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.
刘剑涛. 个性化推荐系统中用户多态聚类研究[J]. 现代图书情报技术, 2012, 28(2): 18-22.
Liu Jiantao. Research on Users' Polymorphic Clustering in Personality Recommendation System. New Technology of Library and Information Service, 2012, 28(2): 18-22.
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