A dual hybrid recommender system based on SCoR and the random forest

We propose a Dual Hybrid Recommender System based on SCoR, the Synthetic Coordinate Recommendation system, and Random Forest method. By combining user ratings user/item features, SCoR is initially employed to provide recommendation which fed into Forest. The two systems are combined by splitting training set ?equivalent? parts, one of used train while other This initial approach does not exhibit good performance due reduced training. resulted drawback alleviated proposed dual system which, using an innovative method, exploits entire for Forest, resulting recommender that subsequently efficiently combined. Experimental results demonstrate high Movielens datasets.