Value filtering - PowerPoint PPT Presentation

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Title:

Value filtering

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Similarity of document to user's interest. Keyword vector, LSI (Latent Semantic Index) ... for 'http://c.s/g.html' by 'John Doe' ratings (violence 3 sex 2) ... – PowerPoint PPT presentation

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Slides: 16
Provided by: Jungh1
Learn more at: http://oak.cs.ucla.edu
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Tags: filtering | value

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Title: Value filtering


1
Value filtering
  • Problem
  • Vast amount of data
  • Traditional method is not enough
  • Hard to find the valuable information

2
Goal
  • Help users with information search
  • Attach value to documents
  • Filter out garbage information

3
Outline
  • Filtering model
  • User-Filter interaction model
  • Information domain
  • Movie, Music, Web, Legal Cite, ...
  • Filtering criteria
  • Content-based, Context-based, ...
  • Metadata architecture
  • Value distribution

4
Filtering model
  • Query/Response model

Filter
Query
Information
Response
5
Filtering model
  • Push model

Filter
Useful Information
6
Filtering model
This way, Please!
  • Guide model

?
7
Information domain
  • Different domain -gt different method
  • Legal cite, Entertainment, Newsgroup, ...
  • Utility/cost
  • ex) Legal cite Defense System Movie
  • Technological barrier
  • ex) Text document Movie

8
Filtering criteria
  • Content-based
  • Access history-based
  • Context-based
  • User-preference based

9
Content-based
  • Similarity of document to users interest
  • Keyword vector, LSI (Latent Semantic Index)
  • Most systems
  • Search Engines
  • Tapestry (Xerox Palo Alto), FAB (Stanford),
    WebWatcher (CMU),

10
Access history-based
  • Analysis of users access pattern
  • Personal access pattern
  • Personalized access history of each user
  • Letizia (MIT Media Lab)
  • Global access pattern
  • Global access pattern to each document
  • WebWatcher (CMU), Path-profile (Microsoft), KSS
    (Stanford), ...

11
Context-based
  • Hyper-link Context
  • Mostly for Guide Model
  • KSS (Stanford), WebGlimpse (U of Arizona),
    WebWatcher (CMU)
  • Social-Network Context
  • Referral Web (ATT Lab)

12
User preference-based
  • Personal profile matching
  • Find similar taste user
  • Global preference measure
  • Find globally famous/popular document
  • Explicit Feedback
  • Users explicit feedback on the document
  • Implicit Feedback
  • Automatic extraction of users preference

13
User preference-based
  • GroupLens (U of Minnesota), Ringo (MIT)
  • Explicit user-profile matching (user voting)
  • Google (Stanford)
  • Implicit Global preference (Hyperlink)
  • PHOAKS (ATT Lab)
  • Semi implicit Global preference (Netnews)

14
Metadata architecture
  • Architecture for Value exchange
  • PICS (Platform for internet content selection)
  • W3C Standardization effort
  • Inspired by Adult Site Filtering
  • Publisher, Rater, Filter
  • User selects Rater
  • Stanford ComMentor

15
PICS
  • (PICS-1.1 http//ra/v1.0/
  • labels
  • on "1994.11.05"
  • until 1995.12.31"
  • for "http//c.s/g.html"
  • by "John Doe"
  • ratings (violence 3 sex 2))
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