Item-based-adp: análise e melhoramento do algoritmo de filtragem colaborativa item-based
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFG |
dARK ID: | ark:/38995/001300000561x |
Texto Completo: | http://repositorio.bc.ufg.br/tede/handle/tede/4133 |
Resumo: | Memory-based algorithms are the most popular among the collaborative filtering algorithms. They use as input a table containing ratings given by users to items, known as the rating matrix. They predict the rating given by user a to an item i by computing similarities of the ratings among users or similarities of the ratings among items. In the first case Memory-Based algorithms are classified as User-based algorithms and in the second one they are labeled as Item-based algorithms. The prediction is computed using the ratings of k most similar users (or items), also know as neighbors. Memory-based algorithms are simple to understand and to program, usually provide accurate recommendation and are less sensible to data change. However, to obtain the most similar neighbors for a prediction they have to process all the data which is a serious scalability problem. Also they are sensitive to the sparsity of the input. In this work we propose an efficient and effective Item-Based that aims at diminishing the sensibility of the Memory-Based approach to both problems stated above. The algorithm is faster (almost 50%) than the traditional Item-Based algorithm while maintaining the same level of accuracy. However, in environments that have much data to predict and few to train the algorithm, the accuracy of the proposed algorithm surpass significantly that of the traditional Item-based algorithms. Our approach can also be easily adapted to be used as User-based algorithms. |
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Rosa, Thierson Coutohttp://lattes.cnpq.br/4414718560764818Rosa, Thierson CoutoCamilo Júnior, Celso GonçalvesPereira, Denilson Alveshttp://lattes.cnpq.br/6594252534841093Aleixo, Everton Lima2015-02-06T20:35:41Z2014-09-02ALEIXO, Everton Lima. Item-based-adp: análise e melhoramento do algoritmo de filtragem colaborativa item-based. 2014. 96 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2014.http://repositorio.bc.ufg.br/tede/handle/tede/4133ark:/38995/001300000561xMemory-based algorithms are the most popular among the collaborative filtering algorithms. They use as input a table containing ratings given by users to items, known as the rating matrix. They predict the rating given by user a to an item i by computing similarities of the ratings among users or similarities of the ratings among items. In the first case Memory-Based algorithms are classified as User-based algorithms and in the second one they are labeled as Item-based algorithms. The prediction is computed using the ratings of k most similar users (or items), also know as neighbors. Memory-based algorithms are simple to understand and to program, usually provide accurate recommendation and are less sensible to data change. However, to obtain the most similar neighbors for a prediction they have to process all the data which is a serious scalability problem. Also they are sensitive to the sparsity of the input. In this work we propose an efficient and effective Item-Based that aims at diminishing the sensibility of the Memory-Based approach to both problems stated above. The algorithm is faster (almost 50%) than the traditional Item-Based algorithm while maintaining the same level of accuracy. However, in environments that have much data to predict and few to train the algorithm, the accuracy of the proposed algorithm surpass significantly that of the traditional Item-based algorithms. Our approach can also be easily adapted to be used as User-based algorithms.Algoritmos baseados em memória são os mais populares entre os algoritmos de filtragem colaborativa. Eles usam como entrada uma tabela contendo as avaliações feitas pelos usuários aos itens, conhecida como matriz de avaliações. Eles predizem a avaliação dada por um usuário a a um item i, computando a similaridade de avaliações entre a e outros usuários ou entre i e outros itens. No primeiro caso, os algoritmos baseados em memória são classificados como algoritmos baseados em usuários (User-based) e no segundo caso são rotulados como algoritmos baseados em itens (Item-Based). A predição é computada usando as avaliações dos k usuários (ou itens) mais similares, também conhecidos como vizinhos. Algoritmos baseados em memória são simples de entender e implementar. Normalmente produzem boas recomendações e são menos sensíveis a mudança nos dados. Entretanto, para obter os vizinhos mais similares para a predição, eles necessitam processar todos os dados da matriz, o que é um sério problema de escalabilidade. Eles também são sensíveis a densidade dos dados. Neste trabalho, nós propomos um algoritmo eficiente e eficaz baseado em itens que visa diminuir a sensibilidade dos algoritmos baseados em memória para ambos os problemas acima referidos. Esse algoritmo é mais rápido (quase 50%) do que o algoritmo baseado em itens tradicional, mantendo o mesmo nível de acurácia. Entretanto, em ambientes onde existem muitos dados para predizer e poucos para treinar o algoritmo, a acurácia do algoritmo proposto supera significativamente a do algoritmo tradicional baseado em itens. Nossa abordagem pode ainda ser facilmente adaptada para ser utilizada como o algoritmo baseado em usuários.Submitted by Erika Demachki (erikademachki@gmail.com) on 2015-02-06T20:35:15Z No. of bitstreams: 2 Dissertação - Everton Lima Aleixo - 2014.pdf: 2375638 bytes, checksum: accbd56745e040e23362d951a1336538 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5)Approved for entry into archive by Erika Demachki (erikademachki@gmail.com) on 2015-02-06T20:35:41Z (GMT) No. of bitstreams: 2 Dissertação - Everton Lima Aleixo - 2014.pdf: 2375638 bytes, checksum: accbd56745e040e23362d951a1336538 (MD5) license_rdf: 23148 bytes, checksum: 9da0b6dfac957114c6a7714714b86306 (MD5)Made available in DSpace on 2015-02-06T20:35:41Z (GMT). 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dc.title.eng.fl_str_mv |
