https://repositorio.ufba.br/handle/ri/36099
Tipo: | Dissertação |
Título: | Exploiting linked data in Dbpedia to reduce prediction error in matrix factorization recommenders |
Título(s) alternativo(s): | Explorando Linked Data na DBpedia para reduzir Erro Predito em Recomendadores baseados em Fatorização de Matriz Exploração de dados vinculados na Dbpedia para reduzir erro de previsão em recomendadores de factorização matricial |
Autor(es): | Pereira, Victor Martinez Vidal |
Primeiro Orientador: | Durão, Frederico Araujo |
metadata.dc.contributor.referee1: | Durão, Frederico Araujo |
metadata.dc.contributor.referee2: | Pereira, Adriano César Machado |
metadata.dc.contributor.referee3: | Coimbra, Danilo Barbosa |
Resumo: | Recommender Systems provide suggestions for items that are most likely of interest to users. Providing personalized recommendations is a challenge that can be addressed by filtering algorithms among which Collaborative Filtering (CF) has demonstrated much progress in the last few years. By using Matrix Factorization (MF) techniques, CF methods reduce prediction error by using optimization algorithms. However, they usually face problems such as data sparsity and prediction error. Studies point to the use of data available in Semantic Web as a path to improve recommender systems and address the challenges related to CF techniques. Motivated by these premises, the present work, conducted by me at RecSys Research Group at UFBA, developed a data pipeline along with an algorithm that processes the Ratings Matrix combining semantic similarities of Linked Open Data (LOD) and estimates missing ratings. The experiments took subsets of 1000 samples from three di↵erent datasets (Movielens, LastFM and LibraryThing), calculated two semantic similarity metrics, Linked Data Similarity Distance (LDSD) and Resource Similarity (RESIM), and applied three MF-based algorithms (SVD, SVD++ and NMF). Results suggest the proposed pipeline is able to reduce Root Mean Square Error (RMSE) of all subsets with statistical confidence supported by parametric test one-way ANOVA followed by Tukey’s multiple comparison test. |
Abstract: | Recommender Systems provide suggestions for items that are most likely of interest to users. Providing personalized recommendations is a challenge that can be addressed by filtering algorithms among which Collaborative Filtering (CF) has demonstrated much progress in the last few years. By using Matrix Factorization (MF) techniques, CF methods reduce prediction error by using optimization algorithms. However, they usually face problems such as data sparsity and prediction error. Studies point to the use of data available in Semantic Web as a path to improve recommender systems and address the challenges related to CF techniques. Motivated by these premises, the present work, conducted by me at RecSys Research Group at UFBA, developed a data pipeline along with an algorithm that processes the Ratings Matrix combining semantic similarities of Linked Open Data (LOD) and estimates missing ratings. The experiments took subsets of 1000 samples from three di↵erent datasets (Movielens, LastFM and LibraryThing), calculated two semantic similarity metrics, Linked Data Similarity Distance (LDSD) and Resource Similarity (RESIM), and applied three MF-based algorithms (SVD, SVD++ and NMF). Results suggest the proposed pipeline is able to reduce Root Mean Square Error (RMSE) of all subsets with statistical confidence supported by parametric test one-way ANOVA followed by Tukey’s multiple comparison test. |
Palavras-chave: | Sistemas de recomendação Fatorização de matrizes Dados abertos Erro predito |
CNPq: | CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::METODOLOGIA E TECNICAS DA COMPUTACAO::SISTEMAS DE INFORMACAO |
Idioma: | eng |
País: | Brasil |
Editora / Evento / Instituição: | Universidade Federal da Bahia |
Sigla da Instituição: | UFBA |
metadata.dc.publisher.department: | Instituto de Computação - IC |
metadata.dc.publisher.program: | Programa de Pós-Graduação em Ciência da Computação (PGCOMP) |
Citação: | PEREIRA, Victor Martinez Vidal. Exploiting linked data in Dbpedia to reduce prediction error in matrix factorization recommenders. 2022. 64 f. Dissertação (Mestrado em Ciências da Computação) Instituto de Computação, Universidade Federal da Bahia, Salvador, Ba, 2022. |
URI: | https://repositorio.ufba.br/handle/ri/36099 |
Data do documento: | 21-Jun-2022 |
Aparece nas coleções: | Dissertação (PGCOMP) |
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