Exploiting Recommendation on Social Media Networks

Swati A. Adhav, Sheetal A. Takale

Abstract: Social media where the user interacts with each other to form social networks is becoming very popular these days. In social media networks, users are influenced by others for various reasons. For example, the colleagues have a strong impact on one-s work, while the friends have a strong impact on one-s daily life. Social influence mining in social networks is important in real world applications such as photo recommendation. Since the Social network is multi-modal and heterogeneous, it is insignificant to effectively exploit the social media information to learn the topic distribution for users and images accurately. To tackle this Problem, Exploiting Recommendation on Social Media Networks is used to find topical influential for users and images with the help of Hypergraph Learning approach. The Hypergraph learning method is used to model user, images and social link relationship. It combines the content of images and social links to determine the topic-specific influence for users and images in the social media network. Exploiting Recommendation on Social Media Networks consists of three learning stages Hypergraph Construction, topic distribution learning, and topic sensitive influence ranking.

Keywords: Hypergraph Construction, topic distribution learning, topic sensitive influence ranking

How to Cite?: Swati A. Adhav, Sheetal A. Takale, "Exploiting Recommendation on Social Media Networks", Volume 4 Issue 6, June 2015, International Journal of Science and Research (IJSR), Pages: 318-322, https://www.ijsr.net/getabstract.php?paperid=SUB155077, DOI: https://dx.doi.org/10.21275/SUB155077