Skip to main content
SpringerLink
Log in

Optimizing target selection complexity of a recommendation system by skyline query and multi-criteria decision analysis

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Various factors related to user consideration cause a target selection problem that may lead users to receive unexpected or confusing results. Traditionally, the recommendation system is constructed to help the user filter out unrelated targets and recommend targets that may be of interest to the user. However, the complexity of target selection requires a more advanced decision-making analysis to offer support. Determining how to optimize the target selection complexity of a recommendation system has become a critical challenge. This study proposes a novel approach using skyline query and multi-criteria decision analysis to recommend Top-k targets for user selection. Skyline query domination reduces the complexity of target selection by filtering out non-dominant candidates and keeping the dominant candidates for multi-criteria decision analysis. After the skyline query processing, the multi-criteria decision analysis is optimized, producing a Top-k ranking order of the candidate targets. The experiment illustrates an empirical case study to verify the effectiveness of the proposed approach. The contribution is optimizing the target selecting complexity of the recommendation system to solve the target selection problem.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Adapted from [24, 25]

Fig. 3

Similar content being viewed by others

References

  1. Li YM, Lin LF, Lin YH (2014) A recommender mechanism for social knowledge navigation in an online encyclopedia. Inf Process Manag 50(5):634–652. https://doi.org/10.1016/j.ipm.2014.04.006

    Article  Google Scholar 

  2. Lu J, Wu D, Mao M, Wang W, Zhang G (2015) Recommender system application developments: a survey. Decis Support Syst 74:12–32. https://doi.org/10.1016/j.dss.2015.03.008

    Article  Google Scholar 

  3. Han J, Kamber M (2012) Data mining: concepts and techniques, 3rd edn. Morgan Kaufmann Publishers, Los Altos

    MATH  Google Scholar 

  4. Ke CK, Liu DR (2011) Context-based knowledge support for problem-solving by rule-inference and case-based reasoning. Int J Innov Comput Inf Control 7(7A):3615–3632

    Google Scholar 

  5. Liu L, Xu J, Liao SS, Chen H (2014) A real-time personalized route recommendation system for self-drive tourists based on vehicle to vehicle communication. Expert Syst Appl 41(7):3409–3417. https://doi.org/10.1016/j.eswa.2013.11.035

    Article  Google Scholar 

  6. Ishizaka A, Nemery P (2013) Multi-criteria decision analysis: methods and software. Wiley, New York

    Book  Google Scholar 

  7. Aruldoss M, Lakshmi TM, Venkatesan VP (2013) A survey on multi criteria decision making methods and its applications. Am J Inf Syst 1(1):31–43

    Google Scholar 

  8. Martin A, Zarate P, Camillieri G (2017) A multi-criteria recommender system based on users’ profile management. In: Zopounidis C, Doumpos M (eds) Multiple criteria decision making. Springer, Cham. https://doi.org/10.1007/978-3-319-39292-9_5

    Chapter  Google Scholar 

  9. Kalyvas C, Tzouramanis T (2017) A survey of skyline query processing. https://arxiv.org/abs/1704.01788

  10. Chang CM (2015) A study on the optimization of application recommendation mechanism using skyline. Master thesis, National Taichung University of Science and Technology, Taiwan (R.O.C.)

  11. Google (2018) Google Play. https://play.google.com/store/apps. Accessed July 2013

  12. Isinkaye FO, Folajimi YO, Ojokoh BA (2015) Recommendation systems: principles, methods and evaluation. Egypt Inform J 16:261–273

    Article  Google Scholar 

  13. Li YM, Chou CL, Lin LF (2014) A social recommender mechanism for location-based group commerce. Inf Sci 274:125–142

    Article  Google Scholar 

  14. Özyer T, Zhang M, Alhajj R (2011) Integrating multi-objective genetic algorithm based clustering and data partitioning for skyline computation. Appl Intell 35(1):110–122. https://doi.org/10.1007/s10489-009-0206-7

    Article  Google Scholar 

  15. Dong LG, Cui XW, Wang ZF, Cheng SW (2010) Finding k-dominant skyline cube based on sharing-strategy. In: Proceedings of 2010 IEEE Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp 1694–1698

