Abstract
With the development of electronic commerce, a lot of recommendation techniques has been developed. Collaborative filtering(CF) is one of the most important technologies. However, traditional collaborative filtering suffers sparsity and scalability problems, which results in poor quality of prediction in recommendation systems. To solve these problems, this paper proposed a recommendation algorithm combining clustering method and slope one scheme. This approach uses clustering algorithms to partition the set of items to several clusters based on user rating data, and then we use slope one scheme to predict ratings independently for unknown items based on which cluster the items belong to. We make experiments on the standard benchmark Movielens data sets and compare our approach with the basic slope one scheme. The results show that our algorithm outperforms the slope one scheme.
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Mi, Z., Xu, C. (2012). A Recommendation Algorithm Combining Clustering Method and Slope One Scheme. In: Huang, DS., Gan, Y., Premaratne, P., Han, K. (eds) Bio-Inspired Computing and Applications. ICIC 2011. Lecture Notes in Computer Science(), vol 6840. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24553-4_23
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DOI: https://doi.org/10.1007/978-3-642-24553-4_23
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