Providing a User-Based Behavior Model to Recommend a Movie Using the Social Network Analysis (Case Study: CinemaMarket)

Due to the increasing share of consumption and watching videos - especially movies and series - in the basket of Iranian households, several systems have been set up to facilitate people's access to these videos. One of the most important types of these systems is the video-on-demand system which has taken unprecedented growth in attracting audiences in recent years. Just as the multiplicity of content in these systems causes users to be diverse and satisfied, this multiplicity can be more confusing for them to find interesting content. Therefore, the need for recommendation systems to further predict user interests and provide consistent content is felt more and more day by day. The purpose of this study is to provide an efficient method of recommending videos based on user viewing data in the video-on-demand system.

In this research, a new bidding algorithm based on users' tastes and video watching data in the video-on-demand system is presented. This algorithm is based on the concepts and indicators of social network analysis. How this algorithm works is that first the similarity of the videos is calculated based on the percentage of movies viewed by the user and based on that the similarity matrix of the movies is formed. In the next step, based on the similarity matrix of the films, the communication graph of the films is formed, and in the next step, while discovering the communities in the graph, the centrality indicators of each film are calculated.