Adaptive learning algorithm based price prediction model for auction lots-deep clustering based interval quoting.

This article addresses the problem of interval pricing for auction items by constructing an auction item price prediction model based on an adaptive learning algorithm. Firstly, considering the confusing class characteristics of auction item prices, a dynamic inter-class distance adaptive learning model is developed to identify confusing classes by calculating the differences in prediction values across multiple classifiers for target domain samples. The difference in the predicted values of the target domain samples on multiple classifiers is used to calculate the classification distance, distinguish the confusing classes, and make the similar samples in the target domain more clustered.

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

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.