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A Novel Hybrid Music Recommendation System using K-Means Clustering and PLSA


  • Computer Science and Engineering Department, Chandigarh University, Mohali - 140413, Punjab, India


We propose a hybrid approach of music recommendation according to which user will get recommendations of the songs searched by the other users alike to the existing user on content basis and also songs listened by that similar set users on context basis. Here we will try to find the similar users on the basis of context using K-Means clustering technique and among those users we will find the most frequent or most listened songs on the basis of content using PLSA technique. To fulfil this task we have created a dummy dataset and taking assumption that it is valid for experimental point of view. In order to evaluate the utility of recommendations produced by using proposed approach of music recommender system, we have computed three metrics i.e. Precision, Recall and F-1 Score. The result of proposed methodology shows the promising result as evaluated by parameters i.e. precision, recall and F1-score on dummy dataset. It can be tested for the real dataset of users from future perspective which will definitely going to take some time because of availability of such data.


Content Based, Context Based, Hybrid Music Recommendations, K-Means, PLSA.

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  • Yading S, Dixon S, Pearce M. A survey of music recommendation systems and future perspectives. 9th International Symposium on Computer Music Modeling and Retrieval; London. 2012 Jun 19-22. p. 395–410.
  • Yajie H, Ogihara M. NextOne Player: A music recommendation system based on user behavior. ISMIR; 2011. p. 103–8.
  • Douglas E, Lamere P, Bertin-Mahieux T, Green S. Automatic generation of social tags for music recommendation. Advances in Neural Information Processing Systems; 2008. p. 385–92.
  • Seungmin R, Song S, Hwang E, Kim M. COMUS: Ontological and rule-based reasoning for music recommendation system. Advances in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg. 2009; 5476:859– 66.
  • Kunsu K, Lee D, Yoon TB, Lee JH. A music recommendation system based on personal preference analysis. IEEE First International Conference on the. Applications of Digital Information and Web Technologies, ICADIWT; Ostrava. 2008 Aug 4-6. p. 102–6.
  • Kazuyoshi Y, Goto M, Komatani K Ogata T, Okuno HG. An efficient hybrid music recommender system using an incrementally trainable probabilistic generative model. IEEE Transactions on Audio, Speech and Language Processing. 2008; 16(2):435–47.
  • Cheng-Che L, Tseng VS. A novel method for personalized music recommendation. Expert Systems with Applications. 2009; 36(6):10035–44.
  • Bo S, Wang D, Li T, Ogihara M. Music recommendation based on acoustic features and user access patterns. IEEE Transactions on Audio, Speech and Language Processing. 2009; 17(8):1602–11.
  • Yoon T, Lee S, Yoon KH, Kim D, Lee JH. A personalized music recommendation system with a time-weighted clustering. 4th International IEEE Conference on Intelligent Systems IS’08; Varna. 2008 Sep 6-8. p. 10–48–52.
  • Hung-Chen C, Chen ALP. A music recommendation system based on music and user grouping. Journal of Intelligent Information Systems. 2005; 24(2-3):113–32.
  • Ziwon H, Lee K, Lee K. Music recommendation using text analysis on song requests to radio stations. Expert Systems with Applications. 2014; 41(5):2608–18.
  • Singh G, Boparai RDS, Kathpal M. A novel hybrid K-Means PLSA technique for music recommender. Indian Journal of Science and Technology. 2016 Apr; 9(16):1–4.


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