Vulcont: A recommender system based on context history ontology

Abstract The usage of recommenders systems is already widespread. Every day people are exposed to different item offerings based on the prediction of their interests and decisions. Context information, such as location, goals, and close entities, plays a key role in the recommendations' accurac...

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Main Authors: Ismael M. G. Cardoso, Jorge L. V. Barbosa, Bruno M. Alves, Lucas P. S. Dias, Luan C. Nesi
Format: Article
Language:English
Published: Wiley 2022-02-01
Series:IET Software
Subjects:
Online Access:https://doi.org/10.1049/sfw2.12034
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author Ismael M. G. Cardoso
Jorge L. V. Barbosa
Bruno M. Alves
Lucas P. S. Dias
Luan C. Nesi
author_facet Ismael M. G. Cardoso
Jorge L. V. Barbosa
Bruno M. Alves
Lucas P. S. Dias
Luan C. Nesi
author_sort Ismael M. G. Cardoso
collection DOAJ
description Abstract The usage of recommenders systems is already widespread. Every day people are exposed to different item offerings based on the prediction of their interests and decisions. Context information, such as location, goals, and close entities, plays a key role in the recommendations' accuracy. The use of context histories allows one to identify similar context histories and predict contexts. This article proposes Vulcont, a recommender system based on a context histories' ontology. Vulcont merges the benefits of ontology reasoning with context histories to measure the context history similarity, based on the semantic and ontology properties provided by the context’s domain. Vulcont considers synonymous and classes' relations to measure similarity. After that, a collaborative filtering approach identifies sequences' frequency to identify potential items for recommendation. The proposed recommendation is evaluated and discussed in four scenarios in an offline experiment, which explores the semantic value of context histories. The main contribution of Vulcont is the use of semantic relations and the properties of ontology in a similarity measurement of context histories, which is a data structure more complete than that of single contexts.
format Article
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institution Kabale University
issn 1751-8806
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language English
publishDate 2022-02-01
publisher Wiley
record_format Article
series IET Software
spelling doaj-art-07b9e45f888e4e54b82fa5114b3953f72025-02-03T06:47:25ZengWileyIET Software1751-88061751-88142022-02-0116111112310.1049/sfw2.12034Vulcont: A recommender system based on context history ontologyIsmael M. G. Cardoso0Jorge L. V. Barbosa1Bruno M. Alves2Lucas P. S. Dias3Luan C. Nesi4Applied Computing Graduate Program University of Vale do Rio dos Sinos São Leopoldo Rio Grande do Sul BrazilApplied Computing Graduate Program University of Vale do Rio dos Sinos São Leopoldo Rio Grande do Sul BrazilApplied Computing Graduate Program University of Vale do Rio dos Sinos São Leopoldo Rio Grande do Sul BrazilApplied Computing Graduate Program University of Vale do Rio dos Sinos São Leopoldo Rio Grande do Sul BrazilApplied Computing Graduate Program University of Vale do Rio dos Sinos São Leopoldo Rio Grande do Sul BrazilAbstract The usage of recommenders systems is already widespread. Every day people are exposed to different item offerings based on the prediction of their interests and decisions. Context information, such as location, goals, and close entities, plays a key role in the recommendations' accuracy. The use of context histories allows one to identify similar context histories and predict contexts. This article proposes Vulcont, a recommender system based on a context histories' ontology. Vulcont merges the benefits of ontology reasoning with context histories to measure the context history similarity, based on the semantic and ontology properties provided by the context’s domain. Vulcont considers synonymous and classes' relations to measure similarity. After that, a collaborative filtering approach identifies sequences' frequency to identify potential items for recommendation. The proposed recommendation is evaluated and discussed in four scenarios in an offline experiment, which explores the semantic value of context histories. The main contribution of Vulcont is the use of semantic relations and the properties of ontology in a similarity measurement of context histories, which is a data structure more complete than that of single contexts.https://doi.org/10.1049/sfw2.12034collaborative filteringontologies (artificial intelligence)recommender systemsdata structures
spellingShingle Ismael M. G. Cardoso
Jorge L. V. Barbosa
Bruno M. Alves
Lucas P. S. Dias
Luan C. Nesi
Vulcont: A recommender system based on context history ontology
IET Software
collaborative filtering
ontologies (artificial intelligence)
recommender systems
data structures
title Vulcont: A recommender system based on context history ontology
title_full Vulcont: A recommender system based on context history ontology
title_fullStr Vulcont: A recommender system based on context history ontology
title_full_unstemmed Vulcont: A recommender system based on context history ontology
title_short Vulcont: A recommender system based on context history ontology
title_sort vulcont a recommender system based on context history ontology
topic collaborative filtering
ontologies (artificial intelligence)
recommender systems
data structures
url https://doi.org/10.1049/sfw2.12034
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AT brunomalves vulcontarecommendersystembasedoncontexthistoryontology
AT lucaspsdias vulcontarecommendersystembasedoncontexthistoryontology
AT luancnesi vulcontarecommendersystembasedoncontexthistoryontology