Title: Fast fuzzy C-means clustering and deep Q network for personalised web directories recommendation

Authors: M. Robinson Joel; M. Navaneethakrishnan; R. Priscilla; G. Gandhi Jabakumar

Addresses: Department of Computer Science and Engineering, KCG College of Technology, Chennai, India ' Department of Computer Science and Engineering, St. Joseph college of Engineering Sriperumbudur, Chennai, India ' Department of Artificial Intelligence and Data Science, St Joseph's Institute of Technology, Chennai, India ' Department of Computer Science and Business Systems, Anand Institute of Higher Technology, Chennai, Tamil Nadu, India

Abstract: This paper proposes an efficient solution for personalised web directories recommendation using fast FCM+DQN. At first, web directory usage file obtained from given dataset is fed into the accretion matrix computation module, where visitor chain matrix, visitor chain binary matrix, directory chain matrix and directory chain binary matrix are formulated. In this, directory grouping is accomplished based on fast FCM and matching among query and group is conducted based on Kumar Hassebrook and Kulczynski similarity. The user preferred directory is restored at this stage and at last, personalised web directories are recommended to the visitors by means of DQN. The proposed approach has received superior results with respect to maximum accuracy of 0.910, minimum mean squared error (MSE) of 0.0206 and root mean squared error (RMSE) of 0.144. Although the system offered magnificent outcomes, it failed to order web directories in the form of highly, medium and low interested directories.

Keywords: web personalisation; personalised web directories recommendation; fast fuzzy C-means; FCM; deep Q network; DQN; Kulczynski similarity metric; root mean squared error; RMSE.

DOI: 10.1504/IJAHUC.2024.142163

International Journal of Ad Hoc and Ubiquitous Computing, 2024 Vol.47 No.3, pp.176 - 189

Received: 25 Oct 2023
Accepted: 08 May 2024
Published online: 10 Oct 2024 *

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