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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-02-01
|
| Series: | IET Software |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/sfw2.12034 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832546712989728768 |
|---|---|
| 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 |
| id | doaj-art-07b9e45f888e4e54b82fa5114b3953f7 |
| institution | Kabale University |
| issn | 1751-8806 1751-8814 |
| 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 |
| work_keys_str_mv | AT ismaelmgcardoso vulcontarecommendersystembasedoncontexthistoryontology AT jorgelvbarbosa vulcontarecommendersystembasedoncontexthistoryontology AT brunomalves vulcontarecommendersystembasedoncontexthistoryontology AT lucaspsdias vulcontarecommendersystembasedoncontexthistoryontology AT luancnesi vulcontarecommendersystembasedoncontexthistoryontology |