informationssuche in sozialen netzen ralf schenkel joint work with tom crecelius, mouna kacimi,...
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Informationssuche in sozialen Netzen
Ralf Schenkel
Joint work with Tom Crecelius, Mouna Kacimi, Sebastian Michel, Thomas Neumann, Josiane Parreira, Marc Spaniol, Gerhard Weikum
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February 2, 2009 Perspektivenvorlesung
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)
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February 2, 2009 Perspektivenvorlesung
Some StatisticsFlickr: (as of Nov 2008)• 3+ billion photos, 3 million new photos per dayFacebook: (as of Nov 2008)• 10+ billion photos, 30+ million new photos per day• 120 million active users• 150,000 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
StudiVZ.net: (as of Nov 2008)• 11 million users• 300 million images, 1 million added daily
Huge volume of highly dynamic data
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February 2, 2009 Perspektivenvorlesung
Showcase: librarything.com
RatingsTagsBooks
Others
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February 2, 2009 Perspektivenvorlesung
librarything.com: Social Interaction
Explicit Friends
Similar Users
Comments
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February 2, 2009 Perspektivenvorlesung
librarything.com: Tag Clouds
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February 2, 2009 Perspektivenvorlesung
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)
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February 2, 2009 Perspektivenvorlesung
librarything.com: Search
Search automatically expanded with similar tags(synonyms)
Search automatically expanded with similar tags(synonyms)
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February 2, 2009 Perspektivenvorlesung
Librarything.com: Recommendations
Recommendations depend on user and tags(but not on social context)
Recommendations depend on user and tags(but not on social context)
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February 2, 2009 Perspektivenvorlesung
Librarything.com: Recommendations
Explanation for the recommendationExplanation for the recommendation
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February 2, 2009 Perspektivenvorlesung
Librarything.com: Explanations
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February 2, 2009 Perspektivenvorlesung
Librarything.com: Explanations
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February 2, 2009 Perspektivenvorlesung
Outline
• Search in Social Tagging Networks– Graph Model
– Different Information Needs
• Effective Query Scoring
• Efficient Query Evaluation
• Summary & Further Challenges
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February 2, 2009 Perspektivenvorlesung
Querying Social Tagging Networks
travelvldb
travelnorway
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February 2, 2009 Perspektivenvorlesung
Querying Social Tagging Networks
travelvldb
travelnorway
harrypotter
harrypotter
harrypotter
harrypotter
traveltrip
travelicde
travelmexico
travel
travelnorway
travelvldb
probabilitydata miningfoundations
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February 2, 2009 Perspektivenvorlesung
Information Need 1: Globally Popular
travelvldb
travelnorway
harrypotter
harrypotter
harrypotter
harrypotter
traveltrip
travelicde
travelmexico
travel
travelnorway
travelvldb
probabilitydata miningfoundations
Most frequently tagged items „best“Tags by all users equally important
harry potter
or ?
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February 2, 2009 Perspektivenvorlesung
Information Need 2: Similar Users
harrypotter
harrypotter
harrypotter
harrypotter
traveltrip
travelicde
travelmexico
travelvldb
travel
travelnorway
travelnorway
travelvldb
probabilitydata miningfoundations
travel
or ?
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February 2, 2009 Perspektivenvorlesung
Information Need 2: Similar Users
harrypotter
harrypotter
harrypotter
harrypotter
traveltrip
travelicde
travelmexico
travelvldb
travel
travelnorway
travelnorway
travelvldb
probabilitydata miningfoundations
travel
or ?Tags by users with similar tags/items(„brothers in spirit“)
more important
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February 2, 2009 Perspektivenvorlesung
Information Need 3: Trusted Friends
harrypotter
harrypotter
harrypotter
traveltrip
travelicdetravel
vldb
travel
travelnorway
travelnorway
travelvldb
probabilityselling
probabilitydata miningfoundations
probabilityselling
probabilityselling
probability harrypotter
travelmexico
or ?
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February 2, 2009 Perspektivenvorlesung
Information Need 3: Trusted Friends
harrypotter
harrypotter
harrypotter
traveltrip
travelicdetravel
vldb
travel
travelnorway
travelnorway
travelvldb
probabilityselling
probabilitydata miningfoundations
probabilityselling
probabilityselling
probability harrypotter
travelmexico
or ?
