A hybrid recommendation framework utilizing domain-adaptive RoBERTa embeddings for enhanced personalization in e-commerce

Abstract

With the rapid growth of e-commerce and online platforms, delivering personalised and accurate recommendations remains a challenge due to sparse interaction data and diverse user interests. This paper proposes HyReC, a unique hybrid recommendation framework that integrates content-based and collaborative filtering while maintaining computational efficiency. Domain-adaptive RoBERTa embeddings are used to extract semantic representations from textual content, capturing user and item preferences from descriptions and reviews. A Deep Neural Network (DNN) model uses user-item interactions to generate latent behavioural embeddings, which are enriched behavioural statistical features such as mean rating, rating variance (standard deviation), interaction frequency, and skewness. Heterogeneous embeddings are fused using a Bahdanau attention mechanism, enabling the model to dynamically weight content, collaborative, and statistical signals. The fused representation is then used to generate recommendations through a Learning-to-Rank layer, depending on application scale. A model is trained using the Adam optimiser to ensure fast convergence and stable performance. Experimental evaluation on the Amazon Baby dataset demonstrates that HyReC achieves superior performance of 0.15, MAE of 0.10, MSE of 0.023, R² of 0.98, Pearson Correlation of 0.99, MAPE of 1.5%, and F1-score of 0.98, outperforming state-of-the-art models such as LSTM, RBM + KNN, GNN, and GAT. Experiments on benchmark datasets demonstrate that the proposed framework improves recommendation accuracy, diversity, and robustness compared to baseline models, effectively addressing data sparsity, user interest drift, and heterogeneous content.