Authors: Bolanle Adefowoke Ojokoh; Oluwatosin Olatunbosun Aboluje; Tobore Igbe
Addresses: Department of Computer Science, Federal University of Technology, Akure, Nigeria; Department of Mathematics and Computer Science, Elizade University, Ilara-Mokin, Nigeria ' Department of Computer Science, Federal University of Technology, Akure, Nigeria ' Center for Minimal Invasive Surgery, Shenzhen Institute of Advanced Technology, Chinese Academy of Science, Nanshan District, Shenzhen, China
Abstract: In this paper, Pearson's correlation coefficient is employed for collaborative filtering due to its ability to manipulate numerical data as well as determine linear relationship among existing users. Its steps involve a user-user representation, similarity generation and prediction generation with a goal to produce a predicted opinion of the active user about a specific item. Concept of parental control is also incorporated for enhancement. Evaluation of the system was done using precision, recall, F-measure, discounted cumulative gain (DCG), idealised discounted cumulative gain (IDCG), normalised discounted cumulative gain (nDCG) and mean absolute error (MAE). Three hundred fortysix datasets were used, out of which 126 were gathered from local video shops and 220 were extracted from internet movie database (IMDb). These were used for the experiments and the results generated through mining of data obtained from profiles and ratings of system users prove the system's average ranking quality of the collaborative filtering algorithm is 95.9%.
Keywords: recommendation; collaborative filtering; correlation coefficient; evaluation; movies.
International Journal of Business Intelligence and Data Mining, 2020 Vol.17 No.3, pp.298 - 320
Received: 16 Aug 2017
Accepted: 18 Jan 2018
Published online: 24 Apr 2020 *