Tuning Personalized PageRank for Semantics-aware Recommendations based on Linked Open Data

Tuning Personalized PageRank for
Semantics-aware Recommendations
based on Linked Open Data
Cataldo Musto, Giovanni Semeraro, Marco de Gemmis, Pasquale Lops
(Università degli Studi di Bari ‘Aldo Moro’, Italy - SWAP Research Group)
ESWC 2017
14th Extended Semantic
Web Conference
Portoroz (Slovenia)
June 1, 2017

Cataldo Musto, Giovanni Semeraro, Marco de Gemmis, Pasquale Lops.
Tuning Personalized PageRank for Semantics-aware Recommendations based on Linked Open Data. ESWC 2017, Portoroz, 01.06.2017
Recommender Systems
Technology able to push relevant items (movies, news, books, etc.)
to the user according to her preferences.
2

Recommender Systems
Largely adopted in industry
3

Recommendation Paradigms
Collaborative
Filtering
Content-based
RecSys
4

Collaborative
Filtering
Exploits the preferences of
the community to generate
recommendations.
Insight: to suggest items
liked by users similar to the
target one
5

Content-based
RecSys
Exploit descriptive features
of the items (e.g. genre of a
book, director of a movie) to
generate recommendations.
Insight: to suggest items
similar to those the user
already liked
6

+
Hybrid Recommender Systems
Combine different recommendation paradigms to
provide recommendations.
Advantage: to merge the strength of each paradigm in a
unique representation
7

8
Graph-based RecSys
Focus of this work.

9
Graph-based RecSys
They can combine collaborative (user preferences) and
content-based features in a unique and powerful formalism

10
Graph-based RecSys
How to model collaborative and
content-based data features as a graph?

11
i4
Users = nodes
Items = nodes
Preferences = edges
Collaborative
data model for
Graph-based
RecSys
u1
i1
u2 i2
u3 i3
u4
i4
Graph-based RecSys

12
i4
Users = nodes
Items = nodes
Preferences = edges
What about
content-based
features?
u1
i1
u2 i2
u3 i3
u4
i4
Graph-based RecSys

13
i4
Users = nodes
Items = nodes
Preferences = edges
We need a data
source to feed
our items with
descriptive
features
u1
i1
u2 i2
u3 i3
u4
i4
Graph-based RecSys

14
Linked Open Data (cloud)
http://dbpedia.org
Our ﬁrst contribution: we introduce DBpedia
in a hybrid graph-based representation

15
Wikipedia
unstructured content
example
(Wikipedia page)

16
example (Wikipedia page)
Wikipedia
unstructured content

17
DBpedia
Structured Representation
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!

18
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!
DBpedia
Structured Representation

19
How to introduce DBpedia data points
in our graph-based representation?

20
Key concept: mapping

21
i4
u1
u2
u3
u4
Introducing Linked Open Data
graph-based data model - bipartite representation

22
i4
graph-based data model - DBpedia mapping
i4
u1
u2
u3
u4

23
i4
graph-based data model - DBpedia mapping
i4
u1
u2
u3
u4
dbr:Django_Unchained
dbr:Kill_Bill
dbr:Eyes_Wide_Shut
dbr:The_Matrix

24
i4
u1
u2
u3
u4
dcterms:subject
Films about
Rebellions
dbprop:director
Quentin Tarantino
dbprop:director
graph-based data model - LOD-boosted representation
1999 ﬁlms
http://dbpedia.org/resource/1999_ﬁlms
dcterms:subject
dcterms:subject
http://dbpedia.org/resource/Quentin_Tarantino

25Cataldo Musto, Giovanni Semeraro, Marco de Gemmis, Pasquale Lops.
Contribution 1: Tripartite Graph-based Representation
encoding user preferences and descriptive features
gathered from the LOD cloud
Graph-based RecSys

Graph-based RecSys
Research Question 1: how do the features gathered from the
LOD cloud impact on the quality of the representation?

Graph-based RecSys
Research Question 2: are all of the features equally important? Is it
possible to automatically select the most promising ones?

Graph-based RecSys
X
X
Research Question 2: are all of the features equally important? Is it
possible to automatically select the most promising ones?

How to get the recommendations?

