Semantics-aware Graph-based Recommender Systems exploiting Linked Open Data

Semantics-aware Graph-based
Recommender Systems exploiting
Linked Open Data
Cataldo Musto, Pasquale Lops, Pierpaolo Basile,
Marco de Gemmis Giovanni Semeraro
(Università degli Studi di Bari ‘Aldo Moro’, Italy - SWAP Research Group)
UMAP 2016
24th Conference on User Modeling,
Adaptation and Personalization
Halifax (Canada)
July 15, 2016

2
Linked Open Data
Cataldo Musto, Pasquale Lops, Pierpaolo Basile, Marco de Gemmis, Giovanni Semeraro
Semantics-aware Graph-based Recommender Systems Exploiting Linked Open Data. UMAP 2016, Halifax (Canada). 15.07.2016

3
Linked Open Data
Methodology to publish, share and link
structured data on the Web
Deﬁnition

4
Linked Open Data
Cornerstones
1.
2.
Use of RDF to publish data on the Web
Re-Use of existing properties to express
an agreed semantics and connect data sources

5
Linked Open Data (cloud)
What is it?
A (large) set of interconnected semantic datasets

6
What kind of datasets?
Each bubble is a dataset!

7
How many data?
9960 datasets and 149 billions triplessource: http://stats.lod2.eu
today!

8
DBpedia is the structured RDF mapping of Wikipedia
http://dbpedia.org
It is the core of the LOD cloud.
DBpedia

9
Example: unstructured content from Wikipedia
example (Wikipedia page)

10
How are these data represented?
The Matrix
Don Davis
http://dbpedia.org/resource/Category:Films_shot_in_Australia
Films shot in
Australia
dcterms:subject
dbpedia-owl:m
usicCom
poser
http://dbpedia.org/resource/Don_Davis_(composer)

dcterms:subject
dcterm
s:subject
dbo:runtimedbpedia-owl:director
dcterm
s:subject
dcterm
s:subject
Dystopian Films136
American Action
Thriller Films
Cyberpunk Films The Wachowskis
http://dbpedia.org/resource/The_Wachowskis
http://dbpedia.org/resource/Dystopian_FIlms
http://dbpedia.org/resource/Cyberpunk_Films
http://dbpedia.org/resource/American_Action_Thriller_FIlms
http://dbpedia.org/resource/Films_About_Rebellions
Films about
Rebellions
Several interesting (non-trivial) features come into play!

11
How are these data represented?
The Matrix
Don Davis
http://dbpedia.org/resource/Category:Films_shot_in_Australia
Films shot in
Australia
dcterms:subject
dbpedia-owl:m
usicCom
poser
http://dbpedia.org/resource/Don_Davis_(composer)

dcterms:subject
dcterm
s:subject
dbo:runtimedbpedia-owl:director
dcterm
s:subject
dcterm
s:subject
Dystopian Films136
American Action
Thriller Films
Cyberpunk Films The Wachowskis
http://dbpedia.org/resource/The_Wachowskis
http://dbpedia.org/resource/Dystopian_FIlms
http://dbpedia.org/resource/Cyberpunk_Films
http://dbpedia.org/resource/American_Action_Thriller_FIlms
Films about
Rebellions
Several interesting (non-trivial) features come into play!

12
Research Questions
(1) Can we use Linked Open Data for
Recommender Systems?

13
Research Questions
(2) Is it possible to automatically select the most promising
properties among those available in the LOD cloud?

