用矩阵分解聚类数据

2009 International Conference on Computational Aspects of Social Networks Pub Date : 2009-06-24 DOI:10.1109/CASON.2009.11

H. Abdulla, M. Polovincak, V. Snás̃el

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引用次数: 2

摘要

在网络上有很多搜索引擎，当被询问时，它们会返回一个很长的搜索结果列表，根据它们与给定查询的相关性进行排名。Web用户必须浏览列表并依次检查标题和(短)片段，以确定他们需要的结果。本文介绍了矩阵分解(奇异值分解(SVD)和非负矩阵分解(NMF))是如何很好地解决搜索结果聚类问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Using a Matrix Decomposition for Clustering Data

There are many search engines in the web and when asked, they return a long list of search results, ranked by their relevancies to the given query. Web users have to go through the list and examine the titles and (short) snippets sequentially to identify their required results. In this paper we present how usage of Matrix Decomposition (Singular Value Decomposition (SVD) and Nonnegative Matrix Factorization (NMF)) can be good solution for the search results clustering.

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来源期刊

2009 International Conference on Computational Aspects of Social Networks

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