Deep reinforcement learning models in auction item price prediction: an optimisation study of a cross-interval quotation strategy

Abstract

In the contemporary digitalization landscape and technological advancement, the auction industry undergoes a metamorphosis, assuming a pivotal role as a transactional paradigm. Functioning as a mechanism for pricing commodities or services, the procedural intricacies and efficiency of auctions directly influence market dynamics and participant engagement. Harnessing the advancing capabilities of artificial intelligence (AI) technology, the auction sector proactively integrates AI methodologies to augment efficacy and enrich user interactions. This study delves into the intricacies of the price prediction challenge within the auction domain, introducing a sophisticated RL-GRU framework for price interval analysis. The framework commences by adeptly conducting quantitative feature extraction of commodities through GRU, subsequently orchestrating dynamic interactions within the model’s environment via reinforcement learning techniques. Ultimately, it accomplishes the task of interval division and recognition of auction commodity prices through a discerning classification module. Demonstrating precision exceeding 90% across publicly available and internally curated datasets within five intervals and exhibiting superior performance within eight intervals, this framework contributes valuable technical insights for future endeavours in auction price interval prediction challenges.