Title: Effective user preference mining-based personalised movie recommendation system

Authors: V. Subramaniyaswamy; R. Logesh; D. Malathi; V. Vijayakumar; Hamid Reza Karimi; Marimuthu Karuppiah

Addresses: School of Computing, SASTRA Deemed University, Thanjavur, India ' School of Computing, SASTRA Deemed University, Thanjavur, India ' School of Computing, SASTRA Deemed University, Thanjavur, India ' School of Computing Science and Engineering, Vellore Institute of Technology, Chennai, India ' Department of Mechanical Engineering, Politecnico di Milano, Milan, Italy ' School of Computing Science and Engineering, Vellore Institute of Technology, Vellore, India

Abstract: One of the primary issues of many websites is the suggestion of multiple choices to the users at the same time, which makes the task more complex and time consuming to find the right product. Web mining and recommendation system based on user behaviour helps users by providing essential information without asking explicitly. Several movie recommendation systems are available to suggest movies, but often they do not do that effectively. To achieve enhanced effectiveness and efficiency, user's movie ratings were retrieved, cleaned, formatted and grouped into proper, meaningful session and data profile was developed. In this paper, we have developed a new ontology for clear and better understanding of the movie domain. The user data consisting of movie ratings is used to recommend movies for the users. For the classification of users, we use adaptive K-nearest neighbour (AKNN) approach and post classification process; movies are recommended to the active target user. The obtained results of the proposed recommendation approach are compared with existing baseline methods, and the results prove that the presented approach to be proficient.

Keywords: recommender systems; personalisation; adaptive KNN; ontology; web mining; classification.

DOI: 10.1504/IJCAET.2020.109521

International Journal of Computer Aided Engineering and Technology, 2020 Vol.13 No.3, pp.371 - 387

Received: 29 Nov 2017
Accepted: 02 Apr 2018

Published online: 11 Sep 2020 *

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