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

• Type: Article in International Conference Proceedings Book
• Date: 2011

Authors

Domingues, MA (Author) Other; Jorge, AM (Author) FCUP; Soares, C (Author) FEP

Conference proceedings International

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

Pages: 92-95

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

Lyon, 22 August 2011 through 27 August 2011

Indexing

ISI Web of Knowledge

Scopus - 2 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific