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Promoting Diversity in Content Based Recommendation Using Feature Weighting and LSH

This work proposes an efficient Content-Based (CB) product recommendation methodology that promotes diversity. A heuristic CB approach incorporating feature weighting and Locality-Sensitive Hashing (LSH) is used, along with the TF-IDF method and functionality of tuning the importance of product feat...

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Detalles Bibliográficos
Autores principales: Beleveslis, Dimosthenis, Tjortjis, Christos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256377/
http://dx.doi.org/10.1007/978-3-030-49161-1_38
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author Beleveslis, Dimosthenis
Tjortjis, Christos
author_facet Beleveslis, Dimosthenis
Tjortjis, Christos
author_sort Beleveslis, Dimosthenis
collection PubMed
description This work proposes an efficient Content-Based (CB) product recommendation methodology that promotes diversity. A heuristic CB approach incorporating feature weighting and Locality-Sensitive Hashing (LSH) is used, along with the TF-IDF method and functionality of tuning the importance of product features to adjust its logic to the needs of various e-commerce sites. The problem of efficiently producing recommendations, without compromising similarity, is addressed by approximating product similarities via the LSH technique. The methodology is evaluated on two sets with real e-commerce data. The evaluation of the proposed methodology shows that the produced recommendations can help customers to continue browsing a site by providing them with the necessary “next step”. Finally, it is demonstrated that the methodology incorporates recommendation diversity which can be adjusted by tuning the appropriate feature weights.
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spelling pubmed-72563772020-05-29 Promoting Diversity in Content Based Recommendation Using Feature Weighting and LSH Beleveslis, Dimosthenis Tjortjis, Christos Artificial Intelligence Applications and Innovations Article This work proposes an efficient Content-Based (CB) product recommendation methodology that promotes diversity. A heuristic CB approach incorporating feature weighting and Locality-Sensitive Hashing (LSH) is used, along with the TF-IDF method and functionality of tuning the importance of product features to adjust its logic to the needs of various e-commerce sites. The problem of efficiently producing recommendations, without compromising similarity, is addressed by approximating product similarities via the LSH technique. The methodology is evaluated on two sets with real e-commerce data. The evaluation of the proposed methodology shows that the produced recommendations can help customers to continue browsing a site by providing them with the necessary “next step”. Finally, it is demonstrated that the methodology incorporates recommendation diversity which can be adjusted by tuning the appropriate feature weights. 2020-05-06 /pmc/articles/PMC7256377/ http://dx.doi.org/10.1007/978-3-030-49161-1_38 Text en © IFIP International Federation for Information Processing 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Beleveslis, Dimosthenis
Tjortjis, Christos
Promoting Diversity in Content Based Recommendation Using Feature Weighting and LSH
title Promoting Diversity in Content Based Recommendation Using Feature Weighting and LSH
title_full Promoting Diversity in Content Based Recommendation Using Feature Weighting and LSH
title_fullStr Promoting Diversity in Content Based Recommendation Using Feature Weighting and LSH
title_full_unstemmed Promoting Diversity in Content Based Recommendation Using Feature Weighting and LSH
title_short Promoting Diversity in Content Based Recommendation Using Feature Weighting and LSH
title_sort promoting diversity in content based recommendation using feature weighting and lsh
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7256377/
http://dx.doi.org/10.1007/978-3-030-49161-1_38
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