Parallelization of Hybrid Content Based and Collaborative Filtering Method in Recommendation System with Apache Spark

https://doi.org/10.22146/ijccs.38596

Rakhmad Ikhsanudin(1*), Edi Winarko(2)

(1) Master Program of Computer Science; FMIPA UGM, Yogyakarta
(2) Department of Computer Science and Electronics, FMIPA UGM, Yogyakarta
(*) Corresponding Author

Abstract


Collaborative Filtering as a popular method that used for recommendation system. Improvisation is done in purpose of improving the accuracy of the recommendation. A way to do this is to combine with content based method. But the hybrid method has a lack in terms of scalability. The main aim of this research is to solve problem that faced by recommendation system with hybrid collaborative filtering and content based method by applying parallelization on the Apache Spark platform.Based on the test results, the value of hybrid collaborative filtering method and content based on Apache Spark cluster with 2 node worker is 1,003 which then increased to 2,913 on cluster having 4 node worker. The speedup got more increased to 5,85 on the cluster that containing 7 node worker.


Keywords


recomendation system; hybrid content based and collaborative filtering method; Apache Spark

Full Text:

PDF


References

[1] F. Ricci, L. Rokach, and B. Shapira, Recommender Systems Handbook. 2015.

[2] A. Segal, Z. Katzir, and K. Gal, “EduRank : A Collaborative Filtering Approach to Personalization in E-learning,” Proc. 7th Int. Conf. Educ. Data Min., no. Edm, pp. 68–75, 2014.

[3] P. Wang, H., Zhang, “Hybrid Recommendation Model Based on Incremental Collaborative Filtering and Content- based Algorithms,” 2017 IEEE 21st Int. Conf. Comput. Support. Coop. Work Des., 2017. [Online]. Available: https://ieeexplore.ieee.org/document/8066717/. [Accessed: 2- Sept- 2018]

[4] S. Schelter, C. Boden, and V. Markl, “Scalable similarity-based neighborhood methods with MapReduce,” in Proceedings of the 6th ACM conference on Recommender systems - RecSys ’12, 2012, p. 163.

[5] P. Ghuli, A. Ghosh, and R. Shettar, “A Collaborative Filtering Recommendation Engine in a Distributed Environment,” Proc. - 2014 Int. Conf. Contemp. Comput. Informatics, pp. 568–574, 2014.

[6] Y. Shang, Z. Li, W. Qu, Y. Xu, Z. Song, and X. Zhou, “Scalable Collaborative Filtering Recommendation Algorithm with MapReduce,” in 2014 IEEE 12th International Conference on Dependable, Autonomic and Secure Computing, 2014, pp. 103–108.

[7] P. A. Riyaz and S. M. Varghese, “A Scalable Product Recommendations Using Collaborative Filtering in Hadoop for Bigdata,” Procedia Technol., 2016.

[8] E. Casey, “Scalable Collaborative Filtering Recommendation Algorithms on Apache Spark,” Claremont McKenna College, 2014.

[9] W. G. S. Parwita, “Hybrid Recommendation System Memanfaatkan Penggalian Frequent Itemset dan Perbandingan Keyword,” vol. 9, no. 2, pp. 19–21, 2015.

[10] B. Sarwar, G. Karypis, J. Konstan, and J. Reidl, “Item-based collaborative filtering recommendation algorithms,” in Proceedings of the tenth international conference on World Wide Web - WWW ’01, 2001, pp. 285–295.

[11] M. Claypool, T. Miranda, A. Gokhale, P. Murnikov, D. Netes, and M. Sartin, “Combining content-based and collaborative filters in an online newspaper,” Proc. Recomm. Syst. Work. ACM SIGIR, pp. 40–48, 1999.

[12] N. Bharill, A. Tiwari, and A. Malviya, “Fuzzy Based Clustering Algorithms to Handle Big Data with Implementation on Apache Spark,” in Proceedings - 2016 IEEE 2nd International Conference on Big Data Computing Service and Applications, BigDataService 2016, 2016. [Online]. Available: https://ieeexplore.ieee.org/document/7474361/. [Accessed: 2- Sept- 2018]



DOI: https://doi.org/10.22146/ijccs.38596

Article Metrics

Abstract views : 2875 | views : 2259

Refbacks

  • There are currently no refbacks.




Copyright (c) 2019 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.



Copyright of :
IJCCS (Indonesian Journal of Computing and Cybernetics Systems)
ISSN 1978-1520 (print); ISSN 2460-7258 (online)
is a scientific journal the results of Computing
and Cybernetics Systems
A publication of IndoCEISS.
Gedung S1 Ruang 416 FMIPA UGM, Sekip Utara, Yogyakarta 55281
Fax: +62274 555133
email:ijccs.mipa@ugm.ac.id | http://jurnal.ugm.ac.id/ijccs



View My Stats1
View My Stats2