Machine learning approach for personalized recommendations on online platforms: uniplaces case study

Detalhes bibliográficos
Autor(a) principal: Villar, Marta Maria Cabral Menéres Posser
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/113411
Resumo: Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
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spelling Machine learning approach for personalized recommendations on online platforms: uniplaces case studyMachine LearningData MiningPredictive ModelsSupervised LearningProject Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceThe goal of this project is to develop a model to personalize the user recommendations of an online marketplace named Uniplaces. This online business offers properties for medium and long-term stays, where landlords can directly rent their place to customers (mainly students). Whenever a student makes a reservation, the booking must be approved by the property owner. The current acceptance rate is 25%. The model is a response to this low acceptance rate, and it will have to show to each student the properties that are more likely to be accepted by the landlord. As a secondary objective, the model seeks to identify the reasons behind the landlord’s decision to accept or reject bookings. The model will be constructed using information from the users, landlord and the property itself kindly provided by Uniplaces. This information will pre-process with data cleaning, transformation and features reduction (where two techniques were applied: dimensionality reduction, features selection). After the data processing, several models were applied to the normalized data. The predictive models that will be applied are already being used on other online markets and platforms like Airbnb, Netflix or LinkedIn, namely Support Vector Machine, Neural Networks, Decision Tree, Logistic Regression and Gradient Boosting. The probability of acceptance proved to be very easy to predict, all the models predict 100% of the test dataset when using the Principal Component Analysis as the Dimensionality Reduction technique. This can be explained mainly by the fact that the new calculated features have a strong correlation with the target variable. All the algorithms predict 100% of the target variable when using Principal Component Analysis as a technique of dimensionality reduction.Castelli, MauroRUNVillar, Marta Maria Cabral Menéres Posser2021-03-08T13:35:16Z2021-02-252021-02-25T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/113411TID:202664309enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:56:27Zoai:run.unl.pt:10362/113411Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:42:18.415722Repositório 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 Machine learning approach for personalized recommendations on online platforms: uniplaces case study
title Machine learning approach for personalized recommendations on online platforms: uniplaces case study
spellingShingle Machine learning approach for personalized recommendations on online platforms: uniplaces case study
Villar, Marta Maria Cabral Menéres Posser
Machine Learning
Data Mining
Predictive Models
Supervised Learning
title_short Machine learning approach for personalized recommendations on online platforms: uniplaces case study
title_full Machine learning approach for personalized recommendations on online platforms: uniplaces case study
title_fullStr Machine learning approach for personalized recommendations on online platforms: uniplaces case study
title_full_unstemmed Machine learning approach for personalized recommendations on online platforms: uniplaces case study
title_sort Machine learning approach for personalized recommendations on online platforms: uniplaces case study
author Villar, Marta Maria Cabral Menéres Posser
author_facet Villar, Marta Maria Cabral Menéres Posser
author_role author
dc.contributor.none.fl_str_mv Castelli, Mauro
RUN
dc.contributor.author.fl_str_mv Villar, Marta Maria Cabral Menéres Posser
dc.subject.por.fl_str_mv Machine Learning
Data Mining
Predictive Models
Supervised Learning
topic Machine Learning
Data Mining
Predictive Models
Supervised Learning
description Project Work presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business Intelligence
publishDate 2021
dc.date.none.fl_str_mv 2021-03-08T13:35:16Z
2021-02-25
2021-02-25T00:00:00Z
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/10362/113411
TID:202664309
url http://hdl.handle.net/10362/113411
identifier_str_mv TID:202664309
dc.language.iso.fl_str_mv eng
language eng
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eu_rights_str_mv openAccess
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
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