NANO SCIENTIFIC RESEARCH CENTRE PVT.LTD., AMEERPET, HYD
WWW.NSRCNANO.COM, 09640648777, 09652926926
JAVA PROJECTS LIST--2013
JAVA 2013 IEEE PAPERS
Ranking Model Adaptation For
Domain-Specific Search
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.
EXISTING SYSTEM
The existing broad-based ranking
model provides a lot of common information in ranking documents only few
training samples are needed to be labeled in the new domain. From the
probabilistic perspective, the broad-based ranking model provides a prior
knowledge, so that only a small number of labeled samples are sufficient for
the target domain ranking model to achieve the same confidence. Hence, to
reduce the cost for new verticals, how to adapt the auxiliary ranking models to
the new target domain and make full use of their domain-specific features,
turns into a pivotal problem for building effective domain-specific ranking
models.
PROPOSED SYSTEM
Proposed System focus whether we can
adapt ranking models learned for the existing broad-based search or some
verticals, to a new domain, so that the amount of labeled data in the target
domain is reduced while the performance requirement is still guaranteed, how to
adapt the ranking model effectively and efficiently and how to utilize
domain-specific features to further boost the model adaptation. The first
problem is solved by the proposed rank-ing adaptability measure, which
quantitatively estimates whether an existing ranking model can be adapted to the
new domain, and predicts the potential performance for the adaptation. We
address the second problem from the regularization framework and a ranking
adaptation SVM algorithm is proposed. Our algorithm is a black box ranking
model adaptation, which needs only the predictions from the existing ranking
model, rather than the internal representation of the model itself or the data
from the auxiliary domains. With the black-box adaptation property, we achieved
not only the flexibility but also the efficiency. To resolve the third problem,
we assume that documents similar in their domain specific feature space should
have consistent rankings.
Advantage:
1. Model adaptation.
2. Reducing the labeling cost.
3. Reducing the computational cost.
Modules:
1. Ranking
Adaptation Module.
2. Explore
ranking adaptability Module.
3. Ranking
adaptation with domain specific search Module.
4. Ranking
Support Vector Machine Module.
System Configuration:
HARDWARE REQUIREMENTS:
Hardware - Pentium
RAM - 1GB
Hard Disk - 20 GB
SOFTWARE REQUIREMENTS:
Operating System : Windows XP
Technology : Java 1.6
Web Technologies : Html, JavaScript, CSS
Web Server : Tomcat
Database : My SQL
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