Enhancing Context-Aware Recommender Systems Through Deep Feature Interaction Learning

Abstract

In the domain of context-aware recommender systems, understanding and leveraging feature interactions is crucial for enhancing recommendation quality. Feature interactions delve into the complex interdependencies among user characteristics, item attributes, and contextual factors like time and location. Traditional models often struggle to effectively combine these diverse features, potentially leading to suboptimal recommendations. To tackle this issue, we propose enhancing context-aware recommender systems through deep feature interaction learning. Our model, which combines BiLSTM and Hybrid Attention mechanisms, offers a sophisticated architecture designed to exploit deep feature interactions effectively. This approach ensures that our system captures essential contextual dynamics, thereby improving the effectiveness of the recommendation process. Experimental results across multiple datasets validate the efficacy of our approach, showing significant improvements in key metrics such as $\mathrm{AUC}$ and $\text{LogLoss}$ compared to traditional and contemporary models. These achievements underscore our model's ability to deliver nuanced and adaptively tailored recommendations, marking a valuable contribution to the field of recommender systems.