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.
Citation:
Madushanki, J.G.I. (2022). Personalised movie recommendation based on multi model data integration [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/21482