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Click Models for Web Search - Lecture 1 · PDF fileLecture 1 Lecture 2 Lecture 4 Practical 2 Lecture 5 Lecture 3 Lecture 2 Practical 1 AC{IM{MdR Click Models for Web Search 10. ...

Jun 04, 2018

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  • Introduction Basic click models Click probabilities

    Click Models for Web SearchLecture 1

    Aleksandr Chuklin, Ilya Markov Maarten de Rijke

    [email protected] [email protected] [email protected]

    University of AmsterdamGoogle Research Europe

    ACIMMdR Click Models for Web Search 1

  • Introduction Basic click models Click probabilities

    Aleksandr Chuklin

    Currently at Google Zurich

    Previously at Yandex Moscow

    Research interests: user experience evaluation and modelling

    Participated at RuSSIR 2009 in Petrozavodsk and RuSSIR2011 in Saint Petersburg

    ACIMMdR Click Models for Web Search 2

  • Introduction Basic click models Click probabilities

    Ilya Markov

    Postdoctoral researcher at the University of Amsterdam

    PhD at the University of Lugano

    Research interests: heterogeneous search environments

    Distributed IR, federated search, aggregated searchUser behavior, user-oriented evaluation

    Teach MSc courses on IR and Web Search

    ACIMMdR Click Models for Web Search 3

  • Introduction Basic click models Click probabilities

    Long-term relations with RuSSIR

    RuSSIR 2007, student

    RuSSIR 2010, lecturer on Distributed IR (with Fabio Crestani)

    RuSSIR 2011, member of organizing committee

    RuSSIR 2015, chair of program committee

    RuSSIR 2016, lecturer

    ACIMMdR Click Models for Web Search 4

  • Introduction Basic click models Click probabilities

    Course on Information Retrieval in St. Petersburg

    http://compsciclub.ru/courses/

    information-retrieval/2016-autumn/

    ACIMMdR Click Models for Web Search 5

    http://compsciclub.ru/courses/information-retrieval/2016-autumn/http://compsciclub.ru/courses/information-retrieval/2016-autumn/

  • Introduction Basic click models Click probabilities

    Maarten de Rijke

    Currently at the University of Amsterdam

    Ongoing collaborations with Bloomberg Labs, Google,Microsoft Research, Yandex Moscow

    Research interests: semantic search, online learning to rank

    Always looking for strong new PhD students

    ACIMMdR Click Models for Web Search 6

  • Introduction Basic click models Click probabilities

    The book

    http://clickmodels.weebly.com/the-book.html

    ACIMMdR Click Models for Web Search 7

    http://clickmodels.weebly.com/the-book.html

  • Introduction Basic click models Click probabilities

    Other course materials

    clickmodels.weebly.com/russir-2016-course.html

    Demos and practical sessions:

    clickmodels.weebly.com/russir-2016-setup.html

    github.com/markovi/PyClick

    ACIMMdR Click Models for Web Search 8

    http://clickmodels.weebly.com/russir-2016-course.htmlhttp://clickmodels.weebly.com/russir-2016-setup.htmlhttps://github.com/markovi/PyClick

  • Introduction Basic click models Click probabilities

    Course content

    Basic Click Models

    Parameter Estimation Evaluation

    Data and ToolsResultsApplications

    Advanced Models

    Recent Studies

    Future Research

    ACIMMdR Click Models for Web Search 9

  • Introduction Basic click models Click probabilities

    Lectures

    Basic Click Models

    Parameter Estimation Evaluation

    Data and ToolsResultsApplications

    Advanced Models

    Recent Studies

    Future Research

    Lecture 1 Lecture 2

    Lecture 4Practical 2 Lecture 5

    Lecture 3Practical 1Lecture 2

    ACIMMdR Click Models for Web Search 10

  • Introduction Basic click models Click probabilities

    Course overview

    Basic Click Models

    Parameter Estimation Evaluation

    Data and ToolsResultsApplications

    Advanced Models

    Recent Studies

    Future Research

    ACIMMdR Click Models for Web Search 11

  • Introduction Basic click models Click probabilities

    This lecture

    Basic Click Models

    Parameter Estimation Evaluation

    Data and ToolsResultsApplications

    Advanced Models

    Recent Studies

    Future Research

    ACIMMdR Click Models for Web Search 12

  • Introduction Basic click models Click probabilities

    Lecture outline

    1 Introduction

    2 Basic click models

    3 Click probabilities

    ACIMMdR Click Models for Web Search 13

  • Introduction Basic click models Click probabilities

    Web search

    ACIMMdR Click Models for Web Search 14

  • Introduction Basic click models Click probabilities

    Why clicks?

    ACIMMdR Click Models for Web Search 15

  • Introduction Basic click models Click probabilities

    Why clicks?

    Reflect user interests

    Help to improve search

    Help to evaluate search

    Ongoing and future research: other user search interactions

    mouse movementsscrollingtouch gestures

    ACIMMdR Click Models for Web Search 16

  • Introduction Basic click models Click probabilities

    What can we do with clicks?

