Finding Dense and Isolated Submarkets in a Sponsored Search

Report
Modelling Relevance and User Behaviour in
Sponsored Search using
Click-Data
Adarsh Prasad, IIT Delhi
Advisors: Dinesh Govindaraj
SVN Vishwanathan*
Group: Revenue and Relevance
*-Visiting Researcher from Purdue
Overview
• Click-Data seems to be the perfect source of information
when deciding which Ads to show in answer to a query. It can
be thought as the result of users voting in favour of the
documents they find interesting.
• This information can be fed into the ranker, to tune search
parameters or even use as training points as for the ranker.
• The aim of the project is to develop a model which takes in
Click-Data and generates output in the form of constraints or
updated ranking score as input to the ranker.
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Motivation
• Quality of training points is of critical importance for learning a ranking
function
• Currently, labeled data collected using human judges. Human-labeling is
time-consuming and labor-intensive.
• Need to ensure “temporal relevance” of Ads i.e. Something relevant
today might not be relevant 6 months later, therefore labeling must be
repeated and there is a need for automation of labeling process
Main Difficulty – Presentation Bias
•Results at lower positions are less likely to be clicked even if they
are relevant.(Position)
•Clicks depend on other Ads being shown.(Externalities)
Example[1]
Query: myspace
URL = www.myspace.com
Market = U.K.
Ranking 1
Pos 1: uk.myspace.com: ctr = 0.97
Pos 2: www.myspace.com: ctr = 0.11
[1] Oliver Chapelle et al. A Dynamic Bayesian Click Model for Web Search Ranking
Ranking 2:
Pos 1 : www.myspace.com : ctr = 0.97
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Procedure
For learning a web search function, clicks can be used as a target[2] or as a
feature[3]
• Use of Click Data as target : Useful for markets with few editorial
Judgments.
• Train on pairwise preferences: Two Sets of preferences:
PE from editorial judgments and PC coming from click modeling.
Minimize:
Target
1. Deriving Preference Relations on
the basis of click-pattern and
feeding them as constraints to
ranker (Rocky-Road)
• Position and Order-of-Click
based Constraints[4]
• Aggregate Constraints
Feature
1. Sample Clicked Ads and label them as
relevant.
2. Types of Sampling:
• Random
• Position based Weighted : User Clicking
ml-4 Ad stronger signal of relevance as
compared to user clicking ml-1
3. Feed them to the Binary Classifier
[2] Joachims et al. Optimizing Search Engines using Clickthrough Data
[3] Agichtein et al. Improving web search ranking via incorporating User Behaviour
[4] Joachims et al. Accurately interpreting ClickThrough Data as Implicit Feedback
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Results
 −
Fisher Score = √( +  )


EXACTMATCH BROADMATCH
PHRASEMATCH
Sampling
+0.39%
+1.02%
Position and
Order Constraints
+1.22%
+5.93%
+4.15%
+0.38%
Aggregate
Constraints
+0.2%
+5.17%
+0.77%
+0.5%
-0.06%
Log Loss (Label Based)
Sampling
SAME
SUPERSET
+5.72%
+4.22%
Position and
+3.1%
Order Constraints
+2.28%
Aggregate
Constraints
+5.28%
+7.4%
SMARTMATCH
-0.5%
Weighted LL
DISJOINT
-6.28%
-3.9%
-11.3%
Sampling
+0.001%
Position and
+3.07%
Order Constraints
Aggregate
Constraints
+1.75%
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Background on Click Models
• Use CTR (click-through rate) data.
• Pr(click) = Pr(examination) x Pr(click | examination)
Relevance
• Need user browsing models to estimate Pr(examination)
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Notation
• Φ(i) : result at position i
• Examination event:
• Click event:
1, if theuser examined (i)
Ei  
0, otherwise
1, if theuser clickedon  (i)
Ci  
0, otherwise
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Examination Hypothesis
Richardson et al, WWW 2007:
Pr(Ci = 1) = Pr(Ei = 1) Pr(Ci = 1 | Ei = 1)
• αi : position bias
• Depends solely on position.
• Can be estimated by looking at CTR of the same result in different
positions.
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Using Prior Clicks
R1
R2
R3
R4
R5
:
Clicks
Pr(E5 | C1) = 0.3
R1
R2
R3
R4
R5:
Clicks
Pr(E5 | C1,C3) = 0.5
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Examination depends on prior clicks
• Cascade model
• Dependent click model (DCM)
• User browsing model (UBM) [Dupret & Piwowarski, SIGIR
2008]
• More general and more accurate than Cascade, DCM.
• Conditions Pr(examination) on closest prior click.
• Bayesian browsing model (BBM) [Liu et al, KDD 2009]
• Same user behavior model as UBM.
• Uses Bayesian paradigm for relevance.
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User browsing model (UBM)
• Use position of closest prior click to predict Pr(examination).
Pr(Ei = 1 | C1:i-1) = αi β i,p(i)
position bias
p(i) = position of
closest prior click
Pr(Ci = 1 | C1:i-1) = Pr(Ei = 1 | C1:i-1) Pr(Ci = 1 | Ei = 1)
Prior clicks don’t
affect relevance.
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Other Related Work
• Examination depends on prior clicks and prior relevance
• Click chain model (CCM)
• General click model (GCM)
• Post-click models
• Dynamic Bayesian model
• Session utility model
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User Browsing in Sponsored
Search
• Is user browsing in sponsored search similar to browsing in Web Search??
• Generally, the assumption in organic search is that users examine and click in a
linear top-to-bottom fashion.
• We observed that for sponsored search where the number of returned results is
few, a fair share (~ 30%) of users click out of order.
• Users behaving in a non-linear fashion is a strong signal, which may contain
important information.
• Combining position and temporal behavior of user.
The statistic(x) that has been counted
is the difference between the positions
of temporal clicks.
Example:
if the user clicks on ml1 and then ml2 then x = -1
if ml2 and then ml1 then x=1 and so on.
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A New Model
• Allow users to move in a non-linear fashion
• Also, incorporate the notion of externalities, i.e. perceived
relevance changes with other clicks.
For learning our parameters, we
can use EM Algorithm.
(1) In E step, we estimate our
hidden parameters by a
forward-backward algorithm.
(2) In M step- We have closed
form solutions to maximize the
expected log-likelihood.
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