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A dynamic fuzzy group recommender system based on intuitionistic fuzzy choquet integral aggregation

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Abstract

In this study, a new dynamic fuzzy group recommender system (DFGRS) is investigated, which aims to deal with some critical challenges in the group recommender system (GRS), including the user preference uncertainty, item attractiveness tendency, and group members’ interaction and fairness. The proposed approach of the DFGRS applies intuitionistic fuzzy sets (IFSs) to represent user preferences on the considered items by taking into account user hesitancy and inconsistency in ratings. It also incorporates a novel dynamic similarity mechanism for taking into account users’ preference tendency and item attractiveness in predicting unknown user-item preferences. The model’s parameters are optimized using a modified Bayesian method. This combination creates a more realistic and concise prediction of unknown user ratings. Then, to form a balanced solution in the consensus phase of the DFGRS, an intuitionistic fuzzy Choquet integral aggregation operation (IFCAO) is suggested. This approach enables the DFGRS to generate recommendations that balance between optimal average group users’ preferences and fairness among them. Furthermore, the proposed IFCAO includes the group members’ interaction impact due to the proposed capacity function. In addition, several other intuitionistic fuzzy aggregation operations can be utilized that inherit features of common aggregation strategies defined for a traditional GRS. The experimental results in this paper demonstrate the performance of various aggregation approaches for the consensus phase of DFGRS. The obtained performance analysis results highlight the advantage and the potential of the DFGRS based on the proposed Choquet integral aggregation operation. Overall, the proposed DFGRS presents a novel approach for addressing challenges in group recommendation and could have important practical applications in various areas.

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References

  • Angelov P (1995) Crispification: defuzzification of intuitionistic fuzzy sets. Busefal 64:51–55

    Google Scholar 

  • Atanassov KT (1999) Intuitionistic fuzzy sets—theory and applications, studies in fuzziness and soft computing, vol 35. Physica-Verlag

  • Atanassov KT (2016) Intuitionistic fuzzy sets. Int J Bioautom 20:1

    Google Scholar 

  • Cantador I, Castells P (2012) Group recommender systems: New perspectives in the social web. In: Recommender systems for the social web, intelligent systems reference library, vol 32. Springer, New York, pp 139–157

  • Capuano N, Chiclana F, Herrera-Viedma E, Fujita H, Loia V (2019) Fuzzy group decision making for influence-aware recommendations. Comput Hum Behav 101:371–379

    Article  Google Scholar 

  • Castro J, Toledo RY, Martínez L (2018) A fuzzy approach for natural noise management in group recommender systems. Expert Syst Appl 94:237–249

    Article  Google Scholar 

  • Chen J, Liu Y, Li D (2016) Dynamic group recommendation with modified collaborative filtering and temporal factor. Int Arab J Inform Technol 13(2):294–301

    Article  Google Scholar 

  • Chen Y, Cheng L, Chuang C (2008) A group recommendation system with consideration of interactions among group members. Expert Syst Appl 34(3):2082–2090

    Article  Google Scholar 

  • Christensen IA, Schiaffino SN (2014) Social influence in group recommender systems. Online Inf Rev 38(4):524–542

    Article  Google Scholar 

  • Dara S, Chowdary CR, Kumar C (2020) A survey on group recommender systems. J Intell Inform Syst 54(2):271–295

    Article  Google Scholar 

  • Delic A, Masthoff J, Neidhardt J, Werthner H (2018) How to use social relationships in group recommenders: Empirical evidence. In: 26th Conference on User Modeling, Adaptation and Personalization (UMAP), pp 121–129

  • El-Morsy S (2023) Stock portfolio optimization using pythagorean fuzzy numbers. J Oper Strateg Anal 1(1):8–13

    Article  Google Scholar 

  • Farris FA (2010) The gini index and measures of inequality. Am Math Monthly 117(10):851–864

    Article  MathSciNet  Google Scholar 

  • Felfernig A, Boratto L, Stettinger M, Tkalčič M (2018) Evaluating group recommender systems. Group recommender systems: an introduction, pp 59–71

