Movie Recommendation Algorithm Using Social Network Analysis to Alleviate Cold-Start Problem

With the rapid increase of information on the World Wide Web, finding useful information on the internet hasbecome a major problem. The recommendation system helps users make decisions in complex data areas wherethe amount of data available is large. There are many methods that have been proposed in the recommendersystem. Collaborative filtering is a popular method widely used in the recommendation system. However,collaborative filtering methods still have some problems, namely cold-start problem. In this paper, we proposea movie recommendation system by using social network analysis and collaborative filtering to solve thisproblem associated with collaborative filtering methods. We applied personal propensity of users such as age,gender, and occupation to make relationship matrix between users, and the relationship matrix is applied tocluster user by using community detection based on edge betweenness centrality. Then the recommendedsystem will suggest movies which were previously interested by users in the group to new users. We show shownthat the proposed method is a very efficient method using mean absolute error. KCI Citation Count: 1