Moresysgoal: Movie Recommendation System Using Technique Supplemented by Content With Goal Programming

Abstract

In recent years, internet grows at an accelerating rate. In addition, a new flow of information, which has various types of data, takes place at internet. Therefore, the end users may not find the relevant information satisfying their interests. As a result, recommendation systems, one of the approaches, appeared to help users for this manner. MoresysGOAL is one of the examples for these systems, and stands for movie recommendation system with goal programming. It aims to improve the state-of-art collaborative filtering algorithms unless they have enough dense dataset. Hence, MoresysGOAL has a successful combination of content-based and collaborative filtering approaches for increasing performance of the recommendation system. This thesis focuses on serving a successful solution to users considering two parts. The first part is related to the similarity calculation of the contents are supplemented by goal programming. Moreover, the proposed system has the content information of the movies which also play a role to support collaborative filtering algorithms. These collaborative methods form the second part by means of predicting movies to satisfy user tastes. Lastly, MoresysGOAL is a web-based application for recommending prediction lists of movies to the end users.