Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Advanced Technology and Social Media Influence on Research, Industry and Community Read chapter Read first chapter

2018 | OriginalPaper | Chapter

An Improved Collaborative Filtering Recommendation Algorithm for Big Data

Authors : Hafed Zarzour, Faiz Maazouzi, Mohamed Soltani, Chaouki Chemam

Published in: Computational Intelligence and Its Applications

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

With the increase of volume, velocity, and variety of big data, the traditional collaborative filtering recommendation algorithm, which recommends the items based on the ratings from those like-minded users, becomes more and more inefficient. In this paper, two varieties of algorithms for collaborative filtering recommendation system are proposed. The first one uses the improved k-means clustering technique while the second one uses the improved k-means clustering technique coupled with Principal Component Analysis as a dimensionality reduction method to enhance the recommendation accuracy for big data. The experimental results show that the proposed algorithms have better recommendation performance than the traditional collaborative filtering recommendation algorithm.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Implementing a Semantic Approach for Events Correlation in SIEM Systems
Literature
1.
Rashid, A.M., Albert, I., Cosley, D., Lam, S.K., McNee, S.M., Konstan, J.A., Riedl, J.: Getting to know you. In: Proceedings of the 7th International Conference on Intelligent User Interfaces - IUI 2002 (2002)
2.
Merve Acilar, A., Arslan, A.: A collaborative filtering method based on artificial immune network. Expert Syst. Appl. 36(4), 8324–8332 (2009)CrossRef
3.
Chen, L., Hsu, F., Chen, M., Hsu, Y.: Developing recommender systems with the consideration of product profitability for sellers. Inf. Sci. 178(4), 1032–1048 (2008)CrossRef
4.
Jalali, M., Mustapha, N., Sulaiman, M.N., Mamat, A.: WebPUM: a web-based recommendation system to predict user future movements. Expert Syst. Appl. 37(9), 6201–6212 (2010)CrossRef
5.
Smith, B., Linden, G.: Two decades of recommender systems at amazon.com. IEEE Internet Comput. 21(3), 12–18 (2017)CrossRef
6.
Koohi, H., Kiani, K.: A new method to find neighbor users that improves the performance of collaborative filtering. Expert Syst. Appl. 83, 30–39 (2017)CrossRef
7.
Pourkamali-Anaraki, F., Becker, S.: Preconditioned data sparsification for big data with applications to PCA and k-means. IEEE Trans. Inf. Theory 63(5), 1 (2017)MathSciNetCrossRef
8.
Gupta, P., Sharma, A., Jindal, R.: Scalable machine-learning algorithms for big data analytics: a comprehensive review. Wiley Interdisc. Rev.: Data Min. Knowl. Disc. 6(6), 194–214 (2016)
9.
Bagchi, S.: Performance and quality assessment of similarity measures in collaborative filtering using mahout. Proced. Comput. Sci. 50, 229–234 (2015)CrossRef
10.
Verma, J.P., Patel, B., Patel, A.: Big data analysis: recommendation system with Hadoop framework. In: 2015 IEEE International Conference on Computational Intelligence and Communication Technology (2015)
11.
Gantner, Z., Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: MyMediaLite. In: Proceedings Of The Fifth Acm Conference On Recommender Systems - Recsys 2011 (2011)
12.
Meng, S., Dou, W., Zhang, X., Chen, J.: KASR: a keyword-aware service recommendation method on mapreduce for big data applications. IEEE Trans. Parallel Distrib. Syst. 25(12), 3221–3231 (2014)CrossRef
13.
Cheng, D., Rao, J., Guo, Y., Jiang, C., Zhou, X.: Improving performance of heterogeneous mapreduce clusters with adaptive task tuning. IEEE Trans. Parallel Distrib. Syst. 28(3), 774–786 (2017)CrossRef
14.
Lee, O.J., Hong, M.S., Jung, J.J., Shin, J., Kim, P.: Adaptive collaborative filtering based on scalable clustering for big recommender systems. Acta Polytech. Hung. 13(2), 179–194 (2016)
15.
Bande, VM., Pakle, K.: CSRS: Customized service recommendation system for big data analysis using map reduce. In: 2016 International Conference on Inventive Computation Technologies (ICICT) (2016)
16.
Zarzour, H., Al-Sharif, Z., Al-Ayyoub, M., Jararweh, Y.: A new collaborative filtering recommendation algorithm based on dimensionality reduction and clustering techniques. In: 9th International Conference on Information and Communication Systems (ICICS) (2018)
17.
Dakhel, GM., Mahdavi, M.: A new collaborative filtering algorithm using k-means clustering and neighbors’ voting. In: 11th International Conference on Hybrid Intelligent Systems, HIS (2011)
18.
Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22(1), 5–53 (2004)CrossRef
19.
20.
Villalba, S.D., Cunningham, P.: An evaluation of dimension reduction techniques for one-class classification. Artif. Intell. Rev. 27(4), 273–294 (2007)CrossRef
21.
Adeniyi, D.A., Wei, Z., Yongquan, Y.: Automated web usage data mining and recommendation system using K-nearest neighbor (KNN) classification method. Appl. Comput. Inform. 12(1), 90–108 (2016)CrossRef
22.
Hallinan, B., Striphas, T.: Recommended for you: the Netflix prize and the production of algorithmic culture. New Media Soc. 18(1), 117–137 (2014)CrossRef
Metadata
Title
An Improved Collaborative Filtering Recommendation Algorithm for Big Data
Authors
Hafed Zarzour
Faiz Maazouzi
Mohamed Soltani
Chaouki Chemam
Copyright Year
2018
Book
Computational Intelligence and Its Applications

Print ISBN: 978-3-319-89742-4

Electronic ISBN: 978-3-319-89743-1

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-89743-1_56

Premium Partner

    Image Credits