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

• Type: Article in International Scientific Journal
• Date: 2013

Authors

Marcos Aurelio Domingues (Author) Other; Alipio Mario Jorge (Author) FCUP; Carlos Soares (Author) FEUP

Journal

Title: Information Processing and Management

Vol. 49

Pages: 698-720

ISSN: 0306-4573

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 25 Citations

Scopus - 31 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 23