A collaborative content-based movie recommender system. (24th April 2020)

• Record Type: Journal Article
• Authors: Ojokoh, Bolanle Adefowoke; Aboluje, Oluwatosin Olatunbosun; Igbe, Tobore
• Is Part Of: International journal of business intelligence and data mining. Volume 17:Number 3(2020)
• Page Start: 298
• Page End: 320
• Publication Date: 2020-04-24
• Subjects: recommendation -- collaborative filtering -- correlation coefficient -- evaluation -- movies; 006.312
• Languages: English
• ISSNs: 1743-8187

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%.