Graph-based RecSys
Recommendations are obtained by identifying the most relevant
(item) nodes for a target user, according to the graph topology.
?

Graph-based RecSys
Recommendations are obtained by identifying the most relevant
(item) nodes for a target user, according to the graph topology.
?
How can we obtain such
information?

Graph-based RecSys
Variant of the original PageRank
?
A possible solution:
Personalized PageRank

Graph-based RecSys
?
A possible solution:
Personalized PageRank
Rationale: Relevant nodes (items) can be identiﬁed through
Random Walks. But they have to be inﬂuenced by
previous users behaviors (preferences!).

34
Graph-based RecSys
A large probability (e.g. 80%) is assigned a priori to speciﬁc items
(the items the user liked)
Weights are distributed according to a simple heuristic

35
Graph-based RecSys
A large probability (e.g. 80%) is assigned a priori to speciﬁc items
(the items the user liked)

36
Graph-based RecSys
The rest is even distributed among the remaining nodes

37
Graph-based RecSys
Recommendation pipeline
How to get the Recommendations?

38
Graph-based RecSys
- Calculate Personalized PageRank score for each item node.
- Sort PageRank scores in a descending order.
- Select top-k recommendations

39
Graph-based RecSys

40
Graph-based RecSys
Workﬂow

Graph-based RecSys
It is likely that The Matrix is suggested to u1, since it
is more (and better) connected in the graph

42
Graph-based RecSys
One step back: is this the best weighting scheme?

43
Graph-based RecSys
Is it correct to give a tiny probability to the properties
connected to the items we liked?

44
Graph-based RecSys
Contribution 2: PageRank Weighting Schemas
tuning recommendation algorithm, by giving more importance to the
properties gathered from the LOD

45
Graph-based RecSys
We can distribute the a priori probabilities
by following different heuristics

46
Graph-based RecSys
80% to the items we liked, 20% to the other nodes
(baseline)

47
Graph-based RecSys
60% to the items we liked, 20% to the properties gathered
from the LOD cloud, 20% to the other nodes
++
++
--
--

48
Graph-based RecSys
40% to the items we liked, 40% to the properties gathered
from the LOD cloud, 20% to the other nodes
+++
+++
---
---

49
Graph-based RecSys
Research Question 3: how does the recommendation algorithm
perform on varying of the weighting schemes?

50
Experiments

51
Research Questions
Do graph-based
recommender systems
beneﬁt of the introduction
of LOD-based features?
How does our methodology
perform when features
selection is used to
automatically select the
most promising features?
1/2
1.
2.

52
Research Questions
3.
4.
2/2
How does the
recommendation algorithm
perform on varying of the
weighting schemes?
How does our
methodology perform
with respect to state-of-
the-art algorithms?

53
Experimental Evaluation
Description of the dataset
MovieLens 1M
6,040 users
3,883 movies
1,000,209 ratings
57.51% positive ratings
165.59 ratings/user (avg.)
269.88 ratings/item (avg.)
96.4% sparsity

54
Description of the dataset
DBbook
6,181 users
6,733 movies
72,732 ratings
45.86% positive ratings
11.71 ratings/user (avg.)
10.74 ratings/item (avg.)
99.85% sparsity

55
DBpedia mapping
3,300 movies (85%) and 6,600 books (98%) were
mapped to DBpedia by querying a SPARQL
endpoint with the title of the item.

56
DBpedia mapping
60 LOD properties were extracted for MovieLens
70 LOD properties were extracted for DBbook

57
Graph Representations :: Recap
G
Basic Graph with
collaborative data points

58
GLOD Graph extended with all the properties
gathered from the LOD cloud

59
GLOD+FS
Graph encoding only the most relevant properties
selected by a feature selection technique FS

60
GLOD+FS
Selection of the top-10 properties through features
selection (Information Gain and PCA)
C.Musto, P. Basile, P. Lops, M. de Gemmis, G. Semeraro:
Introducing linked open data in graph-based recommender systems.
Inf. Process. Manage. 53(2): 405-435 (2017)

