„IP“ is not always „Internet Protocol“ A long and a very short example for IP problems in...

„IP“ is not always „Internet Protocol“A long and a very short example for IP problems in Web 2.0 research

Ralf Schenkel

Joint work with Tom Crecelius, Mouna Kacimi, Sebastian Michel, Thomas Neumann, Josiane Parreira, Marc Spaniol, Gerhard Weikum

September 25, 2008 Dagstuhl Perspectives Workshop Web 2.0

Social Tagging NetworksDefinition: Social Tagging NetworkWebsite where people• publish + tag information• review + rate information• publish their interests• maintain network of friends• interact with friends

Common examples:• Flickr (images)• YouTube (videos)• del.icio.us (bookmarks)• Librarything (books)

• Discogs (CDs)• CiteULike (papers)• Facebook• Myspace (media)

http://www.flickr.com/

http://www.librarything.com/

http://www.discogs.com/

http://de.facebook.com/r.php

Part 1: Search in Social Tagging Networks

(long)


Some Statistics

Flickr: (as of Nov 2007)• 2+ billion photosFacebook: (as of Apr 2007)• 1.8 billion photos• 31 million active users• 100,000 new users per day

Myspace: (as of Apr 2007)• 135 million users (6th largest country on Earth)• 2+ billion images (150,000 req/s), millions added daily• 25 million songs• 60TB videos

Huge volume of highly dynamic data


Showcase: librarything.com

RatingsTagsBooks

Others


librarything.com: Social Interaction

Explicit Friends

Similar Users

Comments


librarything.com: Tag Clouds


librarything.com: Search

Search results independent of the querying user(and the social context)

Search results independent of the querying user(and the social context)


Outline

• Introduction

• Modelling Social Tagging Networks– Graph Model

– Different Information Needs

• Effective Query Scoring

• Efficient Query Evaluation

• Summary & Further Challenges


Social Network Model

travelNorway

travelChina queueing

theory

USERS

ITEMS

TAGS


Social Network Model

travelNorway


theory

USERS

ITEMS

TAGS

travel travel

tripvldb

travel probability

queuestravel

probability

harrypotter


Information Need 1: Global

travelNorway


theory

USERS

ITEMS

TAGS

travel travel

tripvldb

travel probability

queuestravel

probability

harrypotter

harry potter

Tags by all users equally important


Information Need 2: Similar Users

travelNorway


theory

USERS

ITEMS

TAGS

travel travel

tripvldb

travel probability

queuestravel

probability

harrypotter

travel

?Tags by users with similar tags/items

more important


Information Need 3: Trusted Friends

travelNorway


theory

USERS

ITEMS

TAGS

travel travel

tripvldb

travel probability

queuestravel

probability

harrypotter

probability

?Tags by closely related users

more important


Wishlist for Social-Aware Social Search• Search results depend on

– Global popularity of items– Collection context of the querying user (books, tags)– Social context of the querying user (trusted friends)

• Automatic tag expansion (beyond synonyms)• Scalable query processing• Explanation of results

(similar wishlist for social recommendations)


Fast Forward…

Imagine a 20 minutes talk aboutquantified friendship measures,

personalized scoring models,dynamic tag expansion,

scalable query processing, …

Essence:

• Context-aware personalized search

• Tags from closely related users are more important

• Different kinds of „relatedness“ possible[SIGIR 2008]


Experimental Evaluation: Effectiveness

Systematic evaluation of result quality difficult

Three possible setups:• Manual queries + human assessments• Queries+assessments derived from external info

(ex: DMOZ categories)• Automated assessments from context of user

– Items tagged by friends– Items tagged in the future

?


Prototype Implementation

[SIG

IR D

emo

2008

], [V

LDB

Dem

o 20

08]


Preliminary User StudyLibraryThing user study: [Data Engineering Bulletin, June 2008]• 6 librarything users with reasonably large library and friend sets• Overall 49 queries• Crawled (part of) librarything: ~1,3 mio books, ~15 mio tags,

~12,000 users, ~18,000 friends• Measured NDCG[10]

0.0 0.2 0.5 0.8 1.0

0.0 0.546 0.572 0.568 0.565 0.565

0.2 0.564 0.572 0.579 0.581 -

0.5 0.539 0.552 0.559 - -

0.8 0.515 0.546 - - -

1.0 0.465 - - - -

(1-α)(graph)

(1-α) (content)

Authors of the paper


We need a benchmark collection, but…• Everybody „has“ data from Flickr, librarything• Data contains private information by definition• Data cannot be successfully anonymized (AOL)• Data must not be anonymized

(we need the users to assess results)• Data must be large scale

(a few volunteers are not enough)• Collection must be completely offline available

for stability of results (including images,…)

Part 2: Web Archiving

(very short)


Online Information is Volatile• Huge amount of information available online only

today• Easily lost (hardware failure, software failure,

human failure, deletion, attack, …)• Easily unaccessible (anybody knows Interleaf?)• Easily manipulated• How will historians learn about the 21th century?

Strong need for long-term preservationof the evolving Web

„IP“ is not always „Internet Protocol“ A long and a very short example for IP problems in...

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