Tags by closely related andwell-known users more important
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February 2, 2009 Perspektivenvorlesung
Towards Social-Aware Social Search
Search results may depend on– Global popularity of items– Spiritual context of the querying user
(users with similar books and/or tags)– Social context of the querying user
(known and trusted friends)
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February 2, 2009 Perspektivenvorlesung
Outline
• Search in Social Tagging Networks
• Effective Query Scoring– Quantifying Friendship Strengths
– User-specific Scoring Functions
– Experimental Evaluation
• Efficient Query Evaluation
• Summary & Further Challenges
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February 2, 2009 Perspektivenvorlesung
NotationU set of usersT set of tagsI set of items
tags(u): tags used by user uitems(u): items tagged by user u
items(t): items tagged with tag t by at least one user
df(t): number of items tagged with tag ttfu(i,t): number of times user u tagged item i with tag ttf(i,t): number of times item i was tagged with tag t
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February 2, 2009 Perspektivenvorlesung
Quantifying Friendship Strengths• Global „friendship“ strength:
||
1)',(
UuuPglobal
• Spiritual friendship strength
• Social friendship strength
• Integrated friendship strength
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February 2, 2009 Perspektivenvorlesung
Spritual Friendship Strength
|)'(||)(|
|)'()(|2)',(
utagsutags
utagsutagsuuPspirit
|)'(||)(|
|)'()(|2)',(
uitemsuitems
uitemsuitemsuuPspirit
Several alternatives:
• based on overlap of tag usage:
• based on overlap of tagged items:
For all:
• Pspirit(u,u):
• normalization such that
uu
spirit uuP'
1)',( tags(u): tags used by user uitems(u): items tagged by user u
u u‘
)',( uuPspirit overlap in interests of u and u‘
• overlap of behavior (tagging, searching, rating, …)
u u‘
harrypotterwizard
deathlyhallows
philosopherstone
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February 2, 2009 Perspektivenvorlesung
Graph-Based Friendship Strength
u1
u2
u3
u4
u5
u6
u7
1),( 1 ii uuw
1),(),,(1
11 1
juuwuuwj
kiiii kkj
)(min
1)',(
'path pw
uuPuup
social
• set Psocial(u,u):=0
• normalization such that
uu
social uuP'
1)',(
u2
Pso
cial(
,u‘)
||
1
U
u3 u4 u5 u6 u7u‘
)',( uuPsocial distance of u and u‘ in user network
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February 2, 2009 Perspektivenvorlesung
Integrated Friendship StrengthQuery-dependent mixture of• spiritual friendship strength• social friendship strength• background model (global)
(0,1; +1)
)',()',(||
1)1()',( uuPuuP
UuuF spiritualsocial
Pint(u,u‘)
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February 2, 2009 Perspektivenvorlesung
Excursion: Scoring in Text Retrieval
)(),(),( tidftitftiscore
Importance of t in the collection(the less frequent, the better)
Importance of t for item i(the more frequent, the better)
General scoring framework:
5.0)(
5.0)(||log
),(
),()1(),(
1
1
tdf
tdfI
titfk
titfktiscore
Hand-tuned instance: Okapi BM25
n
jjn tiscorettiscore
11 ),(),(
Linear combination for query scores
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February 2, 2009 Perspektivenvorlesung
Towards a User-specific Score
Uu
u titftitf ),(),(
Uu
u titfU
U ),(||
1||
Uu
uu titfuuFUtisf'
),()',(||),(
Convert into user-specific social frequency:
global friendship strength
5.0)(
5.0)(||log
),(
),()1(),(
1
1
tdf
tdfI
tisfk
tisfktiscore
u
uu
Compute user-specific social score
[SIGIR 2008]
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February 2, 2009 Perspektivenvorlesung
Including Tag Expansion
Problem: Users use different tags for similar things poor recall (missing relevant results)
Solution:1. Define notion of similar tags2. Expand queries with similar tags3. Modify scoring function for expanded queries
Example:MPI, MPII, MPI-INF, MPI-CS, Max-Planck-Institut, D5, AG5, DB&IS, MMCI, UdS, Saarland University, …
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February 2, 2009 Perspektivenvorlesung
Heuristics for finding similar tagsCo-Occurrence heuristics:Tags t1 and t2 similar if they occur (almost) always together
|)(||)(|
|)()(|2),(
21
2121 titemstitems
titemstitemsttsim
|)(|
|)()(|]|[),(
2
212121 titems
titemstitemsttPttsim
Specialization heuristics:Tag t2 specialization of t1 if t1 occurs (almost) whenever t2 occurs
Example: t1=Europe, t2=Germany
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February 2, 2009 Perspektivenvorlesung
Scoring Expanded QueriesNaive approach:For query tag t, add similar tags t‘ with sim(t,t‘)>δ to query
Better: auto-tuning incremental expansionFor query tag t, consider only expansion withhighest combined score per item
)',()',(max),('
tiscorettsimtiscoreTt
„international crime“ expanded by „mafia camorra yakuza …“ But:„transportation disaster“ expanded by „train car bus plane …“Result quality drops due to topic drift
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February 2, 2009 Perspektivenvorlesung
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
?
