Impact of Auxiliary Information in Generative Artificial Intelligence Models for Cross-Domain Recommender Systems

Recommender systems (RSs) are significant in enhancing the experiences of users across different online platforms. One of the major problems faced by the conventional RSs is difficulties in getting precise preferences for users, mostly for the users that has limited previous interaction data, and this eventually affects the performance of the conventional techniques to solve the data sparsity problem. To address this challenge, this study proposes an Auxiliary-Aware Conditional GAN (AUXIGAN) model that integrates heterogeneous auxiliary information into both the generator and discriminator networks to enhance representation learning to enhance the performance of the cross-domain recommender systems (CDRS). Most researchers consider only the rating matrix of users-items and ignore the impact of auxiliary information on the interaction functions, which is very significant to the recommendation accuracy to solve data sparsity problems. The proposed novel technique considers features concatenation, attention-based fusion networks, contrastive representation learning, knowledge transfer, and multi-modal embedding alignment techniques to enhance the user-item interaction matrix. Our experiments on benchmark datasets show that the proposed model significantly outperformed state-of-the-art RSs models, the key metrics utilized are: RMSE, Precision, Recall, and MAE, which show the influence of incorporating auxiliary information into the GAN-based CDRS. In conclusion, the integration of auxiliary information on generative adversarial networks models represents a substantial advancement in the field of CDRS, and the results of the proposed models on two real-world datasets show that the proposed model significantly outperforms collaborative filtering and other GAN-based techniques.