Customer preference analysis from online reviews by a 2-additive Choquet integral-based preference disaggregation model
Abstract
Online reviews have become an important data source for analyzing consumers’ preferences. Consumer preference analysis assists product managers to understand consumers’ propensity for different product attributes and make consumer-oriented market strategies. Existing studies on consumer preference analysis used simple additive algorithms to represent the relationship between overall ratings and attribute ratings, but ignored the interactions between attributes. In addition, not all attribute ratings were given by consumers when calculating the overall ratings of a product. To fill these gaps, a preference model based on the extended 2-additive Choquet integral is constructed. The 2-additive Choquet integral can reflect the importance of attributes and the interactions between pairs of attributes when integrating attribute ratings. In cases where consumers choose only a subset of product attributes to rate a product, we introduce the scale parameter into the 2-additive Choquet integral to characterize the relationship between different attribute subsets. Afterwards, a preference disaggregation paradigm based on nonlinear programming is provided to solve the preference model. Finally, the proposed method is validated by experimental analysis using the dataset collected from TripAdvisor.com. Experimental outcomes indicate that our approach can deduce consumers’ preferences and approximate the evaluation behavior of consumers efficiently.
First published online 22 November 2022
Keyword : consumer preferences, online reviews, preference disaggregation, multiple attribute decision aiding, 2-additive Choquet integral
How to Cite
Liao, H., Yang, Q., & Wu, X. (2023). Customer preference analysis from online reviews by a 2-additive Choquet integral-based preference disaggregation model. Technological and Economic Development of Economy, 29(2), 411–437. https://doi.org/10.3846/tede.2022.17972
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Pereira, M. A., Figueira, J. R., & Marques, R. C. (2020). Using a Choquet integral-based approach for incorporating decision-maker’s preference judgments in Data Envelopment Analysis model. European Journal of Operational Research, 284(3), 1016–1030. https://doi.org/10.1016/j.ejor.2020.01.037
Shi, Y., Larson, M., & Hanjalic, A. (2014). Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges. ACM Computing Surveys (CSUR), 47(1), 1–45. https://doi.org/10.1145/2556270
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Wu, X. L., & Liao, H. C. (2021). Modeling personalized cognition of customers in online shopping. Omega, 104, 102471. https://doi.org/10.1016/j.omega.2021.102471
Xiang, Z., Du, Q. Z., Ma, Y. F., & Fan, W. G. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58, 51–65. https://doi.org/10.1016/j.tourman.2016.10.001
Zhang, C. X., Xu, Z. S., Gou, X. J., & Chen, S. X. (2021). An online reviews-driven method for the prioritization of improvements in hotel services. Tourism Management, 87, 104382. https://doi.org/10.1016/j.tourman.2021.104382
Zhu, B., Guo, D. F., & Ren, L. (2022). Consumer preference analysis based on text comments and ratings: A multi-attribute decision-making perspective. Information & Management, 59(3), 103626. https://doi.org/10.1016/j.im.2022.103626
Arcidiacono, S. G., Corrente, S., & Greco, S. (2021). Robust stochastic sorting with interacting criteria hierarchically structured. European Journal of Operational Research, 292(2), 735–754. https://doi.org/10.1016/j.ejor.2020.11.024
Beliakov, G., Pradera, A., & Calvo, T. (2007). Aggregation functions: A guide for practitioners. Springer.
Branke, J., Corrente, S., Greco, S., Słowinski, R., & Zielniewicz, P. (2016). Using Choquet integral as preference model in interactive evolutionary multiobjective optimization. European Journal of Operational Research, 250(3), 884–901. https://doi.org/10.1016/j.ejor.2015.10.027
Chateauneuf, A., & Jaffray, J. Y. (1989). Some characterizations of lower probabilities and other monotone capacities through the use of Möbius inversion. Mathematical Social Sciences, 17(3), 263–283. https://doi.org/10.1016/0165-4896(89)90056-5
Choquet, G. (1954). Theory of capacities. Annales de l’Institut Fourier, 5, 131–295. https://doi.org/10.5802/aif.53
Chung, J., & Rao, V. R. (2012). A general consumer preference model for experience products: Application to internet recommendation services. Journal of Marketing Research, 49(3), 289–305. https://doi.org/10.1509/jmr.09.0467
Doumpos, M., & Zopounidis, C. (2011). Preference disaggregation and statistical learning for multicriteria decision support: A review. European Journal of Operational Research, 209(3), 203–214. https://doi.org/10.1016/j.ejor.2010.05.029
Dyer, J. S., & Smith, J. E. (2021). Innovations in the science and practice of decision analysis: The role of management science. Management Science, 67(9), 5364–5378. https://doi.org/10.1287/mnsc.2020.3652
Fürnkranz, J., & Hüllermeier, E. (2010). Preference learning. Springer. https://doi.org/10.1007/978-3-642-14125-6
Ghaderi, M., Ruiz, F., & Agell, N. (2017). A linear programming approach for learning non-monotonic additive value functions in multiple criteria decision aiding. European Journal of Operational Research, 259(3), 1073–1084. https://doi.org/10.1016/j.ejor.2016.11.038
