Boosting collaborative filtering based on missing data imputation using item´s genre information

Collaborative filtering (CF) is one of the most successful technologies in recommender systems, and widely used in many personalized recommender applications, such as digital library, e-commerce, news sites, and so on. However, most collaborative filtering algorithms suffer from data sparsity problem which leads to inaccuracy of recommendation. This paper is with an eye to missing data imputation strategy in nearest-neighbor CF. We propose an effective CF framework based on missing data imputation before conducting CF process, which utilizes item´s genre information. In the experimental evaluations, 19 item´s genres are employed in the imputation stage. The results show that the proposed approaches effectively alleviate the negative impact of data sparsity, and perform better prediction accuracy than traditional widely-used CF algorithms.