Cold Start to Warm Glow: Tackling Sparsity and Scalability in Modern Recommender Systems

Bala Shanmukha Sowmya Javvadhi, Manas Kumar Yogi

doi:https://doi.org/10.59232/DST-V4I1P102

Research Article | Open Access | Download Full Text

Volume 4 | Issue 1 | Year 2025 | Article Id: DST-V4I1P102 DOI: https://doi.org/10.59232/DST-V4I1P102

Cold Start to Warm Glow: Tackling Sparsity and Scalability in Modern Recommender Systems

Bala Shanmukha Sowmya Javvadhi, Manas Kumar Yogi

Received	Revised	Accepted	Published
07 Jan 2025	08 Feb 2025	12 Mar 2025	31 Mar 2025

Citation

Bala Shanmukha Sowmya Javvadhi, Manas Kumar Yogi. “Cold Start to Warm Glow: Tackling Sparsity and Scalability in Modern Recommender Systems.” DS Journal of Digital Science and Technology, vol. 4, no. 1, pp. 11-24, 2025.

Abstract

Scalability and data sparsity pose substantial problems for contemporary recommendation engines that work with users with low or no engagement with available items. Solving these problems involves using multiple hybrid recommendations, deep learning, and optimization methods to improve system efficiency and accuracy standards. Recommender systems encounter two primary difficulties, including cold-start problems because new users and items possess minimal data history, and the data sparsity limitation impacting collaborative filtering performance. Large-scale applications need scalable solutions because this stands as a vital concern. Research indicates that the combination of applied transfer learning techniques with reinforcement learning models together with meta-learning methods succeeds in producing better recommender systems outcomes for both new users and items. This research analyses three methods: transfer learning, graph-based models, and reinforcement learning to decrease sparsity, together with scalable systems that use distributed processing and parallel systems. An extensive review of contemporary approaches exists within this paper regarding cold-start problem mitigation, data sparsity solutions, and scalable recommender system design for precise user-oriented recommendations.

Keywords

Cold-start problem, Data sparsity, Hybrid models, Personalization, Recommender systems.

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