1. Top-N Recommendations
from Implicit Feedback
leveraging Linked Open Data
Vito Claudio Ostuni, Tommaso Di Noia, Eugenio Di Sciascio, Roberto Mirizzi
ostuni@deemail.poliba.it, t.dinoia@poliba.it, disciascio@poliba.it, mirizzi@deemail.poliba.it
Polytechnic University of Bari - Bari (ITALY)
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

2. Outline
 Introduction and motivation
 SPrank: Semantic Path-based ranking
 Data model and Problem formulation
 Path-based features
 Learning the ranking function
 Experimental Evaluation
 Contributions and Conclusion
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

3. Linked Open Data
• Initiative for publishing and connecting data on the Web using
Semantic Web technologies;
• >30 billion of RDF triples from hundreds of data sources;
• Semantic Web done right [ http://www.w3.org/2008/Talks/0617-lod-tbl/#(3) ]
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

4. Linked Open Data
• Initiative for publishing and connecting data on the Web using
Semantic Web technologies;
• >30 billion of RDF triples from hundreds of data sources;
• Semantic Web done right [ http://www.w3.org/2008/Talks/0617-lod-tbl/#(3) ]
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

5. Hong Kong in DBpedia
db:Hong_Kong
db:thumbnail
subject
predicate
object
8134 triples
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

6. Hong Kong in DBpedia
db:Hong_Kong
db:thumbnail
Skyscrapers over 350 meters in Hong Kong?
select * where {
?s dbpedia-owl:location <http://dbpedia.org/resource/Hong_Kong>.
?s dcterms:subject
category:Skyscrapers_over_350_meters. }
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

7. Hong Kong in DBpedia
db:Hong_Kong
db:thumbnail
db:location
db:International_Commerce_centre
db:thumbnail
db:Central_Plaza_(Hong_Kong)
dcterms:subject
db:thumbnail
db:category:Skyscrapers_over_350_meters)
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

8. Motivation
Traditional Ontological/Semantic Recommender Systems:
• make use of limited domain ontologies;
• rely on explicit feedback data;
• address the rating prediction task.
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

9. Motivation
Traditional Ontological/Semantic Recommender Systems:
• make use of limited domain ontologies;
• rely on explicit feedback data;
• address the rating prediction task.
But…
• a lot of structured semantic data on the Web;
• Implicit feedback are easier to collect;
• Top-N Recommendations is a more realistic task.
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

10. Motivation
Traditional Ontological/Semantic Recommender Systems:
• make use of limited domain ontologies;
• rely on explicit feedback data;
• address the rating prediction task.
But…
• a lot of structured semantic data on the Web;
• Implicit feedback are easier to collect;
• Top-N Recommendations is a more realistic task.
Challenge:
• compute Top-N Item Recommendations from implicit feedback
exploiting the Web of Data.
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

11. Our approach
• Usage of structured semantic data freely available on the Web
(Linked Open Data) to describe items
DBpedia ontology
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

12. Our approach
• Analysis of complex relations between the user preferences and
the target item (extraction of path-based features)
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

13. Our approach
• Analysis of complex relations between the user preferences and
the target item (extraction of path-based features)
• Formalization of the Top-N Item recommendation problem from
implicit feedback in a Learning To Rank setting
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

14. Data model
Implicit Feedback Matrix
^
S
I1
i2
i3
i4
u1
1
1
0
0
u2
1
0
1
0
u3
0
1
1
0
u4
0
1
0
Knowledge Graph
1
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

15. Data model
Implicit Feedback Matrix
^
S
I1
i2
i3
i4
u1
1
1
0
0
u2
1
0
1
0
u3
0
1
1
0
u4
0
1
0
Knowledge Graph
1
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

16. Data model
Implicit Feedback Matrix
^
S
I1
i2
i3
i4
u1
1
1
0
0
u2
1
0
1
0
u3
0
1
1
0
u4
0
1
0
Knowledge Graph
1
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

17. Problem formulation

u
^

u
^
I  {i  I | s ui  1}
Set of relevant items for u
I  {i  I | s ui  0}
Set of irrelevant items for u


Iu *  Iu
Sample of irrelevant items for u
xui 
Feature vector
D

^



 xui , s ui  i  ( I u  I u * )
TR 
u
Training Set
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

18. Path-based features
path acyclic sequence of relations ( s , .. rl , .. rL )
u3 s i2 p2 e1 p1 i1
xui ( j ) 
 (s, p2 , p1)
# pathui ( j )
D
 # path
d 1
ui
(d )
Frequency of pathj in the sub-graph
related to u ad i
• The more the paths, the more the item is relevant.
• Different paths have different meaning.
• Not all types of paths are relevant.
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

