Search Results Clustering Using Nonnegative Matrix Factorization (NMF)

Authors:
Hussam Dahwa Abdulla;Martin Polovincak;Vaclav Snasel
Affiliations:
-;-;-
Venue:
ASONAM '09 Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining
Year:
2009

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Cited 1

Nonnegative shared subspace learning and its application to social media retrieval

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining

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Abstract

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 Nonnegative Matrix Factorization(NMF) can be good solution for the search results clustering.