Precomputed Clustering for Movie Recommendation System in Real Time

A recommendation system delivers customized data (articles, news, images, music, movies, etc.) to its users. As the interest of recommendation systems grows, we started working on the movie recommendation systems. Most research efforts in the fields of movie recommendation system are focusing on discovering the most relevant features from users, or seeking out users who share same tastes as that of the given user as well as recommending the movies according to the liking of these sought users or seeking out users who share a connection with other people (friends, classmates, colleagues, etc.) and make recommendations based on those related people’s tastes. However, little research has focused on recommending movies based on the movie’s features. In this paper, we present a novel idea that applies machine learning techniques to construct a cluster for the movie by implementing a distance matrix based on the movie features and then make movie recommendation in real time. We implement some different clustering methods and evaluate their performance in a real movie forum website owned by one of our authors. This idea can also be used in other types of recommendation systems such as music, news, and articles.

[1]  Sheetal Vij,et al.  A Hybrid Web Recommendation System based on the Improved Association Rule Mining Algorithm , 2013, ArXiv.

[2]  Adam Prügel-Bennett,et al.  An Improved Switching Hybrid Recommender System Using Naive Bayes Classifier and Collaborative Filtering , 2010 .

[3]  Kyoung-jae Kim,et al.  A recommender system using GA K-means clustering in an online shopping market , 2008, Expert Syst. Appl..

[4]  S. B. Aher,et al.  Combination of machine learning algorithms for recommendation of courses in E-Learning System based on historical data , 2013, Knowl. Based Syst..

[5]  P. Jaccard.  THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1 , 1912 .

[6]  Aysenur Birturk,et al.  A Tag-Based Hybrid Music Recommendation System Using Semantic Relations and Multi-domain Information , 2011, 2011 IEEE 11th International Conference on Data Mining Workshops.

[7]  Jianyong Wang,et al.  Personalizing Web Page Recommendation via Collaborative Filtering and Topic-Aware Markov Model , 2010, 2010 IEEE International Conference on Data Mining.

[8]  Giuseppe Sansonetti,et al.  Context-aware movie recommendation based on signal processing and machine learning , 2011, CAMRa '11.

[9]  William W. Cohen,et al.  Recommendation as Classification: Using Social and Content-Based Information in Recommendation , 1998, AAAI/IAAI.

[10]  Alejandro Bellogín,et al.  Discovering Relevant Preferences in a Personalised Recommender System using Machine Learning Techniques , 2008 .

[11]  Gregory Dudek,et al.  Context Dependent Movie Recommendations Using a Hierarchical Bayesian Model , 2009, Canadian Conference on AI.

[12]  Xiongcai Cai,et al.  Collaborative Filtering for People to People Recommendation in Social Networks , 2010, Australasian Conference on Artificial Intelligence.

[13]  Feng-Cheng Lin,et al.  Recommendation system for localized products in vending machines , 2011, Expert Syst. Appl..

[14]  Delbert Dueck,et al.  Clustering by Passing Messages Between Data Points , 2007, Science.

[15]  Mehregan Mahdavi,et al.  Providing an Effective Collaborative Filtering Algorithm Based on Distance Measures and Neighbors ’ Voting , 2013 .

[16]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.