Authors:
(1) Saketh Reddy Karra, University of Illinois Chicago, 601 S Morgan St, Chicago, IL 60607, United States;
(2) Theja Tulabandhula, University of Illinois Chicago, 601 S Morgan St, Chicago, IL 60607, United States. Authors: Authors: (1) Saketh Reddy Karra, University of Illinois Chicago, 601 S Morgan St, Chicago, IL 60607, United States; (2) Theja Tulabandhula, University of Illinois Chicago, 601 S Morgan St, Chicago, IL 60607, United States. Table of Links Abstract and 1 Introduction Abstract and 1 Introduction 2. Related Work 2. Related Work 3. Preliminaries 3. Preliminaries 4. The InteraSSort Framework 4. The InteraSSort Framework 5. Illustration 5. Illustration 6. Conclusion and References 6. Conclusion and References 5. Illustration In this section, we discuss the components needed to run our experiments followed by an illustrative example. 5.1. Ta-Feng dataset 5.1. Ta-Feng dataset Ta-Feng [2] is a grocery shopping dataset released by ACM RecSys. The dataset contains detailed transactional records of users over a period of 4 months, from November 2000 to February 2001. The total number of transactions in this dataset is 817, 741 which are associated with 32, 266 users and 23, 812 products. 5.2. Multinomial logit (MNL) 5.2. Multinomial logit (MNL) 5.3. LLM 5.3. LLM We employ the gpt-3.5-turbo variant from the GPT model series as our primary LLM. The model is publicly accessible through the OpenAI API [3]. 5.4. Illustrative example 5.4. Illustrative example We demonstrate the data flow in the InteraSSort framework using a user question: ‘What is the optimal assortment for the Ta-Feng Dataset using the MNL model?’, as shown in Figure 4. The question is entered as an input prompt via user interface. Utilizing the LLM’s function-calling capability, the input is parsed (i.e., identifying ‘Ta-Feng’ as the dataset and ‘MNL’ as the choice model). Based on the parsed input, InteraSSort efficiently leverages parameter data for the Ta-Feng dataset and invokes the relevant function. This function then processes the arguments, executes the MNL optimization script, and communicates the outcomes through the interface. Whenever a user poses a follow-up question in the form of any additional constraint, such as ‘I want an optimal assortment where assortment size is limited to 5 products’, the system makes use of the decomposed inputs from the previous interaction, along with the constraint limiting the size of optimal assortment and passes these as arguments to the relevant function. This function then reruns the optimization script, with the set of updated arguments and returns the solution, to the LLM which communicates to the user. InteraSSort ‘What is the optimal assortment for the Ta-Feng Dataset using the MNL model?’ InteraSSort ‘I want an optimal assortment where assortment size is limited to 5 products’, 6. Conclusion In this paper, we introduced InteraSSort, an interactive framework designed to empower planners with limited optimization expertise in deriving insightful solutions to the assortment planning problem. InteraSSort facilitates interactive optimization by generating responses to variations of the optimization problem based on user requests. By harnessing the inherent strengths of instruction-tuned LLMs such as comprehension and reasoning, InteraSSort excels in interpreting user requests and breaking them down into distinct function parameters, that enable flexible assortment planning. Subsequently, InteraSSort intelligently calls and executes the most appropriate optimization tools and translates the solutions into concise, easily interpretable responses for the user. Overall, InteraSSort enables working with assortment planning problem effectively through interaction, and the framework can be easily extended to other marketing problems in the field of operations management. InteraSSort InteraSSort InteraSSort InteraSSort References Anand, R., Aggarwal, D., & Kumar, V. (2017). A comparative analysis of optimization solvers. Journal of Statistics and Management Systems, 20 , 623–635. De Mauro, A., Sestino, A., & Bacconi, A. (2022). Machine learning and artificial intelligence use in marketing: a general taxonomy. Italian Journal of Marketing, 2022 , 439–457. Duarte, V., Zuniga-Jara, S., & Contreras, S. (2022). Machine learning and marketing: A systematic literature review. IEEE Access,. Fraiwan, M., & Khasawneh, N. (2023). A review of chatgpt applications in education, marketing, software engineering, and healthcare: Benefits, drawbacks, and research directions. arXiv preprint arXiv:2305.00237,. Frieder, S., Pinchetti, L., Griffiths, R.-R., Salvatori, T., Lukasiewicz, T., Petersen, P. C., Chevalier, A., & Berner, J. (2023). Mathematical capabilities of chatgpt. arXiv preprint arXiv:2301.13867,. Jain, V., Rai, H., Subash, P., & Mogaji, E. (2023). The prospects and challenges of chatgpt on marketing research and practices. Emmanuel, The Prospects and Challenges of ChatGPT on Marketing Research and Practices (March 23, 2023),. Kok, A. G., Fisher, M. L., & Vaidyanathan, R. (2015). Assortment planning: Review of literature and industry practice. Retail supply chain management: Quantitative models and empirical studies, (pp. 175–236). Li, B., Fang, G., Yang, Y., Wang, Q., Ye, W., Zhao, W., & Zhang, S. (2023a). Evaluating chatgpt’s information extraction capabilities: An assessment of performance, explainability, calibration, and faithfulness. arXiv preprint arXiv:2304.11633 ,. Li, B., Mellou, K., Zhang, B., Pathuri, J., & Menache, I. (2023b). Large language models for supply chain optimization. arXiv preprint arXiv:2307.03875,. Liang, Y., Wu, C., Song, T., Wu, W., Xia, Y., Liu, Y., Ou, Y., Lu, S., Ji, L., Mao, S. et al. (2023). Taskmatrix. ai: Completing tasks by connecting foundation models with millions of apis. arXiv preprint arXiv:2303.16434,. Luce, R. D. (2012). Individual choice behavior: A theoretical analysis. Courier Corporation. OpenAI (2023). Gpt-4 technical report. arXiv:2303.08774. Qin, Y., Hu, S., Lin, Y., Chen, W., Ding, N., Cui, G., Zeng, Z., Huang, Y., Xiao, C., Han, C. et al. (2023). Tool learning with foundation models. arXiv preprint arXiv:2304.08354,. Rossi, P. E., & Allenby, G. M. (2003). Bayesian statistics and marketing. Marketing Science, 22 , 304–328. Schick, T., Dwivedi-Yu, J., Dess`ı, R., Raileanu, R., Lomeli, M., Zettlemoyer, L., Cancedda, N., & Scialom, T. (2023). Toolformer: Language models can teach themselves to use tools. arXiv preprint arXiv:2302.04761,. Shen, Y., Song, K., Tan, X., Li, D., Lu, W., & Zhuang, Y. (2023). Hugginggpt: Solving ai tasks with chatgpt and its friends in huggingface. arXiv preprint arXiv:2303.17580,. Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozi`ere, B., Goyal, N., Hambro, E., Azhar, F. et al. (2023). Llama: Open and efficient foundation language models. arXiv preprint arXiv:2302.13971,. Tulabandhula, T., Sinha, D., & Karra, S. (2022). Optimizing revenue while showing relevant assortments at scale. European Journal of Operational Research, 300 , 561–570. Verma, S., Sharma, R., Deb, S., & Maitra, D. (2021). Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1 , 100002. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. This paper is available on arxiv under CC by 4.0 Deed (Attribution 4.0 International) license. available on arxiv available on arxiv [2] https://www.kaggle.com/datasets/chiranjivdas09/ta-feng-grocery-dataset [3] https://platform.openai.com/

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