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SI 503 Search and Retrieval. Prof. George W. Furnas Social, Organizational and MultiAgent Search and Retrieval. Outline for Today. Administrivia =====+===== Overview - Furnas Org Memory - Qiping MultiAgent Search - Handel Social Filtering - Furnas

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si 503 search and retrieval
SI 503 Search and Retrieval

Prof. George W. Furnas

Social, Organizational and MultiAgent Search and Retrieval

outline for today
Outline for Today
  • Administrivia

=====+=====

  • Overview - Furnas
  • Org Memory - Qiping
  • MultiAgent Search - Handel
  • Social Filtering - Furnas
  • Summary and Discussion - Furnas and Class

=====+=====

  • Next week’s readings
administrivia
Administrivia
  • Exams back next week
    • together with statistical analyses
    • answer sheet
  • Paper topics emailed out in the next few days
    • choose one
    • if you really want to do something else, you can come talk to us but your grading will be less calibrated...
overview
Overview

Search is not just something done by single individuals

We will look at several versions of search concerning groups:

  • Groups of individual searchers with own goals, but sharing what they learn along the way to help each other

- Social/Collaborative Filtering/Recommending

  • Groups whose goal is to work together to search

- MultiAgent Search and Cooperative Information Gathering

  • Groups whose goals are much more than search, but who must store and retrieve information in service of those goals

- Organizational Memory

multiagent search and cooperative information gathering
MultiAgent Search and Cooperative Information Gathering

Groups whose goal is to work together to search

  • Take it away, Mark...
problems in search
Problems in Search
  • Rarely done in a vaccum
    • Larger Context of task at hand
  • Sometimes other people are involved
    • Group Projects
    • Group Goals
coordination needs
Coordination Needs
  • Must Coordinate actions between
    • Multiple users
    • Users & system
  • Coordination involves intention-to
    • Who does what,
    • How are those intentions stated?
growth of the search domain
Growth of the Search Domain
  • Growth of on-line information
    • Different interfaces, vocabulary, search styles, and results
  • Many searches use different databases
    • i.e. Web: AltaVista, HotBot, Yahoo
  • Coordination between these databases
    • Who actually does the search?
agents
Agents
  • Actions (Search) can be done by
    • You or An agent
      • Agents: Computer or Human
  • Agents do things on your behalf
    • Travel Plans
      • Multi-airline, hotel, rental car, etc
    • Searches
      • Multi-database search
  • Actions can evolve as well
umdl agent architecture 1
UMDL Agent Architecture 1
  • Through a user agent, a user does a search.
      • Group of agents comes together to fulfill the search request
      • This group of agents work to:
        • Choose databases, execute the query, returning results
  • User never sees behind the scenes
    • Coordination here is between the autonomous agents
umdl agent architecture 2
UMDL Agent Architecture 2
  • Why use this structure?
    • Scale
    • Reliability
    • Extensibility
    • Administrative Control
      • Decentralized
umdl agent architecture 3
UMDL Agent Architecture 3
  • Problems
    • Overhead
      • Especially noticable in small-scale situations
    • Malicious Agents
    • Vocabulary Problem
      • Searching multiple, different collections
ccrumble
CCRumble
  • Virtual Environment
    • Like a graphical, distributed MUD
  • Single environment with
    • Users
    • Agents (“bots”)
    • Other objects
  • This is my research focus right now
    • Example: one participant (me), two bots, (Crumble and ImgBot)
ccrumble2
CCRumble
  • Enable smooth transitions between
    • Group / small group / individual work
  • Real-time interaction and sharing
  • Persistent objects
    • Come back & continue work
    • Share with others async
moving from ann arbor
Moving from Ann Arbor
  • UPS versus a Moving company
  • UPS
    • can move a lot of packages each day to many different destinations
      • No special attention to issues of any one package
  • Moving Company
    • Move specific contents to a specific destination
      • Special attention to each particular move
umdl versus ccrumble
UMDL versus CCRumble
  • UMDL
    • Multi-Agent Search
    • designed for well-defined, one shot queries
    • refinement / reconceptualization not supported directly
    • Coordination takes place without user knowledge or intervention
umdl versus ccrumble1
UMDL versus CCRumble
  • CCRumble
    • Collaborative Information Gathering
      • coordination takes place with all participants
    • On-going feedback to/from all members of the interaction
    • Developed for longer-term information gathering
      • Iterative refinement & reconceptualization the task, teams recombine
    • Iterative searching model
conclusion
Conclusion
  • Groups are important in search
  • Groups can evolve over time
    • Their goals, tasks, and members
    • They may contain agents as well
  • Coordination and intentions are crucial
    • Must design systems to support these ideas
organizational memory
Organizational Memory
  • Groups whose goals are much more than search, but who must store and retrieve information in service of those goals
  • Take it away, Qiping...
outline
Outline
  • Why study OM (Organizational Memory)?
  • What is OM?
  • How to design OMIS?
open quote
Open Quote
  • “The Ford Motor Company today is very different from the same company of 1970, yet many essential characteristics remain so that Ford is Ford, for better or worse. The persistence of organizational features suggest that organizations have the means to retain and transmit information from past to future members of the social system. This capability we might call the organization memory. “ (Stein, 1995)
why study om
Why study OM?
  • OM is important
    • maintain strategic direction over time
    • help organizations to learn
    • strengthen the identity of the organization
    • help newcomers
  • Understanding OM is important
    • to managers
    • to designers.
what is organizational memory
What is Organizational Memory?
  • Individual vs. Organizational memory
  • Debate on OM
    • Does OM exist?
    • Passive storage vs. active remembering
  • Three perspectives:
    • Walsh (Organizational behavior)
    • Krippendorff (communication: classic)
    • Stein (information technology and organization)
walsh ungson s view 1996
Walsh & Ungson’s view (1996)
  • Organization memory is composed of:
  • the structure of its retention facility
  • the information contained in it
  • the processes of information acquisition and retrieval
  • its consequential effects
krippendorff s view
Krippendorff’s view
  • Three types of social memory
    • Temporal memory
    • Memory involving records
    • Structural memory
  • Why do we care about them?
    • Records memory is not the whole story
krippendorff s view cont d
Krippendorff’s view (cont’d)
  • Exercises 1: (Record memory)
    • Please write down the numbers