  • Filev DP, Yager RR (1991) A generalized defuzzification method via BAD distributions. Int J Intell Syst 6(7):687–697

  • Giap CN, Son NN, Nguyen LG, Chau HTM, Tuan TM, Son LH (2022) A new approach for fairness increment of consensus-driven group recommender systems based on choquet integral. Int J Data Warehouse Min 18(1):1–22

    Google Scholar 

  • Grzegorzewski P (2004) Distances between intuitionistic fuzzy sets and/or interval-valued fuzzy sets based on the hausdorff metric. Fuzzy Sets Syst 148(2):319–328

    Article  MathSciNet  Google Scholar 

  • Htun NN, Lecluse E, Verbert K (2021) Perception of fairness in group music recommender systems. In: 26th International Conference on Intelligent User Interfaces, pp 302–306

  • Ihsan M, Saeed M, Rahman A (2023) Optimizing hard disk selection via a fuzzy parameterized single-valued neutrosophic soft set approach. J Oper Strateg Anal 1(2):62–69

    Article  Google Scholar 

  • Jia J, Yao Y, Lei Z, Liu P (2021) Dynamic group recommendation algorithm based on member activity level. Sci Program 2021, 1969118:1–1969118:18

  • Jin C, Mi J, Li F, Zhang J (2022) Hybrid recommender system with core users selection. Soft Comput 26(24):13925–13939

    Article  Google Scholar 

  • Jin Z, Zhang Y, Mu W, Wang W, Jin H (2018) Leveraging the dynamic changes from items to improve recommendation. In: 7th International Conference on Conceptual Modeling (ER). Lecture Notes in Computer Science, vol 11157. Springer, New York, pp 507–520

  • Katarya R (2017) A systematic review of group recommender systems techniques. In: IEEE International Conference on Intelligent Sustainable Systems (ICISS), pp 425–428

  • Kim JK, Kim HK, Oh HY, Ryu YU (2010) A group recommendation system for online communities. Int J Inf Manage 30(3):212–219

    Article  Google Scholar 

  • Krawczak M, Szkatuła G (2020) On matching of intuitionistic fuzzy sets. Inf Sci 517:254–274

    Article  MathSciNet  Google Scholar 

  • Lin X, Zhang M, Zhang Y, Gu Z, Liu Y, Ma S (2017) Fairness-aware group recommendation with pareto-efficiency. In: 11th ACM Conference on Recommender Systems (RecSys), pp 107–115

  • Mahanta J, Panda S (2021) A novel distance measure for intuitionistic fuzzy sets with diverse applications. Int J Intell Syst 36(2):615–627

    Article  Google Scholar 

  • Masthoff J (2004) Group modeling: selecting a sequence of television items to suit a group of viewers. In: Personalized digital television: targeting programs to individual viewers, human-computer interaction series, vol 6. Kluwer/Springer, New York, pp 93–141

  • Masthoff J (2011) Group recommender systems: combining individual models. In: Recommender Systems Handbook. Springer, New York, pp 677–702

  • Öner SC, Öztaysi B, Oner M (2020) Social platform based interval valued intuitionistic fuzzy location recommendation system. J Intell Fuzzy Syst 38(1):1027–1042

  • Palanikumar M, Kausar N, Ahmed SF, Edalatpanah SA, Ozbilge E, Bulut A (2023) New applications of various distance techniques to multi-criteria decision-making challenges for ranking vague sets. AIMS Math 8(5):11397–11424

    Article  MathSciNet  Google Scholar 

  • Patro SGK, Mishra BK, Panda SK, Kumar R, Long HV, Taniar D (2023) Cold start aware hybrid recommender system approach for e-commerce users. Soft Comput 27(4):2071–2091

    Article  Google Scholar 

  • Qiu P, Sorourkhah A, Kausar N, Cagin T, Edalatpanah SA (2023) Simplifying the complexity in the problem of choosing the best private-sector partner. Systems 11(2):80

    Article  Google Scholar 

  • Rabiu I, Salim N, Da’u A, Osman A (2020) Recommender system based on temporal models: a systematic review. Appl Sci 10(7):2204