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February 2, 2009 Perspektivenvorlesung
Prototype [VLDB/SIGIR 2008 demo]
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February 2, 2009 Perspektivenvorlesung
Preliminary User StudyLibraryThing user study: [Data Engineering Bulletin, June 2008]• 6 librarything users with reasonably large library and friend sets• Overall 49 queries like „mystery magic“, „wizard“, „yakuza“• 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 - - - -
α (social)
(spiritual)
• Result quality generally very high• Combination of spiritual and social friends is best
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February 2, 2009 Perspektivenvorlesung
Outline
• Search in Social Tagging Networks
• Effective Query Scoring
• Efficient Query Evaluation– Threshold Algorithms
– ContextMerge
– Experimental Evaluation
• Summary & Further Challenges
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February 2, 2009 Perspektivenvorlesung
Algorithmic Overview
• Input: query q={t1…tn} for user u, α,
• Output: k items with highest scores
• Goals:– Avoid computing all results– Minimize disk I/O and CPU load– Utilize precomputed information on disk
+ „harry potter“
……………………..
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February 2, 2009 Perspektivenvorlesung
Excursion: Threshold Algorithms for Text IR
Input:• query q={t1…tn}
• lists L(tp) with pairs <i,score(i,tp)>, sorted by score(i,tp)↓
Output: k items with highest aggregated score
Family of Threshold Algorithms:• scan lists in parallel• maintain partial candidate results with score bounds• terminate as soon as top-k results are stable
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February 2, 2009 Perspektivenvorlesung
Example: Top-1 for 2-term query (NRA)L1 L2 top-1 item
min-k:
candidates
A: 0.9
G: 0.3
H: 0.3
I: 0.25
J: 0.2
K: 0.2
D: 0.15
D: 1.0
E: 0.7
F: 0.7
B: 0.65
C: 0.6
A: 0.3
G: 0.2
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February 2, 2009 Perspektivenvorlesung
Example: Top-1 for 2-term query (NRA)top-1 item
min-k:
candidates
0.9 ?A:
score: [0.9;1.9]
0.9
A: 0.9
G: 0.3
H: 0.3
I: 0.25
J: 0.2
K: 0.2
D: 0.15
D: 1.0
E: 0.7
F: 0.7
B: 0.65
C: 0.6
A: 0.3
G: 0.2
? ??:
score: [0.0;1.9]
L1 L2
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February 2, 2009 Perspektivenvorlesung
Example: Top-1 for 2-term query (NRA)top-1 item
