An efficient and accurate recommendation strategy using degree classification criteria for item-based collaborative filtering

Abstract

An efficient and accurate recommender system provides online users with a variety of personalized recommendation services, thus effectively improving the satisfaction and experience of users. However, there is a trade-off between the accuracy and efficiency in recommender systems. Accordingly, this study introduces a recommendation strategy to address this limitation. The extended degree classification criteria are first proposed to assign items to more fine-grained classes. Later, item similarity measure is deployed to quickly evaluate similarities between items within the same class, which greatly reduces the runtime of similarity calculation. To obtain a better recommendation result, a Hellinger distance (HD) based item similarity is presented to calculate item similarity from the perspective of rating probability distribution. Additionally, a sigmoid function is considered in the HD similarity to emphasize the importance of the co-rated items and effectively distinguish differences between a pair of items. The experimental results on two benchmark datasets show that the proposed similarity method using the classification criteria has better performance in both accuracy and efficiency compared to other methods. Also, the results verify the effectiveness of the proposed classification criteria, especially the runtime of item-based CF method is reduced by at least 61% while maintaining a relatively stable or higher accuracy.