Open Access

ARTICLE

User Interaction Based Recommender System Using Machine Learning

R. Sabitha¹, S. Vaishnavi^2,*, S. Karthik¹, R. M. Bhavadharini³

1 Department of Computer Science and Engineering, SNS College of Technology, Coimbatore, 641035, India
2 Department of Information Technology, Sri Ramakrishna Engineering College, Coimbatore, 641022, India
3 Department of Computer Science and Engineering, SRM Eswari Engineering College, Chennai, 600089, India

* Corresponding Author: S. Vaishnavi. Email:

Intelligent Automation & Soft Computing 2022, 31(2), 1037-1049. https://doi.org/10.32604/iasc.2022.018985

Received 28 March 2021; Accepted 03 July 2021; Issue published 22 September 2021

Abstract

In the present scenario of electronic commerce (E-Commerce), the in-depth knowledge of user interaction with resources has become a significant research concern that impacts more on analytical evaluations of recommender systems. For staying in aggressive E-Commerce, various products and services regarding distinctive requirements must be provided on time. Moreover, because of the large amount of product information available online, Recommender Systems (RS) are required to analyze the availability of consumers, which improves the decision-making of customers with detailed product knowledge and reduces time consumption. With that note, this paper derives a new model called User Interaction based Recommender System (UI-RS) that utilizes the data from multiple sources and opinion-based analysis for sensing the consumer needs and interests. For that, Content-Based Filtering (CBF) analyses various products and determines the likeliness of products based on User Interaction to recommend that to consumers. Then, the product information from multiple sources is combined with Dempster-Shafer (D-S) evidence theory, and then, decision making for product recommendation is performed with CBF. Moreover, the modified Radial Basis Function Neural Networks (RBFNN) technique has been incorporated for measuring product recommendations. The results show that the proposed model produces better results in providing accurate recommendations to Consumers with a higher rate of coverage and precision, thereby enhancing significant growth in E-Commerce.

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Citations