Personalised movie recommendation based on multi model data integration

Abstract

Recommendation systems plays an essential role in the modern era, and it is a part of routine life where it guides the users in a personalised manner towards interesting and useful objects in a large collection of possible options. The aim of the movie recommendation system is to help movie lovers by generating suggestions on what movie to watch. If movie recommender systems are not in place, movie lovers need to spend time on choosing a movie by going through long lists of movies, which is a time consuming task. Therefore, a lot of research has been conducted to generate movie recommendations using different approaches including pure recommendation techniques and hybrid techniques. However, the recommendations generated through these approaches lack personalisation and accuracy.

This thesis presents our approach to generate personalised movie recommendations using multi model data integration to improve the personalisation and accuracy. Different data sources are integrated as inputs when designing this research. A content-based filtering technique collaborated with genetic algorithm-based optimization was utilized for implementation of this research. A precision value of 0.65 was obtained while evaluating the multi-model data integration-based movie recommender system with genetic algorithm-based optimization.