    Article  Google Scholar 

  • Radhamani C (2020) Crispification of temporal intuitionistic fuzzy sets. In: AIP Conference Proceedings, vol 2277. AIP Publishing

  • Sánchez LQ, Recio-García JA, Díaz-Agudo B, Jiménez-Díaz G (2013) Social factors in group recommender systems. ACM Trans Intell Syst Technol 4(1), 8:1–8:30

  • Serbos D, Qi S, Mamoulis N, Pitoura E, Tsaparas P (2017) Fairness in package-to-group recommendations. In: 26th ACM International Conference on World Wide Web (WWW), pp 371–379

  • Shabani A, Jamkhaneh EB (2014) A new generalized intuitionistic fuzzy number. J Fuzzy Set Valued Anal 24:1–10

    MathSciNet  Google Scholar 

  • Smarandache F (2022) Soft set product extended to hypersoft set and indetermsoft set cartesian product extended to indetermhypersoft set. J fuzzy Exten Appl 3(4):313–316

    Google Scholar 

  • Son LH, Thong NT (2015) Intuitionistic fuzzy recommender systems: an effective tool for medical diagnosis. Knowl Based Syst 74:133–150

    Article  Google Scholar 

  • Tan C, Chen X (2010) Intuitionistic fuzzy choquet integral operator for multi-criteria decision making. Expert Syst Appl 37(1):149–157

    Article  Google Scholar 

  • Toledo RY, Martínez L (2017) Fuzzy tools in recommender systems: a survey. Int J Comput Intell Syst 10(1):776–803

    Article  Google Scholar 

  • Verma R (2021) On intuitionistic fuzzy order-\(\alpha \) divergence and entropy measures with mabac method for multiple attribute group decision-making. J Intell Fuzzy Syst 40(1):1191–1217

    Article  Google Scholar 

  • Xia M, Xu Z, Zhu B (2012) Generalized intuitionistic fuzzy bonferroni means. Int J Intell Syst 27(1):23–47

    Article  Google Scholar 

  • Xiao F (2019) A distance measure for intuitionistic fuzzy sets and its application to pattern classification problems. IEEE Trans Syst Man Cybern Syst 51(6):3980–3992

    Article  Google Scholar 

  • Xu Z, Cai X (2012) Intuitionistic fuzzy information aggregation. In: Intuitionistic Fuzzy Information Aggregation. Springer, New York, pp 1–102

  • Xu Z, Yager RR (2006) Some geometric aggregation operators based on intuitionistic fuzzy sets. Int J Gen Syst 35(4):417–433

    Article  MathSciNet  Google Scholar 

  • Ye J (2011) Cosine similarity measures for intuitionistic fuzzy sets and their applications. Math Comput Modell 53(1–2):91–97

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This work was supported by Vietnam Academy of Science and Technology, under Grant VAST01.10/22-23.

Funding

Funding was provided by Vietnam Academy of Science and Technology (Grant No. VAST01.10.22-23).

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Authors and Affiliations

  1. Institute of Information Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam

    Nguyen Nhu Son & Nguyen Long Giang

  2. Graduate University of Science and Technology, Vietnam Academy of Science and Technology, Hanoi, Vietnam

    Cu Nguyen Giap

  3. Faculty of Economic information systems and E-commerce, Thuongmai University, Hanoi, Vietnam

    Cu Nguyen Giap

  4. VNU Information Technology Institute, Vietnam National University, Hanoi, Vietnam

    Le Hoang Son

  5. Faculty of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam

    Tran Manh Tuan

  6. Department of Digital Systems, University of Thessaly, Larissa, Greece

    Vassilis C. Gerogiannis & Dimitrios Tzimos

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  1. Nguyen Nhu Son
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  2. Cu Nguyen Giap
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  4. Nguyen Long Giang
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  6. Vassilis C. Gerogiannis
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  7. Dimitrios Tzimos
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Correspondence to Cu Nguyen Giap or Le Hoang Son.

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Son, N.N., Giap, C.N., Son, L.H. et al. A dynamic fuzzy group recommender system based on intuitionistic fuzzy choquet integral aggregation. Soft Comput (2024). https://doi.org/10.1007/s00500-023-09485-y

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