RT Journal Article
T1 Improving recommendations for online retail markets based on ontology evolution
A1 Alaa, Rana
A1 Gawish, Mariam
A1 Fernández Veiga, Manuel
K1 1203.04 Inteligencia Artificial
K1 1203.17 Informática
K1 1203.18 Sistemas de Información, Diseño Componentes
AB 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.
PB Electronics
SN 20799292
YR 2021
FD 2021-07-11
LK http://hdl.handle.net/11093/2336
UL http://hdl.handle.net/11093/2336
LA eng
NO Electronics, 10(14): 1650 (2021)
NO Agencia Estatal de Investigación | Ref. PID2020-113795RB-C33
DS Investigo
RD 06-oct-2024