International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Hybrid Recommendation System for Spotify using Content-Based Filtering and Collaborative-Based Filtering |
|---|---|
| ABSTRACT | This project presents a hybrid Spotify song recommendation system designed to improve personalized music discovery for users. The system combines content-based filtering using audio features and metadata with collaborative filtering based on user listening behavior. Raw Spotify datasets are cleaned and processed to create feature matrices and user-item interaction matrices for efficient similarity computation. Cosine similarity identifies relationships between songs and users. A weighted hybrid scoring method combines both recommendation approaches to address cold-start and sparsity issues. The system offers configurable top-k recommendations to balance relevance and diversity. A user-friendly web interface allows users to input a song and quickly receive recommendations. The visual presentation of results enhances usability and engagement. Experimental analysis shows better recommendation accuracy and diversity compared to single-method approaches. Overall, the system provides a scalable and effective solution for personalized music recommendation. |
| AUTHOR | DARSHAN KUMAR M M, PUJALA THANUSH, DR. PRASHANTHA G R Department of Computer Science and Engineering (Data Science), Bapuji Institute of Engineering & Technology, Davanagere, Karnataka, India |
| VOLUME | 180 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1401014 |
| pdf/14_Hybrid Recommendation System for Spotify using Content-Based Filtering and Collaborative-Based Filtering.pdf | |
| KEYWORDS | |
| References | [1] Sheela Kathavate, “Music Recommendation System using Content and Collaborative Filtering Methods,” International Journal of Engineering Research & Technology (IJERT), 2021 [2] K. Yoshii, M. Goto, K. Komatani, T. Ogata and H. G. Okuno, “Hybrid Collaborative and Content-based Music Recommendation Using Probabilistic Model with Latent User Preferences,” Proceedings of ISMIR, 2006 [3] N. V. D. Malleswari, K. Gayatri, K. Y. S. Kumar et al., “Music Recommendation System using Hybrid Approach,” IJARIIE, 2023. [4] Mahaboob Basha Sk, S. Sriharsha, L. Vyshnavi and G. Dhathrik, “User Based Spotify Recommendation System using Machine Learning Algorithms,” IJISRT, 2024 [5] Jain K. N. et al., “Music Recommendation System,” IJARIIE, 2022 [6] “A Survey of Music Recommendation System,” International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), 2018 [7] “Content-driven Music Recommendation: Evolution, State of the Art and Challenges,” ScienceDirect Journal Article, 2024. [8] S. Vashistha et al., “A Novel Music Recommendation System Using Filtering Techniques,” Informatica Journal, 2024 [9] “Spotify Recommendation System,” IRJMETS, 2024 [10] “Inside Spotify’s Recommendation System: A Complete Guide,” Music Tomorrow Blog, 2025 [12] rishti Vashistha, Deepika Varshney, Eva Sarin and Simran Kaur, “A Novel Music Recommendation System Using Filtering Techniques,” Informatica, 2024 [13] Adi Riyan Prasetya and Desi Ramayanti, “A Model-based Music Recommender System using Collaborative Filtering Technique,” International Journal of Computer Applications (IJCA), 2024 [14] Singh and Porwal, “Hybrid Music Recommendation System Utilizing Item Recognition and Content Analysis,” 2022 [15] Y. Chen et al., “A Music Recommendation System Based on Collaborative Filtering Algorithms,” IEEE Conference Paper, 2022. |
