ROPPSA: TV Program Recommendation Based on Personality and Social Awareness

Mathematical Problems in Engineering. 2020;2020 DOI 10.1155/2020/1971286

 

Journal Homepage

Journal Title: Mathematical Problems in Engineering

ISSN: 1024-123X (Print); 1563-5147 (Online)

Publisher: Hindawi Limited

LCC Subject Category: Technology: Engineering (General). Civil engineering (General) | Science: Mathematics

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML, ePUB, XML

 

AUTHORS


Nana Yaw Asabere (Accra Technical University, Accra, Ghana)

Amevi Acakpovi (Accra Technical University, Accra, Ghana)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 26 weeks

 

Abstract | Full Text

The rapid growth of mobile television (TV), smart TV, and Internet Protocol Television (IPTV) content due to the convergence of broadcasting and the Internet requires effective recommendation methods to select appropriate TV programs/channels. Many previous methods have been proposed to address this issue. However, imperative factors such as the utilization of personality traits and social properties to recommend programs for TV viewers remain a challenge. Consequently, in this paper, we propose a recommender algorithm called Recommendation of Programs via Personality and Social Awareness (ROPPSA) for TV viewers. ROPPSA utilizes normalization and folksonomy procedures to generate group recommendations for TV viewers who have common similarities in terms of personality traits and tie strength with a Target TV Viewer (TTV). Therefore, ROPPSA improves TV viewer cold-start and data sparsity situations by utilizing their personality traits and tie strengths. We conducted extensive experiments on a relevant dataset using standard evaluation metrics to substantiate our ROPPSA recommendation method. Results of our experimentation procedure depict the advantage, recommendation accuracy, and outperformance of ROPPSA in comparison with other contemporary methods in terms of precision, recall, f-measure (F1), and arithmetic mean (AM).