NDLI: Mixture model with multiple centralized retrieval algorithms for result merging in federated search

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Mixture model with multiple centralized retrieval algorithms for result merging in federated search

Content Provider	ACM Digital Library
Author	Si, Luo Hong, Dzung
Abstract	Result merging is an important research problem in federated search for merging documents retrieved from multiple ranked lists of selected information sources into a single list. The state-of-the-art result merging algorithms such as Semi-Supervised Learning (SSL) and Sample-Agglomerate Fitting Estimate (SAFE) try to map document scores retrieved from different sources to comparable scores according to a single centralized retrieval algorithm for ranking those documents. Both SSL and SAFE arbitrarily select a single centralized retrieval algorithm for generating comparable document scores, which is problematic in a heterogeneous federated search environment, since a single centralized algorithm is often suboptimal for different information sources. Based on this observation, this paper proposes a novel approach for result merging by utilizing multiple centralized retrieval algorithms. One simple approach is to learn a set of combination weights for multiple centralized retrieval algorithms (e.g., logistic regression) to compute comparable document scores. The paper shows that this simple approach generates suboptimal results as it is not flexible enough to deal with heterogeneous information sources. A mixture probabilistic model is thus proposed to learn more appropriate combination weights with respect to different types of information sources with some training data. An extensive set of experiments on three datasets have proven the effectiveness of the proposed new approach.
Starting Page	821
Ending Page	830
Page Count	10
File Format	PDF
ISBN	9781450314725
DOI	10.1145/2348283.2348393
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-08-12
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Mixture model Result merging Federated search
Content Type	Text
Resource Type	Article

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