AI Transforms 800K+ Grocery Transactions into Smart Insights

Table of Links

Abstract and 1 Introduction

2. Related Work

3. Preliminaries

4. The InteraSSort Framework

5. Illustration

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

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)

5.3. 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

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.

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.

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[2] https://www.kaggle.com/datasets/chiranjivdas09/ta-feng-grocery-dataset

[3] https://platform.openai.com/