Improving Semantic Similarity Measure Within a Recommender System Based-on RDF Graphs

TL;DR

This paper proposes an approach to enhance semantic similarity measurement within recommender systems using RDF graphs, aiming to improve recommendation quality by leveraging structured knowledge representations.

Contribution

It introduces a novel method for improving semantic similarity calculations in recommender systems based on RDF graph structures.

Findings

Enhanced accuracy of semantic similarity measurement

Improved recommendation relevance

Effective use of RDF graphs in similarity computation

Abstract

In today's era of information explosion, more users are becoming more reliant upon recommender systems to have better advice, suggestions, or inspire them. The measure of the semantic relatedness or likeness between terms, words, or text data plays an important role in different applications dealing with textual data, as in a recommender system. Over the past few years, many ontologies have been developed and used as a form of structured representation of knowledge bases for information systems. The measure of semantic similarity from ontology has developed by several methods. In this paper, we propose and carry on an approach for the improvement of semantic similarity calculations within a recommender system based-on RDF graphs.