61Cataldo Musto, Pierpaolo Basile, Marco de Gemmis, Pasquale Lops, Giovanni Semeraro, Simone Rutigliano.
Automatic Selection of Linked Open features in Graph-based Recommender Systems. CBRecSys 2015 Workshop, Vienna, 20.09.2015
Graph Topologies - Comparison
G G_LOD G_LOD+IG G_LOD+PCA
MovieLens
Nodes 9,625 30,204 18,146 13,288
Edges 460,124 509,481 480,526 465,272
Most of the edges are due to the collaborative part of
the data model. Small number of properties added
through G_LOD

Graph Topologies - Comparison
G G_LOD G_LOD+IG G_LOD+PCA
Dbbook
Nodes 12,649 211,611 88,669 28,164
Edges 33,189 534,841 142,334 67,411
Huge number of nodes and edges injected in G_LOD.
Features selection strongly ﬁlters them.

63
Weighting Schemes
80/20 Original Weighting Scheme
60/20/20 20% for LOD properties

Experimental Protocol
Algorithm Personalized PageRank
Data Split
5-fold Cross Validation for
MovieLens, Train/Test for DBbook
Graph Topologies G, GLOD, GLOD+PCA, GLOD+IG
Weighting Schemes 80/20 - 60/20/20 - 40/40/20 - 20/60/20
Evaluation Metrics F1@5

Experiment 1
65
Impact of LOD-based features and FS techniques.

F1@5
G
G_LOD
G_LOD+PCA
G_LOD+IG
53,5 53,75 54 54,25 54,5
54,04
53,98
54,06
53,96
Experiment 1
66
Impact of LOD-based features :: F1-measure
Improvement only on both datasets
MovieLens
DBbook
F1@5
G
G_LOD
G_LOD+PCA
G_LOD+IG
54 54,5 55 55,5 56
55,28
55,08
54,94
55,07

F1@5
G
G_LOD
G_LOD+PCA
G_LOD+IG
53,5 53,75 54 54,25 54,5
54,04
53,98
54,06
53,96
Experiment 1
67
MovieLens: improvement due to the LOD
MovieLens
F1@5
G
G_LOD
G_LOD+PCA
G_LOD+IG
54 54,5 55 55,5 56
55,28
55,08
54,94
55,07
DBbook

F1@5
G
G_LOD
G_LOD+PCA
G_LOD+IG
54 54,5 55 55,5 56
55,28
55,08
54,94
55,07
F1@5
G
G_LOD
G_LOD+PCA
G_LOD+IG
53,5 53,75 54 54,25 54,5
54,04
53,98
54,06
53,96
Experiment 1
68
Expected behavior: representation unbalanced towards
collaborative data points
MovieLens
DBbook

F1@5
G
G_LOD
G_LOD+PCA
G_LOD+IG
53,5 53,75 54 54,25 54,5
54,04
53,98
54,06
53,96
Experiment 1
69
DBbook: LOD + FS lead to the best results
MovieLens
F1@5
G
G_LOD
G_LOD+PCA
G_LOD+IG
54 54,5 55 55,5 56
55,28
55,08
54,94
55,07
DBbook

F1@5
G
G_LOD
G_LOD+PCA
G_LOD+IG
53,5 53,75 54 54,25 54,5
54,04
53,98
54,06
53,96
Experiment 1
70
Reason: noisy properties gathered from the LOD cloud. FS helps.
MovieLens
F1@5
G
G_LOD
G_LOD+PCA
G_LOD+IG
54 54,5 55 55,5 56
55,28
55,08
54,94
55,07
DBbook

Take-Home Message
71
Linked Open Data and Features
Selection techniques have a good
impact on the effectiveness of the
recommendations

Experiment 2
72
Impact of different weighting schemes

Experiment 2
73
Impact of Feature Selection :: MovieLens :: F1@5
G-LOD
G-LOD-PCA
G-LOD-IG
Baseline
60-20-20
40-40-20
20-60-20
Baseline
60-20-20
40-40-20
20-60-20
Baseline
60-20-20
40-40-20
20-60-20
53,5 53,75 54 54,25
54
53,93
53,8
54,09
54,01
53,8
54,07
54,03
53,99
54,04
53,98
54,06

