Skip to main content
SpringerLink
Log in

How to Recommend Online Medical Service Smarter and Faster? A Novel Decision-Making Method Considering Users' Linguistic Preference and Trust Propagation

  • Published:
International Journal of Fuzzy Systems Aims and scope Submit manuscript

Abstract

The existing online medical platforms are striving to provide various kinds of services to satisfy users' dynamic demands. With the exponential growth of information, users usually suffer from information overload, confronting with high search costs and time costs. In order to reduce users' search cost and assist users to retrieve appropriate medical service efficiently, recommending online medical services to users smarter and faster has becoming a valuable research topic. Unfortunately, certain imprecise and subjective information always exist in obtaining users' preferences. Moreover, the effect of social trust on an individual user's decision-making is often omitted in previous methods, which may result in inaccurate recommendation results. This study proposes a method for recommending the most appropriate medical services to users, in which users' linguistic preferences, assessments and social trust information are considered. The procedure of the proposed method consists of two steps: (1) to acquire various users' linguistic preference based on Best–Worst Method (BWM) and fuzzy set theory, and (2) to recommend the corresponding medical services using trust-aware collaborative filtering (CF) technique. Firstly, the fuzzy BWM is adopted to determine the attribute weights based on various users' linguistic preferences. Secondly, by combing the linguistic evaluation matrix and users' social trust information, the trust-based similarity network is constructed, which is then merged into CF technique for predicting final rating and recommending online medical services. Finally, a case study is conducted to demonstrate the applications of the proposed method.

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
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Yang, Y., Zhang, X., Lee, P.: Improving the effectiveness of online healthcare platforms: An empirical study with multi-period patient-doctor consultation data. Int. J. Prod. Econ. 207(1), 70–80 (2019)

    Google Scholar 

  2. Hallikas, J., Immonen, M., Pynneonen, M., Mikkonen, K.: Service purchasing and value creation: towards systemic purchases. Int. J. Prod. Econ. 147(1), 53–61 (2014)

    Google Scholar 

  3. Liu, D.R., Shih, Y.Y.: Integrating AHP and data mining for product recommendation based on customer lifetime value. Information and Management 42(3), 387–400 (2005)

    Google Scholar 

  4. Zhang, Y., Chen, M., Huang, D., Wu, D., Li, Y.: IDoctor: personalized and professionalized medical recommendations based on hybrid matrix factorization. Futur. Gener. Comput. Syst. 66(1), 30–35 (2017)

    Google Scholar 

  5. Zhang, W.Y., Zhang, S., Chen, Y.G., Pan, X.W.: Combining social network and collaborative filtering for personalized manufacturing service recommendation. Int. J. Prod. Res. 51(22), 6702–6719 (2013)

    Google Scholar 

  6. Wang, C.H.: A market-oriented approach to accomplish product positioning and product recommendation for smart phones and wearable devices. Int. J. Prod. Res. 53(8), 2542–2553 (2015)

    Google Scholar 

  7. Ziegler CN, Mcnee SM, Konstan JA, Lausen G.: Improving recommendation lists through topic diversification. In: Proceedings of the 14th International Conference on World Wide Web. Chiba, Japan, pp. 22–32 (2005)

  8. Cai, Z.Q., Hu, H.: Session-aware music recommendation via a generative model approach. Soft. Comput. 22(3), 1023–1031 (2018)

    MATH  Google Scholar 

  9. Mohammadi, M., Rezaei, J.: Bayesian best-worst method: a probabilistic group decision making model. Omega 96, 102075 (2020)

    Google Scholar 

  10. Liu, P., Zhu, B., Wang, P.: A weighting model based on best–worst method and its application for environmental performance evaluation. Appl. Soft Comput. 103, 107168 (2021)

    Google Scholar 

  11. Liu, P.D., Hendalianpour, A., Fakhrabadi, M., et al.: Integrating IVFRN-BWM and goal programming to allocate the order quantity considering discount for green supplier. Int. J. Fuzzy Syst. 24(2), 989–1011 (2022)

    Google Scholar 

  12. Faizi, S., Sałabun, W., Nawaz, S., Rehman, A., Wątróbski, J.: Best-Worst method and Hamacher aggregation operations for intuitionistic 2-tuple linguistic sets. Expert Syst. Appl. 181, 115088 (2021)

    Google Scholar 

  13. Dong, J., Wan, S., Chen, S.M.: Fuzzy best-worst method based on triangular fuzzy numbers for multi-criteria decision-making. Inf. Sci. 547, 1080–1104 (2021)

    MathSciNet  MATH  Google Scholar 

  14. Wankhede, VA, & Vinodh, S.: Analysis of industry 4.0 challenges using best worst method: A case study. Computers & Industrial Engineering, 159, 107487 (2021)

  15. Kotiloglu, S., Lappas, T., Pelechrinis, K., Repoussis, P.P.: Personalized multi-period tour recommendations. Tour. Manage. 62(5), 76–88 (2017)

