Access and Sharing Contents Through the Social Network: A POMDP Approach. (2nd February 2017)

• Record Type: Journal Article
• Authors: Mekouar, Soufiana; Bouyakhf, El‐Houssine; El‐Hammani, Sihame; Ibrahimi, Khalil
• Is Part Of: Computational intelligence. Volume 33:Number 4(2017)
• Page Start: 680
• Page End: 705
• Publication Date: 2017-02-02
• Subjects: partially observable Markov decision process -- social network -- access patterns to content -- YouTube; Artificial intelligence -- Periodicals; Computational linguistics -- Periodicals; 006.3
• DOI: 10.1111/coin.12110
• Languages: English
• ISSNs: 0824-7935

Abstract

Abstract : Users interact in the social network by exchanging useful information. The huge amount of traffic generated requires the design of an accurate model for good management. Hence, knowing the consumers' access patterns is of a great interest for content owners. We propose a study of the access and sharing patterns based on the content's popularity. We suppose that popularity is inferred by the observation of the number of views. Our goal is twofold. On the one hand, we aim to help users select the optimal action that allows content owners to decide to change or not the used social network to another to increase their profits and, on the other hand, to assist consumers, according to their area of interest, to decide to access or not a posted content. We establish a threshold structure of the optimal policies based on a trade‐off study between profits (money, access to useful information, etc.) and costs (time spent to edit or consult content, etc.). The experimental results of the proposed analytical model show that both owners and consumers maximize their utility by choosing the best strategies.