Abstract
Multi-criteria decision-making (MCDM) techniques are increasing in product recommendation decisions, which typically entail several factors. This study aims to demonstrate the application of a novel strategy based on MCDM techniques as the core element of a consumer Decision Support System by suggesting the most appropriate items from a given set of alternatives. Ranking products based on online product ratings and consumer preferences is an important area of study, but there are currently few studies on this topic. This paper proposes a method for ranking products using multi-attribute online ratings. We propose a novel mobile recommendation-ranking system-based (MCDM) method to recommend the best alternative. Our proposed model differs from previous works in the following ways: (a) Rating information of each feature is used to identify user preferences and complementary criteria; (b) Criteria weights are determined by Shannon entropy (c) The complex proportional assessment method is employed to rank the alternatives and solve the best mobile recommendation problem. (d) The sensitivity study results showed that the rankings produced by the various MCDM approaches were highly consistent with the rankings of the evaluated compromise candidates. Demonstration of the proposed approach through a mobile phone selection case study. In our experiments, we have found that our approach provides a reliable ranking while reducing time and space complexity, indicating that our optimization model is accurate and efficient. With its superior product comparison skills and ability to offer a recommendation to the user as a final ranking of alternatives, a decision-making system like this may prove to be the optimal long-term answer for e-commerce sites and websites.
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References
Zha ZJ, Yu J, Tang J et al (2014) Product aspect ranking and its applications. IEEE Trans Knowl Data Eng 26:1211–1224. https://doi.org/10.1109/TKDE.2013.136
Liu Y, Bi JW, Fan ZP (2017) Ranking products through online reviews: a method based on sentiment analysis technique and intuitionistic fuzzy set theory. Inf Fusion 36:149–161. https://doi.org/10.1016/J.INFFUS.2016.11.012
Punetha N, Jain G (2023) Game theory and MCDM-based unsupervised sentiment analysis of restaurant reviews. Appl Intell 53:1–22. https://doi.org/10.1007/s10489-023-04471-1
Chen YS, Chuang HM, Sangaiah AK et al (2019) A study for project risk management using an advanced MCDM-based DEMATEL-ANP approach. J Ambient Intell Humaniz Comput 10:2669–2681. https://doi.org/10.1007/S12652-018-0973-2/FIGURES/7
Punetha N, Jain G (2023) Aspect and orientation-based sentiment analysis of customer feedback using mathematical optimization models. Knowl Inf Syst 65:1–30. https://doi.org/10.1007/s10115-023-01848-z
Punetha N, Jain G (2023) Game theory and MCDM-based unsupervised sentiment analysis of restaurant reviews. Appl Intell https://doi.org/10.1007/s10489-023-04471-1
Chawra VK, Gupta GP (2021) Optimized coverage-aware trajectory planning for AUVs for efficient data collection in underwater acoustic sensor networks. Evol Intell https://doi.org/10.1007/s12065-021-00667-x
Punetha N, Jain G (2023) Sentiment analysis of stock prices and News Headlines using the MCDM Framework. In: 2022 4th international conference on artificial intelligence and speech technology (AIST), pp 1–4. https://doi.org/10.1109/aist55798.2022.10065221
Vaz WS (2022) A comparative approach to multiobjective optimization of AISC thin-walled members. Evol Intell 15:655–668. https://doi.org/10.1007/s12065-020-00541-2
Kang D, Park Y (2014) Review-based measurement of customer satisfaction in mobile service: sentiment analysis and VIKOR approach. Expert Syst Appl 41:1041–1050. https://doi.org/10.1016/J.ESWA.2013.07.101
Peng Y, Kou G, Li J et al (2014) A Fuzzy PROMETHEE approach for mining customer reviews in Chinese. Arab J Sci Eng 39:5245–5252. https://doi.org/10.1007/S13369-014-1033-7
Preethi JS, Abirami AM, Askarunisa A et al (2015) Applying MCDM techniques for ranking products based on online customer feedback. Int J Knowl Based Comput Syst https://doi.org/10.21863/ijkbcs/2015.3.2.009
Rotter P (2014) Relevance feedback based on n-tuplewise comparison and the ELECTRE methodology and an application in content-based image retrieval. Multimed Tools Appl 72:667–685. https://doi.org/10.1007/S11042-013-1384-1/FIGURES/13
Rajak AK, Niraj M, Kumar S (2016) Designing of fuzzy expert heuristic models with cost management toward coordinating AHP, fuzzy TOPSIS and FIS approaches. Sadhana Acad Proc Eng Sci 41:1209–1218. https://doi.org/10.1007/s12046-016-0548-x
Abirami AM, Askarunisa A (2017) Sentiment analysis model to emphasize the impact of online reviews in healthcare industry. Online Inf Rev 41:471–486. https://doi.org/10.1108/OIR-08-2015-0289/FULL/HTML
Liang R, Wang J (2019) A linguistic intuitionistic cloud decision support model with sentiment analysis for product selection in E-commerce. Int J Fuzzy Syst 21:3. https://doi.org/10.1007/S40815-019-00606-0
Yadav S, Pathak VK, Gangwar S (2019) A novel hybrid TOPSIS-PSI approach for material selection in marine applications. Sadhana Acad Proc Eng Sci https://doi.org/10.1007/s12046-018-1020-x
