Movie Recommendation Based on Graph Traversal Algorithms

Media content recommendation is nowadays a common problem. Traditional algorithms based on collaborative filtering require an up-to-date dataset of users and their preferences, which is difficult to gather for huge database of items. Content-based approach suffers from the complex computation of similarity among items. In this paper we propose an approach to recommendation with a focus on the natural change of user´s interests in movies. We make use of a graph representation and experimented with modified graph algorithms. We design a representation of the data about movies in a graph structure and a method which uses our data model for recommendation. We propose four recommendation algorithms which are capable to find recommendations based on initial nodes, which selection is based on the user´s current interests. We implemented these algorithms and experimentally evaluated them with real users.