14
i4
u1
u2
u3
u4
Methodology
Graph-based Data Model - original representation
Original
Graph-based
data model
Users and Items are
connected according
to users’ preferences

15
i4
u1
u2
u3
u4
Methodology
Graph-based Data Model - DBpedia Mapping
If we are able to
map the items in
the dataset with
the
entities in the
LOD cloud, our
representation can
be extended with
new data points

16
i4
u1
u2
u3
u4
dcterms:subject
Films about
Rebellions
dbprop:director
Quentin Tarantino
dbprop:director
Methodology
Graph-based Data Model - LOD-boosted representation
1999 ﬁlms
http://dbpedia.org/resource/1999_ﬁlms
dcterms:subject
dcterms:subject

17
i4
u1
u2
u3
u4
dcterms:subject
Films about
Rebellions
dbprop:director
Quentin Tarantino
dbprop:director
Methodology
example - LOD-boosted representation
1999 ﬁlms
dcterms:subject
dcterms:subject
Many new information
can be injected in the
graph

18
i4
u1
u2
u3
u4
dcterms:subject
Films about
Rebellions
dbprop:director
Quentin Tarantino
dbprop:director
Methodology
1999 ﬁlms
dcterms:subject
dcterms:subject
How to get
recommendations?

19
Graph-based Recommendation Algorithm
Insight:
- Calculate PageRank score for each item node.
- Sort PageRank scores in a descending order.
- Select top-k recommendations
PageRank with Priors
. T. H. Haveliwala. Topic-Sensitive PageRank: A
Context-Sensitive Ranking Algorithm for Web
Search. IEEE Trans. Knowl. Data Eng., 15(4):
784–796, 2003.  
Reference

20
i4
u1
u2
u3
u4
dcterms:subject
Films about
Rebellions
dbprop:director
Quentin Tarantino
dbprop:director
Methodology
1999 ﬁlms
dcterms:subject
dcterms:subject
Is it possibile to
automatically select
the most promising
properties?

21
i4
u1
u2
u3
u4
dcterms:subject
Films about
Rebellions
dbprop:director
Quentin Tarantino
dbprop:director
Methodology
1999 ﬁlms
dcterms:subject
dcterms:subject
X
X

22
i4
u1
u2
u3
u4
dcterms:subject
Films about
Rebellions
dbprop:director
Quentin Tarantino
dbprop:director
Methodology
1999 ﬁlms
dcterms:subject
dcterms:subject
X
X
X

23
Experiments

24
Research Questions
Do graph-based
recommender systems
beneﬁt of the introduction
of LOD-based features?
Do graph-based
recommender systems
exploiting LOD beneﬁt of
the adoption of feature
selection techniques?
1/2
1.
2.

25
Research Questions
3.
4.
2/2
Is there any correlation
between the choice of the
FS technique and the
behavior of the algorithm?
(e.g., better diversity or
better F1) ?
How does our
methodology perform
with respect to state-of-
the-art algorithms?

26
Experimental Evaluation
Description of the dataset
MovieLens 100k
983 users
1,682 movies
100,000 ratings
55.17% positive ratings
84.43 ratings/user (avg.)
48.48 ratings/item (avg.)
93.7% sparsity

27
Description of the dataset
DBbook dataset
6,181 users
6,733 movies
72,372 ratings
45,85% positive ratings
11.70 ratings/user (avg.)
10.74 ratings/item (avg.)
99.8% sparsity

28
Graph Representations :: Recap
G
Basic Graph with
collaborative data points

29
GLOD Graph extended with all the properties
gathered from the LOD cloud

30
GLOD+FS
Graph encoding only the most relevant properties
selected by a feature selection technique FS

31
Experimental Protocol
Algorithm PageRank with Priors
Data Split
5-fold Cross Validation for MovieLens
Train/Test for DBbook
Graph Representation G, GLOD, GLOD+FS
Feature Selection Techniques
PageRank, Chi-Square, Information
Gain, Gain Ratio, mRMR, PCA, SVM
#Selected Features top-10, top-30, top-50 properties
Evaluation Metrics F1, Intra-List Diversity, Run Time

Experiment 1
32
Impact of LOD-based features.

