Abstract
In this paper, two new notions called core-concept and core-concept lattice are proposed and applied to collaborative recommendation system. The core-concept lattice is constructed based on the core-concept, which is extracted from rating matrix between users and items in collaborative recommendation systems. Compared with traditional FCA, it is obviously that the extraction of core-concept very easy and fast. We present the improved nearest neighbors algorithm, it use core-concept lattice as an index to the recommendation’s ratings matrix. The improved nearest neighbors algorithm could remarkably accelerate finding the nearest neighbors. Therefore, it could evidently improve efficiency of recommendation.
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Li, K., Du, Y., Xiang, D. (2007). Collaborative Recommending Based on Core-Concept Lattice. In: Castillo, O., Melin, P., Ross, O.M., Sepúlveda Cruz, R., Pedrycz, W., Kacprzyk, J. (eds) Theoretical Advances and Applications of Fuzzy Logic and Soft Computing. Advances in Soft Computing, vol 42. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72434-6_59
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DOI: https://doi.org/10.1007/978-3-540-72434-6_59
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