Culturally Enhanced Collaborative Filtering

We propose an enhanced collaborative filtering method using Hofstede’s cultural dimensions, calculated for 111 countries. We employ 4 of these dimensions, which are correlated to the costumers’ buying behavior, in order to detect users’ preferences for items. In addition, several advantages of this method demonstrated for data sparseness and cold-start users, which are important challenges in collaborative filtering. We present experiments using a real dataset, Book Crossing Dataset. Experimental results shows that the proposed algorithm provide significant advantages in terms of improving recommendation quality. Keywords—Collaborative filtering, Cross-cultural, E-commerce, Recommender systems