Experiment 2
74
G-LOD
G-LOD-PCA
G-LOD-IG
Baseline
60-20-20
40-40-20
20-60-20
Baseline
60-20-20
40-40-20
20-60-20
Baseline
60-20-20
40-40-20
20-60-20
53,5 53,75 54 54,25
54
53,93
53,8
54,09
54,01
53,8
54,07
54,03
53,99
54,04
53,98
54,06
Improvement when PCA and IG are exploited

Experiment 2
75
G-LOD
G-LOD-PCA
G-LOD-IG
Baseline
60-20-20
40-40-20
20-60-20
Baseline
60-20-20
40-40-20
20-60-20
Baseline
60-20-20
40-40-20
20-60-20
53,5 53,75 54 54,25
54
53,93
53,8
54,09
54,01
53,8
54,07
54,03
53,99
54,04
53,98
54,06
Interesting outcome: even if we have many collaborative data points, different
weighting schemes positively inﬂuence the recommendations

Experiment 2
76
Impact of Feature Selection :: DBbook :: F1@5
G-LOD
G-LOD-PCA
G-LOD-IG
Baseline
60-20-20
40-40-20
20-60-20
Baseline
60-20-20
40-40-20
20-60-20
Baseline
60-20-20
40-40-20
20-60-20
54,5 54,8 55,1 55,4
55,24
55,04
54,96
55,26
54,99
55,04
55,33
55,03
54,98
55,28
55,07
54,94

Experiment 2
77
Impact of Feature Selection :: DBbook :: F1@5
G-LOD
G-LOD-PCA
G-LOD-IG
Baseline
60-20-20
40-40-20
20-60-20
Baseline
60-20-20
40-40-20
20-60-20
Baseline
60-20-20
40-40-20
20-60-20
54,5 54,8 55,1 55,4
55,24
55,04
54,96
55,26
54,99
55,04
55,33
55,03
54,98
55,28
55,07
54,94
Improvement: IG is the best-performing conﬁguration

Take-Home Message
78
Different Weighting Schemes further
improve (even if with tiny gaps) the
effectiveness of the recommendations

Experiment 3
79
Comparison to State of the art
U2U-KNN (User to User CF)
I2I-KNN (Item to Item CF)
POPULAR (Popularity-based baseline)
BPRMF (Bayesian Personalized Ranking) [+]
BPRMF+Side information
[+] S. Rendle, C.Freudenthaler, Z. Gantner, L. Schmidt-Thieme: BPR:
Bayesian Personalized Ranking from Implicit Feedback. UAI 2009.

Experiment 3
80
Comparison to State of the Art :: MovieLens
50
51,25
52,5
53,75
55
F1@5
54,09
51,4
52,18
51,79
52,2
50,43
I2I-KNN U2U-KNN BPRMF BPRMF+Side
POPULAR PR (G_LOD+IG+40/40)
PageRank with Priors boosted with LOD
is the best-performing approach

Experiment 3
81
Comparison to State of the Art :: MovieLens
50
51,25
52,5
53,75
55
F1@5
54,09
51,4
52,18
51,79
52,2
50,43
Even approaches based on Matrix Factorization
are overcame by our methodology

Experiment 3
82
50
51,5
53
54,5
56
F1@5
55,33
52,9653,0452,9
51,93
51,11
Behavior conﬁrmed on DBbook
Comparison to State of the Art :: DBbook

Conclusions

Recap
84
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
5. Introduction of different weighting schemes, to
emphasize properties gathered from the LOD cloud
INVESTIGATION ABOUT THE EFFECTIVENESS OF LINKED OPEN DATA IN
GRAPH-BASED RECOMMENDER SYSTEMS

Lessons Learned
85
Evaluation
1. Personalized PageRank beneﬁt of the injection of data
points coming from the LOD cloud
2. Feature Selection techniques improve the results.
3. Properties gathered from the LOD are worth to be
emphasized, since they improve the effectiveness of the
algorithm
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

questions?
Cataldo Musto, PhD
cataldo.musto@uniba.it
@cataldomusto
http://www.di.uniba.it/~swap

Tuning Personalized PageRank for Semantics-aware Recommendations based on Linked Open Data

More Related Content

What's hot

Similar to Tuning Personalized PageRank for Semantics-aware Recommendations based on Linked Open Data

More from Cataldo Musto

Recently uploaded

Tuning Personalized PageRank for Semantics-aware Recommendations based on Linked Open Data