Item-based-adp: análise e melhoramento do algoritmo de filtragem colaborativa item-based

Detalhes bibliográficos
Autor(a) principal: Aleixo, Everton Lima
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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spelling 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
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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.
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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.
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