Item-based-adp: análise e melhoramento do algoritmo de filtragem colaborativa item-based |
dc.title.alternative.eng.fl_str_mv |
Item-based-adp: analysis and improvent of collaborative filtering algorithm item-based |
title |
Item-based-adp: análise e melhoramento do algoritmo de filtragem colaborativa item-based |
spellingShingle |
Item-based-adp: análise e melhoramento do algoritmo de filtragem colaborativa item-based Aleixo, Everton Lima Sistemas de recomendação Acurácia Filtragem colaborativa Baseado em memória Recommender systems Accuracy Collaborative filtering Memory-based CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
title_short |
Item-based-adp: análise e melhoramento do algoritmo de filtragem colaborativa item-based |
title_full |
Item-based-adp: análise e melhoramento do algoritmo de filtragem colaborativa item-based |
title_fullStr |
Item-based-adp: análise e melhoramento do algoritmo de filtragem colaborativa item-based |
title_full_unstemmed |
Item-based-adp: análise e melhoramento do algoritmo de filtragem colaborativa item-based |
title_sort |
Item-based-adp: análise e melhoramento do algoritmo de filtragem colaborativa item-based |
author |
Aleixo, Everton Lima |
author_facet |
Aleixo, Everton Lima |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
Rosa, Thierson Couto |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/4414718560764818 |
dc.contributor.referee1.fl_str_mv |
Rosa, Thierson Couto |
dc.contributor.referee2.fl_str_mv |
Camilo Júnior, Celso Gonçalves |
dc.contributor.referee3.fl_str_mv |
Pereira, Denilson Alves |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/6594252534841093 |
dc.contributor.author.fl_str_mv |
Aleixo, Everton Lima |
contributor_str_mv |
Rosa, Thierson Couto Rosa, Thierson Couto Camilo Júnior, Celso Gonçalves Pereira, Denilson Alves |
dc.subject.por.fl_str_mv |
Sistemas de recomendação Acurácia Filtragem colaborativa Baseado em memória |
topic |
Sistemas de recomendação Acurácia Filtragem colaborativa Baseado em memória Recommender systems Accuracy Collaborative filtering Memory-based CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
dc.subject.eng.fl_str_mv |
Recommender systems Accuracy Collaborative filtering Memory-based |
dc.subject.cnpq.fl_str_mv |
CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO |
description |
Memory-based algorithms are the most popular among the collaborative filtering algorithms. They use as input a table containing ratings given by users to items, known as the rating matrix. They predict the rating given by user a to an item i by computing similarities of the ratings among users or similarities of the ratings among items. In the first case Memory-Based algorithms are classified as User-based algorithms and in the second one they are labeled as Item-based algorithms. The prediction is computed using the ratings of k most similar users (or items), also know as neighbors. Memory-based algorithms are simple to understand and to program, usually provide accurate recommendation and are less sensible to data change. However, to obtain the most similar neighbors for a prediction they have to process all the data which is a serious scalability problem. Also they are sensitive to the sparsity of the input. In this work we propose an efficient and effective Item-Based that aims at diminishing the sensibility of the Memory-Based approach to both problems stated above. The algorithm is faster (almost 50%) than the traditional Item-Based algorithm while maintaining the same level of accuracy. However, in environments that have much data to predict and few to train the algorithm, the accuracy of the proposed algorithm surpass significantly that of the traditional Item-based algorithms. Our approach can also be easily adapted to be used as User-based algorithms. |
publishDate |
2014 |
dc.date.issued.fl_str_mv |
2014-09-02 |
dc.date.accessioned.fl_str_mv |
2015-02-06T20:35:41Z |
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.citation.fl_str_mv |
ALEIXO, Everton Lima. Item-based-adp: análise e melhoramento do algoritmo de filtragem colaborativa item-based. 2014. 96 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2014. |
dc.identifier.uri.fl_str_mv |
http://repositorio.bc.ufg.br/tede/handle/tede/4133 |
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ark:/38995/001300000561x |
identifier_str_mv |
ALEIXO, Everton Lima. Item-based-adp: análise e melhoramento do algoritmo de filtragem colaborativa item-based. 2014. 96 f. Dissertação (Mestrado em Ciência da Computação) - Universidade Federal de Goiás, Goiânia, 2014. ark:/38995/001300000561x |
url |
http://repositorio.bc.ufg.br/tede/handle/tede/4133 |
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por |
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3671711205811204509 |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
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http://creativecommons.org/licenses/by-nc-nd/4.0/ |
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openAccess |
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Universidade Federal de Goiás |
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Programa de Pós-graduação em Ciência da Computação (INF) |
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UFG |
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Brasil |
dc.publisher.department.fl_str_mv |
Instituto de Informática - INF (RG) |
publisher.none.fl_str_mv |
Universidade Federal de Goiás |
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