Abstract-Recommender systems have contributed to the success of personalized websites as they can automatically and efficiently select items or services adapted to the user's interest from huge datasets. However, these systems suffer of issues related to small number of evaluations; cold start system and data sparsity. Several approaches have been explored to find solutions to related issues. The advent of the Linked Open Data (LOD) initiative has spawned a wide range of open knowledge bases freely accessible on the Web. They provide a valuable source of information that can improve conventional recommender systems, if properly exploited. In this paper, we aim to demonstrate that adding semantic information from LOD enhance the effectiveness of traditional collaborative filtering. To evaluate the accuracy of the semantic approach, experiments on standard benchmark dataset was conducted. The obtained results indicate that the accuracy and quality of the recommendation are improved compared with existing approaches.
To take advantage of Linked Open Data (LOD), Fridi and Benslimane have proposed the addition of this type of semantic information to improve the efficiency of traditional collaborative filtering in conventional RSs REF .
Towards Semantics-Aware Recommender System: A LOD-Based Approach
International Journal of Modern Education and Computer Science
Computer Science