Interaction
LBSC 796/INFM 718R
Douglas W. Oard 
Week 4, October 1, 2007
Moore’s Law
Human Cognition
Where is the bottleneck?
Interaction Points
Information Needs
Anomalous State of Knowledge
•	Belkin: Searchers do not clearly understand
–	The problem itself
–	What information is needed to solve the problem
•	The query results from a clarification process

•	Dervin’s “sense making”: 
Bates’ “Berry Picking” Model
Broder’s Web Query Taxonomy
•	Navigational (~20%)
–	Reach a particular site (“known item”)
•	Informational (~50%)
–	Acquire static information (“topical”)
•	Transactional (~30%)
–	Perform a Web-mediated activity (“service”)
Some Desirable Features
•	Make exploration easy

•	Relate documents with why they are retrieved

•	Highlight relationships between documents
Agenda
•	Query formulation

•	Selection

•	Examination

•	Source selection

•	Project 3
Query Formulation
•	Command Language
•	Form Fill-in
•	Menu Selection
•	Direct Manipulation
•	Natural Language
WESTLAW® Query Examples
Form-Based Query Specification
Direct Manipulation Spec.
VQUERY (Jones 98)
The “Back” Button
•	Behavior is counterintuitive to many users
PadPrints
•	Tree-based history of recently visited Web pages
–	History map placed to left of browser window
–	Node = title + thumbnail
–	Visually shows navigation history
•	Zoomable: ability to grow and shrink sub-trees

Visual Browsing History in PadPrints
PadPrints Thumbnails
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
Agenda
•	Query formulation

•	Selection

•	Examination

•	Source selection

•	Project 3
A Selection Interface Taxonomy
•	One dimensional lists
–	Content: title, source, date, summary, ratings, ...
–	Order:    retrieval status value, date, alphabetic, ...
–	Size:	  scrolling, specified number, score threshold
•	 Two dimensional displays
–	Construction: clustering, starfield, projection
–	Navigation:    jump, pan, zoom
•	Three dimensional displays
–	Contour maps, fishtank VR, immersive VR
Google: KeyWord In Context (KWIC)
Summarization
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

Selection/Examination Tasks
•	“Indicative” tasks
–	Recognizing what you are looking for
–	Determining that no answer exists in a source
–	Probing to refine mental models of system operation

•	“Informative” tasks
–	Vocabulary acquisition
–	Concept learning
–	Information use
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
Extraction-Based Summarization
•	Robust technique for making disfluent summaries
•	Four broad types:
–	Query-biased vs. generic
–	Term-oriented vs. sentence-oriented
•	Combine evidence for selection:
–	Salience: similarity to the query
–	Specificity: IDF or chi-squared
–	Emphasis: title, first sentence
Goldilocks and the Three Summaries…
•	The entire document: too much!
•	The exact answer: too little!

•	The surrounding paragraph: just right…
Ask: Suggested Query Refinements
Open Directory Project
SWISH
 
Text Classification
•	Problem: automatically sort items into bins
•	Machine learning approach
–	Obtain a training set with ground truth labels
–	Use a machine learning algorithm to “train” a classifier
•	kNN, Bayesian classifier, SVMs, decision trees, etc.
–	Apply classifier to new documents
•	System assigns labels according to patterns learned in the training set
Machine Learning
k Nearest Neighbor (kNN) Classifier
kNN Algorithm
•	Select k most similar labeled documents

•	Have them “vote” on the best label:
–	Each document gets one vote, or 
–	More similar documents get a larger vote

•	How can similarity be defined?

