, the model parameters cannot be well estimated and users’ preference cannot be well modeled. It has been shown that the data sparsity problem in recommender systems can be alleviated by transferring knowledge from other domains or tasks ( Cantador et al, 2015 ) and integrating heterogeneous external knowledge ( Guo et al, 2020 ).…”

“…In the context of recommender systems, we can group works that utilize pre-training mechanisms to improve the precision of recommendation into two categories: feature-based models and fine-tuning models. The feature-based models generally use pre-trained models to obtain features from side-information (e.g., the content of items and knowledge graphs) for users and items ( Guo et al, 2020 ). The fine-tuning models leverage the user-item interaction records to pre-train a deep transferable neural model, which is subsequently fine-tuned to downstream recommendation tasks ( Chen et al, 2019c ).…”

A Systematic Review of Recommender Systems and Their Applications in Cybersecurity

“…Altogether, as a result of the aforementioned bibliographic methods, 393 research items were marked as potentially relevant and further analyzed. Out of them, 86 works were selected for the survey due to their quality and appropriateness to this survey topics, among them 12 other surveys [ 3 , 15 , 16 , 17 , 18 , 23 , 24 , 25 , 26 , 27 , 28 , 29 ]. The papers most relevant to the application of recommender systems for attack mitigation are discussed in the Section 4 .…”

A generative adversarial network for single and multi-hop distributional knowledge base completion

“…An alternative research objective is to learn community level KG representations where each community is represented with a low-dimensional vector [49,50]. These methods are used in applications such as community detection [51] and recommendations [52]. However, an important challenge of these methods is to sample communities for training because they are unknown beforehand [49].…”