Annette JR, Banu A (2019) Ranking cloud render farm services for a multi criteria recommender system. Sadhana Acad Proc Eng Sci https://doi.org/10.1007/s12046-018-0981-0
Prasad RV, Rajesh R, Thirumalaikumarasamy D (2020) Selection of coating material for magnesium alloy using Fuzzy AHP-TOPSIS. Sadhana Acad Proc Eng Sci https://doi.org/10.1007/s12046-019-1261-3
Muruganantham A, Gandhi GM (2020) Framework for social media analytics based on multi-criteria decision making (MCDM) model. Multimed Tools Appl 79:3913–3927. https://doi.org/10.1007/S11042-019-7470-2/TABLES/4
Aldhaban F, Daim T, Harmon R, Basoglu N (2020) Technology adoption in emerging regions: case of the smartphone in Saudi Arabia. Int J Innov Technol Manag https://doi.org/10.1142/S0219877020500030
Vyas V, Uma V, Ravi K (2020) Aspect-based approach to measure performance of financial services using voice of customer. J King Saud Univ Comput Inform Sci https://doi.org/10.1016/j.jksuci.2019.12.009
Yang L, Li Y, Wang J, Sherratt RS (2020) Sentiment analysis for E-Commerce product reviews in chinese based on sentiment lexicon and deep learning. IEEE Access 8:23522–23530. https://doi.org/10.1109/ACCESS.2020.2969854
Cicconi P, Castorani V, Germani M et al (2020) A multi-objective sequential method for manufacturing cost and structural optimization of modular steel towers. Eng Comput 0:475–497. https://doi.org/10.1007/s00366-019-00709-0
Zhang D, Li Y, Wu C (2020) An extended TODIM method to rank products with online reviews under intuitionistic fuzzy environment. J Oper Res Soc 71:322–334. https://doi.org/10.1080/01605682.2018.1545519
Liang J, Yang J (2021) Application of the AHP method on the optimization with undesirable priorities. Eng Comput 1:1–17. https://doi.org/10.1007/S00366-021-01359-X
Khosravi R, Teymourtash · AR, Mohammad · et al (2021) Numerical study and optimization of thermohydraulic characteristics of a graphene-platinum nanofluid in finned annulus using genetic algorithm combined with decision-making technique. Eng Comput 37:2473–2491. https://doi.org/10.1007/s00366-020-01178-6
Qin Y, Wang X, Xu Z (2022) Ranking tourist attractions through online reviews: a novel method with intuitionistic and hesitant fuzzy information based on sentiment analysis. Int J Fuzzy Syst 24:755–777. https://doi.org/10.1007/s40815-021-01131-9
Heidary Dahooie J, Raafat R, Qorbani AR, Daim T (2021) An intuitionistic fuzzy data-driven product ranking model using sentiment analysis and multi-criteria decision-making. Technol Forecast Soc Change 173:121158. https://doi.org/10.1016/j.techfore.2021.121158
Bueno I, Carrasco RA, Ureña R, Herrera-Viedma E (2022) A business context aware decision-making approach for selecting the most appropriate sentiment analysis technique in e-marketing situations. Inf Sci 589:300–320. https://doi.org/10.1016/J.INS.2021.12.080
Alipour-Vaezi M, Tavakkoli-Moghaddam R, Mohammadnazari Z (2022) Optimization of a television advertisement scheduling problem by multi-criteria decision making and dispatching rules. Multimed Tools Appl 81:11755–11772. https://doi.org/10.1007/S11042-022-12027-7/TABLES/12
Boran FE, Genç S, Kurt M, Akay D (2009) A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Syst Appl 36:11363–11368. https://doi.org/10.1016/j.eswa.2009.03.039
Wu C, Zhang D (2019) Ranking products with IF-based sentiment word framework and TODIM method. Kybernetes 48:990–1010. https://doi.org/10.1108/K-01-2018-0029
Büyüközkan G, Feyzioǧlu O, Gocer F (2016) Evaluation of hospital web services using intuitionistic fuzzy AHP and intuitionistic fuzzy VIKOR. IEEE Int Conf Ind Eng Eng Manag 2016(Decem):607–611. https://doi.org/10.1109/IEEM.2016.7797947
Beheshti M, Amoozad Mahdiraji H, Zavadskas EK (2016) Strategy portfolio optimisation: a copras g-modm hybrid approach. Transform Bus Econ 15:500–519
Maliene V, Dixon-Gough R, Malys N (2018) Dispersion of relative importance values contributes to the ranking uncertainty: sensitivity analysis of multiple criteria decision-making methods. Appl Soft Comput J 67:286–298. https://doi.org/10.1016/j.asoc.2018.03.003
Chitsaz N, Banihabib ME (2015) Comparison of different multi criteria decision-making models in prioritizing flood management alternatives. Water Resour Manage 29:2503–2525. https://doi.org/10.1007/s11269-015-0954-6
Şahin M (2021) A comprehensive analysis of weighting and multicriteria methods in the context of sustainable energy. Int J Environ Sci Technol 18:1591–1616. https://doi.org/10.1007/S13762-020-02922-7/TABLES/6
Célio SRL, Rotela Junior P, Aquila G et al (2021) Toward a robust optimal point selection: a multiple-criteria decision-making process applied to multi-objective optimization using response surface methodology. Eng Comput 37:2735–2761. https://doi.org/10.1007/s00366-020-00973-5
Zheng Y, Xu Z, He Y, Liao H (2018) Severity assessment of chronic obstructive pulmonary disease based on hesitant fuzzy linguistic COPRAS method. Appl Soft Comput J 69:60–71. https://doi.org/10.1016/J.ASOC.2018.04.035
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Punetha, N., Jain, G. Integrated shannon entropy and COPRAS optimal model-based recommendation framework. Evol. Intel. 17, 385–397 (2024). https://doi.org/10.1007/s12065-023-00886-4
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DOI: https://doi.org/10.1007/s12065-023-00886-4