Title: Double fuzzy clustering driven context neural network optimised with chimp optimisation algorithm for movie rating recommendation system

Authors: K. Krishnaveni; S. Siva Ranjani

Addresses: Department of Computer Science and Engineering, Sethu Institute of Technology, Pulloor, Kariapatti – 626115, Virudhunagar District, India ' Department of Computer Science and Engineering, Sethu Institute of Technology, Pulloor, Kariapatti – 626115, Virudhunagar District, India

Abstract: This paper proposes a pioneering approach called double fuzzy clustering driven context neural network optimised by chimp optimisation algorithm for movie rating recommendation (DFCCNN-COA-MRR). Motivated by the need to enhance recommendation accuracy and mitigate cold start issues, this model integrates double fuzzy clustering with context-aware neural network architecture, bolstered by the chimp optimisation algorithm for weight parameter optimisation. Leveraging the MovieLens 100k dataset, feature extraction and clustering are conducted to form contextual clusters, enabling more precise recommendations. The performance of the proposed DFCCNN-COA-MRR algorithm attains 33.01%, 37.82% and 36.73% high accuracy, 1.16%, 5.07% and 2.71% lower error rate and 32.92%, 35.65% and 33.15% better precision comparing to the existing methods like DRPRA-MRR, EGJSM-CgS-MRR and CAR-RA-MRR respectively. Through this work, contribute a novel recommendation model that successfully addresses key challenges in collaborative filtering, thereby advancing state-of-the-art in recommendation system research.

Keywords: grapheme-based anisotropic polarisation meta-filter; force-invariant improved feature extraction method; double fuzzy clustering driven context neural networks; chimp optimisation algorithm.

DOI: 10.1504/IJBIC.2025.148388

International Journal of Bio-Inspired Computation, 2025 Vol.26 No.1, pp.10 - 23

Received: 18 Jul 2023
Accepted: 25 Mar 2024
Published online: 03 Sep 2025 *

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