Chinese medical websites help patients search for satisfactory doctors via the Internet regardless of time and location. Existing website systems recommend the same doctors for all patients using a global ranking but disregard patient preferences and online reviews. Additionally, these models do not consider the effects of interdependencies among criteria when making recommendations. We propose a systematic decision support model to improve such recommendations using intuitionistic fuzzy sets (IFSs) with the Bonferroni mean (BM) to address interdependencies. Our system accommodates patient preferences using multiple intuitionistic normal clouds (INCs). A case study using production data from haodf.com, the largest such website, shows that our model improves the diversity and coverage of doctor recommendations while considering patient preferences when compared to the existing haodf.com approach. This pattern continued with testing using data from several other Chinese healthcare sites. Our proposal is thus both applicable and readily implemented to improve the recommendations of these websites.
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Li J, Wang JQ. Multi-criteria outranking methods with hesitant probabilistic fuzzy sets. Cogn Comput. 2017;9(5):611–25.
Li X, Chen X. D-intuitionistic hesitant fuzzy sets and their application in multiple attribute decision making. Cogn Comput. 2018;10(3):496–505.
Ji P, Zhang H, Wang JQ. A projection-based outranking method with multi-hesitant fuzzy linguistic term sets for hotel location selection. Cogn Comput. 2018;10:737–51. https://doi.org/10.1007/s12559-018-9552-2.
Farhadinia B. A multiple criteria decision making model with entropy weight in an interval-rransformed hesitant fuzzy environment. Cogn Comput. 2017;9(4):513–25.
Liu P, Li H. Interval-valued intuitionistic fuzzy power Bonferroni aggregation operators and their application to group decision making. Cogn Comput. 2017;9(4):492–512.
Liu P, Zhang X. A novel picture fuzzy linguistic aggregation operator and its application to group decision-making. Cogn Comput. 2018;10(2):242–59.
Tao Z, Han B, Chen H. On intuitionistic fuzzy copula aggregation operators in multiple-attribute decision making. Cogn Comput. 2018;1:1–15. https://doi.org/10.1007/s12559-018-9545-1.
Resnick P, Varian HR. Recommender systems. Commun ACM. 1997;40(3):56–8.
Zhang Z, Zhao X, Wang G. FE-ELM: a new friend recommendation model with extreme learning machine. Cogn Comput. 2017;9(5):659–70.
Schuckert M, Liu X, Law R. Hospitality and tourism online reviews: recent trends and future directions. J Travel Tour Mark. 2015;32(5):608–21.
Liu X, Lu R, Ma J, Chen L, Qin B. Privacy-preserving patient-centric clinical decision support system on naïve bayesian classification. IEEE Journal of Biomedical & Health Informatics. 2016;20(2):655–68.
Wang MX, Wang JQ. New online recommendation approach based on unbalanced linguistic label with integrated cloud. Kybernetes. 2018;47(7):1325–47. https://doi.org/10.1108/K-06-2017-0211.
Wang JQ, Zhang X, Zhang HY. Hotel recommendation approach based on the online consumer reviews using interval neutrosophic linguistic numbers. J Intell Fuzzy Syst. 2018;34(1):381–94. https://doi.org/10.3233/JIFS-171421.
Guy I and Carmel D (2015). Social recommender systems. In: Xavier A, Josep M P (eds). Recommender Systems Handbook.pp 511–43.
Li YM, Wu CT, Lai CY. A social recommender mechanism for e-commerce: combining similarity, trust, and relationship. Decis Support Syst. 2013;55(3):740–52.
Davoodi E, Kianmehr K, Afsharchi M. A semantic social network-based expert recommender system. Appl Intell. 2013;39(1):1–13.
Xiao W, Yao S and Wu S. Improving on recommend speed of recommender systems by using expert users. Control and Decision Conference (CCDC), 2016 Chinese 2016: 2425–30.
Atanassov KT. Intuitionistic fuzzy sets. Fuzzy Sets Syst. 1986;20(1):87–96. https://doi.org/10.1016/S0165-0114(86)80034-3.
Xu Z. Intuitionistic fuzzy aggregation operators. Ieee T Fuzzy Syst. 2007;15(6):1179–87.
Wei G. Some induced geometric aggregation operators with intuitionistic fuzzy information and their application to group decision making. Appl Soft Comput. 2010;10(2):423–31.
Atanassov K, Gargov G. Interval valued intuitionistic fuzzy sets. Fuzzy Sets Syst. 1989;31(3):343–9.
