HUMANE INFORMATION SEEKING:

GOING BEYOND THE IR WAY

JIN YOUNG KIM @ SNU DCC

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Jin Young Kim

• Graduate of SNU EE / Business

• 5th Year Ph.D Student in UMass Computer Science

• Starting as a Applied Researcher at Microsoft Bing

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Today’s Agenda

• A brief introduction of IR as a research area

• An example of how we design a retrieval model

• Other research projects and recent trends in IR

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BACKGROUND

An Information Retrieval Primer

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Information Retrieval?

• The study of how an automated system can enable

its users to access, interact with, and make sense of

information.

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User

Query

DocumentVisit

IssueSurface

IR Research in Context

• Situated between human interface and

system / analytics research

• Aims at satisfying user’s information needs

• Based on large-scale system infrastructure & analytics

• Need for convergence research!

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Information Retrieval

Large-scale

System Infra.

Large-scale

(Text)Analytic

s

End-user Interface

(UX / HCI / InfoViz)

Major Problems in IR

• Matching

• (Keyword) Search : query – document

• Personalized Search : (user+query) – document

• Contextual Advertising : (user+context) – advertisement

• Quality

• Authority/ Spam / Freshness

• Various ways to capture them

• Relevance Scoring

• Combination of matching and quality features

• Evaluation is critical for optimal performance

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User

Query

DocumentVisit

IssueSurface

HUMANE INFORMATION

RETRIEVAL

Going Beyond the IR Way

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You need the freedom of expression.You need someone who understands.

Information seeking requires a communication.

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Information Seeking circa 2012

Search engine accepts keywords only.Search engine doesn’t understand you.

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Toward Humane Information Seeking

Rich User Interactions

Rich User Modeling

Profile

Context

Behavior

Search

Browsing

Filtering

from Query to SessionRich User ModelingHCIR Way:

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Action Response

Action Response

Action Response

USER SYSTEM

Interaction

History

Filtering / Browsing

Relevance Feedback

…

Filtering Conditions

Related Items

…

User

Model

Rich User InteractionIR Way:The

ProfileContextBehavior

HCIR = HCI + IR

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The Rest of Talk…

Web Search

Personal SearchImproving search and browsing for known-item findingEvaluating interactions combining search and browsing

User modeling based on reading level and topicProviding non-intrusive recommendations for browsing

Book SearchAnalyzing interactions combining search and filtering

PERSONAL SEARCH

Retrieval And Evaluation Techniques

for Personal Information [Thesis]

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Example: Desktop Search

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Example: Search over Social Media

Ranking using Multiple

Document Types for

Desktop Search [SIGIR10]

Evaluating Search in

Personal Social Media

Collections [WSDM12]

Structured Document Retrieval: Background

• Field Operator / Advanced Search Interface

• User’s search terms are found in multiple fields

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Understanding Re-finding Behavior in Naturalistic Email

Interaction Logs. Elsweiler, D, Harvey, M, Hacker., M [SIGIR'11]

Structured Document Retrieval: Models

• Document-based Retrieval Model

• Score each document as a whole

• Field-based Retrieval Model

• Combine evidences from each field

q1 q2 ... qm

Document-based Scoring Field-based Scoring

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• Field Relevance

• Different field is important for different query-term

‘james’ is relevant

when it occurs in <to>

‘registration’ is relevant

when it occurs in <subject>

Improved Matching for Email SearchStructured Documents[CIKM09, ECIR09,12]

Estimating the Field Relevance

• If User Provides Feedback

• Relevant document provides sufficient information

• If No Feedback is Available

• Combine field-level term statistics from multiple sources

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content

title

from/to

Relevant Docs

content

title

from/to

Collection

content

title

from/to

Top-k Docs

+ ≅

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Retrieval Using the Field Relevance

• Comparison with Previous Work

• Ranking in the Field Relevance Model

q1 q2 ... qm

f1

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f1

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q1 q2 ... qm

f1

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...

P(F1|q1)

P(F2|q1)

P(Fn|q1)

P(F1|qm)

P(F2|qm)

P(Fn|qm)

Per-term Field Weight

Per-term Field Score

sum

multiply

• Retrieval Effectiveness (Metric: Mean Reciprocal Rank)

DQL BM25F MFLM FRM-C FRM-T FRM-R

TREC 54.2% 59.7% 60.1% 62.4% 66.8% 79.4%

IMDB 40.8% 52.4% 61.2% 63.7% 65.7% 70.4%

Monster 42.9% 27.9% 46.0% 54.2% 55.8% 71.6%

Evaluating the Field Relevance Model

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40.0%

45.0%

50.0%

55.0%

60.0%

65.0%

70.0%

75.0%

80.0%

DQL BM25F MFLM FRM-C FRM-T FRM-R

TREC

IMDB

Monster

Fixed Field Weights Per-term Field Weights

Evaluation Challenges for Personal Search• Evaluation of Personal Search

• Each based on its own user study

• No comparative evaluation was performed yet

• Solution: Simulated Collections

• Crawl CS department webpages, docs and calendars

• Recruit department people for user study

• Collecting User Logs

• DocTrack: a human-computation search game

• Probabilistic User Model: a method for user simulation

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[CIKM09,SIGIR10,CIKM11]

DocTrack Game

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Find It!

