Social Browsing on Flickr by wanghonghx

VIEWS: 7 PAGES: 58

									                    The Social Web or
                 how flickr changed my life



        Kristina Lerman
USC Information Sciences Institute
      http://www.isi.edu/~lerman
Web 1.0




          2
Web 2.0




          3
           Elements of Social Web

 Users contribute content
  • Images (Flickr, Zoomr), news stories (Digg, Reddit), bookmarks
    (Delicious, Bibsonomy), videos (YouTube, Vimeo), …
 Users add metadata to content
  • Tags: annotate content with freely chosen keywords
  • Discussion: leave comments
  • Evaluation: active through voting or passive through views &
    favorites
 Users create social networks
  • Add other users as friends/contacts
  • Sites provide an easy interface to track friends’ activities
 Transparency
  • Publicly navigable content and metadata


                                                                     4
             Flickr




                         submitter




                      tags


discussion

                       image stats
                                     5
User profile




               6
User’s tags

               Tags are keyword-
                based metadata
                added to content
                 • Help users organize
                   their own data
                 • Facilitate searching
                   and browsing for
                   information
                 • Freely chosen by user




                                     7
User’s favorite images
(by other photographers)




                           8
                      So what?

By exposing human activity, Social Web allows users to
  exploit the intelligence and opinions of others to
  solve problems
   • New way of interacting with information
       − Social Information Processing
   • Exploit collective effects
       − Word of mouth to amplify good information
   • Amenable to analysis
       − Design optimal social information processing systems




Challenge for AI: harness the power of collective
  intelligence to solve information processing
  problems
                                                                9
      Outline for the rest of the talk

User-contributed metadata can be used to solve
  following information processing problems

 Discovery
  Collectively added tags used for information discovery
 Personalization
  User-added metadata, in the form of tags and social networks, used to
    personalize search results
 Recommendation
  Social networks for information filtering
 Dynamics of collaboration
  Mathematical study of collaborative rating system


                                                                     10
Discovery
personalization
recommendation
dynamics of collaboration


with: Anon Plangrasopchok
            Information discovery

 Goal: Automatically find resources that provide some
  functionality
   • weather conditions, flight tracking, geocoding, …


 Simpler goal: Find resources that provide the same
  functionality as the seed, e.g., http://flytecomm.com
   • Improve robustness of information integration applications
   • Increase coverage of the applications



 Approach: Leverage user-contributed tags to
  discover new resources similar to the seed


                                                                  12
           Anatomy of Delicious



resource




                                  popular
                                  tags
 user
notes



                                            user
                                              tags

                                                     13
             Probabilistic approach

 Find a compressed description of the source
   • Extract “latent topics” in a collection of sources, using Probabilistic
     Generative Model
 Compute pair-wise similarity between the seed and a source
  using compressed description


       Sources

                              Probabilistic
                              Model

                 Tags                  Compressed description
    Users
                                Compute
                                Source                 Similar sources
                                Similarity             (sorted)
                                                                               14
             Alternative models



     ITM                      pLSA                    MWA

                                                       Z
    U       R                   R


    I       Z                   Z
                                                  U         R

        T                       T                      T
                Nt                  Nt                      Nb
                     D                   D
 [Plangrasopchok &       [Hoffman, in UAI’99]   [Wu+, in WWW’06]
Lerman, in IIWeb’07]




                                                                   15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58

								
To top