Open Access

ARTICLE

Improving Recommendation for Effective Personalization in Context-Aware Data Using Novel Neural Network

R. Sujatha^1,*, T. Abirami²

1 Department of Information Technology, M. Kumarasamy College of Engineering, Karur, Tamilnadu, India
2 Department of Information Technology, Kongu Engineering College, Perundurai, Tamilnadu, India

* Corresponding Author: R. Sujatha. Email:

Computer Systems Science and Engineering 2023, 46(2), 1775-1787. https://doi.org/10.32604/csse.2023.031552

Received 21 April 2022; Accepted 03 November 2022; Issue published 09 February 2023

Abstract

The digital technologies that run based on users’ content provide a platform for users to help air their opinions on various aspects of a particular subject or product. The recommendation agents play a crucial role in personalizing the needs of individual users. Therefore, it is essential to improve the user experience. The recommender system focuses on recommending a set of items to a user to help the decision-making process and is prevalent across e-commerce and media websites. In Context-Aware Recommender Systems (CARS), several influential and contextual variables are identified to provide an effective recommendation. A substantial trade-off is applied in context to achieve the proper accuracy and coverage required for a collaborative recommendation. The CARS will generate more recommendations utilizing adapting them to a certain contextual situation of users. However, the key issue is how contextual information is used to create good and intelligent recommender systems. This paper proposes an Artificial Neural Network (ANN) to achieve contextual recommendations based on user-generated reviews. The ability of ANNs to learn events and make decisions based on similar events makes it effective for personalized recommendations in CARS. Thus, the most appropriate contexts in which a user should choose an item or service are achieved. This work converts every label set into a Multi-Label Classification (MLC) problem to enhance recommendations. Experimental results show that the proposed ANN performs better in the Binary Relevance (BR) Instance-Based Classifier, the BR Decision Tree, and the Multi-label SVM for Trip Advisor and LDOS-CoMoDa Dataset. Furthermore, the accuracy of the proposed ANN achieves better results by 1.1% to 6.1% compared to other existing methods.

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Keywords

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