Recap: Relevance Feedback CS276A - Stanford University · 2002-11-06 · CS276A Text Information...

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CS276AText Information Retrieval, Mining, and Exploitation

Lecture 95 Nov 2002

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Recap: Relevance Feedback� Rocchio Algorithm:

� Typical weights: alpha = 8, beta = 64, gamma = 64� T radeoff alpha vs beta/gamma: If we have a lot of judged

documents, we want a higher beta/gamma.� But we usually don’t …

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Pseudo Feedback

documents

retrievedocuments

top kdocuments

apply relevance feedback

label top kdocs relevant

initialquery

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Pseudo-Feedback: Performance

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Today’s topics

� User Interfaces� Browsing� Visualization

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The User in Information Access

Stop

Informationneed Explore results

Formulate/Reformulate

Done?

Query

Send to system

Receive results

yes

no

User

Find startingpoint

�

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yes

no

Focus of

most IR! Stop



Done?

Query

Send to system

Receive results

User

Find startingpoint

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Information Access in Context

Stop

High-LevelGoal

Synthesize

Done?

Analyze

yes

no

User

Information Access

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Stop



Done?

Query

Send to system

Receive results

yes

no

User

Find startingpoint

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Starting points

� Source selection� Highwire press� Lexis-nexis� Google!

� Overviews� Directories/hierarchies� Visual maps� Clustering

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Highwire Press

Source Selection

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Hierarchical browsing

Level 2

Level 1

Level 0

�

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Visual Browsing: Themescape

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Browsing

x

x

xxxx

x

x

x

xx

x

x x

Starting point

Credit: William Arms, Cornell

Answer16

Scatter/Gather

� Scatter/gather allows the user to find a set of documents of interest through browsing.

� Take the collection and scatter it into n clusters.� Pick the clusters of interest and merge them.� Iterate

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Scatter/Gather

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Scatter/gather

�

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How to Label Clusters

� Show titles of typical documents� Titles are easy to scan� Authors create them for quick scanning!� But you can only show a few titles which may not

fully represent cluster� Show words/phrases prominent in cluster

� More likely to fully represent cluster� Use distinguishing words/phrases� But harder to scan

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Visual Browsing: Hyperbolic Tree

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Visual Browsing: Hyperbolic Tree

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Study of Kohonen Feature Maps

� H. Chen, A. Houston, R. Sewell, and B. Schatz, JASIS 49(7)

� Comparison: Kohonen Map and Yahoo� Task:

� “Window shop” for interesting home page� Repeat with other interface

� Results:� Starting with map could repeat in Yahoo (8/11)� Starting with Yahoo unable to repeat in map (2/14)

Credit: Marti Hearst

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Study (cont.)

� Participants liked:� Correspondence of region size to # documents� Overview (but also wanted zoom)� Ease of jumping from one topic to another � Multiple routes to topics� Use of category and subcategory labels

Credit: Marti Hearst 24

Study (cont.)� Participants wanted:

� hierarchical organization� other ordering of concepts (alphabetical)� integration of browsing and search� corresponce of color to meaning � more meaningful labels� labels at same level of abstraction� fit more labels in the given space� combined keyword and category search� multiple category assignment (sports+entertain)


�

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Browsing

� Effectiveness depends on� Starting point� Ease of orientation (are similar docs “close” etc,

intuitive organization)� How adaptive system is

� Compare to physical browsing (library, grocery store)

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Searching vs. Browsing

� Information need dependent� Open-ended (find an interesting quote on the virtues

of friendship) -> browsing� Specific (directions to Pacific Bell Park) -> searching

� User dependent� Some users prefer searching, others browsing

(confirmed in many studies: some hate to type)� You don’t need to know vocabulary for browsing.

� System dependent (some web sites don’t support search)

� Searching and browsing are often interleaved.

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Searchers vs. Browsers

� 1/3 of users do not search at all� 1/3 rarely search (or urls only)� Only 1/3 understand the concept of search� (ISP data from 2000)

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Exercise

� Observe your own information seeking behavior� WWW� University library� Grocery store

� Are you a searcher or a browser?� How do you reformulate your query?

� Read bad hits, then minus terms� Read good hits, then plus terms� Try a completely different query� …

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Stop



Done?

Query

Send to system

Receive results

yes

no

User

Find startingpoint

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Query Specification

� Recall:� Relevance feedback� Query expansion� Spelling correction� Query-log mining based

� Interaction styles for query specification� Queries on the Web� Parametric search� Term browsing

�

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Query Specification: Interaction Styles

� Shneiderman 97� Command Language� Form Fillin� Menu Selection� Direct Manipulation� Natural Language

� Example:� How do each apply to Boolean Queries


Command-Based Query Specification

� command at tr i but e val ue connec t or …

� f i nd pa shneid er man and t w user#� What are the attribute names?� What are the command names?� What are allowable values?


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Form-Based Query Specification (Altavista)


Form-Based Query Specification (Melvyl)


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Form-based Query Specification (Infoseek)

Credit: Marti Hearst 36��

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�

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Menu-based Query Specification(Young & Shneiderman 93)


Query Specification/Reformulation

� A good user interface makes it easy for the user to reformulate the query

� Challenge: one user interface is not ideal for all types of information needs

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Types of Information Needs

� Need answer to question (who won the game?)� Re-find a particular document� Find a good recipe for tonight’s dinner� Authoritative summary of information (HIV

review)� Exploration of new area (browse sites about

Baja)

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Queries on the WebMost Frequent on 2002/10/26

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Queries on the Web (2000)

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Intranet Queries (Aug 2000)� 3351 bearfacts� 3349 telebears� 1909 extension� 1874 schedule+of+classes� 1780 bearlink� 1737 bear+facts� 1468 decal� 1443 infobears� 1227 calendar� 989 career+center� 974 campus+map� 920 academic+calendar� 840 map