Govindan, K., & Jepsen, M. B. (2016). ELECTRE: A comprehensive literature review on methodologies and applications. European Journal of Operational Research, 250(1), 1–29. https://doi.org/10.1016/j.ejor.2015.07.019
Grabisch, M. (1996). The application of fuzzy integrals in multicriteria decision making. European Journal of Operational Research, 89(3), 445–456. https://doi.org/10.1016/0377-2217(95)00176-X
Grabisch, M. (1997). k-order additive discrete fuzzy measures and their representation. Fuzzy Sets and Systems, 92(2), 167–189. https://doi.org/10.1016/S0165-0114(97)00168-1
Grabisch, M., Kojadinovic, I., & Meyer, P. (2008). A review of methods for capacity identification in Choquet integral based multi-attribute utility theory applications of the Kappalab R package. European Journal of Operational Research, 186(2), 766–785. https://doi.org/10.1016/j.ejor.2007.02.025
Greco, S., Matarazzo, B., & Słowiński, R. (2016). Decision rule approach. In S. Greco, M. Ehrgott, & J. R. Figueira (Eds.), International series in operations research & management science: Vol. 233. Multiple criteria decision analysis: State of the art surveys (pp. 497–552). Springer-Verlag. https://doi.org/10.1007/978-1-4939-3094-4_13
Grigoroudis, E., Noel, L., Galariotis, E., & Zopounidis, C. (2021). An ordinal regression approach for analyzing consumer preferences in the art market. European Journal of Operational Research, 290(2), 718–733. https://doi.org/10.1016/j.ejor.2020.08.031
Guo, M., Liao, X., & Liu, J. (2019). A progressive sorting approach for multiple criteria decision aiding in the presence of non-monotonic preferences. Expert Systems with Applications, 123, 1–17. https://doi.org/10.1016/j.eswa.2019.01.033
Guo, M. Z., Liao, X. W., Liu, J. P., & Zhang, Q. P. (2020). Consumer preference analysis: A data-driven multiple criteria approach integrating online information. Omega, 96, 102074. https://doi.org/10.1016/j.omega.2019.05.010
Hung, K., Guillet, B. D., & Zhang, H. Q. (2019). Understanding luxury shopping destination preference using conjoint analysis and traditional item-based measurement. Journal of Travel Research, 58(3), 411–426. https://doi.org/10.1177/0047287518760259
Kadziński, M., & Tervonen, T. (2013). Robust multi-criteria ranking with additive value models and holistic pair-wise preference statements. European Journal of Operational Research, 228(1), 169–180. https://doi.org/10.1016/j.ejor.2013.01.022
Keeney, R. L., & Raiffa, H. (1976). Decisions with multiple objectives: Preferences and value tradeoffs. Wiley.
Lee, S. K., Cho, Y. H., & Kim, S. H. (2010). Collaborative filtering with ordinal scale-based implicit ratings for mobile music recommendations. Information Sciences, 180(11), 2142–2155. https://doi.org/10.1016/j.ins.2010.02.004
Li, X., Liu, H. F., & Zhu, B. (2020). Evolutive preference analysis with online consumer ratings. Information Science, 541, 332–344. https://doi.org/10.1016/j.ins.2020.06.048
Liu, J. P., Liao, X. W., Kadziński, M., & Słowiński, R. (2019). Preference disaggregation within the regularization framework for sorting problems with multiple potentially non-monotonic criteria. European Journal of Operational Research, 276(3), 1071–1089. https://doi.org/10.1016/j.ejor.2019.01.058
Mayag, B., & Bouyssou, D. (2020). Necessary and possible interaction between criteria in a 2-additive Choquet integral model. European Journal of Operational Research, 283(1), 308–320. https://doi.org/10.1016/j.ejor.2019.10.036
Pazzani, M. J., & Billsus, D. (2007). Content-based recommendation systems. In P. Brusilovsky, A. Kobsa, & W. Nejdl (Eds.), Lecture notes in computer science: Vol. 4321. The adaptive web (pp. 325–341). Springer. https://doi.org/10.1007/978-3-540-72079-9_10
Pelegrina, G. D., Duarte, L. T., Grabisch, M., & Romano, J. M. T. (2020). The multilinear model in multicriteria decision making: The case of 2-additive capacities and contributions to parameter identification. European Journal of Operational Research, 282(3), 945–956. https://doi.org/10.1016/j.ejor.2019.10.005
Pereira, M. A., Figueira, J. R., & Marques, R. C. (2020). Using a Choquet integral-based approach for incorporating decision-maker’s preference judgments in Data Envelopment Analysis model. European Journal of Operational Research, 284(3), 1016–1030. https://doi.org/10.1016/j.ejor.2020.01.037
Shi, Y., Larson, M., & Hanjalic, A. (2014). Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges. ACM Computing Surveys (CSUR), 47(1), 1–45. https://doi.org/10.1145/2556270
Tsai, C. F., Chen, K., Hu, Y. H., & Chen, W. K. (2020). Improving text summarization of online hotel reviews with review helpfulness and sentiment. Tourism Management, 80, 104–122. https://doi.org/10.1016/j.tourman.2020.104122
Wu, X. L., & Liao, H. C. (2021). Modeling personalized cognition of customers in online shopping. Omega, 104, 102471. https://doi.org/10.1016/j.omega.2021.102471
Xiang, Z., Du, Q. Z., Ma, Y. F., & Fan, W. G. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58, 51–65. https://doi.org/10.1016/j.tourman.2016.10.001
Zhang, C. X., Xu, Z. S., Gou, X. J., & Chen, S. X. (2021). An online reviews-driven method for the prioritization of improvements in hotel services. Tourism Management, 87, 104382. https://doi.org/10.1016/j.tourman.2021.104382
Zhu, B., Guo, D. F., & Ren, L. (2022). Consumer preference analysis based on text comments and ratings: A multi-attribute decision-making perspective. Information & Management, 59(3), 103626. https://doi.org/10.1016/j.im.2022.103626