Hybrid time centric recommendation model for e-commerce applications using behavioral traits of user

B. R. Sreenivasa () and C. R. Nirmala ()
Additional contact information
B. R. Sreenivasa: Bapuji Institute of Engineering & Technology, Visvesvaraya Technological University
C. R. Nirmala: Bapuji Institute of Engineering & Technology, Visvesvaraya Technological University

Information Technology and Management, 2023, vol. 24, issue 2, No 3, 133-146

Abstract: Abstract In today's online market, recommendation systems have become universal and are an aspect of any online shopping portal. The traditional approach uses the subscriber's historical knowledge, and this technique is not adequate for resolving problems with a cold start. These issues include recommendations for non-registered users or newly added customers and new items added. Session-based recommendations based on recurrent neural networks are gaining popularity for product recommendations. This is due to recurrent neural networks' ability to record sequential feature data more effectively throughout the current session, which results in more similarity between consumer behaviour sequences. Nevertheless, most state-of-the-art recurring neural networking systems completely ignore the long-term details of multiple sessions and concentrate solely on short-term communication in a single session. This paper presents a hybrid time-centric prediction model to address research issues that learn the customers' short and long-term behaviours. Experiments on the recsys challenge data set are carried out to assess the efficiency of the hybrid time-centric prediction models over the existing hybrid models in terms of HitRate and Mean-Reciprocal Rate.

Keywords: Deep learning; Machine learning; Bayesian personalized recommendation; Behaviour modelling; Recommendation system; Recurrent neural network; Session-based (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10799-022-00358-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:infotm:v:24:y:2023:i:2:d:10.1007_s10799-022-00358-8

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10799

DOI: 10.1007/s10799-022-00358-8

Access Statistics for this article

Information Technology and Management is currently edited by Raymond Patterson and Erik Rolland

More articles in Information Technology and Management from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().