Personalizing the Digital Library Experience

  • Published on
    03-Jan-2016

  • View
    15

  • Download
    2

DESCRIPTION

Personalizing the Digital Library Experience. Nicholas J. Belkin, Jacek Gwizdka, Xiangmin Zhang SCILS, Rutgers University nick@belkin.rutgers.edu http://scils.rutgers.edu/imls/poodle. Goals of Personalization. - PowerPoint PPT Presentation

Transcript

  • Personalizing the Digital Library ExperienceNicholas J. Belkin, Jacek Gwizdka, Xiangmin Zhang SCILS, Rutgers Universitynick@belkin.rutgers.edu http://scils.rutgers.edu/imls/poodle

  • Goals of PersonalizationTo make the users interaction with information as effective and pleasurable as possibleTo tailor the users interaction with information to the users characteristics, preferences, the specific circumstances of the interaction, and the users goals

  • Types of PersonalizationWith respect to predictions of usefulness/ relevance of items, e.g.modify queryre-rank resultsWith respect to interaction, e.g.different interface designs for different tasksdifferent interface designs for different individuals

  • Facets of PersonalizationViewing/saving/evaluating behaviorsTaskProblem statePersonal characteristicsPersonal preferencesContext/situation

  • Viewing, etc. BehaviorsImplicit evidence (Kelly & Teevan)Time on pageClick-throughPrevious usesOthers like the interactantExplicit evidenceRelevance feedback (of various sorts)

  • TaskEveryday or leading or work taskComplexity, difficulty, type (Bystrom, et al.)Information seeking taskChoice of strategies, sources (Bates, Pejtersen, berrypicking)Information searching taskMoves, shifts (Bates; Xie)

  • Problem StateWhat has been done beforePrevious searchesStage in the Problem Solving Process (Kuhlthau; Vakkari)What is being done nowImmediately past behavior in searching, other concurrent activities

  • Personal CharacteristicsKnowledge of topic, of taskDemography gender, ageIndividual differencesCognitive abilitiesAffect

  • Personal PreferencesFor types of interactionMixed or single initiativeFor styles of interactionDisplay, navigationFor support for interactionActive, passiveIntegrated, separateFor types of informationGenre, level

  • Context, SituationLocationPhysical environmentMobile, staticSalienceUrgencyTime of day, of week, of month, of season, Other interactantsGroup conditionsSocial norms

  • Overall Goals for PersonalizationDetermining significant aspects of each facetDetermining means for identifying these aspectsDetermining means for implementation of supportIntegrating all facets of personalization into single system frameworks

  • Evidence for PersonalizationExplicit evidence, e.g.Relevance judgmentsStatements of goals, problems, etc.Location; time of day, week, month, yearImplicit evidence, e.g.Dwell timeClickthroughPast searches, usesConcurrent activities

  • Interpreting Implicit EvidenceDwell time is evidence of usefulness / relevance / interestingnessBut needs to be interpreted in terms of task (Kelly, 2004; White & Kelly, 2006)Is dependent on individual characteristics (Kelly, 2004)In general, evidence from any one facet could affect interpretation of evidence from any other facetAll evidence is probably individual-dependent

  • Our ApproachInvestigate in depth aspects of specific facets, e.g.TaskDomain knowledgeCognitive characteristicsInvestigate the interactions among the different facetsImplement and test within an integrative system frameworkUsing a client-side personalization assistant

  • Initial InvestigationThree months of logs of all computer use and searching behavior for each of seven Ph.D. studentsJudgments, by subjects, of usefulness of pages viewed as results of searching, with task type, duration and stage of task, topic, and familiarity with topic Both from Kelly (2004).

  • Data AnalysisExploratory analysis of relationships among dwell time and each of: task and topic familiarity; task stage; and, task duration, to determine most accurate dwell time value for predicting usefulnessExploratory analysis of current and past behavior as indicator of task type, task stage, and task/topic familiarity

  • Results to Date for Task Stage

    t test results for dwell time (log) for useful and non-useful documents for task stages of each subject (UT = Usefulness threshold on 7-point scale)

    sub

    stage

    UT

    t

    df

    p

    Useful

    Non-useful

    d

    n

    M(SD)

    n

    M(SD)

    5

    5

    6

    2.99

    31

    .005

    19

    3.28(1.36)

    14

    1.99(1.02)

    1.05

    5

    7 (6)

    2.75

    31

    .01

    18

    3.28(1.39)

    15

    2.08(1.04)

