Mobile app recommendations using deep learning and big data

Detalhes bibliográficos
Autor(a) principal: Pinto, Luís António Galego
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/10362/59930
Resumo: Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research e CRM
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spelling Mobile app recommendations using deep learning and big dataSparkTensorflowSocial NetworksMachine LearningDissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research e CRMRecommender systems were first introduced to solve information overload problems in enterprises. Over the last decades, recommender systems have found applications in several major websites related to e-commerce, music and video streaming, travel and movie sites, social media and mobile app stores. Several methods have been proposed over the years to build recommender systems. The most popular approaches are based on collaborative filtering techniques, which leverage the similarities between consumer tastes. But the current state of the art in recommender systems is deep-learning methods, which can leverage not only item consumption data but also content, context, and user attributes. Mobile app stores generate data with Big Data properties from app consumption data, behavioral, geographic, demographic, social network and user-generated content data, which includes reviews, comments and search queries. In this dissertation, we propose a deep-learning architecture for recommender systems in mobile app stores that leverage most of these data sources. We analyze three issues related to the impact of the data sources, the impact of embedding layer pretraining and the efficiency of using Kernel methods to improve app scoring at a Big Data scale. An experiment is conducted on a Portuguese Android app store. Results suggest that models can be improved by combining structured and unstructured data. The results also suggest that embedding layer pretraining is essential to obtain good results. Some evidence is provided showing that Kernel-based methods might not be efficient when deployed in Big Data contexts.Henriques, Roberto André PereiraRUNPinto, Luís António Galego2019-02-08T16:02:08Z2019-01-182019-01-18T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/59930TID:202167674enginfo: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:RCAAP2024-03-11T04:28:44Zoai:run.unl.pt:10362/59930Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:33:27.358444Repositório 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 Mobile app recommendations using deep learning and big data
title Mobile app recommendations using deep learning and big data
spellingShingle Mobile app recommendations using deep learning and big data
Pinto, Luís António Galego
Spark
Tensorflow
Social Networks
Machine Learning
title_short Mobile app recommendations using deep learning and big data
title_full Mobile app recommendations using deep learning and big data
title_fullStr Mobile app recommendations using deep learning and big data
title_full_unstemmed Mobile app recommendations using deep learning and big data
title_sort Mobile app recommendations using deep learning and big data
author Pinto, Luís António Galego
author_facet Pinto, Luís António Galego
author_role author
dc.contributor.none.fl_str_mv Henriques, Roberto André Pereira
RUN
dc.contributor.author.fl_str_mv Pinto, Luís António Galego
dc.subject.por.fl_str_mv Spark
Tensorflow
Social Networks
Machine Learning
topic Spark
Tensorflow
Social Networks
Machine Learning
description Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Marketing Research e CRM
publishDate 2019
dc.date.none.fl_str_mv 2019-02-08T16:02:08Z
2019-01-18
2019-01-18T00:00:00Z
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/10362/59930
TID:202167674
url http://hdl.handle.net/10362/59930
identifier_str_mv TID:202167674
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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)
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