Abstract
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).
Highlights
Recent decades have witnessed the emergence of novel challenges regarding television (TV) content consumption
TV viewers (TVs) are machine screens that broadcast signals and convert them into multimedia pictures and sounds for educational and entertainment purposes. e evident convergence of Internet and broadcasting has paved the way for the proliferation of different TV technologies such as Internet Protocol TV (IPTV), mobile TV, and smart TV which can be viewed on multiscreen and device ecosystems [1,2,3,4,5,6]
Both T3 and T4 are relevant social group TV recommendation methods which involved similar approaches regarding the establishment of group preference profiles/models from individual TV viewer profiles which relate to common social similarity and interests in terms of TV programs. e main goal of our experimentation procedure was to verify the effectiveness of ROPPSA and improve recommendation accuracy by alleviating issues of data sparsity and cold-start in TV program recommendation. e experimental results below present evidence on how the above goals were achieved
Summary
Recent decades have witnessed the emergence of novel challenges regarding television (TV) content consumption. E evident convergence of Internet and broadcasting has paved the way for the proliferation of different TV technologies such as Internet Protocol TV (IPTV), mobile TV, and smart TV which can be viewed on multiscreen and device ecosystems [1,2,3,4,5,6]. Such situations create difficulty for TV producers to predict viewers’ interests and preferences in certain circumstances [1,2,3,4,5,6]
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