Cultural Distance awared Collaborative Filtering Algorithm in Recommendation System

Abstract

Recently most of the existing studies on recommendation system are based on historical rating matrix and specific characteristics, such as location, season and weather. Different from these studies, we introduce an abstract feature about cultural backgrounds and values, called cultural distance, into the recommendation system to understand user intent better and improve the precision of recommendation results. We design a novel similarity representation which combine the item-based collaborative filtering and cultural distance to recommend items for users. We also propose a collaborative filtering-based missing cultural distance prediction algorithm to improve the precision of recommendation further. To evaluate the performance of our proposed algorithm, we execute experiments based on a large-scale real-world dataset, the results show that our algorithm can improve the precision by 10% accurate compared to existing recommendation approaches.