Search Text Mining Web Site Usability

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Search Text Mining Web Site Usability. Marti Hearst SIMS. BAILANDO Projects. Better Access to Information using Language Analysis and Novel Dynamic Organizations. Current BAILANDO Projects. CHA-CHA: Web Search results in Context LINDI: UI support for Search Text Data Mining - PowerPoint PPT Presentation

Transcript of Search Text Mining Web Site Usability

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SearchText Mining

Web Site Usability

Marti HearstSIMS

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BAILANDO Projects

Better Access to Information using Language Analysis and

Novel Dynamic Organizations

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Current BAILANDO Projects CHA-CHA:

Web Search results in Context

LINDI: UI support for Search Text Data Mining

TANGO: Automated Web Site Usability

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Search UIs

Combine Browsing & SearchPlace Search Results in Context

LargeCategoryHierarchies

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Cha-Cha Students: Mike Chen, Jamie Laflen, Jason Hong, Jimmy Lin, Shiang Chen

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Medical Category Hierarchy

M igraine M S

Disease

Carotid Artery Spinal Cord

Anatom y

T am oxifin Steroids

Drugs

M edicine

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DynaCat (Pratt, Hearst, & Fagan 99)

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DynaCat Study Design

Three queries 24 cancer patients Compared three interfaces

ranked list, clusters, categories Results

Participants strongly preferred categories Participants found more answers using categories Participants took same amount of time with all three interfaces

Similar results have been verified by another study by Chen and Dumais (CHI 2000)

Cat-a-Cone Interface(Hearst & Karadi 97)

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Improving Search via Large Category Hierarchies

How to show intersections across category

types? How to preview related categories in a user-

tailored, dynamic manner?

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Information retrieval

Text Data Mining

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Information retrieval

Selection or rejection of existing documents based on a function of word match.

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Text Data Mining

Relationships between information in documents can create new facts, not previously known.

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Imagine

You are a medical researcherYour patient hasspinal inflammationnumbness in fingerslow TC levelsnegative results for all tests

How can you help her?

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Idea

A new way of searching text.

Link pieces of information together

to formulate hypotheses …

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LINDILinking Information for New DIscoveries

Students: Barbara Rosario, David Blei Three main parts

Search UI for building and reusing hypothesis seeking strategies.

Statistical language analysis techniques for interpreting the text.

Backend for interfacing with various databases and translating different formats.

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Gathering Evidence

Spinal Inflammation

Numbness in fingers

Low TC Levels

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Gathering Evidence

Spinal Inflammation

Numbness in fingers

Low TC Levels

Find diseasesassociatedwith each

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Supporting Cascaded Search Operations

Spinal Inflammation

Numbness in fingers

Low TC Levels

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New Language Analysis First use category labels to retrieve candidate

documents Then use language analysis to detect causal

relationships between concepts Title:

Magnesum deficiency implicated in increased stress levels. Interpretation:

<nutrient><reduction> related-to <increase><symptom> Use these to find relationships and formulate

hypotheses

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Statistical Semantic Parsing

Modern statistical techniques Mainly applied to syntactic structure

Probabilistic knowledge representation Represent hypotheses with different degrees

of certainty.

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Automating Assessment of

Web Site Usability

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Why Worry? Problem: IBM's extranet

Heavy use of help and search Unhappy users

Solution Massive web site redesign Focus on info-organization, not the purchasing

process. Cost: "in the millions"

Results Not announced or trumped up Use of "help" decreased 84% Sales increased 400%

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Web TANGOTool for Assessing NaviGation & Organization

Student: Melody Ivory

Goal: automated support for comparing design alternatives

How: Assess usability of the information architecture

Approximate people’s information-seeking behavior (Monte Carlo simulation)

Output quantitative usability metrics

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Anatomy of Web Site Design

Courtesy of Mark Newman

Information Architecture

NavigationDesign

InformationDesign

GraphicDesign

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Usability EvaluationStandard Techniques

User studies Have people use the interface to complete

some tasks Requires an implemented interface "Discount" vs. Scientific Results

Heuristic Evaluation An expert assesses a design or

implementation according to certain guidelines

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Automated Usability Evaluation Logging/capture

Pro: Easy Con: Requires implemented system Con: Don't know the user task (web) Con: Don't present alternatives Con: Don't distinguish error from success

Analytical Modeling Pro: doable at design phase Con: models an expert Con: academic exercise

Simulation

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Existing Metrics

Web metric analysis tools report on what is easy to measure, e.g.: Predicted download time Depth/breadth of site

We want to worry about Content User goals/tasks

Not available from logs

We also want to compare alternative designs.

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Monte Carlo Simulation

Have a model of information structure Have a set of user goals Want to assess navigation structure

Compare alternatives/tradeoffs Identify bottlenecks Identify critically important pages/links Check all pairs of start/end points Check overall reachability before and after a change.

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Monte Carlo Simulation At each step in the simulation

Assume a probability distribution over a set of next choices. The next choice is a function of:

The current goal The understandability of the choice The overall complexity of the set of choices Prior interaction history

These can use models of "scent" Varying the distribution corresponds to varying properties of

the links Spot-check important choices

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One Monte Carlo simulation step for Design 1, Task 1. Simulation starts from the home page and the target information is at Renter Support.

X

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Monte Carlo simulation results for Design 1, Task 1. Simulation runs start from all pages in the site. Average Navigation times are shown for Tasks 2 & 3.

X

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Using Simulator Results Design Decisions

Use Design 1 Improve Tasks 1 & 2

Next Steps Analyze results for Tasks 1

& 2 Create new Design 1 Repeat simulation to

compare old & new designs

Iterate if necessary

Design 1 Design 2 Task Time Errors Time Errors 1 41 sec 2 38 sec 4 2 38 sec 4 43 sec 5 3 32 sec 2 74 sec 6

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Research Issues: Navigation Predictions Develop IR model for predicting link selection

Requirements Information need (task metadata) Representation of pages (page metadata) Method for selecting links (relevance ranking) Maintaining user’s conceptual model during site traversal

(scent [Fur97,LC98,Pir97]) One possible approach

Information Foraging Theory [PC95,Pir97,PPR96] Functional categorization of pages based on features Prediction of relevance to current page

Consider link connectivity, text similarity & usage

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Other HCC-Related Projects

Using a large digital desk in design Ame Elliot

Using visualization for light design Dan Glaser

User interfaces and computer security Prof. Doug Tygar, Rachna Dahmija