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CS276AText Information Retrieval, Mining, and
Exploitation
Lecture 95 Nov 2002
Recap: Relevance Feedback
Rocchio Algorithm:
Typical weights: alpha = 8, beta = 64, gamma = 64 Tradeoff alpha vs beta/gamma: If we have a lot of
judged documents, we want a higher beta/gamma. But we usually don’t …
Pseudo Feedback
documents
retrievedocuments
top kdocuments
apply relevance feedback
label top kdocs relevant
initialquery
Pseudo-Feedback: Performance
Today’s topics
User Interfaces Browsing Visualization
The User in Information Access
Stop
Information need Explore results
Formulate/Reformulate
Done?
Query
Send to system
Receive results
yes
no
User
Find startingpoint
The User in Information Access
yes
no
Focus of
most IR! Stop
Information need Explore results
Formulate/Reformulate
Done?
Query
Send to system
Receive results
User
Find startingpoint
Information Access in Context
Stop
High-LevelGoal
Synthesize
Done?
Analyze
yes
no
User
Information Access
The User in Information Access
Stop
Information need Explore results
Formulate/Reformulate
Done?
Query
Send to system
Receive results
yes
no
User
Find startingpoint
Starting points
Source selection Highwire press Lexis-nexis Google!
Overviews Directories/hierarchies Visual maps Clustering
Highwire Press
Source Selection
Hierarchical browsing
Level 2
Level 1
Level 0
Visual Browsing: Themescape
Browsing
x
x
xxxx
x
x
x
xx
x
x x
Starting point
Credit: William Arms, Cornell
Answer
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
Scatter/Gather
Scatter/gather
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
Visual Browsing: Hyperbolic Tree
Visual Browsing: Hyperbolic Tree
UWMS Data Mining Workshop
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
UWMS Data Mining Workshop
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
UWMS Data Mining Workshop
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)
Credit: Marti Hearst
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)
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.
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)
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 …
The User in Information Access
Stop
Information need Explore results
Formulate/Reformulate
Done?
Query
Send to system
Receive results
yes
no
User
Find startingpoint
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
Query Specification: Interaction Styles
Shneiderman 97 Command Language Form Fillin Menu Selection Direct Manipulation Natural Language
Example: How do each apply to Boolean Queries
Credit: Marti Hearst
Command-Based Query Specification
command attribute value connector … find pa shneiderman and tw user#
What are the attribute names? What are the command names? What are allowable values?
Credit: Marti Hearst
Form-Based Query Specification (Altavista)
Credit: Marti Hearst
Form-Based Query Specification (Melvyl)
Credit: Marti Hearst
Form-based Query Specification (Infoseek)
Credit: Marti Hearst
Di r
ect
Manip
ula
tion S
pec.
VQ
UER
Y (
J ones
98
)
Credit: Marti Hearst
Menu-based Query Specification(Young & Shneiderman 93)
Credit: Marti Hearst
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
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)
Queries on the WebMost Frequent on 2002/10/26
Queries on the Web (2000)
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
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
Query Specification: Feast or Famine
Famine
Feast
Specifyinga well targetedquery is hard.
Bigger problem for Boolean.
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.
Notice that the output is a (large) table. Various parameters in the table (column headings) may be clicked on to effect a sort.
Parametric search example
Parametric search example
We can add text search.
Interfaces for term browsing
The User in Information Access
Stop
Information need Explore results
Formulate/Reformulate
Done?
Query
Send to system
Receive results
yes
no
User
Find startingpoint
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
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
Context in Collection: Cha-Cha
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
Credit: Marti Hearst
Evaluate ResultsContext in Hierarchy: Cat-a-Cone
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
Evaluate ResultsStructure of document: SeeSoft
Personalization
QueryAugmentation
InterestsInterests
DemographicsDemographics
Click StreamClick Stream
Search HistorySearch History
Application UsageApplication Usage
Result Processing
Outride SchemaUser x Content x
History x Demographics
IntranetSearch
Web Search
Search Engine Schema
Keyword x Doc ID x Link Rank
Outride PersonalizedSearch System
User Query
Result Set
Outride Side Bar Interface
How Long to Get an Answer?
89.6
83.5
81
75.4
38.9
0 10 20 30 40 50 60 70 80 90 100
AOL
Excite
Yahoo!
Outride
Average Task Completion Time in Seconds
SOURCE: ZDLabs/eTesting, Inc. October 2000
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). SOURCE: ZDLabs/eTesting, Inc. October 2000
75.70
91.30
32.83
45.07
0
10
20
30
40
50
60
70
80
90
100
Novice Experts
OthersOutride
Tim
e (S
econ
ds)
User Skill Level
SOURCE: ZDLabs/eTesting, Inc. October 2000
Novices versus Experts(Average Time to Complete Task)
Performance of Interactive Retrieval
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).
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
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
Exercise
Current information retrieval user interfaces are designed for typical computer screens.
How would you design a user interface for a wall-size screen?
Resources
MIR Ch. 10.0 – 10.7Donna Harman, Overview of the fourth text retrieval
conference (TREC 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.