ARRN: Leveraging Demographic Context for Improved Semantic Personalization in Hybrid Recommendation Systems

Abstract

This paper proposes a novel recommendation system model, the Attentive Recurrent Recommender Network (ARRN), that addresses the challenge of incorporating demographic information into recommendations. ARRN leverages user-item interaction data along with age information from the data set to deliver personalized recommendations specifically tailored to different age groups. The approach utilizes embedding techniques and semantic analysis to capture user preferences and behaviors associated with their age. An attention mechanism prioritizes relevant features based on user age groups, enabling ARRN to dynamically adapt recommendations for users with limited interaction history. The paper presents a comprehensive evaluation of ARRN’s performance compared to existing state-of-the-art recommendation algorithms. The results demonstrate that ARRN outperforms existing approaches, particularly for users with limited interaction history, by effectively mitigating the cold-start problem in age-sensitive product domains.