24-06-2012, 09:57 PM
Academic projects at cheaper rates only at Ocular Systems...
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Abstract—With the explosive emergence of vertical search domains, applying the broad-based ranking model directly to different
domains is no longer desirable due to domain differences, while building a unique ranking model for each domain is both laborious for
labeling data and time-consuming for training models. In this paper, we address these difficulties by proposing a regularization based
algorithm called ranking adaptation SVM (RA-SVM), through which we can adapt an existing ranking model to a new domain, so that
the amount of labeled data and the training cost is reduced while the performance is still guaranteed. Our algorithm only requires the
prediction from the existing ranking models, rather than their internal representations or the data from auxiliary domains. In addition,
we assume that documents similar in the domain-specific feature space should have consistent rankings, and add some constraints
to control the margin and slack variables of RA-SVM adaptively. Finally, ranking adaptability measurement is proposed to quantitatively
estimate if an existing ranking model can be adapted to a new domain. Experiments performed over Letor and two large scale datasets
crawled from a commercial search engine demonstrate the applicabilities of the proposed ranking adaptation algorithms and the ranking
adaptability measurement.
Please download the IEEE Paper for this project from the attachment of this post...