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Information Filtering

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Information Filtering

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    1. Information Filtering Evaluation of Filtering Systems IEEE Paper Contest Fall 2002

    2. Introduction to information filtering What is filtering Other info. seeking processes Paradigms Profile Modeling Evaluation of filtering systems Privacy in filtering systems

    3. Other info. seeking processes

    4. Filtering vs. Retrieval

    5. 3 subtasks of Filtering Collection Active Passive Selection Display Interactive Non Interactive

    6. Two paradigms of filtering systems Content-Based SIFT InfoScope Social Tapestry Uses a Client/Server mechanism to generate a ranked list GroupLens Chicken and the Egg problem

    7. A Typical filtering system

    8. User modeling & Machine Learning User Model Explicit (like SIFT) Implicit (in machine learning) Users behavior Elements of the environment Evidence of Users behavior Explicit feedback Implicit feedback (InfoScope)

    9. sources of implicit evidence about users interests Read/Ignored Saved/Delete Replied or not Reading time

    10. Machine learning approaches Rule induction Instance based Statistical classification Neural networks Genetic algorithms and more

    11. Evaluation strategies Precision and Recall problems: Recall needs total number of rel. docs. Precision does not tell everything.

    12. Utility Functions Linear Utility Functions: LF1=3R - 2N if p(rel)>.4 LF2=3R - N if p(rel)>.25

    13. Major problems The average will be dominated by topics with large retrieved sets. Difficult to compare performance across topics

    14. Solutions Nonlinear Utility functions: NF1= 6R^.5 N NF2= 6R^.8 N Scaling

    15. Scaling Divide by max utility scores for each topic problems: It is flawed by negative scores. Inconsistency with precision and recall.

    16. Suppose we have two systems where: Precision(X)>Precision(Y) Recall (X)> Recall(Y) if U(X) and U(Y) are negative or we use nonlinear utility we can have: U(X) < U(Y) !!!

    17. A more sophisticated formula Us(S,T)= (max(U(S,T),U(S)) -U(S))/(max U(T)-U(S)) Problem: Evaluation highly dependent on the value of S.

    18. TREC 9:Resorting to the good old friend Precision-Oriented function: T9P=(rel. ret. Docs)/ max (target , ret. Docs)

    19. Privacy Privacy becomes an issue when a system collects information about its user Its important either in commercial and personal application

    20. Privacy in content-based Filtering Preventing unauthorized access to profiles Password Encryption preventing reconstruction of useful information about user profile Traffic analysis problem

    21. Privacy in social filtering Using pseudonym Encrypted transmission of annotation to authorized users

    22. resources A Conceptual Framework for Text Filtering Douglas W. Oard & Gary Marchionini Information filtering and information retrieval: two sides of the same coin? Nicholas J. Belkin & W. Bruce Croft The TREC-7 Filtering Track Final Report The TREC-8 Filtering Track Final Report David A. Hull & Stephen Robertson The TREC-9 Filtering Track Final Report Ellen M. Voorhees

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