Skip to main content

Recommendation of a Cloud Service Item Based on Service Utilization Patterns in Jyaguchi

  • Conference paper
Book cover Knowledge and Systems Engineering

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 245))

Abstract

One of the most determining factors for mining the sequences in terms of service mining in cloud services is time sequence. However, this factor is found to be often ignored and recommendation of services in cloud system is done based on the item mining approach. The problem that we discussed in this paper is addressed by applying the concept of time weight factor in the collection of sequence from which we achieved better result of recommendation from relational sequences. In this paper, we describe a recommendation method of service to user based on his service usage pattern in the system. The recommendation algorithm is based on the mining result of TWSMA algorithm which adopts an innovative approach based on sequences of service usage pattern and then characterizes each set of sequences using multidimensional properties based on user id, time series, and usage frequencies. We take advantage of implementing recommendation in Jyaguchi cloud system in which the user are recommended the services according to the log of service used by the users.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Proc. of the Eleventh International Conference on Data Engineering, pp. 3–14 (1995)

    Google Scholar 

  2. Shrestha, S.K., Kudo, Y., Gautam, B.P., Shrestha, D.: Multidimensional Service Weight Sequence Mining based on Cloud Service Utilization in Jyaguchi. In: Proc. of the International Multi Conference of Engineers and Computer Scientists, IMECS 2013, vol. I, pp. 301–306 (2013)

    Google Scholar 

  3. Gautam, B.P.: An Architectural Model for Time Based Resource Utilization and Optimized Resource Allocation in a Jini Based Service Cloud. Master Thesis, Shinshu University, Nagano, Japan (2009)

    Google Scholar 

  4. Gautam, B.P., Shrestha, D.: A Model for the Development of Universal Browser for Proper Utilization of Computer Resources Available in Service Cloud over Secured Environment. In: Proc. of the International Multi Conference of Engineers and Computer Scientists, IMECS 2010, vol. I (2010)

    Google Scholar 

  5. Gautam, B.P., Shrestha, S.K., Paudel, D.R.: Utilization of Jyaguchi Architecture for development of Jini Based Service Cloud. Wakkanai Hokusei Gakuen University Journal (11), 7–21 (2011)

    Google Scholar 

  6. Pinto, H., Han, J., Pei, J., Wang, K., Chen, Q., Dayal, U.: Multidimensional Sequential Pattern Mining. In: Proc. of the Tenth International Conference on Information and Knowledge Management (CIKM 2001), pp. 81–88 (2001)

    Google Scholar 

  7. Pei, J., Han, J., Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q., Dayal, U., Hsu, M.C.: Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach. IEEE Transactions on Knowledge and Data Engineering 16(10), 1424–1440 (2004)

    Google Scholar 

  8. http://en.wikipedia.org/wiki/NetfixPrize

  9. Oku, K., Tung, T.S., Hattori, F.: Collaborative Filtering for Predicting Users Potential Preferences. In: König, A., Dengel, A., Hinkelmann, K., Kise, K., Howlett, R.J., Jain, L.C. (eds.) KES 2011, Part IV. LNCS (LNAI), vol. 6884, pp. 44–52. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  10. Pazzani, M.J., Billsus, D.: Content-Based Recommendation Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)

    Google Scholar 

  11. Burke, R.: Hybrid Web Recommender Systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) The Adaptive Web. LNCS, vol. 4321, pp. 377–408. Springer, Heidelberg (2007)

    Google Scholar 

  12. Zhou, B., Hui, S.C., Fong, A.C.M.: Efficient Sequential Access Pattern Mining for Web Recommendations. International Journal of Knowledge-based and Intelligent Engineering Systems 10(2), 155–168 (2006)

    Google Scholar 

  13. Han, M., Wang, Z., Yuan, J.: Mining Constraint Based Sequential Patterns and Rules on Restaurant Recommendation System. Journal of Computational Information Systems 9(10), 3901–3908 (2013)

    Google Scholar 

  14. Zhou, B., Hui, S.C., Chang, K.: An intelligent recommender system using sequential web access patterns. In: Proc. of the 2004 IEEE Conference on Cybernetics and Intelligent Systems, vol. 1, pp. 393–398 (2004)

    Google Scholar 

  15. Khonsha, S., Sadreddini, M.H.: New hybrid web personalization framework. In: Proc. of IEEE 3rd International Conference on Communication Software and Networks, ICCSN, pp. 86–92 (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shree Krishna Shrestha .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Shrestha, S.K., Kudo, Y., Gautam, B.P., Shrestha, D. (2014). Recommendation of a Cloud Service Item Based on Service Utilization Patterns in Jyaguchi. In: Huynh, V., Denoeux, T., Tran, D., Le, A., Pham, S. (eds) Knowledge and Systems Engineering. Advances in Intelligent Systems and Computing, vol 245. Springer, Cham. https://doi.org/10.1007/978-3-319-02821-7_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-02821-7_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02820-0

  • Online ISBN: 978-3-319-02821-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics