@misc{Renda/Straccia:02a,
	title = {Metasearch: {Rank} vs. {Score} {Based} {Rank} {List} {Fusion} {Methods} (without {Training} {Data})},
	abstract = {Given a set of rankings (a ranking is a linear
ordering of a set of items), 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, one prominent of which is metasearch. 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 query
dependent 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. Preliminary experimental
results seem to indicate that score based methods
outperform rank based methods, while methods based on
training data perform better than those without
training data. In this paper we will compare rank and
score based methods, without training data, in the
context of metasearch. 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 competing 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.},
	publisher = {Istituto di Elaborazione dell'Informazione - CNR},
	author = {Renda, M E and Straccia, U},
	year = {2002},
	note = {Series Number: 2002-TR-07
Place: Pisa},
}

{"_id":"mdk2NZWALLy25RuLu","bibbaseid":"renda-straccia-metasearchrankvsscorebasedranklistfusionmethodswithouttrainingdata-2002","author_short":["Renda, M E","Straccia, U"],"bibdata":{"bibtype":"misc","type":"misc","title":"Metasearch: Rank vs. Score Based Rank List Fusion Methods (without Training Data)","abstract":"Given a set of rankings (a ranking is a linear ordering of a set of items), 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, one prominent of which is metasearch. 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 query dependent 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. Preliminary experimental results seem to indicate that score based methods outperform rank based methods, while methods based on training data perform better than those without training data. In this paper we will compare rank and score based methods, without training data, in the context of metasearch. 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 competing 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.","publisher":"Istituto di Elaborazione dell'Informazione - CNR","author":[{"propositions":[],"lastnames":["Renda"],"firstnames":["M","E"],"suffixes":[]},{"propositions":[],"lastnames":["Straccia"],"firstnames":["U"],"suffixes":[]}],"year":"2002","note":"Series Number: 2002-TR-07 Place: Pisa","bibtex":"@misc{Renda/Straccia:02a,\n\ttitle = {Metasearch: {Rank} vs. {Score} {Based} {Rank} {List} {Fusion} {Methods} (without {Training} {Data})},\n\tabstract = {Given a set of rankings (a ranking is a linear\nordering of a set of items), the task of ranking fusion\nis the problem of combining these lists in such a way\nto optimize the performance of the combination. The\nranking fusion problem is encountered in many\nsituations, one prominent of which is metasearch. It\ndeals with the problem of combining the result lists\nreturned by multiple search engines in response to a\ngiven query, where each item in a result list is\nordered with respect to a search engine and query\ndependent relevance score. Several ranking fusion\nmethods have been proposed in the literature. They can\nbe classified based on whether: (i) they rely on the\nrank; (ii) they rely on the score; and (iii) they\nrequire training data or not. Preliminary experimental\nresults seem to indicate that score based methods\noutperform rank based methods, while methods based on\ntraining data perform better than those without\ntraining data. In this paper we will compare rank and\nscore based methods, without training data, in the\ncontext of metasearch. Our paper will make the\nfollowing contributions: (i) we will report\nexperimental results for the Markov chain rank based\nmethods, for which no large experimental tests have yet\nbeen made; (ii) while it is believed that the rank\nbased method, named Borda Count, is competing with\nscore based methods, we will show that this is not true\nfor metasearch; and (iii) we will show that Markov\nchain based methods compete with score based methods.\nThis is especially important in the context of\nmetasearch as scores are usually not available from the\nsearch engines.},\n\tpublisher = {Istituto di Elaborazione dell'Informazione - CNR},\n\tauthor = {Renda, M E and Straccia, U},\n\tyear = {2002},\n\tnote = {Series Number: 2002-TR-07\nPlace: Pisa},\n}\n\n","author_short":["Renda, M E","Straccia, U"],"key":"Renda/Straccia:02a","id":"Renda/Straccia:02a","bibbaseid":"renda-straccia-metasearchrankvsscorebasedranklistfusionmethodswithouttrainingdata-2002","role":"author","urls":{},"metadata":{"authorlinks":{}},"html":""},"bibtype":"misc","biburl":"https://bibbase.org/zotero/ifromm","dataSources":["N4kJAiLiJ7kxfNsoh"],"keywords":[],"search_terms":["metasearch","rank","score","based","rank","list","fusion","methods","without","training","data","renda","straccia"],"title":"Metasearch: Rank vs. Score Based Rank List Fusion Methods (without Training Data)","year":2002}