19. Path-based features
i1
xu3i1 ?
e1
u1
e3
u2
i2
e2
u3
i3
u4
i4
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
e5
e4

20. Path-based features
path1 (s, s, s) : 1
i1
e1
u1
e3
u2
i2
e2
u3
i3
u4
i4
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
e5
e4

21. Path-based features
path1 (s, s, s) : 2
i1
e1
u1
e3
u2
i2
e2
u3
i3
u4
i4
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
e5
e4

22. Path-based features
path1 (s, s, s) : 2
path2 (s, p2, p1) : 1
i1
e1
u1
e3
u2
i2
e2
u3
i3
u4
i4
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
e5
e4

23. Path-based features
path1 (s, s, s) : 2
path2 (s, p2, p1) : 2
i1
e1
u1
e3
u2
i2
e2
u3
i3
u4
i4
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
e5
e4

24. Path-based features
path1 (s, s, s) : 2
path2 (s, p2, p1) : 2
path3 (s, p2, p3, p1) : 1
i1
e1
u1
e3
u2
i2
e2
u3
i3
u4
i4
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
e5
e4

25. Path-based features
path1 (s, s, s) : 2
path2 (s, p2, p1) : 2
path3 (s, p2, p3, p1) : 1
2
xu3i1 (1) 
5
2
xu3i1 (2) 
5
1
xu3i1 (3) 
5
i1
e1
u1
e3
u2
i2
e2
u3
i3
u4
i4
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
e5
e4

26. Learning the ranking function
Point-wise Learning To Rank
Learn a prediction function f :
D

^
s.t. f ( xui )  sui
Assumption: if f is accurate, then the ranking induced by f should
be close to the desired ranking
• Simplest LTR technique
• Very effective in practice (Yahoo! Learning to Rank Challenge best
solution was extremely randomized trees in a standard regression setting)
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

27. BagBoo
BagBoo: a scalable hybrid bagging-the-boosting model
[D. Pavlov, A. Gorodilov, C. Brunk CIKM2010]
• Combination of Random Forest (Bagging) and Gradient Boosted
Regression Trees (Boosting)
• Combines the high accuracy of gradient boosting with the resistance
to overfitting of random forests
For b=1 to B:
Tb  TR
fb  learn GBRT from Tb
1 B
f   fb
B b 1
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

28. Evaluation Methodology
• Top-N Item recommendation task
• Evaluation methodology similar to:
[Cremonesi, Koren and Turrin, RecSys 2010]
• Evaluation with different user profile size:
given 5
given 10
User
profile
5
User profile
Test Set
10
……
given All
User profile
Test Set
10
Test Set
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

29. Datasets
Subset of Movielens mapped to DBpedia
3,792 users
2,795 movies
104,351 entities
Subset of Last.fm mapped to DBpedia
852 users
6,256 artists
150,925 entities
Mappings
http://sisinflab.poliba.it/mappingdatasets2dbpedia.zip
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

30. Evaluation of different ranking functions
Movielens
0,6
0,5
recall@5
0,4
BagBoo
0,3
GBRT
Sum
0,2
0,1
0
given 5
given 10
given 20
given 30
given 50
given All
user profile size
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

31. Evaluation of different ranking functions
Last.fm
0,6
0,5
recall@5
0,4
BagBoo
0,3
GBRT
Sum
0,2
0,1
0
given 5
given 10
given 20
given All
user profile size
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

32. Comparative approaches
MyMediaLite
• BPRMF, Bayesian Personalized Ranking for Matrix Factorization
• BPRLin, Linear Model optimized for BPR (Hybrid alg.)
• SLIM, Sparse Linear Methods for Top-N Recommender Systems
• SMRMF, Soft Margin Ranking Matrix Factorization
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

33. Comparison with other approaches
Movielens
0,6
0,5
recall@5
0,4
SPrank
BPRMF
0,3
SLIM
BPRLin
0,2
SMRMF
0,1
0
given 5
given 10
given 20
given 30
given 50
user profile size
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China
given All

34. Comparison with other approaches
Last.fm
0,6
0,5
recall@5
0,4
SPrank
BPRMF
0,3
SLIM
BPRLin
0,2
SMRMF
0,1
0
given 5
given 10
given 20
given All
user profile size
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

35. Contributions
SPrank: Semantic Path-based ranking
 Combination of semantic item descriptions from the Web
of Data and implicit feedback
 Mining of the semantic graph using path-based features
 Learning To Rank setting
Future Work:
 Deeper analysis of the path-based features
 Usage of other Learning To Rank approaches
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China

36. Q&A
A Little Semantics Goes a Long Way.
Hendler Hypothesis
RecSys 2013 – 7th ACM Conference on Recommender Systems
October 12-16, 2013 Hong Kong, China