TY  - EJOU
AU  - Kumar, C. Raja 
AU  - Jayanthi, VE. 

TI  - A Novel Fuzzy Rough Sets Theory Based CF Recommendation System
T2  - Computer Systems Science and Engineering

PY  - 2019
VL  - 34
IS  - 3
SN  - 

AB  - Collaborative Filtering (CF) is one of the popular methodology in recommender systems. It suffers from the data sparsity problem, recommendation inaccuracy
and big-error in predictions. In this paper, the efficient advisory tool is implemented for the younger generation to choose their right career based on their
knowledge. It acquires the notions of indiscernible relation from Fuzzy Rough Sets Theory (FRST) and propose a novel algorithm named as Fuzzy Rough
Set Theory Based Collaborative Filtering Algorithm (FRSTBCF). To evaluate the model, data is prepared using the cross validation method. Based on that,
ratings are evaluated by calculating the MAE (mean average error), MSE (means squared error) and RMSE (root means squared error) values. Further the
correctness of the model is measured by finding rates like Accuracy, Specificity, Sensitivity, Precision & False Positive Rate. The proposed FRSTBCF algorithm
is compared with the traditional algorithms experiment results such as Item Based Collaborative Filtering using the cosine similarity (IBCF-COS), IBCF using
the pearson correlation (IBCF-COR), IBCF using the Jaccard similarity (IBCF-JAC) and Singular Value Decomposition approximation (SVD). The proposed
algorithm gives better error rate and its precision value is comparatively identical with the existing system.
KW  - Recommendation System; Collaborative Filtering; Fuzzy Rough Sets Theory; Indiscernibility; prediction

DO  - 10.32604/csse.2019.34.123