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Information Agents 14 October 2003 - PowerPoint PPT Presentation


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Ken Varnum Information Specialist Research Library & Information Services Ford Motor Company kvarnum@ford.com. Information Agents 14 October 2003. Tom Montgomery Technical Expert Infotronics & Systems Analytics Ford Motor Company tmontgo1@ford.com. Presentation Outline. Introduction

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Ken Varnum

Information Specialist

Research Library & Information Services

Ford Motor Company

kvarnum@ford.com

Information Agents14 October 2003

Tom Montgomery

Technical Expert

Infotronics & Systems Analytics

Ford Motor Company

tmontgo1@ford.com


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Presentation Outline

  • Introduction

  • Intelligent Agents

  • Process

  • Monitoring & Tuning

  • Conclusions


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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)


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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


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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


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World Automotive Information

  • Inefficient use of abstractors’ time

  • “One size fits all” approach doesn’t work

  • Not scalable – becomes hard to add new topics


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Intelligent Information Agents

  • Software analog to human agents

    • real estate agent, librarian, salesperson

  • Learn preferences over time


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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


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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


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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


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Intelligent Agents vs. Collaborative Filtering

  • Every document in WAI service is a “new product”

  • Customer’s interests evolve over time


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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


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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


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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


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Agent Interaction

Feedback

Train

Agent

Recommend


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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


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Percent of Content for Users who Received Personalized Content from Individual Agents

Extra Content Distributed


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Percent of Clickthroughs to Content by Users who Received Personalized Content from Individual Agents

Extra Content Selected


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Results Personalized Content from Individual Agents

  • Use of agent improved usage

  • Technology proved to us it work

  • Is core technology in our next version of current awareness service


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Privacy’s Two-Edge Sword Personalized Content from Individual Agents

  • 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


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Next Steps Personalized Content from Individual Agents

  • Expand to larger subscription service

  • Allow users to edit their preferences

    • As an option

    • As a convenience

  • Ability for user to reset preferences


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Thank You Personalized Content from Individual Agents

  • Updated slides available atvarnum.org/agents.ppt

  • Questions?