Searching distributed collections with inference networks
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Analyses of multiple evidence combination
Proceedings of the 20th annual international ACM SIGIR conference on Research and development in information retrieval
The impact of database selection on distributed searching
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
Modeling score distributions for combining the outputs of search engines
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Relevance score normalization for metasearch
Proceedings of the tenth international conference on Information and knowledge management
Condorcet fusion for improved retrieval
Proceedings of the eleventh international conference on Information and knowledge management
Data fusion with estimated weights
Proceedings of the eleventh international conference on Information and knowledge management
Fusion Via a Linear Combination of Scores
Information Retrieval
Fusion of effective retrieval strategies in the same information retrieval system
Journal of the American Society for Information Science and Technology
ProbFuse: a probabilistic approach to data fusion
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Segmentation of search engine results for effective data-fusion
ECIR'07 Proceedings of the 29th European conference on IR research
Extending probabilistic data fusion using sliding windows
ECIR'08 Proceedings of the IR research, 30th European conference on Advances in information retrieval
The linear combination data fusion method in information retrieval
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
Query sampling for learning data fusion
Proceedings of the 20th ACM international conference on Information and knowledge management
Linear combination of component results in information retrieval
Data & Knowledge Engineering
The weighted Condorcet fusion in information retrieval
Information Processing and Management: an International Journal
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Data Fusion is the combination of a number of independent search results, relating to the same document collection, into a single result to be presented to the user. A number of probabilistic data fusion models have been shown to be effective in empirical studies. These typically attempt to estimate the probability that particular documents will be relevant, based on training data. However, little attempt has been made to gauge how the accuracy of these estimations affect fusion performance. The focus of this paper is twofold: firstly, that accurate estimation of the probability of relevance results in effective data fusion; and secondly, that an effective approximation of this probability can be made based on less training data that has previously been employed. This is based on the observation that the distribution of relevant documents follows a similar pattern in most high-quality result sets. Curve fitting suggests that this can be modelled by a simple function that is less complex than other models that have been proposed. The use of existing IR evaluation metrics is proposed as a substitution for probability calculations. Mean Average Precision is used to demonstrate the effectiveness of this approach, with evaluation results demonstrating competitive performance when compared with related algorithms with more onerous requirements for training data.