Abstract—This paper proposes an Item-Based Context-Aware Collaborative Filtering (IB-CACF) approach that combines Energy Distance and Pre-Filtering Contextual Features to improve the effectiveness of recommendation systems. The proposed method addresses the limitations of traditional recommendation systems, which fail to consider contextual factors such as time, location, and companions. Energy Distance measures the similarity between items in the context space, enhancing prediction accuracy. Along with Pre-Filtering Contextual Features, this approach reduces computational cost by filtering out the most relevant contextual factors before similarity calculations. Experimental results on the MovieLens 25M, Amazon, and Yelp datasets demonstrate that this method outperforms existing approaches in terms of both prediction accuracy and computational efficiency.
 
Keywords—item-based, energy distance, context-aware, collaborative filtering, contextual pre-filtering


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