Ontology-based Recommender System of Economic Articles

TL;DR

This paper introduces an ontology-based recommender system for economic articles that personalizes content for decision makers by semantically describing articles and user profiles to improve relevance.

Contribution

It proposes a novel ontology-driven approach to personalize economic article recommendations based on semantic descriptions of content and user profiles.

Findings

Enhanced relevance of recommendations through semantic analysis

Real-time feedback improves content quality

Personalized economic review generation

Abstract

Decision makers need economical information to drive their decisions. The Company Actualis SARL is specialized in the production and distribution of a press review about French regional economic actors. This economic review represents for a client a prospecting tool on partners and competitors. To reduce the overload of useless information, the company is moving towards a customized review for each customer. Three issues appear to achieve this goal. First, how to identify the elements in the text in order to extract objects that match with the recommendation's criteria presented? Second, How to define the structure of these objects, relationships and articles in order to provide a source of knowledge usable by the extraction process to produce new knowledge from articles? The latter issue is the feedback on customer experience to identify the quality of distributed information in real-time and to improve the relevance of the recommendations. This paper presents a new type of recommendation based on the semantic description of both articles and user profile.