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Probe: Learning Users' Personalized Projection Bias in Intertemporal Choices (2303.06016v5)

Published 9 Mar 2023 in cs.IR and cs.AI

Abstract: Intertemporal choices involve making decisions that require weighing the costs in the present against the benefits in the future. One specific type of intertemporal choice is the decision between purchasing an individual item or opting for a bundle that includes that item. Previous research assumes that individuals have accurate expectations of the factors involved in these choices. However, in reality, users' perceptions of these factors are often biased, leading to irrational and suboptimal decision-making. In this work, we specifically focus on two commonly observed biases: projection bias and the reference-point effect. To address these biases, we propose a novel bias-embedded preference model called Probe. The Probe incorporates a weight function to capture users' projection bias and a value function to account for the reference-point effect, and introduce prospect theory from behavioral economics to combine the weight and value functions. This allows us to determine the probability of users selecting the bundle or a single item. We provide a thorough theoretical analysis to demonstrate the impact of projection bias on the design of bundle sales strategies. Through experimental results, we show that the proposed Probe model outperforms existing methods and contributes to a better understanding of users' irrational behaviors in bundle purchases. This investigation can facilitate a deeper comprehension of users' decision-making mechanisms, enable the provision of personalized services, and assist users in making more rational and optimal decisions.

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References (40)
  1. M. Aggarwal, “Modeling a decision-maker’s choice behavior through perceived values,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 3, pp. 1933–1944, 2019.
  2. T. Y. Chang, W. Huang, and Y. Wang, “Something in the air: pollution and the demand for health insurance,” The Review of Economic Studies, vol. 85, no. 3, pp. 1609–1634, 2018.
  3. G. Loewenstein, T. O’Donoghue, and M. Rabin, “Projection bias in predicting future utility,” the Quarterly Journal of economics, vol. 118, no. 4, pp. 1209–1248, 2003.
  4. A. Tversky and I. Simonson, “Context-dependent preferences,” Management science, vol. 39, no. 10, pp. 1179–1189, 1993.
  5. A. Pujahari and D. S. Sisodia, “Handling dynamic user preferences using integrated point and distribution estimations in collaborative filtering,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 10, pp. 6639–6651, 2022.
  6. M. Kaufmann, “Projection bias in effort choices,” Games and Economic Behavior, vol. 135, pp. 368–393, 2022.
  7. X. Wu, Y. Gu, J. Tao, G. Li, J. Han, and N. Xiong, “An effective data-driven cloud resource procurement scheme with personalized reserve prices,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 51, no. 8, pp. 4693–4705, 2019.
  8. R. Venkatesh and V. Mahajaim, “A probabilistic approach to pricing a bundle of products or services,” Journal of Marketing Research, vol. 30, no. 4, pp. 494–508, 1993.
  9. B. A. Harlam, A. Krishna, D. R. Lehmann, and C. Mela, “Impact of bundle type, price framing and familiarity on purchase intention for the bundle,” journal of Business Research, vol. 33, no. 1, pp. 57–66, 1995.
  10. R. N. Giri, S. K. Mondal, and M. Maiti, “Bundle pricing strategies for two complementary products with different channel powers,” Annals of Operations Research, vol. 287, pp. 701–725, 2020.
  11. A. Pathak, K. Gupta, and J. McAuley, “Generating and personalizing bundle recommendations on steam,” in Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2017, pp. 1073–1076.
  12. T. Avny Brosh, A. Livne, O. Sar Shalom, B. Shapira, and M. Last, “Bruce: Bundle recommendation using contextualized item embeddings,” in Proceedings of the 16th ACM Conference on Recommender Systems, 2022, pp. 237–245.
  13. Y. Ma, Y. He, A. Zhang, X. Wang, and T. Chua, “Crosscbr: cross-view contrastive learning for bundle recommendation,” in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2022, pp. 1233–1241.
  14. J. Chang, C. Gao, X. He, D. Jin, and Y. Li, “Bundle recommendation and generation with graph neural networks,” IEEE Transactions on Knowledge and Data Engineering, vol. 35, no. 3, pp. 2326–2340, 2021.
