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Web Metasearch: Rank vs. Score Based Rank Aggregation
Methods
M. Elena Renda and Umberto Straccia
I.S.T.I. - C.N.R. Via G. Moruzzi,1 I-56124 Pisa (PI) ITALY
Contacts:
Elena.Renda_AT_iit.cnr.it
Umberto.Straccia_AT_isti.cnr.it
Abstract. Given a set of rankings, the task of ranking
fusion is the problem of combining these lists in such a way to optimize
the performance of the combination. The ranking fusion problem is
encountered in many situations and, e.g., metasearch is a prominent one.
It deals with the problem of combining the result lists returned by
multiple search engines in response to a given query, where each item in a
result list is ordered with respect to a search engine and a relevance
score. Several ranking fusion methods have been proposed in the
literature. They can be classified based on whether: (i) they rely on the
rank; (ii) they rely on the score; and (iii) they require training data or
not.
Our paper will make the following contributions: (i) we will report
experimental results for the Markov chain rank based methods, for which no
large experimental tests have yet been made; (ii) while it is believed
that the rank based method, named Borda Count, is competitive with score
based methods, we will show that this is not true for metasearch; and
(iii) we will show that Markov chain based methods compete with score
based methods. This is especially important in the context of metasearch as
scores are usually not available from the search engines.
Full article in PDF
© 2003 ACM 1-58113-625-0/03/03.
BibTex
@InProceedings{RendaSac03,
author = "Renda, M. Elena and Straccia, Umberto",
title = "Web Metasearch: Rank vs. Score Based Rank Aggregation Methods",
booktitle = "Proc.\ of the 18th Annual ACM Symposium on Applied
Computing",
address = "Melbourne, Florida",
year = "2003",
publisher = "{ACM} Press",
pages = "841--846"
}
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