User-based collaborative filtering (CF) is a widely used technique to generate recommendations. Lacking sufficient ratings will prevent CF from modeling user preference effectively and finding trustworthy similar users. To alleviate this problems, item-based CF was introduced. However, when number of co-rated items is not enough or new item is added to the system, item-based CF result is not reliable, too. This paper presents a new method based on movies similarity that focuses on improving recommendation performance when dataset is sparse. In this way, we express a new method to measure the similarity between items by utilizing the genre and director of movies. Experiments show the superiority of the measure in cold start condition. © 2014 Springer International Publishing Switzerland.
CITATION STYLE
Pirasteh, P., Jung, J. J., & Hwang, D. (2014). Item-based collaborative filtering with attribute correlation: A case study on movie recommendation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8398 LNAI, pp. 245–252). Springer Verlag. https://doi.org/10.1007/978-3-319-05458-2_26
Mendeley helps you to discover research relevant for your work.