Enhancing Recommendation Systems for the Cold Start Challenge: A Two-Stage Content-Boosted Collaborative Filtering Approach
Authors
Vinay Kumar Matam1, *, A. L. Sreenivasulu2, G. Naga Pavani3
1Assistant Professor, Department of CSE, G. Pulla Reddy Engineering College, Kurnool, India
2Professor, Department of CSE, Vignana Bharathi Institute of Technology, Hyderabad, India
3Assistant Professor, Department of CSE, Anantha Lakshmi Institute of Technology and Sciences, Anantapur, India
*Corresponding author.
Email: vinayforv@gmail.com
Corresponding Author
Vinay Kumar Matam
Available Online 17 March 2025.
- DOI
- 10.2991/978-94-6463-662-8_30How to use a DOI?
- Keywords
- Cold-start; Deep Learning; Personalization; Recommendation Systems; User Preferences
- Abstract
The dynamic world of e-commerce demands efficient recommendation systems to help users discover relevant products. However, a major hurdle arises when new users join a platform. Without any historical data, traditional recommendation techniques struggle to provide personalized suggestions. This ‘cold-start’ challenge remains a significant challenge in the field. Deep learning offers a potential solution by enabling systems to learn from limited information and gives more accurate recommendations. Exploring the application of deep learning to address the cold-start problem across various domains is a promising area of research.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Vinay Kumar Matam AU - A. L. Sreenivasulu AU - G. Naga Pavani PY - 2025 DA - 2025/03/17 TI - Enhancing Recommendation Systems for the Cold Start Challenge: A Two-Stage Content-Boosted Collaborative Filtering Approach BT - Proceedings of the International Conference on Advanced Materials, Manufacturing and Sustainable Development (ICAMMSD 2024) PB - Atlantis Press SP - 362 EP - 376 SN - 2352-5401 UR - https://doi.org/10.2991/978-94-6463-662-8_30 DO - 10.2991/978-94-6463-662-8_30 ID - Matam2025 ER -