Abstract
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.
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Pirasteh, P., Jung, J.J., Hwang, D. (2014). Item-Based Collaborative Filtering with Attribute Correlation: A Case Study on Movie Recommendation. In: Nguyen, N.T., Attachoo, B., Trawiński, B., Somboonviwat, K. (eds) Intelligent Information and Database Systems. ACIIDS 2014. Lecture Notes in Computer Science(), vol 8398. Springer, Cham. https://doi.org/10.1007/978-3-319-05458-2_26
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DOI: https://doi.org/10.1007/978-3-319-05458-2_26
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