min-k:
candidates
0.9 ?A:
score: [0.9;1.9]
0.9
? 1.0D:
score: [1.0;1.9]
1.0
A: 0.9
G: 0.3
H: 0.3
I: 0.25
J: 0.2
K: 0.2
D: 0.15
D: 1.0
E: 0.7
F: 0.7
B: 0.65
C: 0.6
A: 0.3
G: 0.2
? ??:
score: [0.0;1.9]
L1 L2
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February 2, 2009 Perspektivenvorlesung
1.0
Example: Top-1 for 2-term query (NRA)top-1 item
min-k:
candidates0.9 ?A:
score: [0.9;1.9]
0.3 ?G:
score: [0.3;1.3]
? 1.0D:
score: [1.0;1.3]A: 0.9
G: 0.3
H: 0.3
I: 0.25
J: 0.2
K: 0.2
D: 0.15
D: 1.0
E: 0.7
F: 0.7
B: 0.65
C: 0.6
A: 0.3
G: 0.2
? ??:
score: [0.0;1.3]
L1 L2
![Page 43: Informationssuche in sozialen Netzen Ralf Schenkel Joint work with Tom Crecelius, Mouna Kacimi, Sebastian Michel, Thomas Neumann, Josiane Parreira, Marc](https://reader037.vdocuments.us/reader037/viewer/2022110210/56649e795503460f94b79c99/html5/thumbnails/43.jpg)
February 2, 2009 Perspektivenvorlesung
1.0
Example: Top-1 for 2-term query (NRA)top-1 item
min-k:
candidates
0.9 ?A:
score: [0.9;1.6]
? 1.0D:
score: [1.0;1.3]
0.3 ?G:
score: [0.3;1.0]
No more new candidates considered
A: 0.9
G: 0.3
H: 0.3
I: 0.25
J: 0.2
K: 0.2
D: 0.15
D: 1.0
E: 0.7
F: 0.7
B: 0.65
C: 0.6
A: 0.3
G: 0.2
? ??:
score: [0.0;1.0]
L1 L2
![Page 44: Informationssuche in sozialen Netzen Ralf Schenkel Joint work with Tom Crecelius, Mouna Kacimi, Sebastian Michel, Thomas Neumann, Josiane Parreira, Marc](https://reader037.vdocuments.us/reader037/viewer/2022110210/56649e795503460f94b79c99/html5/thumbnails/44.jpg)
February 2, 2009 Perspektivenvorlesung
1.0
Example: Top-1 for 2-term query (NRA)top-1 item
min-k:
candidates
0.9 ?A:
score: [0.9;1.6]
? 1.0D:
score: [1.0;1.3]
Algorithm safely terminates
A: 0.9
G: 0.3
H: 0.3
I: 0.25
J: 0.2
K: 0.2
D: 0.15
D: 1.0
E: 0.7
F: 0.7
B: 0.65
C: 0.6
A: 0.3
G: 0.2
? 1.0D:
score: [1.0;1.25]
0.9 ?A:
score: [0.9;1.55]
? 1.0D:
score: [1.0;1.2]
0.9 ?A:
score: [0.9;1.5]
? 1.0D:
score: [1.0;1.2]0.9 0.4A:
score: [1.3;1.3]
1.3
L1 L2
![Page 45: Informationssuche in sozialen Netzen Ralf Schenkel Joint work with Tom Crecelius, Mouna Kacimi, Sebastian Michel, Thomas Neumann, Josiane Parreira, Marc](https://reader037.vdocuments.us/reader037/viewer/2022110210/56649e795503460f94b79c99/html5/thumbnails/45.jpg)
February 2, 2009 Perspektivenvorlesung
Can we reuse this here?harry
0.95
0.85
0.51
travel
0.87
0.82
0.69
No, scores specific to queryinguser and parameter setting!
