Exploiting linked data in Dbpedia to reduce prediction error in matrix factorization recommenders

Detalhes bibliográficos
Ano de defesa: 2022
Autor(a) principal: Pereira, Victor Martinez Vidal lattes
Orientador(a): Durão, Frederico Araujo lattes
Banca de defesa: Durão, Frederico Araujo lattes, Pereira, Adriano César Machado lattes, Coimbra, Danilo Barbosa lattes
Tipo de documento: Dissertação
Tipo de acesso: Acesso aberto
Idioma: eng
Instituição de defesa: Universidade Federal da Bahia
Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação (PGCOMP) 
Departamento: Instituto de Computação - IC
País: Brasil
Palavras-chave em Português:
Área do conhecimento CNPq:
Link de acesso: https://repositorio.ufba.br/handle/ri/36099
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.