Enhancing recommendation accuracy of item-based collaborative filtering using Bhattacharyya coefficient and most similar item

Abstract

The item-based collaborative filtering technique recommends an item to the user from the rating of k-nearest items. Generally, a random value of k is considered to find nearest neighbor from item-item similarity matrix. However, consideration of a random value for k intuitively is not a rational approach, as different items may have different value of k nearest neighbor. Sparsity in the data set is another challenge in collaborative filtering, as number of co-rated items’ may be few or zero. Due to the above two reasons, collaborative filtering provides inaccurate recommendations, because the predicted rating may tend towards the Mean. The objective of the proposed work is to improve the accuracy by mitigating the above issues. Instead of using a random value of k, we use the most similar neighbor for each target item so as to predict the target item, since finding k for different target item is computationally expensive. Bhattacharyya Coefficient is used as a similarity measure to handle sparsity in the dataset. The performance of the proposed algorithm is tested the datasets of MovieLens and Film Trust, and experimental results reveal better prediction accuracy than the best of the prevalent prediction approaches exist in literature.