This document discusses recommender systems for social tagging systems. It begins with an introduction to tagging and folksonomies. It then discusses problems with tag sparsity and idiosyncrasy. The document outlines different tag recommender systems including nearest neighbor approaches and a graph-based method. It also discusses cross-tagging resources between systems and tag enrichment. Evaluation results show the graph-based weighted average method performs comparably or better than state-of-the-art methods while being more computationally efficient. The document concludes by discussing future work such as cross-tagging and tag enrichment.

1. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Recommender Systems for Social Tagging Systems
Leandro Balby Marinho
Machine Learning Lab
University of Hildesheim
PhD Defense
Leandro Balby Marinho 1 / 32 Machine Learning Lab, University of Hildesheim

2. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Outline
1. Motivation
2. Problems and Contributions
3. Tag Recommender Systems
4. Nearest Neighbor-based Tag Recommendation
5. Cross-Tagging
6. Tag Enrichment
7. Conclusions and Future Work
Leandro Balby Marinho 2 / 32 Machine Learning Lab, University of Hildesheim

3. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Web 2.0 sites more used than e-mail! [Nielsen Online (2009)]
In Web 2.0, the user plays the main role!
Leandro Balby Marinho 2 / 32 Machine Learning Lab, University of Hildesheim

4. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Tags help users to organize and retrieve content.
Leandro Balby Marinho 3 / 32 Machine Learning Lab, University of Hildesheim

5. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Tags also help other users to organize and retrieve their content.
Leandro Balby Marinho 4 / 32 Machine Learning Lab, University of Hildesheim

6. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Folksonomy
A folksonomy is a structure F := (U, R, T, Y )
U ... users
R ... resources
T ... tags
Y ⊆ U × R × T ... tag assignments
X := {(u, r) | ∃t ∈ T : (u, r, t) ∈ Y } ... set of posts
Leandro Balby Marinho 5 / 32 Machine Learning Lab, University of Hildesheim

7. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Outline
1. Motivation
2. Problems and Contributions
3. Tag Recommender Systems
4. Nearest Neighbor-based Tag Recommendation
5. Cross-Tagging
6. Tag Enrichment
7. Conclusions and Future Work
Leandro Balby Marinho 6 / 32 Machine Learning Lab, University of Hildesheim

8. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Problems and Contributions
Tag Sparsity: Users are lazy to tag!
1 − |Y |
|U|×|R|×|T| ≈ 0.99 in all datasets used!
Solution: Tag Recommendation
Leandro Balby Marinho 6 / 32 Machine Learning Lab, University of Hildesheim

9. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Problems and Contributions
Tag Sparsity: Users are lazy to tag!
1 − |Y |
|U|×|R|×|T| ≈ 0.99 in all datasets used!
Solution: Tag Recommendation
Social Network Divide: Compatible social systems are disconnected.
Leandro Balby Marinho 6 / 32 Machine Learning Lab, University of Hildesheim

10. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Problems and Contributions
Tag Sparsity: Users are lazy to tag!
1 − |Y |
|U|×|R|×|T| ≈ 0.99 in all datasets used!
Solution: Tag Recommendation
Social Network Divide: Compatible social systems are disconnected.
Tag Idiosyncrasy: Tags bearing unclear semantics.
Solution: Tag Enrichment.
Leandro Balby Marinho 6 / 32 Machine Learning Lab, University of Hildesheim

11. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Outline
1. Motivation
2. Problems and Contributions
3. Tag Recommender Systems
4. Nearest Neighbor-based Tag Recommendation
5. Cross-Tagging
6. Tag Enrichment
7. Conclusions and Future Work
Leandro Balby Marinho 7 / 32 Machine Learning Lab, University of Hildesheim

12. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Tag Recommender Systems
...change the process from creation to recognition!
Personalized methods take the user preferences for tags into
consideration.
Value for the industry, e.g., youtube, flickr, last.fm, amazon.
Leandro Balby Marinho 7 / 32 Machine Learning Lab, University of Hildesheim

13. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Evaluation and Metric
Xtrain ˙∪Xtest = X ... train/test splits based on posts
For each user, randomly pick one post for test.
Task: For (u, r) ∈ Xtest compute ˆT(u, r)
Metric: Recall((u, r) ∈ Xtest, n) := | ˆT(u,r)∩T(u,r)|
|T(u,r)|
Leandro Balby Marinho 8 / 32 Machine Learning Lab, University of Hildesheim

14. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Formalization
Given (u, r) ∈ Xtest, a tag recommender system first computes:
Utility : {u} × {r} × T → R (1)
And then presents the tags in descending order of their utility:
ˆT(u, r) :=
n
argmax
t∈T
Utility(u, r, t) (2)
Leandro Balby Marinho 9 / 32 Machine Learning Lab, University of Hildesheim

15. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Outline
1. Motivation
2. Problems and Contributions
3. Tag Recommender Systems
4. Nearest Neighbor-based Tag Recommendation
5. Cross-Tagging
6. Tag Enrichment
7. Conclusions and Future Work
Leandro Balby Marinho 10 / 32 Machine Learning Lab, University of Hildesheim

16. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Nearest Neighbor-based (NN) Tag Recommenders
Collaborative Filtering (CF): Similar users tend to like similar things.
Here: Similar users tend to tag alike.
Traditional CF cannot be directly applied to folksonomies unless:
resources
tagsresources
users
users
userstags
Y
πUT YπURY
Leandro Balby Marinho 10 / 32 Machine Learning Lab, University of Hildesheim

17. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Collaborative Filtering for Tag Recommendation
Neighborhood Formation: Nk
u :=
k
argmax
v∈Ur {u}
sim(mu, mv )
Recommendation:
ˆT(u, r) :=
n
argmax
t∈T
v∈Nk
u
sim(mu, mv )δ(v, r, t)
where δ(v, r, t) := 1 if (v, r, t) ∈ Y and 0 else.
Leandro Balby Marinho 11 / 32 Machine Learning Lab, University of Hildesheim

18. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Ensembles of CF
Projections’ Ensemble:
Similarities’ Ensemble:
ˆT(u, r) =
n
argmax
t∈T
v∈Nu
(λsim(mu, mv ) + (1 − λ)sim(zu, zv ))δ(v, r, t)
where mu and mv are rows of πUT Y , and zu and zv rows of πUR Y .
Leandro Balby Marinho 12 / 32 Machine Learning Lab, University of Hildesheim

19. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
A Graph-Based Tag Recommender based on Posts
We represent X as a homogeneous, undirected graph G := (X, E) over
the post set. Posts are related to each other if they share the same user:
Ruser := {(x, x ) ∈ X × X | user(x) = user(x )}
the same resource:
Rres := {(x, x ) ∈ X × X|res(x) = res(x )}
or either share the same user or resource:
Rres
user := Ruser ∪ Rres
where user(x) and res(x) are the user and resource associated with the
post x respectively.
Leandro Balby Marinho 13 / 32 Machine Learning Lab, University of Hildesheim

20. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Relational Graph based on Posts
Leandro Balby Marinho 14 / 32 Machine Learning Lab, University of Hildesheim

21. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Weighting Schemes
For x ∈ Xtest and (x, x ) ∈ E:
1. User-Tag Profile:
φuser-tag
:= (|Y ∩ ({user(x)} × R × {t})|)t∈T
2. Resource-Tag Profile:
φres-tag
:= (|Y ∩ (U × {res(x)} × {t})|)t∈T
Weight:
w(x, x ) :=
φ(x), φ(x )
φ(x) φ(x )
Leandro Balby Marinho 15 / 32 Machine Learning Lab, University of Hildesheim

22. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Relational Classification
Weighted Average (WA) [Marinho et al. (2009)]:
P(t|x) :=
x ∈Nx |t∈T(x ) w(x, x )
x ∈Nx
w(x, x )
where:
Nx := {x ∈ X | (x, x ) ∈ R, T(x) = ∅}
Runtime: O (|T||Nx |))
Leandro Balby Marinho 16 / 32 Machine Learning Lab, University of Hildesheim

23. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Evaluation
Datasets:
dataset |U| |R| |T| Triples |Y | Posts |X|
BibSonomy 116 361 412 10,148 2,522
Last.fm 2,917 1,853 2,045 219,702 75,565
Delicious 37,399 74,874 22,170 7,487,319 3,055,436
Evaluated methods:
Baselines: (Locally) Constant Models (GCT,LCR, LCU).
Ensemble of Locally Constant Models (LCE) [J¨aschke et al. 2008].
TopicRank, FolkRank [J¨aschke et al. 2007]
RTF [Rendle et al. 2009]
PITF [Rendle et al. 2010]
Our NN-based Recommenders
Leandro Balby Marinho 17 / 32 Machine Learning Lab, University of Hildesheim

24. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Results: NN Methods
0
0.2
0.4
0.6
0.8
1
0 2 4 6 8 10
Recall
Number of recommended tags
Top-10 Tag Recommendations in Delicious
WA
CF UT
CF UR
matrixExt
simEns
LCR
GCT
Leandro Balby Marinho 18 / 32 Machine Learning Lab, University of Hildesheim

25. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Results: WA vs. State-of-the-Art
0
0.2
0.4
0.6
0.8
1
0 2 4 6 8 10
Recall
Number of recommended tags
Top-10 Tag Recommendations in BibSonomy
WA
RTF
PITF
FolkRank
LCE
TopicRank
Leandro Balby Marinho 19 / 32 Machine Learning Lab, University of Hildesheim

26. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Results: WA vs. State-of-the-Art
0
0.2
0.4
0.6
0.8
1
0 2 4 6 8 10
Recall
Number of recommended tags
Top-10 Tag Recommendations in Last.fm
PITF
WA
RTF
FolkRank
LCE
TopicRank
Leandro Balby Marinho 20 / 32 Machine Learning Lab, University of Hildesheim

27. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Results: WA vs. State-of-the-Art
0
0.2
0.4
0.6
0.8
1
0 2 4 6 8 10
Recall
Number of recommended tags
Top-10 Tag Recommendations in Delicious
PITF
WA
FolkRank
LCE
TopicRank
Leandro Balby Marinho 21 / 32 Machine Learning Lab, University of Hildesheim

28. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Runtime: WA vs. PITF
BibSonomy Last.fm Delicious
Method Runtime Runtime Runtime
WA < 1 second < 1 minute ≈ 3 minutes
PITF ≈ 5 minutes ≈ 7 hours ≈ 33 days
Leandro Balby Marinho 22 / 32 Machine Learning Lab, University of Hildesheim

29. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
ECML/Discovery Challenge 2009
2nd Place ECML/PKDD Discovery Challenge 2009!
Rank Method Top-5 F1
1 PITF [Rendle et al. (2009)] 0.35594
2 Relational Ensemble [Marinho et al. (2009)]1
0.33185
– WA (not submitted) 0.32519
3 Content-based [Lipczak et al. (2009)] 0.32461
1
With Christine Preisach
Leandro Balby Marinho 23 / 32 Machine Learning Lab, University of Hildesheim

30. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Outline
1. Motivation
2. Problems and Contributions
3. Tag Recommender Systems
4. Nearest Neighbor-based Tag Recommendation
5. Cross-Tagging
6. Tag Enrichment
7. Conclusions and Future Work
Leandro Balby Marinho 24 / 32 Machine Learning Lab, University of Hildesheim

31. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Problem
Use resources overlap to cross tags between systems.
Leandro Balby Marinho 24 / 32 Machine Learning Lab, University of Hildesheim

32. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Tag Recommendation for Cross-Tagging
Cross-Tagging Approaches:
LCR (locally constant per resource).
Collaborative Filtering.
Leandro Balby Marinho 25 / 32 Machine Learning Lab, University of Hildesheim

33. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Evaluation
Tag-Aware-based Evaluation
The better the tags the better a tag-aware recommender that uses
those tags.
Tag-Aware based on HOSVD [Symeonidis et al. (2008)]
Datasets
Blogger.com Last.fm Annotated Blog
|U| 6,620 44,143 3,827
|R| 17,372 17,372 1,323
|T| 0 4,903 422
|Y | 0 254,388 32,900
Leandro Balby Marinho 26 / 32 Machine Learning Lab, University of Hildesheim

34. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Recall on the top-5 resources of HOSVD
n - Number of tags used to annotate the test posts of Blogger.com.
Leandro Balby Marinho 27 / 32 Machine Learning Lab, University of Hildesheim

35. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Outline
1. Motivation
2. Problems and Contributions
3. Tag Recommender Systems
4. Nearest Neighbor-based Tag Recommendation
5. Cross-Tagging
6. Tag Enrichment
7. Conclusions and Future Work
Leandro Balby Marinho 28 / 32 Machine Learning Lab, University of Hildesheim

36. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Problems
First we map tags from a folksonomy to concepts C of an ontology
H : T → C
Then we learn an ontology P such that:
CP := T ˙∪ C
The better the ontology the better a ontology-aware recommender
that uses this ontology.
Taxonomy driven CF [Ziegler et al. (2004)]
Datasets:
dataset |U| |T| |R| |Y |
Last.fm 3,532 7,081 982 130,899
musicmoz - 555 982 -
Leandro Balby Marinho 28 / 32 Machine Learning Lab, University of Hildesheim

37. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Results
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
Trivial Ontology Domain Expert Ontology Learned Ontology
Recall
Leandro Balby Marinho 29 / 32 Machine Learning Lab, University of Hildesheim

38. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Outline
1. Motivation
2. Problems and Contributions
3. Tag Recommender Systems
4. Nearest Neighbor-based Tag Recommendation
5. Cross-Tagging
6. Tag Enrichment
7. Conclusions and Future Work
Leandro Balby Marinho 30 / 32 Machine Learning Lab, University of Hildesheim

39. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Conclusions
Tag Sparsity: Nearest Neighbor Method that
Performs competitively to more sophisticated methods.
Require modest computational effort.
Social Network Divide:
Cross-tagging as a tag recommendation problem.
Personalized cross-tagging better than non-personalized
cross-tagging.
Tag idiosyncrasy: Tag enrichment
Well agreed concepts that match the semantic intention of
users.
Learned ontology better than trivial or domain expert ontology.
New recommender systems-based evaluation protocols.
Leandro Balby Marinho 30 / 32 Machine Learning Lab, University of Hildesheim

40. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Future Work
Optimzed weight learning for WA.
Bidirectional Cross-Tagging.
Optimized Cross-Tagging/Ontology learning.
Leandro Balby Marinho 31 / 32 Machine Learning Lab, University of Hildesheim

41. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Results NN vs. Baselines
0
0.2
0.4
0.6
0.8
1
0 2 4 6 8 10
Recall
Number of recommended tags
Top-10 Tag Recommendations in BibSonomy
WA
CF UT
CF UR
matrixExt
simEns
LCR
GCT
0
0.2
0.4
0.6
0.8
1
0 2 4 6 8 10Recall
Number of recommended tags
Top-10 Tag Recommendations in Last.fm
WA
CF UT
CF UR
matrixExt
simEns
LCR
GCT
Leandro Balby Marinho 32 / 32 Machine Learning Lab, University of Hildesheim

42. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
PageRank for Folksonomias
Based on PageRank [Hotho et al. 2006]
Each hyperedge is broken into three undirected edges:
Now PageRank can be applied:
wt+1 ← λAT
wt + (1 − λ)p
Rank will be dominated by popular nodes (Skewd distribution of tag
assignments)
Leandro Balby Marinho 32 / 32 Machine Learning Lab, University of Hildesheim

43. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
FolkRank
1. First compute vector w(0)
with p = 1.
2. Next compute vector w(1)
with p[u] := 1 + |U|, p[r] := 1 + |R|, and
p[v] := 1 for v = u, r.
3. Finally compute w := w(1)
− w(0)
.
4. Recommendation list ˆT(u, r) is the top-n nodes in the rank
restricted to tags.
Leandro Balby Marinho 32 / 32 Machine Learning Lab, University of Hildesheim

44. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
RTF: Ranking with Tensor Factorization
Tag Recommendation as a tensor completion problem.
Positive tags have higher rank than negative ones [Rendle et al. 2009].
yu,r,t1 > yu,r,t2 ⇔ (u, r, t1) ∈ T+
u,r ∧ (u, r, t2) ∈ T−
u,r
T+
u,r := {t | (u, r) ∈ Xtreino ∧ (u, r, t) ∈ Y }, T−
u,r := {t | (u, r) ∈ Xtreino ∧ (u, r, t)
Leandro Balby Marinho 32 / 32 Machine Learning Lab, University of Hildesheim

45. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Tucker Decomposition Model
ˆY := ˆC ×u
ˆU ×r
ˆR ×t
ˆT
or equivalently:
ˆyu,r,t =
˜u ˜r ˜t
ˆc˜u,˜r,˜t · ˆuu,˜u · ˆrr,˜r · ˆtt,˜t
where the model parameters are:
ˆC ∈ RkU ×kR ×kT
, ˆU ∈ R|U|×kU
, ˆR ∈ R|R|×kR
, ˆT ∈ R|T|×kT
Leandro Balby Marinho 32 / 32 Machine Learning Lab, University of Hildesheim

46. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
PITF: Pairwise Interaction Tensor Factorization
PITF only models the two-way interactions between user and tags as well
as between resources and tags:
ˆau,r,t =
k
f
ˆuu,f · ˆtU
t,f +
k
f
ˆrr,f · ˆtR
t,f
where ˆU ∈ R|U|×k
, ˆR ∈ R|R|×k
, ˆTU
∈ R|T|×k
and ˆTR
∈ R|T|×k
Leandro Balby Marinho 32 / 32 Machine Learning Lab, University of Hildesheim

47. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Complexity
Learning Runtime Complexity
Method Runtime
WA O(1)
FolkRank O(1)
RTF O iter · |Xtrain||T|2
· kU · kR · kT
PITF O(iter · |Xtrain||T|2
· 2k)
Prediction Runtime Complexity
Method Runtime
WA O (|T||Nx | + |T| log(n)))
Folkrank O(iter · (|Y | + |U| + |R| + |T|) + |T| + |T| log(n))
RTF O(|T| · kU + kR · kT · kT )
PITF O(|T|2k + |T| log(n))
Leandro Balby Marinho 32 / 32 Machine Learning Lab, University of Hildesheim

48. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Relation Rewarding
We can reward the best relation by a factor c ∈ R
Leandro Balby Marinho 32 / 32 Machine Learning Lab, University of Hildesheim

49. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Results Cross-Tagging
Leandro Balby Marinho 32 / 32 Machine Learning Lab, University of Hildesheim

50. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Tag Enrichment Approach
Semantic mapping as an ontology matching problem.
P(A, B) ≈ | A ∩ B |
|R| [Doan et al. (2004)]
Jaccard coefficient:
JS(A, B) := P(A ∩ B)/P(A ∪ B) :=
P(A, B)
P(A, B) + P(A, ¯B) + P(¯A, B)
Leandro Balby Marinho 32 / 32 Machine Learning Lab, University of Hildesheim

51. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Ontology learning
Frequent itemset mining for ontology learning [Marinho et al. 2008]2
.
2
Algorithm proposed by Krisztian Buza co-author of [Marinho et al. 2008]
Leandro Balby Marinho 32 / 32 Machine Learning Lab, University of Hildesheim

52. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
Semantic mapping
tags mapped concepts
electro electronica
hip hop hip hop
chillout rock
old skool dance house
anything else but death heavy metal
post-hardcore emo
california punk
political punk
urban hip hop
60s stuff country-rock
relaxing folk rock
explorer experimental rock
rock en espanol latin pop
Leandro Balby Marinho 32 / 32 Machine Learning Lab, University of Hildesheim

53. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
An Extract of Domain Expert Ontology
heavy_metal
death_metal
doom_metal
black_metal
thrash rap-metal
hair_metal
speed_metal
grindcore
metal
Pajek
Leandro Balby Marinho 32 / 32 Machine Learning Lab, University of Hildesheim

54. Motivation Problems Tag Recommender Systems NN Tag Recommendation Cross-Tagging Tag Enrichment Conclusions
An Extract of Learned Ontology
maynard james keenan
powerful
technical death metal
brit-rock
metalcore
nu metal
doom metal
new age
finnishprogressive metal
alternative metal
melodic death metal
melancholic
black metal
progressive
ethereal
swedish metal
gothenburg metal
progressive death metal
german
bands i have seen live
speed metal
nwobhm
heavy
power metal
symphonic metal
guitargasm
death-doom metal
gothic metal
famous frontman
art rock
viking metal
groove metal
melodic metal
violent
aggressive alternative - at work music
moody
faves
a-o-t-w
slipknot
grindcore
great lyrics
gothenburg
dark
g00ds
70s progressive rock
depressing
cold
doom
art-rock
prog
trash metal
depression
brutal death metal
us
loud
sad
korn
soad
mezmerize
fall out boy
rap-metal
seen them live
nu-metal
cello metal
melodic black metal
folk metal
guitar music
symphonic prog
british metal
awesome
zeuhl
female fronted metal
love metal
aggressive
finland
epic
nellis1
symphonic black metal
new metal
ominous
buen metal
bands ive seen live
classic thrash
bands i have seen
prog metal
classic metal
prog rock metal gods
my band inspiration
metal of some persuasionfavorite shitnice music
grooving metal
fav artistsblizzards main tags
symphonic death
grind
melodic power metal
everything
speed
favs
melodic death
heavy_metal
death
progressive_rock
doom_metal
death_metal
thrash
metal
speed_metal
periods
Pajek
Leandro Balby Marinho 32 / 32 Machine Learning Lab, University of Hildesheim