Metalearning for Context-aware Filtering: Selection of Tensor Factorization Algorithms

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2017

Autores

Cunha, T (Autor) Outra; Carlos Soares (Autor) FEUP; de Carvalho, ACPLF (Autor) Outra

Ata de Conferência Internacional

Título: Proceedings of the Eleventh ACM Conference on Recommender Systems - RecSys '17

Páginas: 14-22

Indexação

ISI Web of Knowledge - 6 Citações

Scopus - 9 Citações

Outras Informações

ID Authenticus: P-00M-YFG

DOI: 10.1145/3109859.3109899

Abstract (EN): This work addresses the problem of selecting Tensor Factorization algorithms for the Context-aware Filtering recommendation task using a metalearning approach. The most important challenge of applying metalearning on new problems is the development of useful measures able to characterize the data, i.e. metafeatures. We propose an extensive and exhaustive set of metafeatures to characterize Context-aware Filtering recommendation task. These metafeatures take advantage of the tensor's hierarchical structure via slice operations. The algorithm selection task is addressed as a Label Ranking problem, which ranks the Tensor Factorization algorithms according to their expected performance, rather than simply selecting the algorithm that is expected to obtain the best performance. A comprehensive experimental work is conducted on both levels, baselevel and metalevel (Tensor Factorization and Label Ranking, respectively). The results show that the proposed metafeatures lead to metamodels that tend to rank Tensor Factorization algorithms accurately and that the selected algorithms present high recommendation performance.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 9