F1@5
F1@10
G
G_LOD
G
G_LOD
53 55 57 59 61
60,83
54,24
60,23
53,89
Experiment 1
33
Impact of LOD-based features :: F1-measure
Improvement only on MovieLens
F1@5
F1@10
G
G_LOD
G
G_LOD
53 56 59 62 65
64,21
55,04
64,31
55,02
MovieLens
DBbook

Run Time (min.)
G
G_LOD
50 262,5 475 687,5 900
880
72
Experiment 1
34
Tremendous increase in the run time
Impact of LOD-based features :: Run Time
Run Time (min.)
G
G_LOD
50 662,5 1275 1887,5 2500
2.433
100
MovieLens
DBbook

Experiment 2
35
Impact of Feature Selection techniques

Experiment 2
36
GLOD (baseline) = 54,24
Impact of Feature Selection :: MovieLens :: F1@5
PageRank
mRMR
Chi-Square
SVM
Gain Ratio
Inf. Gain
PCA
10
30
50
10
30
50
10
30
50
10
30
50
10
30
50
10
30
50
10
30
50
53 53,5 54 54,5 55
54,31
54,12
54,06
54,21
54,2
54,21
54,12
54,13
53,96
53,98
54,13
54,19
54,29
54,29
54,06
53,97
53,72
53,82
54,14
53,97
54,18

PageRank
mRMR
Chi-Square
SVM
Gain Ratio
Inf. Gain
PCA
10
30
50
10
30
50
10
30
50
10
30
50
10
30
50
10
30
50
10
30
50
53 53,5 54 54,5 55
54,31
54,12
54,06
54,21
54,2
54,21
54,12
54,13
53,96
53,98
54,13
54,19
54,29
54,29
54,06
53,97
53,72
53,82
54,14
53,97
54,18
Experiment 2
37
Tree out of seven techniques
(and only with 50 features) overcome the baseline

PageRank
mRMR
Chi-Square
SVM
Gain Ratio
Inf. Gain
PCA
10
30
50
10
30
50
10
30
50
10
30
50
10
30
50
10
30
50
10
30
50
53 53,5 54 54,5 55
54,31
54,12
54,06
54,21
54,2
54,21
54,12
54,13
53,96
53,98
54,13
54,19
54,29
54,29
54,06
53,97
53,72
53,82
54,14
53,97
54,18
Experiment 2
38
Typically, the larger the number of features, the better the F1
#50
#50
#50
#50
#50
#30
#30
(best)

PageRank
mRMR
Chi-Square
SVM
Gain Ratio
Inf. Gain
PCA
10
30
50
10
30
50
10
30
50
10
30
50
10
30
50
10
30
50
10
30
50
63,5 63,825 64,15 64,475 64,8
64,286
64,19
64,25
64,26
64,19
64,19
64,18
64,32
64,27
64,31
64,3
64,2
64,27
64,22
64,33
64,45
64,35
64,34
64,23
64,35
64,31
Experiment 2
39
#10
#10
#10
#10
#10
#10
Impact of Feature Selection :: DBbook :: F1@10
#10
GLOD (baseline) = 64,20

PageRank
mRMR
Chi-Square
SVM
Gain Ratio
Inf. Gain
PCA
10
30
50
10
30
50
10
30
50
10
30
50
10
30
50
10
30
50
10
30
50
63,5 63,825 64,15 64,475 64,8
64,286
64,19
64,25
64,26
64,19
64,19
64,18
64,32
64,27
64,31
64,3
64,2
64,27
64,22
64,33
64,45
64,35
64,34
64,23
64,35
64,31
Experiment 2
40
#10
#10
#10
#10
#10
#10
#10
All the techniques
overcome the baseline at least once

PageRank
mRMR
Chi-Square
SVM
Gain Ratio
Inf. Gain
PCA
10
30
50
10
30
50
10
30
50
10
30
50
10
30
50
10
30
50
10
30
50
63,5 63,825 64,15 64,475 64,8
64,286
64,19
64,25
64,26
64,19
64,19
64,18
64,32
64,27
64,31
64,3
64,2
64,27
64,22
64,33
64,45
64,35
64,34
64,23
64,35
64,31
Experiment 2
41
On DBbook best results are obtained with 10 features!
#10
#10
#10
#10
#10
#10
(best)
#10