    0.96

    6

    1

    5

    -3.8

    21

    .001

    21

    0.58(0.71)

    2

    2.52(0.17)

    -2.81

    3

    6 (5)

    2.14

    255

    .03

    29

    2.05(1.07)

    228

    1.60(1.07)

    0.42

    5

    6 (5)

    2.44a

    9.13

    .04

    9

    2.31(1.86)

    221

    1.58(1.12)

    0.83

    6

    5

    2.39a

    129.94

    .02

    84

    1.52(1.32)

    172

    1.13(0.99)

    0.34

    6

    6 (5)

    4.86 a

    5.44

    .004

    4

    1.92(0.24)

    252

    1.24(1.13)

    1.23

    7

    1

    6

    -3.47

    170

    .001

    114

    4.03(0.92)

    58

    3.49(1.01)

    -0.56

    4

    6

    -2.37

    76

    .02

    57

    3.79(1.00)

    21

    3.19(0.99)

    -0.60

    5

    6

    -3.12

    42

    .003

    37

    3.97(0.83)

    8

    2.93(0.96)

    -1.22

  • Results to Date for Task Type

    t-test results for dwell time (log) for useful and non-useful documents for individual tasks of each subject (UT = Usefulness Threshold on 7-point scale; task numbers are specific to each subject)

    sub

    task

    UT

    t

    df

    p

    Useful

    Non-useful

    d

    n

    M(SD)

    n

    M(SD)

    1

    2

    6

    2.21

    125

    .03

    21

    3.44(1.48)

    106

    2.60(1.61)

    0.53

    6

    6

    -2.74

    14

    .02

    6

    1.90(0.85)

    10

    3.11(0.85)

    -1.41

    2

    10

    7

    -2.03

    153

    .04

    116

    3.11(1.42)

    39

    2.59(1.35)

    -0.07

    3

    2

    6

    2.19

    21

    .04

    17

    3.48(1.17)

    6

    2.24(1.26)

    1.04

    3

    6

    2.82a

    106.67

    .006

    90

    3.04(1.48)

    39

    2.42(0.97)

    0.54

    3

    5 (6)

    3.16a

    93.82

    .002

    100

    2.99(1.48)

    29

    2.35(0.75)

    0.67

    9

    6

    -2.36

    13

    .04

    9

    1.67(1.08)

    6

    3.05(1.17)

    -1.24

    9

    5 (6)

    -2.30

    13

    .04

    10

    1.75(1.05)

    5

    3.17(1.27)

    -1.26

    4

    1

    6 (7)

    -2.39

    35

    .02

    25

    1.97(1.49)

    12

    3.41(2.13)

    -0.84

    5

    5 (7)

    -2.52a

    39.46

    .02

    40

    2.70(1.45)

    2

    3.28(0.03)

    -1.83

    6

    3

    5

    2.28

    141

    .02

    117

    2.00(1.07)

    26

    1.48(0.90)

    0.49

    3

    6 (5)

    2.27

    141

    .03

    11

    2.59(0.62)

    132

    1.85(1.07)

    0.71

    12

    6 (5)

    1.92

    164

    .05

    22

    1.87(1.28)

    144

    1.39(1.06)

    0.44

    7

    7

    7 (6)

    2.43

    52

    .02

    32

    3.78(0.89)

    22

    3.16(0.98)

    0.67

    7

    6

    2.76

    52

    .008

    33

    3.8(0.88)

    21

    3.10(0.96)

    0.77

    27

    5 (6)

    8.22a

    5.76

    .000

    2

    4.67(0.19)

    14

    2.86(0.65)

    6.21

    a - not assuming equal variance

  • Results to Date for Topic FamiliarityThe three-way interaction of individual*usefulness*topic familiarity was significant, meaning that considering both the individual and topic familiarity information may be helpful in predicting usefulness by display time.

  • Next StepsBegin three concurrent investigations ofDomain knowledgeTaskCognitive characteristicsEach investigation to consider one additional facetEach to identify:Evidence for particular facetUse of evidence for personalizationInteraction of main facet with one other

  • Then Implement results from previous investigations in prototype systemExperiments to test methods of identification of evidence, and the use of that evidence from all facets simultaneously

  • And FinallyMove from experimental prototype to robust client-side personalization assistantDistribute assistant to subjects in a real work environmentCompare performance, usability, acceptability, etc. between those with, and those without the personalization assistantMake the personalization assistant available as open source software

Recommended

View more >