An improved similarity calculation method for collaborative filtering- based recommendation, considering neighbor’s liking and disliking of categorical attributes of items
Pradeep Kumar Singh, Pijush Kanti Dutta Pramanik, Prasenjit Choudhury · Journal of Information and Optimization Sciences · 2019
Similarity measures play an important role in the accuracy of collaborative filtering based recommendation. Due to non-availability of adequate co-rated users, the accuracy of collaborative filtering decreases because it introduces the sparsity problem. In certain cases, if there is no similar user, recommendation is not possible. In order to abate the issue, we propose a novel approach that calculates the similarity between users not only based on the items rather the attributes of the items. To calculate the similarity more accurately, users’ liking and disliking of the similar attributes of a particular item are considered separately. This approach is particularly useful when there are no co-rated users in the similarity dataset. The performance of the proposed algorithm is tested on the MovieLens dataset using two accuracy metrics, MAE and RMSE.