: harry (=0.2,=0.5)
0.98
0.84
0.45
: harry (=0.0,=0.8)
0.90
0.89
0.56
: harry (=1.0,=0.0)
0.90
0.89
0.56
: harry (=0.5,=0.5)
0.90
0.86
0.64
: harry (=0.0,=1.0)
0.90
0.89
0.56
: harry (=0.2,=0.5)
0.98
0.84
0.45
: harry (=0.0,=0.8)
0.90
0.89
0.56
: harry (=1.0,=0.0)
0.90
0.89
0.56
: harry (=0.5,=0.5)
0.90
0.86
0.64
: harry (=0.0,=1.0)
0.90
0.89
0.56
: harry (=0.2,=0.5)
0.98
0.84
0.45
: harry (=0.0,=0.8)
0.90
0.89
0.56
: harry (=1.0,=0.0)
0.90
0.89
0.56
: harry (=0.5,=0.5)
0.90
0.86
0.64
: harry (=0.0,=1.0)
0.90
0.89
0.56
: harry (=0.2,=0.5)
0.98
0.84
0.45
: harry (=0.0,=0.8)
0.90
0.89
0.56
: harry (=1.0,=0.0)
0.90
0.89
0.56
: harry (=0.5,=0.5)
0.90
0.86
0.64
: harry (=0.0,=1.0)
0.90
0.89
0.56
Number of lists to precompute would explode!(#tags #users parameter space)
![Page 46: Informationssuche in sozialen Netzen Ralf Schenkel Joint work with Tom Crecelius, Mouna Kacimi, Sebastian Michel, Thomas Neumann, Josiane Parreira, Marc](https://reader037.vdocuments.us/reader037/viewer/2022110210/56649e795503460f94b79c99/html5/thumbnails/46.jpg)
February 2, 2009 Perspektivenvorlesung
Revisiting the Social Frequency
Uu
uu titfuuFUtisf'
),()',(||),(
Uuuint titfuuP
UU
'
),()',(||
1)1(||
Uuuint
Uu
u titfuuPU
titfU
'
),()',(||
),()1(||
Uu
uint titfuuPUtitf'
),()',(||),()1(
independent of user u dependent of user u
Uuuspiritual
Uuusocial titfuuPtitfuuPU
''
),()',(),()',(|| Compute sfu(i,t) on the fly from tf(i,t), friends of
u and their tagged documents
![Page 47: Informationssuche in sozialen Netzen Ralf Schenkel Joint work with Tom Crecelius, Mouna Kacimi, Sebastian Michel, Thomas Neumann, Josiane Parreira, Marc](https://reader037.vdocuments.us/reader037/viewer/2022110210/56649e795503460f94b79c99/html5/thumbnails/47.jpg)
February 2, 2009 Perspektivenvorlesung
Top-K in Social Networks: ContextMergePrecomputed lists:• ITEMS(t): pairs <i,tf(i,t)>, sorted by tf(i,t)↓
• USERITEMS(u‘,t): pairs <i,tfu‘(i,t)>, unsorted
• FRIENDS(u): pairs <u‘,F(u,u‘)>, sorted by F(u,u‘)↓
ITEMS(harry): 47 32 26
FRIENDS( ): 0.12 0.10 0.085 …
…
USERITEMS( , harry):
alreadyexist insystems
![Page 48: Informationssuche in sozialen Netzen Ralf Schenkel Joint work with Tom Crecelius, Mouna Kacimi, Sebastian Michel, Thomas Neumann, Josiane Parreira, Marc](https://reader037.vdocuments.us/reader037/viewer/2022110210/56649e795503460f94b79c99/html5/thumbnails/48.jpg)
February 2, 2009 Perspektivenvorlesung
ContextMergeAdapted Threshold Algorithm for query u,t:• Scan ITEMS(t) and FRIENDS(u) in parallel• pick „best“ list
– If ITEMS(t): read next entry– If FRIENDS(u): read USERITEMS(u‘,t) for next friend u‘– Maintain candidates with bounds for min and max score and current results
ITEMS(harry):
47
32
26
…
FRIENDS( ):
0.12
0.10
0.085
…
![Page 49: Informationssuche in sozialen Netzen Ralf Schenkel Joint work with Tom Crecelius, Mouna Kacimi, Sebastian Michel, Thomas Neumann, Josiane Parreira, Marc](https://reader037.vdocuments.us/reader037/viewer/2022110210/56649e795503460f94b79c99/html5/thumbnails/49.jpg)
February 2, 2009 Perspektivenvorlesung
ContextMergeAdapted Threshold Algorithm for query u,t:• Scan ITEMS(t) and FRIENDS(u) in parallel• pick „best“ list
– If ITEMS(t): read next entry– If FRIENDS(u): read USERITEMS(u‘,t) for next friend u‘– Maintain candidates with bounds for min and max score and current results
ITEMS(harry):
47
32
26
…
FRIENDS( ):
0.12
0.10
0.085
…
User-indeppart of sf:
User-specpart of sf:
47
? |U|
computemin score bound
compute max score bound
![Page 50: Informationssuche in sozialen Netzen Ralf Schenkel Joint work with Tom Crecelius, Mouna Kacimi, Sebastian Michel, Thomas Neumann, Josiane Parreira, Marc](https://reader037.vdocuments.us/reader037/viewer/2022110210/56649e795503460f94b79c99/html5/thumbnails/50.jpg)
February 2, 2009 Perspektivenvorlesung
ContextMergeAdapted Threshold Algorithm for query u,t:• Scan ITEMS(t) and FRIENDS(u) in parallel• pick „best“ list
– If ITEMS(t): read next entry– If FRIENDS(u): read USERITEMS(u‘,t) for next friend u‘– Maintain candidates with bounds for min and max score and current results
ITEMS(harry):
47
32
26
…
FRIENDS( ):
0.12
0.10
0.085
…
User-indeppart of sf:
User-specpart of sf:
47
? |U|
User-indeppart of sf:
User-specpart of sf:
?
