Slides
•	Please download the slides from
www.umiacs.umd.edu/~daqingd/lbsc796-w5.ppt
 www.umiacs.umd.edu/~daqingd/lbsc796-w5.rtf
Interactions
LBSC 796/CMSC 838o
Daqing He, Douglas W. Oard 
Session 5, March 8, 2004
Slides
•	Please download the slides from
www.umiacs.umd.edu/~daqingd/lbsc796-w5.ppt
 www.umiacs.umd.edu/~daqingd/lbsc796-w5.rtf
Agenda
•	Interactions in retrieval systems

•	Query formulation

•	Selection

•	Examination

•	Document delivery
System Oriented Retrieval Model
Whose Process Is It? 
•	Who initiates a search process?

•	Who controls the progress? 

•	Who ends a search process?
User Oriented Retrieval Model
Taylor’s Conceptual Framework
•	Four levels of “information needs”
–	Visceral
•	What you really want to know
–	Conscious
•	What you recognize that you want to know
–	Formalized (e.g., TREC topics)
•	How you articulate what you want to know
–	Compromised (e.g., TREC queries)
•	How you express what you want to know to a system
Belkin’s ASK model
•	Users are concerned with a problem
•	But do not clearly understand
–	the problem itself
–	the information need to solve the problem
                          Anomalous State of Knowledge
•	Need clarification process to form a query 
What are humans good at?
•	Sense low level stimuli
•	Recognize patterns
•	Reason inductively
•	Communicate with multiple channels
•	Apply multiple strategies
•	Adapt to changes or unexpected events
What are computers good at?
•	Sense stimuli outside human’s range
•	Calculate fast and mechanical 
•	Store large quantities and recall accurately 
•	Response rapidly and consistently
•	Perform repetitive actions reliably
•	Maintain performance under heavy load and extended time
What should Interaction be?
•	 Synergic 
•	 Humans do things that human are good at
•	 Computers do things that computers are good at
•	 the strength of one covers the weakness of the other
Source Selection
•	 People have their own preference
•	 Different tasks require different sources
•	 Possible choices
–	 ask help from people or machines
–	 browsing or search, or combination
–	 general purpose vs specific domain IR system
–	 different collections

Query Formulation
User’s Goals
•	User’s goals
–	Identify the right query for the current need
•	conscious/formalized need => compromised need

•	How can the user achieve this goal?
–	Infer the right query terms
–	Infer the right composition of terms
System’s Goals
•	Help the user 
–	build links between needs 
–	know more about the system and the collection

How does System Achieve Its Goals?
•	Ask more from the user 
–	Encourage long/complex queries
•	Provide a large text entry area
•	Use forms filling or direct manipulation
–	Initiate interactions
•	Ask questions related to the needs 
•	Engage a dialogue with the user 
•	Infer from relevant items
–	Infer from previous queries
–	Infer from previous retrieved documents
Query Formulation Interaction Styles
•	Shneiderman 97
–	Command Language
–	Form Fillin
–	Menu Selection
–	Direct Manipulation
–	Natural Language
Form-Based Query Specification (Melvyl)
Form-based Query Specification (Infoseek)
Direct Manipulation Spec.
VQUERY (Jones 98)
High-Accuracy Retrieval of Documents
UMD HARD 2003 retrieval model
Dialogues in Need Negotiation
Personalization through User’s Search Contexts
Things That Hurt
•	Obscure ranking methods
–	Unpredictable effects of adding or deleting terms
•	Only single-term queries avoid this problem
•	Counterintuitive statistics
–	“clis”: AltaVista says 3,882 docs match the query
–	“clis library”:           27,025 docs match the query!
•	Every document with either term was counted
Browsing Retrieved Set
Indicative vs. Informative
•	Terms often applied to document abstracts
–	Indicative abstracts support selection
•	They describe the contents of a document
–	Informative abstracts support understanding
•	They summarize the contents of a document

•	Applies to any information presentation
–	Presented for indicative or informative purposes

User’s Browsing Goals
•	Identify documents for some form of delivery
–	An indicative purpose
•	Query Enrichment
–	Relevance feedback (indicative)
•	User designates “more like this” documents
•	System adds terms from those documents to the query
–	Manual reformulation (informative)
•	Better approximation of visceral information need

