Abstract
Collaborative filtering is one of the most successful and popular methodologies in recommendation systems. However, the traditional collaborative filtering has some limitations such as the item sparsity and cold start problem. In this paper, we propose a new methodology for solving the item sparsity problem by mapping users and items to a domain ontology. Our method uses a semantic match with the domain ontology, while the traditional collaborative filtering uses an exact match to find similar users. The results of several experiments show that our method is more precise than the traditional collaborative filtering methodologies.
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Lee, Jw., Nam, KH., Lee, Sg. (2009). Semantics Based Collaborative Filtering. In: Lee, R., Ishii, N. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing. Studies in Computational Intelligence, vol 209. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01203-7_17
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DOI: https://doi.org/10.1007/978-3-642-01203-7_17
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