Run Time (min.) - MovieLens
GLOD
GLOD+PCA
50 262,5 475 687,5 900
581
880
Experiment 2
42
Signiﬁcant decrease
Impact of Feature Selection techniques :: Run Time
Run Time (min.) - DBbook
GLOD
GLOD+IG
50 687,5 1325 1962,5 2600
1.341
2433

Experiment 3
43
Trade-off between F1 and diversity

Experiment 3
44
Trade-off between F1 and diversity
Can the choice of the feature selection technique
endogenously induce an higher diversity (or,
respectively, an higher F1) of the recommendations?

Experiment 3
45
Trade-off between F1 and diversity :: MovieLens :: F1@5
G_LOD = Baseline

Experiment 3
46
PCA maximizes F1, at the expense of a little diversity

Experiment 3
47
Gain Ratio and SVM sacriﬁce F1,
to induce an higher diversity

Experiment 3
48
PageRank obtains a good compromise
between F1 and Diversity

Experiment 3
49
Similar outcomes on DBbook
…but more techniques have a good impact

Experiment 4
50
Comparison to State of the art
BPRMF (Bayesian Personalized Ranking) [+]
U2U-KNN (User to User CF)
I2I-KNN (Item to Item CF)
POPULAR (Popularity-based baseline)
[+] S. Rendle, C.Freudenthaler, Z. Gantner, L. Schmidt-Thieme: BPR:
Bayesian Personalized Ranking from Implicit Feedback. UAI 2009.

Experiment 4
51
Comparison to State of the Art :: MovieLens
50
54
58
62
66
F1@5 F1@10
60,88
54,31
59,16
51,4
59,16
51,78
59,7
52,2
58,35
50,22
I2I-KNN U2U-KNN BPRMF POPULAR PR (G_LOD+PCA)

Experiment 4
52
50
54
58
62
66
F1@5 F1@10
60,88
54,31
59,16
51,4
59,16
51,78
59,7
52,2
58,35
50,22
PageRank with Priors boosted with LOD
is the best-performing approach

Experiment 4
53
50
54
58
62
66
F1@5 F1@10
60,88
54,31
59,16
51,4
59,16
51,78
59,7
52,2
58,35
50,22
Even state-of-the-art approaches based on Matrix
Factorization are overcame by our methodology

Experiment 4
54
50
55
60
65
70
F1@5 F1@10
64,45
55,4
62,72
52,96
62,63
52,9
62,29
51,93
62,1
51,11
Behavior conﬁrmed on DBbook
Comparison to State of the Art :: DBbook

Conclusions
55Cataldo Musto, Pasquale Lops, Pierpaolo Basile, Marco de Gemmis, Giovanni Semeraro

Recap
56
Methodolology
1. PageRank with Priors as base algorithm
2. Mapping of the items with nodes in the Linked
Open Data Cloud
3. Expansion of the data points and injection of new
nodes and edges
4. Use of feature selection to automatically select the
most promising properties
INVESTIGATION ABOUT THE EFFECTIVENESS OF LINKED OPEN DATA IN
GRAPH-BASED RECOMMENDER SYSTEMS

Lessons Learned
57
Evaluation
1. PageRank with Priors benefit of the injection of data points
coming from the LOD cloud
2. Feature Selection techniques improve the results but need
to be properly tuned, since its usage is not always useful
3. A significant connection between the choice of the feature
selection technique and the maximization of a specific
evaluation metric exists
4. PageRank with Priors boosted with LOD significantly
overcomes state-of-the-art approaches
INVESTIGATION ABOUT THE EFFECTIVENESS OF LINKED OPEN DATA IN
GRAPH-BASED RECOMMENDER SYSTEMS

thank you
Cataldo Musto
cataldo.musto@uniba.it
@cataldomusto
http://www.di.uniba.it/~swap

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