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Multi-Purchase Behavior: Modeling, Estimation and Optimization

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Listed:
  • Theja Tulabandhula
  • Deeksha Sinha
  • Saketh Reddy Karra
  • Prasoon Patidar

Abstract

We study the problem of modeling purchase of multiple products and utilizing it to display optimized recommendations for online retailers and e-commerce platforms. We present a parsimonious multi-purchase family of choice models called the Bundle-MVL-K family, and develop a binary search based iterative strategy that efficiently computes optimized recommendations for this model. We establish the hardness of computing optimal recommendation sets, and derive several structural properties of the optimal solution that aid in speeding up computation. This is one of the first attempts at operationalizing multi-purchase class of choice models. We show one of the first quantitative links between modeling multiple purchase behavior and revenue gains. The efficacy of our modeling and optimization techniques compared to competing solutions is shown using several real world datasets on multiple metrics such as model fitness, expected revenue gains and run-time reductions. For example, the expected revenue benefit of taking multiple purchases into account is observed to be $\sim5\%$ in relative terms for the Ta Feng and UCI shopping datasets, when compared to the MNL model for instances with $\sim 1500$ products. Additionally, across $6$ real world datasets, the test log-likelihood fits of our models are on average $17\%$ better in relative terms. Our work contributes to the study multi-purchase decisions, analyzing consumer demand and the retailers optimization problem. The simplicity of our models and the iterative nature of our optimization technique allows practitioners meet stringent computational constraints while increasing their revenues in practical recommendation applications at scale, especially in e-commerce platforms and other marketplaces.

Suggested Citation

  • Theja Tulabandhula & Deeksha Sinha & Saketh Reddy Karra & Prasoon Patidar, 2020. "Multi-Purchase Behavior: Modeling, Estimation and Optimization," Papers 2006.08055, arXiv.org, revised Aug 2023.
  • Handle: RePEc:arx:papers:2006.08055
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    References listed on IDEAS

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    1. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, September.
    2. P. Seetharaman & Siddhartha Chib & Andrew Ainslie & Peter Boatwright & Tat Chan & Sachin Gupta & Nitin Mehta & Vithala Rao & Andrei Strijnev, 2005. "Models of Multi-Category Choice Behavior," Marketing Letters, Springer, vol. 16(3), pages 239-254, December.
    3. A. Gürhan Kök & Marshall L. Fisher & Ramnath Vaidyanathan, 2008. "Assortment Planning: Review of Literature and Industry Practice," International Series in Operations Research & Management Science, in: Narendra Agrawal & Stephen A. Smith (ed.), Retail Supply Chain Management, chapter 0, pages 99-153, Springer.
    4. Iain Dunning & Swati Gupta & John Silberholz, 2018. "What Works Best When? A Systematic Evaluation of Heuristics for Max-Cut and QUBO," INFORMS Journal on Computing, INFORMS, vol. 30(3), pages 608-624, August.
    5. Paat Rusmevichientong & David Shmoys & Chaoxu Tong & Huseyin Topaloglu, 2014. "Assortment Optimization under the Multinomial Logit Model with Random Choice Parameters," Production and Operations Management, Production and Operations Management Society, vol. 23(11), pages 2023-2039, November.
    6. Praveen K. Kopalle & Aradhna Krishna & João L. Assunção, 1999. "The role of market expansion on equilibrium bundling strategies," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 20(7), pages 365-377.
    7. R. L. Plackett, 1975. "The Analysis of Permutations," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 24(2), pages 193-202, June.
    8. D. R. Cox, 1972. "The Analysis of Multivariate Binary Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 21(2), pages 113-120, June.
    9. Puneet Manchanda & Asim Ansari & Sunil Gupta, 1999. "The “Shopping Basket”: A Model for Multicategory Purchase Incidence Decisions," Marketing Science, INFORMS, vol. 18(2), pages 95-114.
    10. Markus Ettl & Pavithra Harsha & Anna Papush & Georgia Perakis, 2020. "A Data-Driven Approach to Personalized Bundle Pricing and Recommendation," Manufacturing & Service Operations Management, INFORMS, vol. 22(3), pages 461-480, May.
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    Cited by:

    1. Yu Xia & Ali Arian & Sriram Narayanamoorthy & Joshua Mabry, 2023. "RetailSynth: Synthetic Data Generation for Retail AI Systems Evaluation," Papers 2312.14095, arXiv.org.

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