A Deep Learning Framework for Enhancing Recommender Systems With Dual-Feedback Integration

Abstract

Collaborative filtering (CF)-based personalized recommendation systems are among the most widely adopted strategies for addressing information overload in modern markets. However, a significant challenge that hinders the effectiveness of these systems is data sparsity. To address this limitation, advanced methodologies such as matrix factorization and deep learning models have been developed to improve CF system performance in sparse data scenarios. Despite their advancements, most existing models rely solely on either implicit or explicit user–item interactions to generate recommendations. Traditional CF approaches often struggle with sparsity, necessitating the development of hybrid methods that integrate both explicit and implicit feedback to enhance performance. To address this gap, this study introduces DeepBlendRec, an innovative deep learning-based framework that leverages both explicit user ratings and implicit user behaviour. By utilizing this dual-input methodology, the model captures richer information regarding user–item interactions, thereby significantly improving recommendation accuracy. DeepBlendRec employs an enhanced autoencoder with a constrained decoder to process implicit ratings, thereby improving reconstruction quality and facilitating the creation of robust latent space representations. Simultaneously, explicit ratings are processed through a multilayer perceptron. The reconstructed outputs are then fused to generate a Top-N recommendation list. Experimental evaluations conducted on the MovieLens datasets demonstrate that DeepBlendRec consistently outperforms existing models across several key performance metrics, including mean-squared error (MSE), root-mean-squared error (RMSE), mean absolute error (MAE), precision, recall, and F1-score. These results highlight the potential of DeepBlendRec to advance the capabilities of recommendation systems in handling data sparsity and improving predictive accuracy.