    ACIMMdR Click Models for Web Search 17

  • Introduction Basic click models Click probabilities

    What can we do with clicks?

    countclick-through rate (CTR)

    Global CTR = # clicks# shown docs

    Rank-based CTR = # clicks at rank r# shown docs at rank r

    Query-document CTR = # u is clicked for q# u is shown for q

    Some notation: u URL (or document), q query

    ACIMMdR Click Models for Web Search 18

  • Introduction Basic click models Click probabilities

    Why click models?

    ACIMMdR Click Models for Web Search 19

  • Introduction Basic click models Click probabilities

    Why click models?

    Scientific modelling is a scientific activity, the aim of which is tomake a particular part or feature of the world easier to understand,define, quantify, visualize, or simulate by referencing it to existingand usually commonly accepted knowledge.

    Wikipedia, Scientific modelling

    ACIMMdR Click Models for Web Search 20

  • Introduction Basic click models Click probabilities

    Why click models?

    Click models make user clicks in web searcheasier to understand, define, quantify, visualize, or simulate

    using (mostly) probabilistic graphical models.

    ACIMMdR Click Models for Web Search 21

  • Introduction Basic click models Click probabilities

    Click log

    Yandex Relevance Prediction Challengehttp://imat-relpred.yandex.ru/en

    ACIMMdR Click Models for Web Search 22

    http://imat-relpred.yandex.ru/en

  • Introduction Basic click models Click probabilities

    Why do we need click models?

    Understand users

    Simulate users

    Approximate document relevance

    Evaluate search

    ACIMMdR Click Models for Web Search 23

  • Introduction Basic click models Click probabilities

    Lecture outline

    1 Introduction

    2 Basic click modelsRandom click modelCTR modelsPosition-based modelCascade modelDynamic Bayesian network modelUser browsing model

    3 Click probabilities

    ACIMMdR Click Models for Web Search 24

  • Introduction Basic click models Click probabilities

    Lecture outline

    2 Basic click modelsRandom click modelCTR modelsPosition-based modelCascade modelDynamic Bayesian network modelUser browsing model

    ACIMMdR Click Models for Web Search 25

  • Introduction Basic click models Click probabilities

    Random click model

    Pclick

    Pclick

    Pclick

    Pclick

    Pclick

    ACIMMdR Click Models for Web Search 26

  • Introduction Basic click models Click probabilities

    Random click model

    Terminology

    Cu binary random variable denoting a click on document uDocument u is clicked: Cu = 1Document u is not clicked: Cu = 0P(Cu = 1) probability of click on document uP(Cu = 0) = 1 P(Cu = 1)

    Random click model (RCM)

    Any document can be clicked with the same (fixed) probability

    P(Cu = 1) = const =

    ACIMMdR Click Models for Web Search 27

  • Introduction Basic click models Click probabilities

    Random click model

    P(Cu1 = 1) =

    P(Cu2 = 1) =

    P(Cu3 = 1) =

    P(Cu4 = 1) =

    P(Cu5 = 1) =

    =# clicks

    # shown docs= Global CTR

    ACIMMdR Click Models for Web Search 28

  • Introduction Basic click models Click probabilities

    Lecture outline

    2 Basic click modelsRandom click modelCTR modelsPosition-based modelCascade modelDynamic Bayesian network modelUser browsing model

    ACIMMdR Click Models for Web Search 29

  • Introduction Basic click models Click probabilities

    Rank-based CTR model

    P(Cu1 = 1) = 1

    P(Cu2 = 1) = 2

    P(Cu3 = 1) = 3

    P(Cu4 = 1) = 4

    P(Cu5 = 1) = 5

    P(Cur = 1) = r =# clicks at rank r

    # shown docs at rank r

    ACIMMdR Click Models for Web Search 30

  • Introduction Basic click models Click probabilities

    Query-document CTR model

    P(Cu1 = 1) = u1q

    P(Cu2 = 1) = u2q

    P(Cu3 = 1) = u3q

    P(Cu4 = 1) = u4q

    P(Cu5 = 1) = u5q

    P(Cu = 1) = uq =# u is clicked for q

    # u is shown for q

    ACIMMdR Click Models for Web Search 31

  • Introduction Basic click models Click probabilities

    CTR models: summary

    Random click model (global CTR):

    P(Cu = 1) =

    Rank-based CTR:

    P(Cur = 1) = r

    Query-document CTR:

    P(Cu = 1) = uq

    ACIMMdR Click Models for Web Search 32

  • Introduction Basic click models Click probabilities

    CTR models: demo

    Demo

    ACIMMdR Click Models for Web Search 33

  • Introduction Basic click models Click probabilities

    Lecture outline

    2 Basic click modelsRandom click modelCTR modelsPosition-based modelCascade modelDynamic Bay

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