0.12·|U|
47
|U|
0.88·|U|
![Page 51: Informationssuche in sozialen Netzen Ralf Schenkel Joint work with Tom Crecelius, Mouna Kacimi, Sebastian Michel, Thomas Neumann, Josiane Parreira, Marc](https://reader037.vdocuments.us/reader037/viewer/2022110210/56649e795503460f94b79c99/html5/thumbnails/51.jpg)
February 2, 2009 Perspektivenvorlesung
Experimental Evaluation: Efficiency• Testbed: 3 large crawls of real social networks
– Flickr: 10 mio pictures, ~50,000 users– Del.icio.us: ~175,000 bookmarks, ~12,000 users– Librarything: ~6.5 mio books, ~10,000 users
• Queries:– 150 frequent tag pairs– for each query pick user with „enough“ results &
friends• Abstract cost measure disk load• Baseline: full merge + sort
![Page 52: Informationssuche in sozialen Netzen Ralf Schenkel Joint work with Tom Crecelius, Mouna Kacimi, Sebastian Michel, Thomas Neumann, Josiane Parreira, Marc](https://reader037.vdocuments.us/reader037/viewer/2022110210/56649e795503460f94b79c99/html5/thumbnails/52.jpg)
February 2, 2009 Perspektivenvorlesung
Experimental Evaluation: Efficiency (=0)
α
2-8 times better than baseline
![Page 53: Informationssuche in sozialen Netzen Ralf Schenkel Joint work with Tom Crecelius, Mouna Kacimi, Sebastian Michel, Thomas Neumann, Josiane Parreira, Marc](https://reader037.vdocuments.us/reader037/viewer/2022110210/56649e795503460f94b79c99/html5/thumbnails/53.jpg)
February 2, 2009 Perspektivenvorlesung
Outline
• Search in Social Tagging Networks
• Effective Query Scoring
• Efficient Query Evaluation
• Summary & Further Challenges
![Page 54: Informationssuche in sozialen Netzen Ralf Schenkel Joint work with Tom Crecelius, Mouna Kacimi, Sebastian Michel, Thomas Neumann, Josiane Parreira, Marc](https://reader037.vdocuments.us/reader037/viewer/2022110210/56649e795503460f94b79c99/html5/thumbnails/54.jpg)
February 2, 2009 Perspektivenvorlesung
Summary• Need for social-aware social search, supporting
– global– social– spiritual
information needs• Social scoring
– integrating global, collection, and social context– including dynamic tag expansion
• ContextMerge: scalable implementation
![Page 55: Informationssuche in sozialen Netzen Ralf Schenkel Joint work with Tom Crecelius, Mouna Kacimi, Sebastian Michel, Thomas Neumann, Josiane Parreira, Marc](https://reader037.vdocuments.us/reader037/viewer/2022110210/56649e795503460f94b79c99/html5/thumbnails/55.jpg)
February 2, 2009 Perspektivenvorlesung
Further Challenges• Meaningful & common benchmark• Incremental maintenance for high dynamics• Extend to ratings, user weights, item weights, …• Extend to non-tags (like image features)• Automatic query parameterization• Meaningful explanations of results• Exploit dynamics (hot topics, evolving groups,….)
Social-Aware Search & Recommendationsat planet scale
![Page 56: Informationssuche in sozialen Netzen Ralf Schenkel Joint work with Tom Crecelius, Mouna Kacimi, Sebastian Michel, Thomas Neumann, Josiane Parreira, Marc](https://reader037.vdocuments.us/reader037/viewer/2022110210/56649e795503460f94b79c99/html5/thumbnails/56.jpg)
February 2, 2009 Perspektivenvorlesung
Thank you.
Questions?