Vol. 8, Special Issue 5 (2019)

The proposed methodology involves the utilization of pre-trained models, such as those pretrained on large-scale language or image datasets, as a foundation for sales prediction tasks. This approach capitalizes on the wealth of information captured by these models during their training on diverse and extensive datasets, enabling them to grasp underlying patterns and features that transcend domain-specific nuances. The transfer of knowledge from these generic models to the sales prediction domain allows for improved generalization, particularly in scenarios where labeled sales data is scarce.

One key advantage of this transfer learning paradigm is its ability to expedite the training process for sales prediction models. By initializing the model with pre-existing knowledge, the convergence to optimal solutions is accelerated, thereby reducing the need for extensive labeled data in the target domain. This efficiency is crucial in real-world sales scenarios where data collection may be resource-intensive and time-consuming.

Furthermore, the paper explores the fine-tuning strategies employed to adapt pre-trained models to the nuances of specific sales domains. This involves updating model parameters using a smaller, domain-specific dataset, ensuring the model becomes attuned to the intricacies of sales-related patterns. The effectiveness of this fine-tuning process is evaluated through empirical studies on diverse sales datasets, showcasing the potential for enhanced performance and generalization.