Dimensions as Virtual Items: Improving the predictive ability of top-N recommender systems

• Tipo: Artigo em Revista Científica Internacional
• Data: 2013

Autores

Marcos Aurelio Domingues (Autor) Outra; Alipio Mario Jorge (Autor) FCUP; Carlos Soares (Autor) FEUP

Revista

Título: Information Processing and Management

Vol. 49

Páginas: 698-720

ISSN: 0306-4573

Editora: Elsevier

Indexação

ISI Web of Knowledge - 25 Citações

Scopus - 31 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: P-005-1AA

DOI: 10.1016/j.ipm.2012.07.009

Abstract (EN): Traditionally, recommender systems for the web deal with applications that have two dimensions, users and items. Based on access data that relate these dimensions, a recommendation model can be built and used to identify a set of N items that will be of interest to a certain user. In this paper we propose a multidimensional approach, called DaVI (Dimensions as Virtual Items), that consists in inserting contextual and background information as new user-item pairs. The main advantage of this approach is that it can be applied in combination with several existing two-dimensional recommendation algorithms. To evaluate its effectiveness, we used the DaVI approach with two different top-N recommender algorithms, Item-based Collaborative Filtering and Association Rules based, and ran an extensive set of experiments in three different real world data sets. In addition, we have also compared our approach to the previously introduced combined reduction and weight post-filtering approaches. The empirical results strongly indicate that our approach enables the application of existing two-dimensional recommendation algorithms in multidimensional data, exploiting the useful information of these data to improve the predictive ability of top-N recommender systems.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 23