  16. Choi H, Jung H, Lee KY, Chung YD (2013) Skyline queries on keyword-matched data. Inf Sci 232:449–463

    Article  Google Scholar 

  17. Lee J, Kim J, Hwang SW (2012) Supporting efficient distributed skyline computation using skyline views. Inf Sci 194:24–37

    Article  Google Scholar 

  18. Lian X, Chen L (2013) Efficient processing of probabilistic group subspace skyline queries in uncertain databases. Inf Syst 38(3):265–285

    Article  Google Scholar 

  19. Xu Z, Xia M (2012) Identifying and eliminating dominated alternatives in multi-attribute decision making with intuitionistic fuzzy information. Appl Soft Comput 12(4):1451–1456

    Article  Google Scholar 

  20. Gandhi M, Muruganantham A (2015) Potential influencers identification using multi-criteria decision making (MCDM) methods. Procedia Comput Sci 57:1179–1188

    Article  Google Scholar 

  21. Barros CP, Wanke P (2015) An analysis of African airlines efficiency with two-stage TOPSIS and neural networks. J Air Transp Manag 44:90–102

    Article  Google Scholar 

  22. Liu CH, Tzeng GH, Lee MH, Lee PY (2013) Improving metro–airport connection service for tourism development: using hybrid MCDM models. Tour Manag Perspect 6:95–107

    Article  Google Scholar 

  23. Ke CK, Chen YL (2012) A message negotiation approach to e-services by utility function and multi-criteria decision analysis. Comput Math Appl 64(5):1056–1064

    Article  Google Scholar 

  24. Chen YL, Ke CK (2016) Optimal mobile device selection for round-robin data exchange via adaptive multi-criteria decision analysis. Comput Electr Eng 54:119–130. https://doi.org/10.1016/j.compeleceng.2016.01.018

    Article  Google Scholar 

  25. Veza I, Celar S, Peronja I (2015) Competences-based comparison and ranking of industrial enterprises using PROMETHEE method. Procedia Eng 100:445–449

    Article  Google Scholar 

  26. Kilic HS, Zaim S, Delen D (2015) Selecting “The Best” ERP system for SMEs using a combination of ANP and PROMETHEE methods. Expert Syst Appl 42(5):2343–2352

    Article  Google Scholar 

  27. Huang TCK, Chen YL, Chang TH (2015) A novel summarization technique for the support of resolving multi-criteria decision making problems. Decis Support Syst 79:109–124

    Article  Google Scholar 

  28. Liu S, Chan FT, Ran W (2016) Decision making for the selection of cloud vendor: an improved approach under group decision-making with integrated weights and objective/subjective attributes. Expert Syst Appl 55:37–47

    Article  Google Scholar 

  29. Silaghi GC, Arenas AE, Silva LM (2007) A utility-based reputation model for service-oriented computing. In: Priol T, Vanneschi M (eds) Towards next generation grids. Springer, New York, pp 63–72

    Chapter  Google Scholar 

  30. Serrai W, Abdelli A, Mokdad L, Serrai A (2018) How to deal with QoS value constraints in MCDM based web service selection. Wiley Online Library. https://doi.org/10.1002/cpe.4512

  31. Mouhir M, Balouki Y, Gadi T (2018) Towards an enhanced user’s preferences integration into ranking process using dominance approach. Vietnam J Compu Sci 5(1):15–25. https://doi.org/10.1007/s40595-017-0098-0

    Article  Google Scholar 

  32. Ezenwoke AA (2018) Design of a QoS-based framework for service ranking and selection in cloud E-marketplaces. Asian J Sci Res 11:1–11. https://doi.org/10.3923/ajsr.2018.1.11

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported in part by the Ministry of Science and Technology, R.O.C., with MOST Grant 107-2221-E-025-005 -.

Author information

Authors and Affiliations

  1. Department of Information Management, National Taichung University of Science and Technology, No. 129, Sec. 3, Sanmin Rd, North Dist., Taichung, 40401, Taiwan, ROC

    Chih-Kun Ke & Chiao-Min Chang

Authors
  1. Chih-Kun Ke
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chiao-Min Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chih-Kun Ke.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ke, CK., Chang, CM. Optimizing target selection complexity of a recommendation system by skyline query and multi-criteria decision analysis. J Supercomput 76, 6453–6474 (2020). https://doi.org/10.1007/s11227-019-02963-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-019-02963-x

Keywords

Search

Navigation