Table of Contents Author Guidelines Submit a Manuscript
Erratum

An erratum for this article has been published. To view the erratum, please click here.

The Scientific World Journal
Volume 2014, Article ID 679849, 11 pages
http://dx.doi.org/10.1155/2014/679849
Research Article

N-Screen Aware Multicriteria Hybrid Recommender System Using Weight Based Subspace Clustering

Department of Information & Communication, Korea Aerospace University, Goyang 412-791, Republic of Korea

Received 21 February 2014; Revised 23 May 2014; Accepted 16 June 2014; Published 24 July 2014

Academic Editor: Martin Lopez-Nores

Copyright © 2014 Farman Ullah et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper presents a recommender system for N-screen services in which users have multiple devices with different capabilities. In N-screen services, a user can use various devices in different locations and time and can change a device while the service is running. N-screen aware recommendation seeks to improve the user experience with recommended content by considering the user N-screen device attributes such as screen resolution, media codec, remaining battery time, and access network and the user temporal usage pattern information that are not considered in existing recommender systems. For N-screen aware recommendation support, this work introduces a user device profile collaboration agent, manager, and N-screen control server to acquire and manage the user N-screen devices profile. Furthermore, a multicriteria hybrid framework is suggested that incorporates the N-screen devices information with user preferences and demographics. In addition, we propose an individual feature and subspace weight based clustering (IFSWC) to assign different weights to each subspace and each feature within a subspace in the hybrid framework. The proposed system improves the accuracy, precision, scalability, sparsity, and cold start issues. The simulation results demonstrate the effectiveness and prove the aforementioned statements.