Abstract
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.
Highlights
The astounding rate of generation of unstructured text data demands efficient algorithms and other methodologies to represent, analyze and extract useful patterns
It is found that knowledge graph-based recommendation systems are becoming popular and widely adopted, where recommendation systems are sub-classes of information filtering systems that suggest items to users based on certain features and conditions
The five major relationships such as “FocusOn”, “BelongTo, “USimilar”, “SSimilar” and “FSimilar” are considered for their experiment and it showed that their proposed approach showed better recommendation performance with least computation time[11]. Another recent approach that explored the higher order user preference on Knowledge Graphs (KGs) for recommendation engines [12] was reported in the recommender system literature by Hogwei Wang et al The authors stated that to avoid cold start problems, researchers normally use side information for building recommendation algorithms and in the proposed approach, the authors used a knowledge graph as the side information
Summary
The astounding rate of generation of unstructured text data demands efficient algorithms and other methodologies to represent, analyze and extract useful patterns. It is found that knowledge graph-based recommendation systems are becoming popular and widely adopted, where recommendation systems are sub-classes of information filtering systems that suggest items to users based on certain features and conditions. International Journal of Machine Learning and Networked Collaborative Engineering, ISSN: 2581-3242 Most recommendation systems such as traditional collaborative filtering based approaches use simple user ratings done on items. These predefined information sources pose some constraints and the quality of the recommendation engines in many cases is degraded. It is undoubtable that it is highly necessary to have systematic research in knowledge graph-based recommendation systems and this paper is an attempt in this direction This survey attempts to review some of the very recently reported approaches to knowledge graphbased recommendation algorithms.
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