Browse > Article
http://dx.doi.org/10.14400/JDC.2019.17.9.375

A Music Recommendation System based on Context-awareness using Association Rules  

Oh, Jae-Taek (Department of Computer Science & Engineering, Kongju National University)
Lee, Sang-Yong (Division of Computer Science & Engineering, Kongju National University)
Publication Information
Journal of Digital Convergence / v.17, no.9, 2019 , pp. 375-381 More about this Journal
Abstract
Recently, the recommendation system has attracted the attention of users as customized recommendation services have been provided focusing on fashion, video and music. But these services are difficult to provide users with proper service according to many different contexts because they do not use contextual information emerging in real time. When applied contextual information expands dimensions, it also increases data sparsity and makes it impossible to recommend proper music for users. Trying to solve these problems, our study proposed a music recommendation system to recommend proper music in real time by applying association rules and using relationships and rules about the current location and time information of users. The accuracy of the recommendation system was measured according to location and time information through 5-fold cross validation. As a result, it was found that the accuracy of the recommendation system was improved as contextual information accumulated.
Keywords
Context-awareness; Contextual Information; Data Sparsity; Association Rules; Recommendation System;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
연도 인용수 순위
1 R. Scoble & S. Israel. (2015). Age of Context. Goyang: JiAndSon.
2 E. N. Ko. (2015). New an Introduction to Information and Communication. Seoul: Hanbit Academy.
3 S. Masanori. (2017). New IT Trend. Seoul: Infopub.
4 H. Y. Ko & N. G. Kim. (2019). Performance Analysis of Detecting buried pipelines in GPR images using Faster R-CNN. Journal of Convergence for Information Technology, 9(5), 21-26.   DOI
5 I. B. Yang. (2019). A study on Driver-vehicle Interface for Cooperative Driving. Journal of Convergence for Information Technology, 9(5), 27-33.   DOI
6 H. S. Choi & Y. H. Cho. (2019). Analysis of Security Problems of Deep Learning Technology. Journal of the Korea Convergence Society, 10(5), 9-16.   DOI
7 D. B. Lee & J. H. Seo. (2019). Classification Performance Improvement of UNSW-NB15 Dataset Based on Feature Selection. Journal of the Korea Convergence Society, 10(5), 35-42.   DOI
8 Apple Inc. (2019). https://www.apple.com/kr/apple-music/features
9 M. Unger. (2015). Latent Context-aware Recommender Systems. RecSys' 15 Proceediing of the 9th ACM Conference on Recommender Systems, 383-386.
10 M. Unger, A. Bar, B. Shapira & L. Rokach. (2016). Toward Latent Context-aware Recommendation Systems. Knowledge-Based Systems, 104(2016), 165-178.   DOI
11 S. Rendle, Z. Gantner, C. Freudenthaler & L. Schmidt-Thieme. (2011). Fast Context-aware Recommendations with Factorization Machines. SIGIR' 11 Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, 635-644.
12 J. M. Luna, M. Pechenizkiy, M. J. D. Jesus & S. Ventura. (2018). Mining Context-aware Association Rules using Grammar-based Genetic Programming. IEEE Transactions on Cybernetics, 48(11), 3030-3044.   DOI
13 N. R. Kim, H. B. Bang, B. Kim, S. H. Lee & J. H. Lee. (2016). Research Trends in Context-aware Recommender Systems. Communications of KIISE, 34(6), 22-29.
14 M. Schedl. (2013). Ameliorating Music Recommendation: Integrating Music Content, Music Context, and User Context for Improved Music Retrieval and Recommendation. MoMM' 13 Proceedings of International Conference on Advances in Mobile Computing & Multimedia, 3-10.
15 M. B. Magara, S. Ojo, S. Ngwira & T. Zuva. (2016). Mplist: Context-aware Music Playlist. 2016 IEEE International Conference on Emerging Technologies and Innovative Business Practices for the Transformation of Societies, 309-316.
16 N. M. Villegas, C. Sanchez, J. Diaz-Cely & G. Tamura. (2017). Characterizing Context-aware Recommender Systems: A Systematic Literature Review. Knowledge-based Systems, 140(15), 173-200.
17 J. Bell. (2016). Machine Learning. Seoul: Gilbut.
18 S. K. Gorakala. (2017). Building Recommendation Engines. Seoul: Acorn.
19 J. Han, M. Kamber & J. Pei. (2015). Data Mining: Concepts and Techniques. UiWang: Acorn.
20 M. Yao, B. Cao & J. Yin. (2011). Process Recommendation based on Association Rules and Transaction Context. 2011 International Conference on Internet Technology and Applications, 1-5.
21 S. W. Kim. (2017). Step-by-step Android Programming. Seoul: Hanbit Academy.
22 Naver Corp. (2019). A Music Genre Encyclopedia. https://terms.naver.com/list.nhn?cid=62892&categoryId=62892&so=st1.dsc&viewType=&categoryType=
23 Kakao Corp. (2019) https://www.melon.com/
24 I. K. Cheon. (2015). Android Programming. Paju: SaengNeung.