� 773 bookstore� 741 class+pass� 738 housing� 721 tele-bears� 716 directory� 667 schedule� 627 recipes� 602 transcripts� 582 tuition� 577 seti� 563 registrar� 550 info+bears� 543 class+schedule� 470 financial+aid

Source: Ray Larson

�

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Intranet Queries� Summary of sample data from 3 weeks of UCB queries� 13.2% Telebears/BearFacts/InfoBears/BearLink (12297)� 6.7% Schedule of classes or final exams (6222)� 5.4% Summer Session (5041)� 3.2% Extension (2932)� 3.1% Academic Calendar (2846)� 2.4% Directories (2202)� 1.7% Career Center (1588)� 1.7% Housing (1583)� 1.5% Map (1393)� Average query length over last 4 months: 1.8 words� This suggests what is difficult to find from the home page

Source: Ray Larson 44

Query Specification: Feast or Famine

Famine

Feast

Specifyinga well targetedquery is hard.

Bigger problem for Boolean.

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Parametric search

� Each document has, in addition to text, some “meta-data” e.g.,� Language = French� Format = pdf� Subject = Physics etc.� Date = Feb 2000

� A parametric search interface allows the user to combine a full-text query with selections on these parameters e.g.,� language, date range, etc.

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Parametric search example

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Parametric search example

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Interfaces for term browsing

�

49 50


Stop



Done?

Query

Send to system

Receive results

yes

no

User

Find startingpoint

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Explore Results

� Determine: Do these results answer my question?� Summarization� More generally: provide context

� Hypertext navigation: Can I find the answer by following a link?

� Browsing and clustering (again)� Browse to explore results

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Explore Results: Context

� We can’t present complete documents in the result set – too much information.

� Present information about each doc� Must be concise (so we can show many docs)� Must be informative

� Typical information about each document� Summary� Context of query words� Meta data: date, author, language, file name/url� Context of document in collection� Information about structure of document

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Context in Collection: Cha-Cha

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Category Labels

� Advantages:� Interpretable� Capture summary information� Describe multiple facets of content� Domain dependent, and so descriptive

� Disadvantages� Do not scale well (for organizing documents)� Domain dependent, so costly to acquire� May mis-match users’ interests


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Evaluate ResultsContext in Hierarchy: Cat-a-Cone

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Explore Results: Summarization

� Query-dependent summarization� KWIC (keyword in context) lines (a la google)

� Query-independent summarization� Summary written by author (if available)� Exploit genre (news stories)� Sentence extraction� Natural language generation

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Evaluate ResultsStructure of document: SeeSoft

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Personalization

QueryAugmentation

InterestsInterests

DemographicsDemographics

Click StreamClick Stream

Search HistorySearch History

Application UsageApplication Usage

Result Processing

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IntranetSearch

Web Search

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Outride PersonalizedSearch System

User Query

Result Set

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How Long to Get an Answer?

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8 3 .5

8 1

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0 1 0 2 0 3 0 4 0 5 0 6 0 7 0 8 0 9 0 1 0 0

A O L

E x c i t e

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G o o g l e

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Search Engine User Actions Difference (%) Outride 11.2 Google 21.2 89.6 Yahoo! 22.4 100.5 AOL 23.1 107.0 Excite 23.3 108.5 Average 22.5 101.4 Table 1. User actions study results. Experienced Users Novice Users Overall

Engine Expert Time

Rank Novice Time

Rank Average Rank % Difference

Outride 32.8 (1) 45.1 (1) 38.9 (1) 0% AOL 92.3 (5) 87.0 (4) 89.6 (5) 130.2% Excite 75.7 (3) 91.3 (5) 83.5 (4) 114.5% Google 72.5 (2) 78.4 (3) 75.4 (2) 93.7% Yahoo! 85.1 (4) 76.9 (2) 81.0 (3) 107.9% Table 2. Overall timing results (in seconds, with placement in parenthesis). � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � � �

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75.70

91.30

32.83

45.07

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10

20

30

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50

60

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90

100

Novice Experts

OthersOutride

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Performance of Interactive Retrieval

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Boolean Queries: Interface Issues

[ Boolean logic is difficult for the average user.[ Much research was done on interfaces facilitating

the creation of boolean queries by non-experts.[ Much of this research was made obsolete by the

web.[ Current view is that non-expert users are best

served with non-boolean or simple +/- boolean(pioneered by altavista).

[ But boolean queries are the standard for certain groups of expert users (eg, lawyers).

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User Interfaces: Other Issues

[ Technical HCI issues\ How to use screen real estate\ One monolithic window or many?\ Undo operator\ Give access to history\ Alternative interfaces for novel/expert users

[ Disabilities

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Take-Away

[ Don’t ignore the user in information retrieval.[ Finding matching documents for a query is only

part of information access and “knowledge work”.[ In addition to core information retrieval,

information access interfaces need to support\ Finding starting points\ Formulation/reformulation of queries\ Exploring/evaluating results

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Exercise

� Current information retrieval user interfaces are designed for typical computer screens.

� How would you design a user interface for a wall-size screen?

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ResourcesMIR Ch. 10.0 – 10.7Donna Harman, Overview of the fourth text retrieval conference

(TR EC 4), National Institute of Standards and Technology.Cutting, Karger, Pedersen, Tukey. Scatter/Gather. ACM SIGIR.Hearst, Cat-a-cone, an interactive interface for specifying searches

and viewing retrieving results in a large category hierarchy, ACM SIGIR.

Recap: Relevance Feedback CS276A - Stanford University · 2002-11-06 · CS276A Text Information...

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