Tuesday 08 April 2025
A team of researchers has developed a novel approach to generating high-quality sequential consumption data, which could have significant implications for various applications such as store recommendation, site selection, and sale forecasting.
The traditional methods used to generate sequence data are limited in their ability to model complex user consumption behaviors. Model-based methods rely on simplified assumptions that fail to capture the intricacies of human decision-making, while data-driven methods can be prone to noise, unobserved behaviors, and dynamic decision spaces.
To address these limitations, the researchers have proposed a framework called EPR- GAIL (Enhanced Preferential Return Generative Adversarial Imitation Learning), which combines the strengths of two existing approaches. The framework consists of a generator that models user consumption behaviors as a complex EPR decision process, consisting of purchase, exploration, and preference decisions.
The key innovation lies in the use of probability distributions from an EPR model to guide the reward function in the discriminator. This allows the generator to learn more accurate patterns and relationships between user behavior and store characteristics, resulting in higher-quality sequence data.
To evaluate the effectiveness of the EPR-GAIL framework, the researchers conducted extensive experiments on two real-world datasets of user consumption behaviors from an online platform. The results showed that EPR-GAIL significantly outperformed the best state-of-the-art baseline by over 19% in terms of data fidelity.
Furthermore, the generated sequence data was found to improve the performance of sale prediction and location recommendation tasks by up to 35.29% and 11.19%, respectively. These results demonstrate the potential of EPR-GAIL to generate high-quality sequence data that can be used to inform business decisions.
The researchers also explored the impact of replacing the EPR knowledge with other expert knowledge, such as distance, price, and popularity. The results showed that while these alternatives can improve performance, they do not match the level of accuracy achieved by the EPR-GAIL framework.
The implications of this research are far-reaching, particularly in the context of e-commerce where accurate sequence data is crucial for informing business decisions. The ability to generate high-quality sequence data could enable companies to develop more effective recommendation systems, improve sales forecasting, and optimize store locations.
While the EPR-GAIL framework has shown promise, further work is needed to fully explore its potential and limitations. Nevertheless, this research represents an important step towards developing more accurate and comprehensive models of user consumption behavior.
Cite this article: “Enhancing Human Mobility Modeling with EPR: A Novel Approach to Simulating Urban Consumption Behaviors”, The Science Archive, 2025.
User Consumption Behaviors, Generative Adversarial Imitation Learning, Epr Decision Process, Purchase Exploration Preference, Probability Distributions, Reward Function, Discriminator, Online Platform, Sale Prediction, Location Recommendation
