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Outline

- Search Engines
- Clickthrough Data
- Learning of Retrieval Functions
- Support Vector Machine (SVM) for

Learning of Ranking Functions

- Experiment Setup
- Offline Experiment
- Online Experiment
- Analysis of The Online Experiment
- Conclusion and Future Work
- References
- Questions

Search Engines

- Search engines utilize ranking systems to list results based on their relevance to the query
- Current ranking systems are not optimized for relevance
- As an alternative solution we can use

Clickthrought Data to find

more relevance

optimized results

Clickthrough Data

What is Clickthrough Data ?

- Clickthrough data is the set of links that the user selects from the list of the links retreived by the search engine to a user-given query.

Why is Clickthrough Data Important?

- These are the most relevant links among the query results
- Easier to acquire than user feedback (since the data is already in the logs of the search engines)

Clickthrough Data (2)

- Users are less likely to click on a link that has a low ranking

(Independent of the actual relevence)

- Users typically scan the first 10 links in the result set [24]

Thus, clickthrough data is not the absolute relevence

value for the query but a good relative relevence value

Clickthrough Data (3)

Example:

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

The Software

10. Lagrangian Support Vector

Machine Home Page

Among the 10 results, only links 1,3 and 7 is chosen (clickthrough data)

Clickthrough Data (4)

link3 < * link2

link7 < * link2

link7 < * link4

link7 < * link5

link7 < * link6

: ranking preferred by the user

(binary relation)

We can generalize this preference

information,

link i < * link j

for all pairs 1 <= j < i, with and

Learning of Retrieval Functions

- Goal:

We have to find a retrival function whose results are close to

- In order to calculate the similarity between any given

and , we have to use a performance metric

- Average Precision (binary relevance)
- Kendall’s

VerySimple

Good Performance Metric

Learning of Retrieval Functions (2)

- Kendall’s
- Between any two ranking functions the distance is,

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

Learning of Retrieval Functions (3)

- Problem Defination of Learning an Appropriate Retrieval Function
- For a fixed (but unknown) distribution of queries and target (user preferred) rankings the goal is,

where is the distribution of queries

Support Vector Machine (SVM) for Learning of Ranking Functions

- Usually machine learning in information learning is based on binary classification.

(A document is either related to the query or not)

- Since the information gathered from clickthrought data is not an absoulte relevancy information we cannot use binary classification

Support Vector Machine (SVM) for Learning of Ranking Functions (2)

- Using a set of queries and user ranking sets (training data) we will select a ranking function among a family (F) of ranking functions

Selection will be based on minimizing

n : # of queries in the training set

Support Vector Machine (SVM) for Learning of Ranking Functions (3)

- Then, we need to find a sound family of ranking functions.
- How to find an F which includes an efficent ranking function (f) ?

Support Vector Machine (SVM) for Learning of Ranking Functions (4)

- A set of functions,
- Where ‘s are description based retrieval functions [10,11]
- ‘s are weight vectors (2D) adjusted by learning

Support Vector Machine (SVM) for Learning of Ranking Functions (5)

- Instead of maximizing directly our goal function we can minimize the Q in our performance measure
- By using calssification SVM’s [7]

minimize

subject to

Experiment Setup

- A baseline meta-search engine called Striver is used for testing purposes
- Striver forwards a query to “MSNSearch, Google, Excite, Altavista and Hotbot”
- Acquires top 100 results from each search engine
- Based on the learned retrival function it selects top 50 of the 500(may be lesser if more than one engine has found a specific document)

Offline Experiment

- Using Striver 112 Queries are recorded
- A huge set of features are used to calculate the description based retrieval functions
- The testing is done with different values of training set queries
- Results from Google and MSNSearch are used for benchmarking purposes

Online Experiment

- Striver is used by a group of people (20 people)
- Based on these people’s queries training set of Striver is composed of 260 queries
- The results are compared with results from Google, MSNSearch and Toprank (a simple meta-search engine)

Online Experiment (2)

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

Analysis of the Online Experiment

- Since all of the users have used the engine for academic searches the learned data is good for searches in academic research topics
- But it may not give that good results for different groups of people
- We can say that learned engine is a customizable engine unlike traditional engines

Future Work and Conclusions

- What is the optimal group size for user custimization?
- Features can be tuned for better performance
- Clustering algorithms can cluster users in WWW into subgroups based on their clickthrough data’s ?
- Can malicious users corrupt the learning process by clicking irrelevant links, how it is avoided?

References

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[15] T. Joachims. Learning to Classify Text Using SupportVector Machines – Methods, Theory, and Algorithms.Kluwer, 2002.

[16] T. Joachims. Unbiased evaluation of retrieval qualityusing clickthrough data. Technical report, CornellUniversity, Department of Computer Science, 2002.http://www.joachims.org.

[17] T. Joachims, D. Freitag, and T. Mitchell.WebWatcher: a tour guide for the world wide web. InProceedings of International Joint Conference onArtificial Intelligence (IJCAI), volume 1, pages 770 –777. Morgan Kaufmann, 1997.

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[19] M. Kendall. Rank Correlation Methods. Hafner, 1955.

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[21] A. Mood, F. Graybill, and D. Boes. Introduction tothe Theory of Statistics. McGraw-Hill, 3 edition, 1974.

[22] L. Page and S. Brin. Pagerank, an eigenvector basedranking approach for hypertext. In 21st AnnualACM/SIGIR International Conference on Researchand Development in Information Retrieval, 1998.

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[24] C. Silverstein, M. Henzinger, H. Marais, andM. Moricz. Analysis of a very large altavista querylog. Technical Report SRC 1998-014, Digital SystemsResearch Center, 1998.

[25] V. Vapnik. Statistical Learning Theory. Wiley,Chichester, GB, 1998.

[26] Y. Yao. Measuring retrieval effectiveness based onuser preference of documents. Journal of the AmericanSociety for Information Science, 46(2):133–145, 1995.

Questions........

?

Experiment Results ?

Clickthrough Data ?

Machine Learning for

Retrieval Functions ?

Retrieval Functions ?

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