number a: A249 8Z047 F4892 P98U23

  • Exercises 2: (Structural memory)
    • 4 volunteers to remember

number a: A249 8Z047 F4892 P98U23

number b: P98U23 8Z047 A249 F4892

  • Exercises 3: (Temporal memory)
    • Spread number a within the group
krippendorff s view1
Krippendorff’s View

Organization

  • Number a
  • number b

Response

Stimuli

structure a

elements

structure b

Temporal pattern

stein s view
Stein’s view
  • Definition
    • OM is the memory by which knowledge from the past is brought to bear on present activities, thus resulting in higher or lower levels of organizational effectiveness.
  • Essential Components of OM
    • Contents of Memory
    • Processes of Memory
contents of organizational memory
Contents of Organizational Memory
  • Declarative types: data, information, knowledge
  • Procedural: organizational routine
  • Systemic: culture, social structure
process of om
Process of OM

Search

Retention

Organizational Knowledge Base

Retrieval

Acquisition

Maintenance

search and retrieval
Search and Retrieval
  • Decision-making and problem solving context
  • Past knowledge is retrieved if:
    • DM values past knowledge
    • DM believes information exists
    • DM can find desired information
    • Cost to find information is less than starting from scratch
how to design omis
How to Design OMIS?
  • Definition of OMIS
  • Rationale
  • Mnemonic function of OM
  • Barriers to OMIS implementation and use
definition of omis
Definition of OMIS
  • “ … a system that functions to provide a means by which knowledge from the past is brought to bear on present activities, thus resulting in increased levels of effectiveness for the organization. “
slide36

Supporting activities leading to organizational effectiveness

OMIS

Layer 1

Intergrative

subsystem

Goal Attainment Subsystem

Pattern Maintenance subsystem

Adaptive subsystem

Layer 2

Mnemonic Functions

(knowledge acquisition, retention, maintenance, search and retrieval)

rationale for it supported om
Rationale for IT Supported OM
  • Rrequirements of post-industrial world
    • increased complexity of the environment
    • increased trubulence of the environment
  • Availability of advanced information Technology
    • expert systems
    • case-based systems
    • DSS/GDSS
  • OMIS can support the retention of core companies
  • OMIS can support organizational effectiveness and learning
mnemonic enabling technologies
Mnemonic enabling technologies
  • Machine learning systems
  • Hypertext and hypermedia
  • Expert systems
  • Case-based systems
  • Information retrieval systems
omis implementation issues
OMIS Implementation Issues
  • OMIS implementation may not be initiated
  • OMIS may not be successfully implemented
    • user resistance
    • incongruent with organization
  • OMIS contents may not be maintained and used
    • motivation to contribute may be lacking
conclusion1
Conclusion
  • What is organization memory?
    • common aspects
    • different aspects
  • How to apply OM?
    • ILS
    • HCI
    • ARM
collaborative filtering virtual community recommending
Collaborative Filtering(Virtual Community Recommending)

Groups of individual searchers with own goals, but sharing what they learn along the way to help each other

  • Take it away, George...
movie recommendations
Movie Recommendations

Suppose you want to find a movie with the help of IT

  • Standard way:
    • could search a database based on features of the movie
    • title, actor, director, genre
    • Were CD-ROM versions of this
  • Problems of Standard way
    • have to figure out what are relevant features, and put them in explicitly
    • features don’t capture quality, aesthetic,...
    • recommendations for groups
another way
Another way

Basic Idea:

  • Find people who like the movies you have liked in the past, and use their opinions on movies you have not yet seen to predict what movies you will like
  • Video Recommender:
    • large database of people’s movie preferences
    • find 30 people closest to you in movie preference
    • weight those people in a statistical regression equation to predict movie preference

AmyPref = .3*GeorgePref + .2*MarkPref + .5*QipingPref

    • recommend movies predicted to be most liked
what is going on here
What is going on here...

