Movie Recommender System using Genetic Algorithm

Jyoti Joshi

Abstract


Recommender systems have become extremely common in recent years, and are utilized in a variety of areas: some popular applications include movies, music, news, books, research articles, search queries, social tags, and products in general. Traditional recommendation techniques in recommender systems mainly use content based or collaborative filtering techniques. These systems only use the product ratings given by the users to predict/recommend new products or items to the user. They do not consider other attributes while generating recommendations for a user.

This article describes a new recommendation system that uses genetic algorithm to learn about the preferences of the users and provides recommendations based on these preferences. This research uses Movie Lens (http://www.movielens.umn.edu) database and the genetic algorithm combines features (22) from different files present in the dataset. These features are then used to train the system. The 22 features are - movie rating, age, sex, occupation and 18 movie genres like action, adventure, animation, children, comedy, crime, documentary, drama, fantasy, film-noir, horror, musical, mystery, romance, sci-fi, thriller, war and western.


Keywords


Content based filtering, Collaborative filtering, Genetic algorithms, Recommender system

Full Text:

PDF

References


Schafer J, Konstan J, Riedl J. Recommender Systems in E-commerce. ACM conference on Electronic Commerce, USA. 1999. pp. 158-166.

Balabanovic M, Shoham Y. FAB: content-based, collaborative recommendation. Communications of the ACM 1997; 40(3): 66-72.

Burke R. Hybrid web recommender systems. The Adaptive Web - Lecture Notes in Computer Science, 2007. pp. 377-408.

Pazzani MJ. A Framework for Collaborative, Content-based and Demographic Filtering. Artificial Intelligence Review 1999; 13(5-6): 394-408.

Mitchell M. An Introduction to Genetic Algorithm. MIT Press, 1998.

Goldberg DE, Holland JH. Genetic algorithms and machine learning. Machine Learning 1988; 3(2-3): 95-9.

Breese JS, Heckerman D, Kadie C. Empirical analysis of predictive algorithms for collaborative filtering. Conference on Uncertainty in Artificial Intelligence, 1998. pp. 43-52.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.