System’s Goals
•	Assist the user to
–	Identify relevant documents
–	Identify potential useful terms 
•	for clarifying the right information need
•	for generating better queries 
Browsing Retrieved Set
A Selection Interface Taxonomy
•	One dimensional lists
–	Content:  title, source, date, summary, ratings, ...
–	Order:  retrieval status value, date, alphabetic, ...
–	Size: scrolling, specified number, RSV threshold
•	 Two dimensional displays
–	Construction: clustering, starfields, projection
–	Navigation:  jump, pan, zoom
•	Three dimensional displays
–	Contour maps, fishtank VR, immersive VR
Extraction-Based Summarization
•	Robust technique for making disfluent summaries
•	Four broad types:
–	Single-document vs. multi-document
–	Term-oriented vs. sentence-oriented
•	Combination of evidence for selection:
–	Salience: similarity to the query
–	Selectivity: IDF or chi-squared
–	Emphasis: title, first sentence
•	For multi-document, suppress duplication
Generated Summaries
•	Fluent summaries for a specific domain
•	Define a knowledge structure for the domain
–	Frames are commonly used
•	Analysis: process documents to fill the structure
–	Studied separately as “information extraction”
•	Compression: select which facts to retain
•	Generation: create fluent summaries
–	Templates for initial candidates
–	Use language model to select an alternative
Google’s KWIC Summary
Teoma’s Query Refine Suggestions
Vivisimo’s Clustering Results
Kartoo’s Cluster Visualization
Cluster Formation
•	Based on inter-document similarity
–	Computed using the cosine measure, for example
•	Heuristic methods can be fairly efficient
–	Pick any document as the first cluster “seed”
–	Add the most similar document to each cluster
•	Adding the same document will join two clusters
–	Check to see if each cluster should be split
•	Does it contain two or more fairly coherent groups?
•	Lots of variations on this have been tried
Starfield
Dynamic Queries:
•	IVEE/Spotfire/Filmfinder (Ahlberg & Shneiderman 93)
Constructing Starfield Displays
•	Two attributes determine the position
–	Can be dynamically selected from a list
•	Numeric position attributes work best
–	Date, length, rating, …
•	Other attributes can affect the display
–	Displayed as color, size, shape, orientation, …
•	Each point can represent a cluster
–	Interactively specified using “dynamic queries”
Projection
•	Depict many numeric attributes in 2 dimensions
–	While preserving important spatial relationships
•	Typically based on the vector space model
–	Which has about 100,000 numeric attributes! 
•	Approximates multidimensional scaling
–	Heuristic approaches are reasonably fast
•	Often visualized as a starfield
–	But the dimensions lack any particular meaning 
Contour Map Displays
•	Display a cluster density as terrain elevation
–	Fit a smooth opaque surface to the data
•	Visualize in three dimensions
–	Project two 2-D and allow manipulation
–	Use stereo glasses to create a virtual “fishtank”
–	Create an immersive virtual reality experience
•	Mead mounted stereo monitors and head tracking
•	“Cave” with wall projection and body tracking

Browsing Retrieved Set
Full-Text Examination Interfaces
•	Most use scroll and/or jump navigation
–	Some experiments with zooming
•	Long documents need special features
–	“Best passage” function helps users get started
•	Overlapping 300 word passages work well
–	“Next search term” function facilitates browsing
•	Integrated functions for relevance feedback
–	Passage selection, query term weighting, …
A Long Document
Document lens
TileBar
SeeSoft
Things That Help
•	Show the query in the selection interface
–	It provides context for the display
•	Explain what the system has done
–	It is hard to control a tool you don’t understand
•	Highlight search terms, for example
•	Complement what the system has done
–	Users add value by doing things the system can’t
–	Expose the information users need to judge utility
Document Delivery
Delivery Modalities
•	On-screen viewing
–	Good for hypertext, multimedia, cut-and-paste, …
•	Printing
–	Better resolution, portability, annotations, …
•	Fax-on-demand
–	Really just another way to get to a printer
•	Synthesized speech
–	Useful for telephone and hands-free applications
Take-Away Messages
•	IR process belongs to users
•	Matching documents for a query is only part of the whole IR process
•	But IR system can help users
•	And IR systems need to support
–	Query formulation/reformulation
–	Document Selection/Examination
Two Minute Paper
•	When examining documents in the selection and examination interfaces, which type of information need (visceral, conscious, formalized, or compromised) guides the user’s decisions?  Please justify your answer.

•	What was the muddiest point in today’s lecture?
Alternate Query Modalities
•	Spoken queries
–	Used for telephone and hands-free applications
–	Reasonable performance with limited vocabularies
•	But some error correction method must be included

•	Handwritten queries
–	Palm pilot graffiti, touch-screens, …
–	Fairly effective if some form of shorthand is used
•	Ordinary handwriting often has too much ambiguity