Exploiting Additional Dimensions as Virtual Items on Top-N Recommender Systems

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2011

Autores

Domingues, MA (Autor) Outra; Jorge, AM (Autor) FCUP; Soares, C (Autor) FEP

Ata de Conferência Internacional

Título: Proceedings - 2011 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2011

Páginas: 92-95

2011 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2011

Lyon, 22 August 2011 through 27 August 2011

Indexação

ISI Web of Knowledge

Scopus - 2 Citações

Outras Informações

ID Authenticus: P-008-0YC

DOI: 10.1109/WI-IAT.2011.55

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 enables the use of common two-dimensional top-N recommender algorithms for the generation of recommendations using additional dimensions (e.g., contextual or background information). We empirically evaluate our approach with two different top-N recommender algorithms, Item-based Collaborative Filtering and Association Rules based, on two real world data sets. The empirical results demonstrate that DaVI enables the application of existing two-dimensional recommendation algorithms to exploit the useful information in multidimensional data. © 2011 IEEE.

Idioma: Inglês

Tipo (Avaliação Docente): Científica