Target Item

Summary so far…

• Query Modeling for Structured Documents

• Using the estimated field relevance improves the retrieval

• User’s feedback can help personalize the field relevance

• Evaluation Challenges in Personal Search

• Simulation of the search task using game-like structures

• Related work : ‘Find It If You Can’ [SIGIR11]

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WEB SEARCH

Characterizing Web Content, User Interests, and

Search Behavior by Reading Level and Topic

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[WSDM12]

Reading level distribution varies across major topical categories

User Modeling by Reading Level and Topic• Reading Level and Topic

• Reading Level: proficiency (comprehensibility)

• Topic: topical areas of interests

• Profile Construction

• Profile Applications

• Improving personalized search ranking

• Enabling expert content recommendation

P(R|d1) P(T|d1)P(R|d1) P(T|d1)

P(R|d1) P(T|d1) P(R,T|u)

Profile matching can predict user’s preference over search results• Metric

• % of user’s preferences predicted by profile matching

• Profile matching measured in KL-Divergence of RT profiles

• Results

• By the degree of focus in user profile

• By the distance metric between user and website

User Group #Clicks KLR(u,s) KLT(u,s) KLRT(u,s)

↑Focused 5,960 59.23% 60.79% 65.27%

147,195 52.25% 54.20% 54.41%

↓Diverse 197,733 52.75% 53.36% 53.63%

Comparing Expert vs. Non-expert URLs• Expert vs. Non-expert URLs taken from [White’09]

Higher Reading Level

Low

er To

pic

Div

ersity

Enabling Browsing for Web Search

• SurfCanyon®

• Recommend results

based on clicks

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Initial results indicate that

recommendations are useful

for shopping domain.

[Work-in-progress]

BOOK SEARCH

Understanding Book Search Behavior on the Web

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[Submitted to SIGIR12]

Understanding Book Search on the Web

• OpenLibrary

• User-contributed online digital library

• DataSet: 8M records from web server log

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Comparison of Navigational Behavior

• Users entering directly show different behaviors from

users entering via web search engines

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Users entering the site directly Users entering via Google

Comparison of Search Behavior

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Rich interaction reduces the query lengthsFiltering induces more interactions than search

LOOKING ONWARD

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Where’s the Future? – Social Search

• The New Bing Sidebar makes search a social activity.

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Where’s the Future? – Semantic Search

• The New Google serves ‘knowledge’ as well as docs.

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Where’s the Future? – Siri-like Agent

• The New Google serves ‘knowledge’ as well as docs.

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Exciting Future is Awaiting US!

• Recommended Readings in IR:

• http://www.cs.rmit.edu.au/swirl12

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Any

Questions

?

Selected Publications

• Structured Document Retrieval• A Probabilistic Retrieval Model for Semi-structured Data [ECIR09]

• A Field Relevance Model for Structured Document Retrieval [ECIR11]

• Personal Search• Retrieval Experiments using Pseudo-Desktop Collections [CIKM09]

• Ranking using Multiple Document Types in Desktop Search [SIGIR10]

• Building a Semantic Representation for Personal Information [CIKM10]

• Evaluating an Associative Browsing Model for Personal Info. [CIKM11]

• Evaluating Search in Personal Social Media Collections [WSDM12]

• Web / Book Search• Characterizing Web Content, User Interests, and Search Behavior by

Reading Level and Topic [WSDM12]

• Understanding Book Search Behavior on the Web [In submission to SIGIR12]

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More at @lifidea, or

cs.umass.edu/~jykim

My Self-tracking Efforts

• Life-optimization Project (2002~2006)

• LiFiDeA Project (2011-2012)

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OPTIONAL SLIDES

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The Great Divide: IR vs. HCI

IR

• Query / Document

• Relevant Results

• Ranking / Suggestions

• Feature Engineering

• Batch Evaluation (TREC)

• SIGIR / CIKM / WSDM

HCI

• User / System

• User Value / Satisfaction

• Interface / Visualization

• Human-centered Design

• User Study

• CHI / UIST / CSCW

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Can we learn from each other?

The Great Divide: IR vs. RecSys

IR

• Query / Document

• Reactive (given query)

• SIGIR / CIKM / WSDM

RecSys

• User / Item

• Proactive (push item)

• RecSys / KDD / UMAP

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The Great Divide: IR in CS vs. LIS

IR in CS

• Focus on ranking &

relevance optimization

• Batch & quantitative

evaluation

• SIGIR / CIKM / WSDM

• UMass / CMU /

Glasgow

IR in LIS

• Focus on behavioral

study & understanding

• User study & qualitative

evaluation

• ASIS&T / JCDL

• UNC / Rutgers / UW

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• What

• How

Problems & Techniques in IR

Data Format (documents, records and linked data) /

Size / Dynamics (static, dynamic, streaming)

User &

Domain

End User (web and library)

Business User (legal, medical and patent)

System Component (e.g., IBM Watson)

Needs Known-item vs. Exploratory Search

Recommendation

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System Indexing and Retrieval

(Platforms for Big Data Handling)

Analytics Feature Extraction

Retrieval Model Tuning & Evaluation

Presentation User Interface

Information Visualization

More about the Matching Problem

• Finding Representations

• Term vector vs. Term distribution

• Topical category, Reading level, …

• Estimating Representations

• By counting terms

• Using automatic classifiers

• Calculating Matching Scores

• Cosine similarity vs. KL-divergence

• Combining multiple reps.

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User

Query

DocumentVisit

IssueSurface