Context-aware media recommendations for smart devices

• Type: Article in International Scientific Journal
• Date: 2015

Authors

Abayomi Moradeyo Otebolaku (Author) FEUP; Maria Teresa Andrade (Author) FEUP

Journal

Title: Journal of Ambient Intelligence and Humanized Computing

Serial No. 12652 Vol. 6 No. 1

Pages: 13-36

ISSN: 1868-5137

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 18 Citations

Scopus - 22 Citations

COMPENDEX

INSPEC

Scientific classification

FOS: Engineering and technology > Electrical engineering, Electronic engineering, Information engineering

CORDIS: Technological sciences > Engineering > Communication engineering > Telecommunications engineering

Other information

Authenticus ID: P-00A-93R

DOI: 10.1007/s12652-014-0234-y

Abstract (EN): The emergence of pervasive computing, the rapid advancements in broadband and mobile networks and the incredible appeals of smart devices are driving unprecedented universal access and delivery of onlinebased media resources. As more and more media services continue to flood the Web, mobile users will continue to waste invaluable time, seeking content of their interest. To deliver relevant media items offering richer experiences to mobile users, media services must be equipped with contextual knowledge of the consumption environment as well as contextual preferences of the users. This article investigates context-aware recommendation techniques for implicit delivery of contextually relevant online media items. The proposed recommendation services work with a contextual user profile and a context recognition framework, using case base reasoning as a methodology to determine user¿s current contextual preferences, relying on a context recognition service, which identifies user¿s dynamic contextual situation from device¿s built-in sensors. To evaluate the proposed solution, we developed a case-study context-aware application that provides personalized recommendations adapted to user¿s current context, namely the activity he/she performs and consumption environment constraints. Experimental evaluations, via the case study application, real-world user data, and onlinebased movie metadata, demonstrate that context-aware recommendation techniques can provide better efficacy than the traditional approaches. Additionally, evaluations of the underlying context recognition process show that its power consumption is within an acceptable range. The recommendations provided by the case study application were assessed as effective via a user study, which demonstrates that users are pleased with the contextual media recommendations.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 24