Implementation of Apriori Algorithm for Music Genre Recommendation

Authors

  • Michael Henry Computer Science Department, BINUS Graduate Program, Master of Computer Science, Bina Nusantara University, Jakarta, Indonesia
  • Wiryanata Chandra Computer Science Department, BINUS Graduate Program, Master of Computer Science, Bina Nusantara University, Jakarta, Indonesia
  • Amalia Zahra Computer Science Department, BINUS Graduate Program, Master of Computer Science, Bina Nusantara University, Jakarta, Indonesia

DOI:

https://doi.org/10.15575/join.v7i1.819

Keywords:

music genre, association rule, apriori algorithm, recommendation system, data mining

Abstract

Music interest is diverse yet enticing to be a part of knowledge discovery. It influences how people feel, study, work, etc. A lot of things are to be considered in producing brand new music with its correlation to its genre. We have already collected the dataset that we can utilize in this research, which is the history of every song listened to by several users in a total of 20.000 records from a million song dataset. This study implements the Apriori algorithm which can handle a large amount of data while simplifying the data to create a recommendation system where the result is a pattern from the music genre according to the interests of each user with the help of the RapidMiner tool. The purpose of this research is that the pattern which has been found can become a reference for music producers in terms of making or distributing their brand-new music. The result of the best combination of genres states that listeners of the rock genre will also hear the pop genre with a combination frequency of 50, support value of 21.2%, and confidence value of 51%.

References

K. E. Barkwell et al., “Big data visualisation and visual analytics for music data mining,†Inf. Vis. - Biomed. Vis. Vis. Built Rural Environ. Geom. Model. Imaging, IV 2018, pp. 235–240, 2018, doi: 10.1109/iV.2018.00048.

F. Liu, S. Zhang, J. Ge, F. Lu, and J. Zou, “Agricultural Major Courses Recommendation Using Apriori Algorithm Applied in China Open University System,†Proc. - 2016 9th Int. Symp. Comput. Intell. Des. Isc. 2016, vol. 1, pp. 442–446, 2016, doi: 10.1109/ISCID.2016.1109.

P. D. Waggoner, “Unsupervised Machine Learning for Clustering in Political and Social Research,†Unsupervised Mach. Learn. Clust. Polit. Soc. Res., 2020, doi: 10.1017/9781108883955.

M. Sandeep Kumar and J. Prabhu, “A hybrid model collaborative movie recommendation system using K-means clustering with ant colony optimisation,†Int. J. Internet Technol. Secur. Trans., vol. 10, no. 3, pp. 337–354, 2020, doi: 10.1504/IJITST.2020.107079.

L. Yao, Z. Xu, X. Zhou, and B. Lev, “Synergies Between Association Rulesand Collaborative Filteringin Recommender System: An Applicationto Auto Industry,†Data Sci. Digit. Bus., pp. 23–40, 2019, doi: 10.1007/978-3-319-95651-0_2.

C. Wang and X. Zheng, “Application of improved time series Apriori algorithm by frequent itemsets in association rule data mining based on temporal constraint,†Evol. Intell., vol. 13, no. 1, pp. 39–49, 2020, doi: 10.1007/s12065-019-00234-5.

A. Gonzalez and F. Forsberg, “Unsupervised Machine Learning : An Investigation of Clustering Algorithms on a Small Dataset,†pp. 1–39, 2017.

S. V Hovale and P. G, “Survey Paper on Recommendation System using Data Mining Techniques,†Int. J. Eng. Comput. Sci., vol. 0869, no. 4, pp. 18–19, 2016, doi: 10.18535/ijecs/v5i5.60.

M. K. Najafabadi, M. N. Mahrin, S. Chuprat, and H. Sarkan, “Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining,†Comput. Human Behav., pp. 113–128, 2017, doi: http://dx.doi.org/10.1016/j.chb.2016.11.010.

S. Alzu’Bi, B. Hawashin, M. Eibes, and M. Al-Ayyoub, “A Novel Recommender System Based on Apriori Algorithm for Requirements Engineering,†2018 5th Int. Conf. Soc. Networks Anal. Manag. Secur. SNAMS 2018, pp. 323–327, 2018, doi: 10.1109/SNAMS.2018.8554909.

M. Muhairat, S. Alzu’bi, B. Hawashin, M. Elbes, and M. Al-Ayyoub, “An intelligent recommender system based on association rule analysis for requirement engineering,†J. Univers. Comput. Sci., vol. 26, no. 1, pp. 33–49, 2020.

F. Ali, T. Ahmad, A. M. Martinez-Enriquez, and A. Muhammad, “Data mining based recommendation system using social websites,†Proc. - 2015 IEEE/WIC/ACM Int. Jt. Conf. Web Intell. Intell. Agent Technol. WI-IAT 2015, vol. 1, pp. 365–368, 2016, doi: 10.1109/WI-IAT.2015.78.

J. Jooa, S. Bangb, and G. Parka, “Implementation of a Recommendation System Using Association Rules and Collaborative Filtering,†Procedia Comput. Sci., vol. 91, no. Itqm 2016, pp. 944–952, 2016, doi: 10.1016/j.procs.2016.07.115.

M. Brilliant, D. Handoko, and Sriyanto, “Implementation of Data Mining Using Association Rules for Transactional Data Analysis,†3rd Int. Conf. Inf. Technol. Bus., pp. 177–180, 2017.

I. B. E. Kouni, W. Karoui, and L. B. Romdhane, “Prucars: Improved association rule-based social recommender systems using overlapping community detection,†Procedia Comput. Sci., vol. 176, pp. 956–965, 2020, doi: 10.1016/j.procs.2020.09.091.

M. Fauzy, K. R. Saleh W, and I. Asror, “Penerapan Metode Association Rule Menggunakan Algoritma Apriori pada Simulasi Prediksi Hujan Wilayah Kota Bandung,†J. Ilm. Teknol. Inf. Terap., vol. II, no. 2, pp. 221–227, 2016.

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Published

2022-06-30

Issue

Vol. 7 No. 1 (2022)

Section

Article

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