Knowledge Graph-based Recommendation Systems: The State-of-the-art and Some Future Directions

International Journal of Machine Learning and Networked Collaborative Engineering

View Publication Info
 
 
Field Value
 
Title Knowledge Graph-based Recommendation Systems: The State-of-the-art and Some Future Directions
 
Creator P. S., Sajisha
V.S., Anoop
K. A., Ansal
 
Subject Knowledge Graphs
Recommendation Systems
Knowledge Representation
Semantic Computing
Machine Learning
 
Description The unprecedented growth of unstructured data poses many challenges in semantic computing, which is an active research area for many years. While unearthing interesting patterns such as entities, relationships, and other metadata are important, it is equally important to represent them in an efficient, easy to access manner. Knowledge Graphs (KGs) are one such mechanism to represent facts extracted from unstructured text. KGs represent entities as nodes and relationships as edges. Such a representation may find applications in many meaning-aware computing applications such as question answering, summarization, etc., to name a few. Very recently, knowledge graph-based recommendation systems have become popular which has many advantages over traditional recommendation engines. This survey is an attempt to summarize and critically evaluate some of the very recent approaches to knowledge graph-based recommendation approaches.
 
Publisher SR Informatics, New Delhi, India
 
Date 2019-11-10
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://www.mlnce.net/index.php/Home/article/view/97
 
Source International Journal of Machine Learning and Networked Collaborative Engineering; Vol. 3 No. 03 (2019): Volume No 03, Issue No 03; 159-167
2581-3242
 
Language eng
 
Relation http://www.mlnce.net/index.php/Home/article/view/97/65
 
Rights Copyright (c) 2020 International Journal of Machine Learning and Networked Collaborative Engineering
 

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