Title
Selecting the n-top retrieval result lists for an effective data fusion
Abstract
Although the application of data fusion in information retrieval has yielded good results in the majority of the cases, it has been noticed that its achievement is dependent on the quality of the input result lists. In order to tackle this problem, in this paper we explore the combination of only the n-top result lists as an alternative to the fusion of all available data. In particular, we describe a heuristic measure based on redundancy and ranking information to evaluate the quality of each result list, and, consequently, to select the presumably n-best lists per query. Preliminary results in four IR test collections, containing a total of 266 queries, and employing three different DF methods are encouraging. They indicate that the proposed approach could significantly outperform the results achieved by fusion all available lists, showing improvements in mean average precision of 10.7%, 3.7% and 18.8% when it was used along with Maximum RSV, CombMNZ and Fuzzy Borda methods.
Year
DOI
Venue
2010
10.1007/978-3-642-12116-6_49
CICLing
Keywords
Field
DocType
result list,input result list,available data,effective data fusion,data fusion,information retrieval,available list,preliminary result,n-top retrieval result list,ranking information,n-top result list,good result,mean average precision
Data mining,Heuristic,Ranking,Information retrieval,Computer science,Fuzzy logic,Sensor fusion,Redundancy (engineering)
Conference
Volume
ISSN
ISBN
6008
0302-9743
3-642-12115-2
Citations 
PageRank 
References 
3
0.37
20
Authors
5