4.6 Article

Improving Recommendations for Online Retail Markets Based on Ontology Evolution

Journal

ELECTRONICS
Volume 10, Issue 14, Pages -

Publisher

MDPI
DOI: 10.3390/electronics10141650

Keywords

online retail ontology; semantic web; ontology; ontology evolution; ontology reasoning; recommendation system

Funding

  1. European Regional Development Fund (ERDF)
  2. Galician Regional Government
  3. Ministerio de Economia, Industria y Competitividad [PID2020-113795RB-C33]
  4. Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad
  5. ERDF funds

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The semantic web is an extension of the current web, providing information with well-defined meanings to facilitate global cooperation and knowledge exchange. Ontologies serve as the backbone of the semantic web, offering a common foundation for resources. Its use of semantics and artificial intelligence results in a Smarter Web, allowing for easier product retrieval and enhancing e-commerce platforms.
The semantic web is considered to be an extension of the present web. In the semantic web, information is given with well-defined meanings, and thus helps people worldwide to cooperate together and exchange knowledge. The semantic web plays a significant role in describing the contents and services in a machine-readable form. It has been developed based on ontologies, which are deemed the backbone of the semantic web. Ontologies are a key technique with which semantics are annotated, and they provide common comprehensible foundation for resources on the semantic web. The use of semantics and artificial intelligence leads to what is known to be Smarter Web, where it will be easy to retrieve what customers want to see on e-commerce platforms, and thus will help users save time and enhance their search for the products they need. The semantic web is used as well as webs 3.0, which helps enhancing systems performance. Previous personalized recommendation methods based on ontologies identify users' preferences by means of static snapshots of purchase data. However, as the user preferences evolve with time, the one-shot ontology construction is too constrained for capturing individual diverse opinions and users' preferences evolution over time. This paper will present a novel recommendation system architecture based on ontology evolution, the proposed subsystem architecture for ontology evolution. Furthermore, the paper proposes an ontology building methodology based on a semi-automatic technique as well as development of online retail ontology. Additionally, a recommendation method based on the ontology reasoning is proposed. Based on the proposed method, e-retailers can develop a more convenient product recommendation system to support consumers' purchase decisions.

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