Feature augmentation based hybrid collaborative filtering using tree boosted ensemble

Abstract

Requirements for recommendation systems are currently on the raise due to the huge information content available online and the inability of users to manually filter required data. This paper proposes a Feature augmentation based hybrid collaborative filtering using Tree Boosted Ensemble (TBE), for prediction. The proposed TBE recommender is formulated in two phases. The first phase creates category based training matrix using similar user profiles, while the second phase employs the boosted tree based model to predict ratings for the items. A threshold based filtering is finally applied to obtain precise recommendations for the user. Experiments were conducted with MovieLens dataset and performances were measured in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The proposed model was observed to exhibit MAE levels of 0.64 and RMSE levels of 0.77 with a variation level of ±0.1. Comparisons with state-of-the-art models indicate that the proposed TBE model exhibits reductions in MAE at 6% to 14% and RMSE at ~0.2.