Sentiment-Driven Knowledge Graph Attention Network for Enhanced Recommendation Systems

Recommender systems are essential for providing personalized content to users across various domains, yet traditional models struggle to capture the full complexity of user preferences, particularly when facing sparse data or implicit feedback. This paper introduces a novel approach called Sentiment-Driven Knowledge Graph Attention Network (SK-GAN), which aims to enhance recommendation accuracy by combining knowledge graph embeddings with sentiment analysis and an attention mechanism. Knowledge graphs enable the representation of intricate relationships between users, items, and contextual factors, while sentiment analysis uncovers the emotional tone behind user interactions, such as product reviews or feedback. The SK-GAN model integrates these two components by first embedding sentiment information into the knowledge graph structure, allowing for the refinement of user preferences. The attention mechanism then selectively highlights the most relevant knowledge nodes based on sentiment, effectively guiding the recommendation process. Experimental results on benchmark datasets demonstrate that SK-GAN significantly improves the precision and relevance of recommendations compared to traditional collaborative filtering and content-based models. By focusing on sentimentdriven insights and knowledge graph-based semantic relationships, the proposed model addresses key challenges such as data sparsity and cold-start issues. The findings suggest that the SK-GAN model offers a promising direction for developing more accurate and context-aware recommendation systems that better align with user preferences and emotions.