  15. Z. He, H. Zhao, T. Yu, S. Kim, F. Du, and J. McAuley, “Bundle mcr: Towards conversational bundle recommendation,” in Proceedings of the 16th ACM Conference on Recommender Systems, 2022, pp. 288–298.
  16. N. Chen, S. Farajollahzadeh, and G. Wang, “Learning consumer preferences from bundle sales data,” arXiv preprint arXiv:2209.04942, 2022.
  17. M. Ettl, P. Harsha, A. Papush, and G. Perakis, “A data-driven approach to personalized bundle pricing and recommendation,” Manufacturing & Service Operations Management, vol. 22, no. 3, pp. 461–480, 2020.
  18. E. O. Young and H. W. Lauw, “Mining competitively-priced bundle configurations,” in 2022 IEEE International Conference on Big Data (Big Data).   IEEE, 2022, pp. 6844–6846.
  19. D. Laibson, “Golden eggs and hyperbolic discounting,” The Quarterly Journal of Economics, vol. 112, no. 2, pp. 443–478, 1997.
  20. J. E. Mazur and D. R. Biondi, “Delay-amount tradeoffs in choices by pigeons and rats: Hyperbolic versus exponential discounting,” Journal of the experimental analysis of behavior, vol. 91, no. 2, pp. 197–211, 2009.
  21. D. R. Amasino, N. J. Sullivan, R. E. Kranton, and S. A. Huettel, “Amount and time exert independent influences on intertemporal choice,” Nature human behaviour, vol. 3, no. 4, pp. 383–392, 2019.
  22. S. J. Gershman and R. Bhui, “Rationally inattentive intertemporal choice,” Nature communications, vol. 11, no. 1, p. 3365, 2020.
  23. M. Schultheis, C. A. Rothkopf, and H. Koeppl, “Reinforcement learning with non-exponential discounting,” Advances in Neural Information Processing Systems, vol. 35, pp. 3649–3662, 2022.
  24. D. Zhou, J. He, and Q. Gu, “Provably efficient reinforcement learning for discounted mdps with feature mapping,” in International Conference on Machine Learning.   PMLR, 2021, pp. 12 793–12 802.
  25. D. Acland and M. R. Levy, “Naiveté, projection bias, and habit formation in gym attendance.” Management Science, vol. 61, no. 1, 2015.
  26. D. Kahneman and A. Tversky, “Prospect theory: An analysis of decision under risk,” Econometrica, vol. 47, no. 2, pp. 263–292, 1979.
  27. P. Ren, Z. Xu, and Z. Hao, “Hesitant fuzzy thermodynamic method for emergency decision making based on prospect theory,” IEEE Transactions on Cybernetics, vol. 47, no. 9, pp. 2531–2543, 2017.
  28. K. Jhala, B. Natarajan, and A. Pahwa, “Prospect theory-based active consumer behavior under variable electricity pricing,” IEEE Transactions on Smart Grid, vol. 10, no. 3, pp. 2809–2819, 2018.
  29. E. K. Macdonald, H. Wilson, V. Martinez, and A. Toossi, “Assessing value-in-use: A conceptual framework and exploratory study,” Industrial Marketing Management, vol. 40, no. 5, pp. 671–682, 2011.
  30. Q. Li, Z. Chen, and H. V. Zhao, “PRIMA++: A probabilistic framework for user choice modelling with small data,” IEEE Transactions on Signal Processing, vol. 69, pp. 1140–1153, 2021.
  31. H. Yang, K. Ma, and J. Cheng, “Rethinking graph regularization for graph neural networks,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 5, 2021, pp. 4573–4581.
  32. R. Merris, “Laplacian matrices of graphs: a survey,” Linear algebra and its applications, vol. 197, pp. 143–176, 1994.
  33. A. E. Hoerl and R. W. Kennard, “Ridge regression: Biased estimation for nonorthogonal problems,” Technometrics, vol. 12, no. 1, pp. 55–67, 1970.
  34. L. Bottou, “Stochastic gradient descent tricks,” in Neural Networks: Tricks of the Trade: Second Edition.   Springer, 2012, pp. 421–436.
  35. S. Taheri and M. Mammadov, “Learning the naive bayes classifier with optimization models,” International Journal of Applied Mathematics and Computer Science, vol. 23, no. 4, pp. 787–795, 2013.
  36. V. K. Chauhan, K. Dahiya, and A. Sharma, “Problem formulations and solvers in linear svm: a review,” Artificial Intelligence Review, vol. 52, no. 2, pp. 803–855, 2019.
  37. G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T. Liu, “Lightgbm: A highly efficient gradient boosting decision tree,” Advances in neural information processing systems, vol. 30, 2017.
  38. C. Ying, M. Qi-Guang, L. Jia-Chen, and G. Lin, “Advance and prospects of adaboost algorithm,” Acta Automatica Sinica, vol. 39, no. 6, pp. 745–758, 2013.
  39. C. Lee and C. Lin, “Large-scale linear ranksvm,” Neural computation, vol. 26, no. 4, pp. 781–817, 2014.
  40. Y. Song, H. Wang, and X. He, “Adapting deep ranknet for personalized search,” in Proceedings of the 7th ACM international conference on Web search and data mining, 2014, pp. 83–92.

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