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Information Agents 14 October 2003

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Ken Varnum Information Specialist Research Library & Information Services Ford Motor Company [email protected] Information Agents 14 October 2003. Tom Montgomery Technical Expert Infotronics & Systems Analytics Ford Motor Company [email protected] Presentation Outline. Introduction

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Presentation Transcript
slide1
Ken Varnum

Information Specialist

Research Library & Information Services

Ford Motor Company

[email protected]

Information Agents14 October 2003

Tom Montgomery

Technical Expert

Infotronics & Systems Analytics

Ford Motor Company

[email protected]

presentation outline
Presentation Outline
  • Introduction
  • Intelligent Agents
  • Process
  • Monitoring & Tuning
  • Conclusions
introduction
Introduction
  • Intelligent agents developed and implemented by Thomas Montgomery, Bardia Madani, and Ken Varnum
  • Based on a collaboration with MIT that combined mathematical modeling and empirical validation
    • MIT: product recommendations (music, furniture)
    • Ford: information retrieval (automotive news)
about rlis
About RLIS
  • Ford’s largest library
    • 9 MLS librarians
    • 3 Programmer/Developers
    • 2 Support staff
    • Branches in England (1) and Germany (2)
  • Serve Ford Motor Company’s global operations
world automotive information
World Automotive Information
  • Original abstracts of automotive news
  • Abstractors select abstracts for inclusion in one of 8 topical “Highlights” sent each week
  • Customers read the abstracts and click through to full text or document request
world automotive information6
World Automotive Information
  • Inefficient use of abstractors’ time
  • “One size fits all” approach doesn’t work
  • Not scalable – becomes hard to add new topics
intelligent information agents
Intelligent Information Agents
  • Software analog to human agents
    • real estate agent, librarian, salesperson
  • Learn preferences over time
intelligent information agents8
Intelligent Information Agents
  • Individual Recommendation Agents (not Collaborative Filtering)
    • Fine grained (users treated as individuals)
    • Driven by attributes of users and products, therefore can recommend new products
intelligent agents vs collaborative filtering
Intelligent Agents vs. Collaborative Filtering
  • CF: Items I interacted with are compared to Items other people interacted with
    • Assumes you are like others (requires others)
    • Requires interaction history prior to recommendation
intelligent agents vs collaborative filtering10
Intelligent Agents vs. Collaborative Filtering
  • IA: Features of what I interacted with are compared to Features of new items
    • Assumes you are unique
    • Can recommend items with no interaction history
intelligent agents vs collaborative filtering11
Intelligent Agents vs. Collaborative Filtering
  • Every document in WAI service is a “new product”
  • Customer’s interests evolve over time
data collection
Data Collection
  • We mine usage logs to learn about user preferences
    • Read full abstract
    • Order photocopy of full text
    • Click through to full text
    • Use of database
  • User doesn’t have to do anything
agent mechanism
Agent Mechanism
  • Each document is turned into a mathematical vector of features:
    • Keywords - Author
    • Publication - Age (days old)
  • Agent compares:
    • Vectors of users’ previously-selected documents
    • Vectors of newly-published documents
user advantages
User Advantages
  • No overhead from user perspective
    • No preference panels
    • No query refining
    • No document rating
  • Users unaware of process
    • Pro: Unobtrusiveness is good
    • Con: User actions can impact their content
agent interaction
Agent Interaction

Feedback

Train

Agent

Recommend

intelligent agent data flow
Intelligent Agent Data Flow

User

Feedback

Click Thru

Log

WAI

DB

Individualized

E-mail

Extract

Features

(Click Thru)

Train

Intelligent Agent

Priorities

New

Docs

Recommend

Extract

Features

from WAI

results
Results
  • Use of agent improved usage
  • Technology proved to us it work
  • Is core technology in our next version of current awareness service
privacy s two edge sword
Privacy’s Two-Edge Sword
  • This works well in a closed environment
    • Corporate environment allows greater use of “personal” data
    • System can know a great deal about users
  • Perhaps less well on public Internet
    • Privacy concerns result in less data about users
    • Internet audiences often object to “invasive” observation of actions
next steps
Next Steps
  • Expand to larger subscription service
  • Allow users to edit their preferences
    • As an option
    • As a convenience
  • Ability for user to reset preferences
thank you
Thank You
  • Updated slides available atvarnum.org/agents.ppt
  • Questions?
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