Personalization of product rankings in e-commerce

Detalhes bibliográficos
Autor(a) principal: Kuka, Josefine Frederike
Data de Publicação: 2019
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/71768
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
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spelling Personalization of product rankings in e-commercePersonalizationRankingMachine LearningLearning to RankClusteringRecommendation systemMatrix FactorizationE-commerceDissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsConsumers face a large number of choices while shopping online. Studies have shown, that they are already expecting to be targeted with content addressing their personal needs. In a web shop, products are presented as lists based on a selected category or as results of a product search. To support the users in their decision making, they can be provided with a personalized product ranking fitted to their current interests. In this piece of work, three levels of personalized product rankings are proposed: explicit personalization, cluster-based personalization and individualization. To estimate the potential effect of the personalization and its required effort, two prototypes for the second and third level are developed and evaluated. The prototypes are based on a previously existing non-personalized ranking, which ranks the products in descending order according to a sales prediction. The cluster-based prototype enhances this product ranking by determining customer clusters beforehand using both situative and behavioural data. The individualized product rankings rely on the combination of the ranking with a recommendation system realized as a matrix factorization. In doing so, the concept of learning to rank is considered. By evaluating the cluster-based and individualized prototype on a sampled data set in comparison to the non-personalized ranking, it is shown that the created personalized rankings are in fact closer to the users’ needs. Furthermore, a subjective evaluation confirms that the cluster-based rankings can reflect the users’ interests in a better way.Neto, Miguel de Castro Simões FerreiraRUNKuka, Josefine Frederike2019-04-222024-04-22T00:00:00Z2019-04-22T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/71768TID:202252035enginfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:33:42Zoai:run.unl.pt:10362/71768Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:35:13.637661Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Personalization of product rankings in e-commerce
title Personalization of product rankings in e-commerce
spellingShingle Personalization of product rankings in e-commerce
Kuka, Josefine Frederike
Personalization
Ranking
Machine Learning
Learning to Rank
Clustering
Recommendation system
Matrix Factorization
E-commerce
title_short Personalization of product rankings in e-commerce
title_full Personalization of product rankings in e-commerce
title_fullStr Personalization of product rankings in e-commerce
title_full_unstemmed Personalization of product rankings in e-commerce
title_sort Personalization of product rankings in e-commerce
author Kuka, Josefine Frederike
author_facet Kuka, Josefine Frederike
author_role author
dc.contributor.none.fl_str_mv Neto, Miguel de Castro Simões Ferreira
RUN
dc.contributor.author.fl_str_mv Kuka, Josefine Frederike
dc.subject.por.fl_str_mv Personalization
Ranking
Machine Learning
Learning to Rank
Clustering
Recommendation system
Matrix Factorization
E-commerce
topic Personalization
Ranking
Machine Learning
Learning to Rank
Clustering
Recommendation system
Matrix Factorization
E-commerce
description Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
publishDate 2019
dc.date.none.fl_str_mv 2019-04-22
2019-04-22T00:00:00Z
2024-04-22T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/71768
TID:202252035
url http://hdl.handle.net/10362/71768
identifier_str_mv TID:202252035
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
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collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
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