Yu SM, Wang J, Wang JQ. An extended TODIM approach with intuitionistic linguistic numbers. Int Trans Oper Res. 2018;25(3):781–805.
Rodríguez A, Ortega F, Concepción R. An intuitionistic method for the selection of a risk management approach to information technology projects. Inform Sci. 2017;375:202–18.
Hu JH, Zhang XH, Yang Y, Liu YM, Chen XH. New doctors ranking system based on VIKOR method. Int Trans Oper Res. 2018. https://doi.org/10.1111/itor.12569.
Hu JH, Yang Y, Zhang XL, Chen XH. Similarity and entropy measures for hesitant fuzzy sets. Int Trans Oper Res. 2018;25(3):857–86. https://doi.org/10.1111/itor.12477.
Yang Y, Hu JH, An QX, Chen XH. Group decision making with multiplicative triangular hesitant fuzzy preference relations and cooperative games method. Int J Uncertain Quantif. 2017;7(3):271–84. https://doi.org/10.1615/Int.J.UncertaintyQuantification.2017020126.
Gao H, Wei G, Huang Y. Dual hesitant bipolar fuzzy Hamacher prioritized aggregation operators in multiple attribute decision making. IEEE Access. 2018;6(1):11508–22.
Tian ZP, Wang J, Wang JQ, Zhang HY. Simplified neutrosophic linguistic multi-criteria group decision-making approach to green product development. Group Decis Negotiation. 2017;26(3):597–627.
Wei G, Lu M. Pythagorean fuzzy power aggregation operators in multiple attribute decision making. Int J Intell Syst. 2018;33(1):169–86.
Wei G, Wei Y. Similarity measures of Pythagorean fuzzy sets based on the cosine function and their applications. Int J Intell Syst. 2018;33(3):634–52.
Wei GW, Gao H. The generalized dice similarity measures for picture fuzzy sets and their applications. Informatica. 2018;29(1):1–18.
Wei G. Picture uncertain linguistic Bonferroni mean operators and their application to multiple attribute decision making. Kybernetes. 2017;46(10):1777–800.
Wei GW. Some cosine similarity measures for picture fuzzy sets and their applications to strategic decision making. Informatica. 2017;28(3):547–64.
Li D, Meng H, Shi X. Membership clouds and membership cloud generators. J Comput Res Dev. 1995;6(32):15–20.
Li D, Du Y. Artificial intelligence with uncertainty. International Conference on Computer and Information Technology. 2008;15(11):2.
Wang G, Xu C, Li D. Generic normal cloud model. Inform Sci. 2014;280:1–15.
Petri I, Li H, Rezgui Y, Chunfeng Y, Yuce B, Jayan B. A HPC based cloud model for real-time energy optimisation. Enterp Inf Syst. 2016;10(1):108–28.
Wang JQ, Yang WE. Multiple criteria group decision making method based on intuitionistic normal cloud by Monte Carlo simulation. Syst Eng Theory Pract. 2013;33(11):2859–65.
Xu Z, Yager RR. Intuitionistic fuzzy Bonferroni means. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics). 2011;41(2):568–78.
Liu P, Liu J, Chen S-M. Some intuitionistic fuzzy Dombi Bonferroni mean operators and their application to multi-attribute group decision making. J Oper Res Soc. 2018;69(1):1–24.
Garg H, Arora R. Bonferroni mean aggregation operators under intuitionistic fuzzy soft set environment and their applications to decision-making. J Oper Res Soc. 2018:1–14.
Liu P, Chen S-M, Liu J. Multiple attribute group decision making based on intuitionistic fuzzy interaction partitioned Bonferroni mean operators. Inform Sci. 2017;411:98–121.
Kim HN, El-Saddik A, Jo GS. Collaborative error-reflected models for cold-start recommender systems. Decis Support Syst. 2011;51(3):519–31.
Huang TCK, Chen YL, Chen MC. A novel recommendation model with Google similarity. Decis Support Syst. 2016;89:17–27.
Negre E, Ravat F, Teste O, Tournier R. Cold-start recommender system problem within a multidimensional data warehouse. IEEE 7th International Conference on Research Challenges in Information Science (RCIS). 2013:1–8.
Li YM, Lin LF, Ho CC. A social route recommender mechanism for store shopping support. Decis Support Syst. 2016;94:97–108.
Zhang Y. GroRec: a group-centric intelligent recommender system integrating social, mobile and big data technologies. IEEE Trans Serv Comput. 2016;9(5):1.