    Google Scholar 

  16. Hwangbo, H., Kim, Y.S., Cha, K.J.: Recommendation system development for fashion retail e-commerce. Electron. Commer. Res. Appl. 28(2), 94–101 (2018)

    Google Scholar 

  17. Wang, M.L., Ma, J.: A novel recommendation approach based on users’ weighted trust relations and the rating similarities. Soft. Comput. 20(10), 3981–3990 (2016)

    Google Scholar 

  18. Parvin, H., Moradi, P., Esmaeili, S.: TCFACO: trust-aware collaborative filtering method based on ant colony optimization. Expert Syst. Appl. 118(6), 152–168 (2019)

    Google Scholar 

  19. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)

    Google Scholar 

  20. Jannach, D., Karakaya, Z., Gedikli, F.: Accuracy improvements for multi-criteria recommender systems. In: Proceedings of the 13th ACM Conference on Electronic Commerce. Valencia, Spain, pp. 674–689 (2012)

  21. Adomavicius, G., Kwon, Y.O.: New recommendation techniques for multicriteria rating systems. IEEE Intell. Syst. 22(3), 48–55 (2007)

    Google Scholar 

  22. Fan, Z.P., Zhang, X., Chen, F.D., Liu, Y.: Multiple attribute decision making considering aspiration-levels: a method based on prospect theory. Comput. Ind. Eng. 65(2), 341–350 (2013)

    Google Scholar 

  23. Fan, Z.P., Xi, Y., Liu, Y.: Supporting consumer’s purchase decision: a method for ranking products based on online multi-attribute product ratings. Soft. Comput. 22(16), 5247–5261 (2018)

    MATH  Google Scholar 

  24. Liu, Y., Fan, Z.P., Zhang, Y.: A method for stochastic multiple criteria decision making based on dominance degrees. Inf. Sci. 181(19), 4139–4153 (2011)

    MathSciNet  MATH  Google Scholar 

  25. Ortega, F, Mayor J, Lopez-Fernandez D, et al.: CF4J 2.0: Adapting collaborative filtering for Java to new challenges of collaborative filtering based recommender systems. Knowledge-Based Systems, 215, 94–99 (2021)

  26. Jian, M., Zhang, C.L., Liu, M.S., et al.: Siamese graph-based dynamic matching for collaborative filtering. Inf. Sci. 611, 185–198 (2022)

    Google Scholar 

  27. Singh, P.K., Othman, E., Ahmed, R., et al.: Optimized recommendations by user profiling using apriori algorithm. Appl. Soft Comput. 106, 107272 (2021)

    Google Scholar 

  28. Wang, F., Zhu, H.B., Srivastava, G., et al.: Robust collaborative filtering recommendation with user-item-trust records. IEEE Trans. Comput. Soc. Syst. 9(4), 986–996 (2022)

    Google Scholar 

  29. Liang, T.T., Chen, M.N., Yin, Y.Y., et al.: Recurrent neural network based collaborative filtering for QoS prediction in IoV. IEEE Trans. Intell. Transp. Syst. 23(3), 2400–2410 (2022)

    Google Scholar 

  30. Chen, T.C.T., Chiu, M.C.: Mining the preferences of patients for ubiquitous clinic recommendation. Health Care Management Science. https://doi.org/10.1007/s10729-018-9441-y (2020)

  31. Huang, Z.X., Lu, X.D., Duan, H.L., Zhao, C.H.: Collaboration-based medical knowledge recommendation. Artif. Intell. Med. 55(1), 13–24 (2012)

    Google Scholar 

  32. Kim, K.J., Ahn, H.: Recommender systems using cluster-indexing collaborative filtering and social data analytics. Int. J. Prod. Res. 55(17), 5037–5049 (2017)

    Google Scholar 

  33. Lu, J., Wu, D.S., Mao, M.S., Wang, W., Zhang, G.Q.: Recommender system application developments: a survey. Decis. Support Syst. 74(6), 12–32 (2015)

    Google Scholar 

  34. Lee, W.P., Ma, C.Y.: Enhancing collaborative recommendation performance by combining user preference and trust-distrust propagation in social networks. Knowl.-Based Syst. 106(6), 125–134 (2016)

    Google Scholar 

  35. Wu, J., Chang, J.L., Cao, Q.W., Liang, C.Y.: A trust propagation and collaborative filtering based method for incomplete information in social network group decision making with type-2 linguistic trust. Comput. Ind. Eng. 127(12), 853–864 (2019)

    Google Scholar 

  36. Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    MATH  Google Scholar 

  37. Pedrycz, W.: Why triangular membership functions? Fuzzy Sets Syst. 64(1), 21–30 (1994)

    MathSciNet  Google Scholar 

  38. Chen, X., Li, Z.W., Fan, Z.P., Zhou, X.Y., Zhang, X.: Matching demanders and suppliers in knowledge service: a method based on fuzzy axiomatic design. Inf. Sci. 346–347(6), 130–145 (2016)