Note that there is a lot of info about the quality of these films around...

...in other viewers’ heads

If we can only draw upon that resource.

a baseline for comparison
A Baseline for Comparison
  • Check with the movie critics (1-5 stars)
  • How well does it predict the ratings of regular people?
  • Correlation, r = 0.22
    • (5% of the variance)
  • Not very good
    • though better than a random-person-as-critic

(r = .16)

performance of the video recommender
Performance of the Video Recommender
  • Correlation, r = 0.62
    • (37% of the variance)
    • explains >7x the var.
  • 3 out of 4 movies it recommends, you would rate 8, 9 or 10.
conclusion 1
Conclusion #1

You can use other people

to help you find the things you like.

Notes:

  • The way you do it informally now
    • ask a friend
  • Could not have happened without computation and communication IT advances
a second effect
A Second Effect

If you played “Onymously” (Not anonymously)

  • Got back names and email addresses of your closest matches
  • Generated great excitement
    • Best friend from Jr High
    • Fiancee
    • Single-and-Available bit...
  • The human element...
conclusion 2
Conclusion #2

You can use the finding of things

to help find people.

  • We are using communication and computational technology to build not just an infosphere, but a socio-infosphere
some comments
Some Comments
  • Other domains: books, music, ...
    • Grouplens, Firefly, etc..
    • Collab website: http://www.sims.berkeley.edu/resources/collab/
    • web pages
web recommender
Web Recommender
  • Rate pages you like with slider
    • data sent of to recommendation engine
  • Can ask for a page of recommended sites
  • Can ask for recommendations on outlinks on any given page
    • recommendation guided browsing
some comments cont
Some Comments (cont.)
  • Other domains: books, music, ...
    • Grouplens, Firefly, etc...[]
    • Collab website: http://www.sims.berkeley.edu/resources/collab/
    • web pages
  • Incentive
    • you win by playing
  • Works best where
    • density of overlap is reasonably high
      • movies are great
    • cooperation stronger than competition
  • Symmetric version
    • consumers find producers
  • Agent version...
dimensions of variation for recommender social filtering systems
Dimensions of Variation for Recommender / Social Filtering Systems

(Resnick and Varian)

  • Technical Design Space:
    • Contents of evaluation
    • Explicit / Implicit ratings
    • Anonymous / Onymous
    • Aggregation method
    • Use of recommendations
  • Domain Space

Characteristics of items evaluated:

    • Type, Number, Lifetime, Cost Structure

Characteristics of evaluators:

    • Recommenders, Density of Recommendations, Consumers, Consumer Taste Variability
summary of group search retrieval
Summary of Group Search&Retrieval

Search is not just an individual exercise

  • Even individual search can be helped by sharing results of explorations of an unfamiliar, under
      • Community Recommending
      • Adaptive Indexing, AnswerGarden
  • Groups can search for things together
    • Teams of artificial agents can help coordinate getting users and sources of information together efficiently
      • UMDL agent architecture
    • Groups of people (and infobots) can search together, fluidly evolving goals and strategies, and reconfiguring momentary group structure
      • Cooperative Information Gathering
      • CCRUMDL
summary of group search retrieval cont
Summary of Group Search&Retrieval (cont.)

Search is not just an individual exercise (cont.)

  • Even individual search can be helped by sharing results of explorations of an unfamiliar, under
  • Groups can search for things together
  • Any groups that endure successfully over time (Organizations) must bring past experience to bear appropriately on the present, requiring some kind of memory (structural, record-based, temporal) with appropriate storage and retrieval
      • Organizational Memory
discussion exercise
Discussion Exercise

The School of Information wants to find speakers for a speaker series

  • Imagine a future world with new IT, and people participating appropriately
  • How can SI find good speakers in that future world?
  • Use whatever you can think of from this class
    • Org memory
    • CIG (Cooperative Information Gathering)
    • Recommenders
    • IR, interface, org, query, interface, cog search
epilog looking to next week
Epilog: Looking to next week
  • A few general theories related to search
    • Russell, Daniel M., et al (1993) (269-276=8pp)
        • Cost structure of sensemaking
    • Pirolli, Peter, and Card, Stuart, (1995) (51-58=8pp)
      • Information foraging in information access environments
    • Dennet, Daniel C. (1996) (48-84=37, 104-145=42, =79pp)
      • Darwin's Dangerous Idea
  • Food Volunteers???