Atanassov KT, Rangasamy P. Intuitionistic fuzzy sets. VII ITKR. 1983.
Atanassov KT, Rangasamy P. Intuitionistic fuzzy sets. Fuzzy Sets & Systems. 1986;20(1):87–96.
Le HS, Thong NT. Intuitionistic fuzzy recommender systems: an effective tool for medical diagnosis. Knowl-Based Syst. 2015;74:133–50. https://doi.org/10.1016/j.knosys.2014.11.012.
Thong NT, Le HS. HIFCF: an effective hybrid model between picture fuzzy clustering and intuitionistic fuzzy recommender systems for medical diagnosis. Expert Syst Appl. 2015;42(7):3682–701.
Guan C, Yuen K K F and Coenen F. Towards an intuitionistic fuzzy agglomerative hierarchical clustering algorithm for music recommendation in folksonomy. IEEE International Conference on Systems, Man, and Cybernetics 2016: 2039–42.
Wang G Y, Xu C L and Li D Y. Generic normal cloud model Inform Sciences 2014; 280: 1–15.
Wang D, Liu DF, Ding H, Singh VP, Wang YK, Zeng XK, et al. A cloud model-based approach for water quality assessment. Environ Res. 2016;148:24–35.
Wang D, Zeng DB, Singh VP, Xu PC, Liu D, Wang YK, et al. A multidimension cloud model-based approach for water quality assessment. Environ Res. 2016;149:113–21.
Zhang LM, Wu XG, Chen QQ, Skibniewski MJ, Zhong JB. Developing a cloud model based risk assessment methodology for tunnel-induced damage to existing pipelines. Stoch Env Res Risk A. 2015;29(2):513–26.
Zhang HY, Ji P, Wang JQ, Chen XH. A neutrosophic normal cloud and its application in decision-making. Cogn Comput. 2016;8(4):649–69.
Agarwal B, Poria S, Mittal N, Gelbukh A, Hussain A. Concept-level sentiment analysis with dependency-based semantic parsing: a novel approach. Cogn Comput. 2015;7(4):487–99.
Giatsoglou M, Vozalis MG, Diamantaras K, Vakali A, Sarigiannidis G, Chatzisavvas KC. Sentiment analysis leveraging emotions and word embeddings. Expert Syst Appl. 2017;69:214–24.
Zhu B, Xu ZS. Hesitant fuzzy Bonferroni means for multi-criteria decision making. J Oper Res Soc. 2013;64(12):1831–40.
Tian ZP, Wang J, Wang JQ, Chen XH. Multicriteria decision-making approach based on gray linguistic weighted Bonferroni mean operator. Int Trans Oper Res. 2018;25(5):1635–58. https://doi.org/10.1111/itor.12220.
Wang J-g, Peng J-j, Zhang H-y, Liu T, Chen X-h. An uncertain linguistic multi-criteria group decision-making method based on a cloud model. Group Decis Negot. 2015;24(1):171–92.
Zhou T, Kuscsik Z, Liu JG, Medo M, Wakeling JR, Zhang YC. Solving the apparent diversity-accuracy dilemma of recommender systems. Proceedings of the National Academy of Sciences of the USA. PNAS. 2010;107(10):4511–5.
Gogna A, Majumdar A. DiABlO: optimization based design for improving diversity in recommender system. Inform Sci. 2017;378:59–74.
Kunaver M, Požrl T. Diversity in recommender systems—a survey. Knowl-Based Syst. 2017;123:154–62.
Ricci F, Rokach L and Shapira B. Introduction to recommender systems handbook. Place: Springer; year.
Shani G and Gunawardana A (2011). Evaluating recommendation systems. In: (eds). Recommender systems handbook.pp 257–97.
Pu P, Faltings B, Chen L, Zhang J and Viappiani P (2011). Usability guidelines for product recommenders based on example critiquing research. In: Xavier A, Josep M P (eds). Recommender Systems Handbook.pp 511–45.
This work was supported by the National Natural Science Foundation of China (Grant numbers 71871229, 71771219) and the Fundamental Research Funds for the Central Universities of Central South University (2018zzts092).
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Yang, Y., Hu, J., Liu, Y. et al. Doctor Recommendation Based on an Intuitionistic Normal Cloud Model Considering Patient Preferences. Cogn Comput 12, 460–478 (2020). https://doi.org/10.1007/s12559-018-9616-3
- Decision support model
- Intuitionistic normal cloud model
- Medical websites
- Doctor recommendation