Using transfer adaptation method for dynamic features expansion in multi-label deep neural network for recommender systems

In this paper, we propose to use a convertible deep neural network (DNN) model with a transfer adaptation
mechanism to deal with varying input and output numbers of neurons. The flexible DNN model serves as a multi-label
classifier for the recommender system as part of the retrieval systems’ push mechanism, which learns the combination of
tabular features and proposes the number of discrete offers (targets). Our retrieval system uses the transfer adaptation,
mechanism, when the number of features changes, it replaces the input layer of the neural network then freezes all gradients
on the following layers, trains only replaced layer, and unfreezes the entire model. The experiments show that using the
transfer adaptation technique impacts stable loss decreasing and learning speed during the training process. Furthermore,
our proposed model demonstrates notable advantages in production scenarios. Specifically, it exhibits enhanced efficiency,
manifesting in accelerated processing times and improved resource utilization, thereby contributing to a more sustainable
and cost-effective training of machine learning solutions in real-world applications.