    MathSciNet  Google Scholar 

  39. Fan, Z.P., Li, Y.H., Wang, X.H., Liu, Y.: Hybrid similarity measure for case retrieval in CBR and its application to emergency response towards gas explosion. Expert Syst. Appl. 41(5), 2526–2534 (2014)

    Google Scholar 

  40. Kaufmann, A., Gupta, M.M.: Introduction to Fuzzy Arithmetic: Theory and Applications. Van Nostrand Reinhold Company, New York (1985)

    MATH  Google Scholar 

  41. Li, D.F.: A ratio ranking method of triangular intuitionistic fuzzy numbers and its application to MADM problems. Comput. Math. Appl. 60(6), 1557–1570 (2010)

    MathSciNet  MATH  Google Scholar 

  42. Rezaei, J.: Best-worst multi-criteria decision-making method. Omega 53(4), 49–57 (2015)

    MathSciNet  Google Scholar 

  43. Saaty, T.L.: The Analytic Hierarchy Process. McGraw Hill Company, New York (1980)

    MATH  Google Scholar 

  44. Mi, X., Tang, M., Liao, H., Shen, W., Lev, B.: The state-of-the-art survey on integrations and applications of the best worst method in decision making: Why, what, what for and what’s next? Omega 87, 205–225 (2019)

    Google Scholar 

  45. Hafezalkotob, A., Hafezalkotob, A.: A novel approach for combination of individual and group decisions based on fuzzy best-worst method. Appl. Soft Comput. 59(10), 316–325 (2017)

    MATH  Google Scholar 

  46. Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning-I. Inf. Sci. 8(3), 199–249 (1975)

    MathSciNet  MATH  Google Scholar 

  47. van Laarhoven, P.J.M., Pedrycz, W.: A fuzzy extension of Saaty’s priority theory. Fuzzy Sets Syst. 11(1–3), 199–227 (1983)

    MathSciNet  MATH  Google Scholar 

  48. Lootsma, F.A.: Saaty’s priority theory and the nomination of a senior professor in operations Research. Eur. J. Oper. Res. 4(6), 380–388 (1980)

    Google Scholar 

  49. Jiang, Y.P., Fan, Z.P.: A method for group decision making with multi-granularity linguistic assessment information. Inf. Sci. 178(4), 1098–1109 (2008)

    MATH  Google Scholar 

  50. Al-Oufi, S., Kim, H.N., El Saddik, A.: A group trust metric for identifying people of trust in online social networks. Expert Syst. Appl. 39(18), 13173–13181 (2012)

    Google Scholar 

  51. Chen, S., Luo, T., Liu, W., Xu, Y..: Incorporating Similarity and Trust for Collaborative Filtering. In: Proceedings of the Sixth International Conference on Fuzzy Systems and Knowledge Discovery. Tianjin, China, pp.487–493 (2009)

  52. Liu, Y., Bi, J.W., Fan, Z.P.: A method for ranking products through online reviews based on sentiment classification and interval-valued intuitionistic fuzzy TOPSIS. Int. J. Inf. Technol. Decis. Mak. 16(6), 1497–1522 (2017)

    Google Scholar 

Download references

Funding

This study was partially supported by the National Natural Science Foundation of China (Program Nos. 71974154), the Natural Science Foundation of Shaanxi Province (Program No. 2019JM-110),  the Shaanxi Provincial Youth Talent Support Program, and the Fundamental Research Funds for the Central Universities (Project Nos. JB190604, 20101235636). Also sponsored by the Seed Foundation of Innovation Practice for Graduate Students in Xidian University.

Author information

Authors and Affiliations

  1. Department of Management Engineering, School of Economics & Management, Xidian University, Xi’an 710071, China

    Xi Chen, Yuan Luo & Wenbo Zhang

  2. School of Foreign Languages, Xidian University, Xi’an 710071, China

    Qirui Wu

Authors
  1. Xi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yuan Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qirui Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Wenbo Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qirui Wu.

Ethics declarations

Conflict of interest

Author Xi Chen declares that she has no conflict of interest. Author Yuan Luo declares that she has no conflict of interest. Author Qirui Wu declares that he has no conflict of interest. Author WenBo Zhang declares that he has no conflict of interest.

Appendix A

Appendix A

See Tables 13, 14, and 15.

Table 13 Notation
Full size table
Table 14 Abbreviation
Full size table
Table 15 The weighted evaluation of all users
Full size table

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, X., Luo, Y., Wu, Q. et al. How to Recommend Online Medical Service Smarter and Faster? A Novel Decision-Making Method Considering Users' Linguistic Preference and Trust Propagation. Int. J. Fuzzy Syst. 25, 2828–2846 (2023). https://doi.org/10.1007/s40815-023-01533-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40815-023-01533-x

Keywords

Search

Navigation