The content-based cluster P2P search model depending on a set distance is proposed in this paper to reduce the query time and redundant messages.
本文提出利用集合差异度实现基于内容聚类的P2P搜索模型提高查询效率和减少冗余消息。
There are three common approaches to solving the recommendation problem: traditional collaborative filtering, cluster models, and search-based methods.
解决推荐问题有三个通常的途径：传统的协同过滤，聚类模型，以及基于搜索的方法。
The RDF-based dynamic semantic search algorithm is discussed, and on the basis a cluster-based RDF dynamic semantic search algorithm is proposed.
对基于RDF的动态语义检索算法进行了探讨，在它的基础上提出了一种基于“簇”的RDF动态语义检索算法。
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