A Survey on Semantic Recommendation System based on Linked Open Data(LOD)

Abstract

A globally accessible Linked Open Data known as LOD is used to manage, reuse, share, exchange, and integrate information from different domains of knowledge. This is accomplished by offering tools that characterize, catalog, organize, and retrieve information, making them readily available for quick consumption, and publishing. Many approaches employed LOD to suggest data to the user that was close to what he was looking for within a vast amount of data. Each of them attested to the fact that the usage of LOD improves the process of making suggestions and results in accurate recommendations. In this paper, a survey is presented to show a recent overview of Linked Open Data methods in recommendation systems and their limitations such as cold start, sparsity, new user issue, and limited content analysis. Also, in this survey, the contributions and results of employing LOD in recommendations are presented to show their impact on recommendation systems. Different datasets were introduced to show the effectiveness of LOD methods on data.