DL-MD-OKT: deep learning-based prediction model for cross-domain recommendation with multi-auxiliary domains through optimal knowledge transfer

Recommendation systems play a crucial role in assisting users by recommending products and services that align with their preferences and needs. However, a common issue faced by the recommender systems is data sparsity, where limited user-item interactions lead to inadequate recommendation accuracy. To address these challenges, we propose a deep learning-based prediction model for a cross-domain recommendation system with multi-auxiliary domains through optimal knowledge transfer (DL-MD-OKT), which ensures consistent and accurate recommendations in cross-domain scenarios. We design a unified framework that integrates the quantum classical with deep residual learning (QC-DRL) model and joint matrix factorization to align user and item latent factor spaces, which can decrease the dispersion inconsistency between every helper and target region. Instead of using selective knowledge transfer, here we introduce optimal knowledge transfer, i.e., the multi objective non-dominated sorting-based butterfly optimization (NDS-BO) algorithm, which is used to optimally transfer more knowledge from the common matrix to a sparse target domain by transferring cluster-level estimation knowledge from multiple auxiliaries. In cross-domain recommendation scenarios, the superiority of DL-MD-OKT is validated with existing recent studies based on evaluation metrics such as mean absolute error (MAE) and root mean squared error (RMSE).