Abstract

Collaborative filtering (CF) techniques have been proven to be one of the successful approaches to building music recommender systems (MRS). The CF’s system work by creates suggestions for users based on their neighbors preferences. On the other hand the online music sites data are highly dynamic. As a result, the listeners face the problem of missing music suggestions. Recommender systems help the users to find their music interests and information they are looking for. In this paper we provide other researchers in that field with new valuable knowledge and insights regarding music recommendations using collaborative filtering technique that has the ability to recommend music to a new user as well as the other existing users. We then present three main categories of collaborative filtering techniques: memory-based, model-based, and the last hybrid method that combining collaborative and content-based.

References

• B. Dey, “Music-Map: A Music Recommendation System Based On Data Mining Technique,” Ver. XI.
• P. No, F. Ahmad, and A. K. M, “Available Online at www.journalijcar.org Review Article REVIEW BASED RECOMMENDER SYSTEM FOR BUSINESS ADVANTAGE USING NATURAL LANGUAGE PROCESSING,” vol. 7, no. 5, 2018.
• M. Nilashi, K. Bagherifard, O. Ibrahim, and H. Alizadeh, “Collaborative Filtering Recommender Systems Collaborative Filtering Recommender Systems,” no. September 2016, 2013.
• V. Manvitha and M. S. Reddy, “Music Recommendation System Using Association Rule Mining and Clustering Technique To Address Coldstart Problem,” vol. 3, no. 7, pp. 6855–6858, 2014.
• M. B. Dias, D. Locher, B. Dias, D. Locher, and W. El-deredy, “The Value of Personalised Recommender Systems to e-Business : A Case Study Categories and Subject Descriptors,” no. January, 2008.
• N. Abdullah, Y. Xu, and S. Geva, “Integrating collaborative filtering and matching-based search for product recommendations,” J. Theor. Appl. Electron. Commer. Res., 2013.
• C. A. Gomez-uribe and N. Hunt, “The Netflix Recommender System : Algorithms , Business Value ,” vol. 6, no. 4, 2015.
• M. Misal and U. Nagaraj, “Recommender System Methods and Feedback Mechanisms : A Survey,” pp. 11710–11716, 2015.
• T. Sultana, N. Sharma, S. Aher, N. Pate, and M. Kinikar, “Recommendation System for Music File.”
• G. Badaro, H. Hajj, W. El-hajj, and L. Nachman, “A Hybrid Approach with Collaborative Filtering for Recommender Systems A Hybrid Approach with Collaborative Filtering for Recommender Systems,” no. July, 2013.
• C. Castelino, R. S. Muthalaly, A. Lopes, and S. Lopes, “Recommender Systems: Increasing Profits and Efficiency in Business,” Int. J. Eng. Trends Technol., vol. 16, 2014.
• K. Choi, Y. Suh, and D. Yoo, “Extended Collaborative Filtering Technique for Mitigating the Sparsity Problem Previous works,” vol. 11, no. October, pp. 631–644, 2016.
• K. Yu, A. Schwaighofer, V. Tresp, X. Xu, and H. P. Kriegel, “Probabilistic Memory-Based Collaborative Filtering,” IEEE Trans. Knowl. Data Eng., 2004.
• A. F. Huda, I. Wasito, T. Basaruddin, and S. Mujiono, “Spatial Clustering Algorithm Based on Neighboring Structure Approach,” vol. 8, no. November, pp. 25–38, 2013.
• M. S. Reddy, “User Based Collaborative Filtering for Music Recommendation System Abstract :,” vol. 2, no. 12, pp. 185–190, 2013.
• P. B. Thorat, “Survey on Collaborative Filtering , Content-based Filtering and Hybrid Recommendation System,” vol. 110, no. 4, pp. 31–36, 2015.
• N. Pereira and S. Varma, “Survey on Content Based Recommendation System.
• G. Advani, “A Novel Method for Music Recommendation using Social Media Tags,” vol. 122, no. 2, pp. 37–43, 2015.