Title: Efficient synergetic filtering in big data set using neural network technique

Authors: B. Mukunthan

Addresses: Department of Computer Science, Sri Ramakrishna College of Arts and Science (Autonomous), Nava India, Coimbatore-641006, Tamil Nadu, India

Abstract: Presently, great accomplishment on speech-recognition, computer-vision and natural-language processing has been achieved by deep-neural networks. To tackle the major trouble in synergetic or collaborative-filtering we concentrated intensively on the techniques based on neural networks. Although a few recent researches have employed deep learning, they mostly used it to carve auxiliary facts, along with textual metaphors of objects and acoustic capabilities of music. We present a popular framework named Artificial Neural Synergetic Filtering (ANSF) to substitute the core makeup with a neural design which could be very efficient to analyse data with a random feature. ANSF is a prevalent matrix-factorisation framework. To improvise it with non-linearity we propose to leverage a multilayer perceptron to investigate customer-object communication function. In-depth experiments on actual global databases display big improvement over the latest techniques. Investigational results manifest that the application of core layers of artificial neural networks gives improved overall performance.

Keywords: synergetic filtering; big data; matrix factorisation; deep neural network; multilayer perceptron.

DOI: 10.1504/IJCAT.2021.114989

International Journal of Computer Applications in Technology, 2021 Vol.65 No.2, pp.134 - 149

Received: 17 Jan 2020
Accepted: 11 May 2020
Published online: 13 May 2021 *

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