Optimizing Search Engines using Clickthrough Data. by Thorsten Joachims. Presentation by M. Şükrü Kuran. Outline. Search Engines Clickthrough Data Learning of Retrieval Functions Support Vector Machine (SVM) for Learning of Ranking Functions Experiment Setup Offline Experiment
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Presentation by M. Şükrü Kuran
Learning of Ranking Functions
Clickthrought Data to find
What is Clickthrough Data ?
Why is Clickthrough Data Important?
(Independent of the actual relevence)
Thus, clickthrough data is not the absolute relevence
value for the query but a good relative relevence value
Results for a search for SVM:
1. Kernel Machines 6. Archives of Support Vector
2. Support Vector Machine Machines
3. SVM-Light Support Vector Machine7. SVM demo Applet
4. Intr. To Support Vector Machines 8. Royal Holloway Support Vector
5. Support Vector Machine and Machine
Kernel Methods Ref. 9. Support Vector Machine
10. Lagrangian Support Vector
Machine Home Page
Among the 10 results, only links 1,3 and 7 is chosen (clickthrough data)
link3 < * link2
link7 < * link2
link7 < * link4
link7 < * link5
link7 < * link6
: ranking preferred by the user
We can generalize this preference
link i < * link j
for all pairs 1 <= j < i, with and
We have to find a retrival function whose results are close to
and , we have to use a performance metric
Good Performance Metric
D : Set of documents in a query result
P : # of concordant pairs in D x D
Q : # of discordant pairs in D x D
m : # of documents/links in D
where is the distribution of queries
(A document is either related to the query or not)
Selection will be based on minimizing
n : # of queries in the training set
More clicks mean that (for Google) users clicked more links in the learned engine
than they do in Google for 29 queries out of 88.
Less clicks mean that (for Google) users clicked less links in the learned engine
than they do in Google for 13 queries out of 88
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Experiment Results ?
Clickthrough Data ?
Machine Learning for
Retrieval Functions ?
Retrieval Functions ?