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Research on Users' Polymorphic Clustering in Personality Recommendation System |
Liu Jiantao |
Library of Huaqiao University, Quanzhou 362021, China |
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Abstract Traditional collaborative filtering algorithm is usually dependent on single kind of user requirement to generate clustering and this may affect the accuracy of recommendation. In view of the problem, this paper proposes a personalized recommendation method in digital library based on users' polymorphic clustering. This method uses an improved Hamming distance to calculate candidate neighbors, then combines polymorphic similarity to cluster again, finally forecasts user' s requirements degree and generates recommendation. The experiments show that recommendation based on polymorphic clustering is more accurate than the single' s.
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Received: 26 December 2011
Published: 23 March 2012
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