Enhanced Content-Based Filtering Using Diverse Collaborative Prediction for Movie Recommendation

• Authors: Mohammed Nazim uddin;Jenu Shrestha;Geun-Sik Jo
• Venue: ACIIDS '09 Proceedings of the 2009 First Asian Conference on Intelligent Information and Database Systems
• Year: 2009

In recommender system, Collaborative filtering or Content-based filtering is one of the most popular methods used to predict items of interest for a user. Each method has their own advantage, though individually they possess several limitations. In order to minimize the limitation, we developed a hybrid recommender system incorporating components from both methods.Our approach includes a diverse-item selection algorithm that uses a diversity metric to select the dissimilar items among the recommended items from collaborative filtering, which together with the input is fed into content-based filtering . We present experimental result on movielens dataset that show how our approach performs better than content-based filtering and Naive hybrid approach.