Title: Tackling data sparsity: a hybrid filtering paradigm for robust recommender systems

Authors: G. Umarani Srikanth; Lijetha C. Jaffrin; Sushmitha Srikanth; Shyam Ramesh

Addresses: Department of CSE, Panimalar Engineering College, Chennai, 600 123, Tamil Nadu, India ' Department of IT, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, 600 062, Tamil Nadu, India ' Department of IT, SSN College of Engineering, Chennai, 603 110, Tamil Nadu, India ' Department of EEE, SSN College of Engineering, Chennai, 603 110, Tamil Nadu, India

Abstract: This paper introduces a hybrid recommender system approach that aims to tackle the problems associated with data sparsity, also referred to as the 'cold start problem', Recommender systems use user preferences to filter information. To improve recommendation accuracy, our method combines user-based and content-based collaborative filtering techniques. More specifically, content-based filtering takes over when there is little data. When there is a high degree of user similarity, user-based collaborative filtering is used to maximise accuracy by suggesting diverse items. This strategy can be used in a variety of fields, including e-commerce, music, books, and film.

Keywords: hybrid filtering; recommender systems; collaborative filtering; SVD; singular value decomposition; machine learning; k-nearest neighbours.

DOI: 10.1504/IJDATS.2025.147515

International Journal of Data Analysis Techniques and Strategies, 2025 Vol.17 No.2, pp.77 - 106

Received: 14 Feb 2023
Accepted: 16 Jan 2024
Published online: 20 Jul 2025 *

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