Personalized movie recommender system through hybrid 2-way filtering with extracted information

Abstract

Personalized recommender systems improve access to relevant items and information by making personalized suggestions based on previous items ofan individual user's likes and dislikes. Most recommender systems use collaborative filtering or content-based filtering to predict new items of interest for auser. This approach has the advantage of being able to recommend previously un-rated items to users with unique interests and to provide explanations forpersonalized recommendations. We describe a personalized movie recommender system that utilizes WebBot, hybrid 2-way filtering, and a machinelearningalgorithm for web page and movie poster's extraction. And we validate our personalized movie recommender system through hybrid 2-way filteringwith extracted information in on-line experiments.