An effective movie recommender system enhanced with time series analysis of user rating behaviour
by Bam Bahadur Sinha; R. Dhanalakshmi
International Journal of Mathematics in Operational Research (IJMOR), Vol. 19, No. 3, 2021

Abstract: Recommender system aims at improvising user satisfaction by taking decision on what movie or item to recommend next. Over time though, learners and learning behaviours shift regularly. This paper introduces a novel behaviour-inspired suggestion algorithm named the TimeFly-PPSE algorithm, which operates on the concept of changing user's motives around time. The suggested model takes temporal knowledge into account and monitors the progression of consumers and items that are useful in providing adequate recommendations. The latter outlines a framework that enrolls the user's shifting behaviour to include guidance for personalisation. TimeFly's findings are contrasted with those of other well-known algorithms. Simulation test on 100K MovieLens dataset shows that utilising TimeFly contributes to recommendations that are exceptionally efficient and reliable.

Online publication date: Tue, 10-Aug-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 Mathematics in Operational Research (IJMOR):
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