 Cat-a-Cone
Cat-a-Cone
•	Key Ideas:
–	Separate documents from category labels
–	Show both simultaneously
–	Link the two for iterative feedback
–	Integrate searching and browsing
•	Distinguish between:
–	Searching for documents
–	Searching for categories
Cat-a-Cone Architecture
The Cluster Hypothesis
Vivisimo: Clustered Results
Kartoo’s Cluster Visualization
Clustering Result Sets
•	Advantages:
–	Topically coherent document sets are presented together
–	User gets a sense for the themes in the result set
–	Supports browsing retrieved hits

•	Disadvantages:
–	May be difficult to understand the theme of a cluster based on summary terms
–	Clusters themselves might not “make sense”
–	Computational cost
Visualizing Clusters
Hierarchical Agglomerative Clustering
Another Way to Look at H.A.C.
The H.A.C. Algorithm
•	Start with each document in its own cluster

•	Until there is only one cluster:
–	Determine the two most similar clusters ci and cj
–	Replace ci and cj with a single cluster ci  cj

•	The history of merging forms the hierarchy
Cluster Similarity
•	Assume a similarity function that determines the similarity of two instances: sim(x,y)
–	What’s appropriate for documents?

•	What’s the similarity between two clusters?
–	Single Link: similarity of two most similar members
–	Complete Link: similarity of two least similar members
–	Group Average: average similarity between members
K-Means Clustering
K-Means
•	Each cluster is characterized by its centroid (center of gravity):

•	Reassignment of documents to clusters is based on distance to the current cluster centroids
K-Means Algorithm
•	Let d be the distance measure between documents

•	Select k random instances {s1, s2,… sk} as seeds

•	Until clustering converges:
–	Assign each instance xi to the cluster cj such that 
d(xi, sj) is minimal
–	Update the seeds to the centroid of each cluster
–	For each cluster cj, sj = (cj) 

K-Means: Discussion
•	How do you select k?

•	Results can vary based on random seed selection
–	Some seeds can result in poor convergence rate, or convergence to sub-optimal clusters
Scatter/Gather
Scatter/Gather
•	Sustem clusters documents into “themes” 
–	Displays clusters by showing:
•	Topical terms
•	Typical titles

•	User chooses a subset of the clusters

•	System re-clusters documents in selected cluster
–	New clusters have different, more refined, “themes”

Summary: Clustering
•	Advantages:
–	Provides an overview of main themes in search results
–	Helps overcome polysemy

•	Disadvantages:
–	Documents can be clustered in many ways
–	Not always easy to understand the theme of a cluster
–	What is the correct level of granularity?
–	More information to present
Recap
•	Clustering
–	Automatically group documents into clusters

•	Classification
–	Automatically assign labels to documents
Agenda
•	Query formulation

•	Selection

•	Examination

•	Source selection

•	Project 3
Examining Individual Documents
Document lens
Distorting Reality
1-D Fisheye Menu
1-D Fisheye Document Viewer
SeeSoft
TileBars
U Mass: Scrollbar-Tilebar
Agenda
•	Query formulation

•	Selection

•	Examination

•	Source selection

•	Project 3

WebTheme
Ben S’ ‘Seamless Interface’ Principles
•	Informative feedback
•	Easy reversal
•	User in control
–	Anticipatable outcomes
–	Explainable results
–	Browsable content
•	Limited working memory load
–	Query context
–	Path suspension
•	Alternatives for novices and experts
•	Scaffolding
My ‘Synergistic Interaction’ Principles
–	Interdependence with process (“interaction models”)
•	Co-design with search strategy
•	Speed
–	System initiative
•	Guided process
•	Exposing the structure of knowledge
–	Support for reasoning
•	Representation of uncertainty
•	Meaningful dimensions
–	Synergy with features used for search
•	Weakness of similarity, Strength of language
–	Easily learned
•	Familiar metaphors (timelines, ranked lists, maps)
Some Good Ideas
•	Show the query in the selection interface
–	It provides context for the display
•	Suggest options to the user
–	Query refinements, for example
•	Explain what the system has done
–	Highlight query terms in the results, 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
Agenda
•	Query formulation

•	Selection

•	Examination

•	Source selection

•	Project 3
Expertise@Maryland
•	Goal
–	Create a system to help research administration identify faculty members with specific research interests

•	Design Criteria
–	Maximize reliance on available information
–	Help the user, but don’t try to replace them
–	Offer immediate utility to untrained users

One 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.