Fine tuning ranking models
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Fine-tuning Ranking Models:. Vitor Jan 29, 2008 Text Learning Meeting - CMU. a two-step optimization approach. With invaluable ideas from …. Motivation. Rank, Rank, Rank… Web retrieval, movie recommendation, NFL draft, etc. Einat ’s contextual search Richard ’s set expansion (SEAL)

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Fine tuning ranking models

Fine-tuning Ranking Models:

Vitor

Jan 29, 2008

Text Learning Meeting - CMU

a two-step optimization approach

With invaluable ideas from ….


Motivation
Motivation

  • Rank, Rank, Rank…

    • Web retrieval, movie recommendation, NFL draft, etc.

    • Einat’s contextual search

    • Richard’s set expansion (SEAL)

    • Andy’s context sensitive spelling correction algorithm

    • Selecting seeds in Frank’s political blog classification algorithm

    • Ramnath’s thunderbird extension for

      • Email Leak prediction

      • Email Recipient suggestion


Help your brothers
Help your brothers!

  • Try Cut Once!, our Thunderbird extension

    • Works well with Gmail accounts

  • It’s working reasonably well

  • We need feedback.


Thunderbird plug-in

Leak warnings:

hit x to remove recipient

Suggestions:

hit + to add

Pause or cancel send of message

Email Recipient Recommendation

Timer: msg is sent after 10sec by default

Classifier/rankers written in JavaScript



Email recipient recommendation1
Email Recipient Recommendation

Threaded

[Carvalho & Cohen, ECIR-08]


Aggregating rankings
Aggregating Rankings

[Aslam & Montague, 2001]; [Ogilvie & Callan, 2003]; [Macdonald & Ounis, 2006]

  • Many “Data Fusion” methods

    • 2 types:

      • Normalized scores: CombSUM, CombMNZ, etc.

      • Unnormalized scores: BordaCount, Reciprocal Rank Sum, etc.

  • Reciprocal Rank:

    • The sum of the inverse of the rank of document in each ranking.


Aggregated ranking results
Aggregated Ranking Results

[Carvalho & Cohen, ECIR-08]



Can we do better
Can we do better?

  • Not using other features, but better ranking methods

  • Machine learning to improve ranking: Learning to rank:

    • Many (recent) methods:

      • ListNet, Perceptrons, RankSvm, RankBoost, AdaRank, Genetic Programming, Ordinal Regression, etc.

    • Mostly supervised

    • Generally small training sets

    • Workshop in SIGIR-07 (Einat was in the PC)


Pairwise based ranking
Pairwise-based Ranking

Goal: induce a ranking function f(d) s.t.

Rank q

d1

d2

d3

d4

d5

d6

...

dT

We assume a linear function f

Therefore, constraints are:


Ranking with perceptrons
Ranking with Perceptrons

  • Nice convergence properties and mistake bounds

    • bound on the number of mistakes/misranks

  • Fast and scalable

  • Many variants[Collins 2002, Gao et al 2005, Elsas et al 2008]

    • Voting, averaging, committee, pocket, etc.

    • General update rule:

    • Here: Averaged version of perceptron


Rank svm
Rank SVM

[Joachims, KDD-02],

[Herbrich et al, 2000]

  • Equivalent to maximing AUC

Equivalent to:





Loss functions
Loss Functions

  • SVMrank

  • SigmoidRank

Not convex


Fine tuning ranking models1
Fine-tuning Ranking Models

Base ranking model

Final model

Base Ranker

Sigmoid Rank

e.g., RankSVM, Perceptron, etc.

Non-convex:

Minimizing a very close approximation for the number of misranks



Results in cc prediction
Results in CC prediction

36 Enron users


Set expansion seal results
Set Expansion (SEAL) Results

[Wang & Cohen, ICDM-2007]

[Listnet: Cao et al. , ICML-07]



Learning curve
Learning Curve

TOCCBCC Enron: user lokay-m


Learning curve1
Learning Curve

CCBCC Enron: user campbel-m


Regularization parameter
Regularization Parameter

s=2

TREC3

TREC4

Ohsumed


Some ideas
Some Ideas

  • Instead of number of misranks, optimize other loss functions:

    • Mean Average Precision, MRR, etc.

    • Rank Term:

    • Some preliminary results with Sigmoid-MAP

  • Does it work for classification?



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