1 / 22

Information Agents 14 October 2003

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

JasminFlorian
Download Presentation

Information Agents 14 October 2003

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. 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

  2. Presentation Outline • Introduction • Intelligent Agents • Process • Monitoring & Tuning • Conclusions

  3. 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)

  4. 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

  5. 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

  6. World Automotive Information • Inefficient use of abstractors’ time • “One size fits all” approach doesn’t work • Not scalable – becomes hard to add new topics

  7. Intelligent Information Agents • Software analog to human agents • real estate agent, librarian, salesperson • Learn preferences over time

  8. 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

  9. 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

  10. 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

  11. Intelligent Agents vs. Collaborative Filtering • Every document in WAI service is a “new product” • Customer’s interests evolve over time

  12. 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

  13. 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

  14. 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

  15. Agent Interaction Feedback Train Agent Recommend

  16. 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

  17. Percent of Content for Users who Received Personalized Content from Individual Agents Extra Content Distributed

  18. Percent of Clickthroughs to Content by Users who Received Personalized Content from Individual Agents Extra Content Selected

  19. Results • Use of agent improved usage • Technology proved to us it work • Is core technology in our next version of current awareness service

  20. 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

  21. 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

  22. Thank You • Updated slides available atvarnum.org/agents.ppt • Questions?

More Related