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SI 503 Search and Retrieval

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  1. SI 503 Search and Retrieval Prof. George W. Furnas Social, Organizational and MultiAgent Search and Retrieval

  2. 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

  3. 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...

  4. 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

  5. MultiAgent Search and Cooperative Information Gathering Groups whose goal is to work together to search • Take it away, Mark...

  6. Problems in Search • Rarely done in a vaccum • Larger Context of task at hand • Sometimes other people are involved • Group Projects • Group Goals

  7. Coordination Needs • Must Coordinate actions between • Multiple users • Users & system • Coordination involves intention-to • Who does what, • How are those intentions stated?

  8. 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?

  9. 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

  10. 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

  11. UMDL Agent Architecture 2 • Why use this structure? • Scale • Reliability • Extensibility • Administrative Control • Decentralized

  12. UMDL Agent Architecture 3 • Problems • Overhead • Especially noticable in small-scale situations • Malicious Agents • Vocabulary Problem • Searching multiple, different collections

  13. 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)

  14. CCRumble

  15. 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

  16. 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

  17. 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

  18. 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

  19. 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

  20. 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...

  21. Outline • Why study OM (Organizational Memory)? • What is OM? • How to design OMIS?

  22. 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)

  23. 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.

  24. 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)

  25. Individual Memory and Organizational Memory (JoAnn Brook)

  26. 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

  27. 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

  28. 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

  29. Krippendorff’s View Organization • Number a • number b Response Stimuli structure a elements structure b Temporal pattern

  30. 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

  31. Contents of Organizational Memory • Declarative types: data, information, knowledge • Procedural: organizational routine • Systemic: culture, social structure

  32. Process of OM Search Retention Organizational Knowledge Base Retrieval Acquisition Maintenance

  33. 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

  34. How to Design OMIS? • Definition of OMIS • Rationale • Mnemonic function of OM • Barriers to OMIS implementation and use

  35. 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. “

  36. 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)

  37. 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

  38. Mnemonic enabling technologies • Machine learning systems • Hypertext and hypermedia • Expert systems • Case-based systems • Information retrieval systems

  39. 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

  40. Conclusion • What is organization memory? • common aspects • different aspects • How to apply OM? • ILS • HCI • ARM

  41. 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...

  42. 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

  43. 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

  44. 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.

  45. How Well Does It Do?

  46. 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)

  47. 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.

  48. Performance of the video recommender r = .22 r = .62

  49. 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

  50. 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...