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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:


Jan 29, 2008

Text Learning Meeting - CMU

a two-step optimization approach

With invaluable ideas from ….



  • 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.

Fine tuning ranking models

Thunderbird plug-in

Leak warnings:

hit x to remove recipient


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 recommendation

Email Recipient Recommendation

36 Enron users

Email recipient recommendation1

Email Recipient Recommendation


[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]

Intelligent email auto completion

Intelligent Email Auto-completion



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









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 function

Loss Function

Loss function1

Loss Function

Loss function2

Loss Function

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.


Minimizing a very close approximation for the number of misranks

Gradient descent

Gradient Descent

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]

Results in letor

Results in Letor

Learning curve

Learning Curve

TOCCBCC Enron: user lokay-m

Learning curve1

Learning Curve

CCBCC Enron: user campbel-m

Regularization parameter

Regularization Parameter





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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