Efficient synergetic filtering in big data set using neural network technique
by B. Mukunthan
International Journal of Computer Applications in Technology (IJCAT), Vol. 65, No. 2, 2021

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.

Online publication date: Thu, 13-May-2021

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computer Applications in Technology (IJCAT):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com