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Automatic Selection of Linked Open Data features in
Graph-based Recommender SystemsCataldo Musto, Pierpaolo Basile, Marco de Gemmis
Pasquale Lops, Giovanni Semeraro, Simone Rutigliano (Università degli Studi di Bari ‘Aldo Moro’, Italy - SWAP Research Group)
CBRecSys 2015 Workshop on New Trends in
Content-based Recommender Systems Vienna (Austria)
September 20, 2015
Outline• Basics
• Linked Open Data • Graph-based Recommendations • PageRank with Priors
• Methodology • Introducing LOD-based features • Selecting LOD-based features
• Experiments • Impact of LOD-based features • Comparison to baselines • Trade-off F1/Diversity
• Conclusions
2Cataldo 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
3
Linked Open Data
Cataldo 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
4
Linked Open Data
What are you talking about?Cataldo 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
(*) basic italian gesture
(*)
5
Linked Open Data
Methodology to publish, share and link structured data on the Web
Definition
Cataldo 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
6
Linked Open Data (cloud)What is it?
A (large) set of interconnected semantic datasetsCataldo 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
7
Linked Open Data (cloud)What kind of datasets?
Cataldo 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
Each bubble is a dataset!
8
Linked Open Data (cloud)How many data?
1048 datasets and 58 billions triplessource: http://stats.lod2.eu
(slide from CBRecSys 2014 presentation)
Cataldo 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
9
Linked Open Data (cloud)How many data?
3426 datasets and 86 billions triplessource: http://stats.lod2.eu
today!
Cataldo 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
10
Linked Open Data (cloud)
DBpedia is the structured mapping of Wikipedia
http://dbpedia.org
It is the core of the LOD cloud.
DBpedia
Cataldo 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
11
Linked Open Data (cloud)Example: unstructured content from Wikipedia
example (Wikipedia page)
Cataldo 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
12
Linked Open Data (cloud)How are these data represented?
Cataldo 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
The Matrix
Don Davis
http://dbpedia.org/resource/Category:Films_shot_in_Australia
Films shot in Australia
dcterms:subject
dbpedia-owl:musicComposer
http://dbpedia.org/resource/Don_Davis_(composer)
dcterms:subject
dcterms:subject
dcte
rms:
subj
ect
dbpe
dia-
owl:d
irect
ordcterms:subject
dcterms:subject
Dystopian Films1999 FilmsAmerican Action Thriller Films
Cyberpunk Films The Wachowskis
http://dbpedia.org/resource/The_Wachowskis
http://dbpedia.org/resource/Dystopian_FIlmshttp://dbpedia.org/resource/1999_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
13
Linked Open Data (cloud)How are these data represented?
Cataldo 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
The Matrix
Don Davis
http://dbpedia.org/resource/Category:Films_shot_in_Australia
Films shot in Australia
dcterms:subject
dbpedia-owl:musicComposer
http://dbpedia.org/resource/Don_Davis_(composer)
dcterms:subject
dcterms:subject
dcte
rms:
subj
ect
dbpe
dia-
owl:d
irect
ordcterms:subject
dcterms:subject
Dystopian Films1999 FilmsAmerican Action Thriller Films
Cyberpunk Films The Wachowskis
http://dbpedia.org/resource/The_Wachowskis
http://dbpedia.org/resource/Dystopian_FIlmshttp://dbpedia.org/resource/1999_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
14
Linked Open Data (cloud)How are these data represented?
Semantic Web cake
Information from the LOD cloud is
represented in RDF
Cataldo 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
15
Linked Open Data (cloud)How are these data represented?
Cataldo 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
The Matrix
Don Davis
http://dbpedia.org/resource/Category:Films_shot_in_Australia
Films shot in Australia
dcterms:subject
dbpedia-owl:musicComposer
http://dbpedia.org/resource/Don_Davis_(composer)
dcterms:subject
dcterms:subject
dbpe
dia-
owl:d
irect
ordcterms:subject
dcterms:subject
Dystopian FilmsAmerican 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
dbo:
runt
ime
136
16
Linked Open Data (cloud)How are these data represented?
Cataldo 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
The Matrix
Don Davis
http://dbpedia.org/resource/Category:Films_shot_in_Australia
Films shot in Australia
dcterms:subject
dbpedia-owl:musicComposer
http://dbpedia.org/resource/Don_Davis_(composer)
\
dcterms:subject
dcterms:subject
dbo:
runt
ime
dbpe
dia-
owl:d
irect
ordcterms:subject
dcterms:subject
Dystopian Films136American 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!
17Cataldo 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
Research Question
Can we use Linked Open Data for Recommender Systems?
18Cataldo 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
Contribution
We investigate the impact of the injection of exogenous knowledge coming from the LOD cloud
in graph-based recommender systems
19Cataldo 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
Graphs
Why graphs?
20Cataldo 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
Graphs
First, because LOD cloud is a graph
21Cataldo 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
Graphs
Graphs are the most straightforward representation for LOD-based data points
22Cataldo 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
Why graphs?
Second, graphs can easily model the recommendation task
23Cataldo 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
i4(Bipartite graph)
Users = nodes Items = nodes
Preferences = edges
Very intuitiverepresentation!
u1
i1
u2 i2
u3 i3u4
i4
Graph-based RecSys
24Cataldo 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
i4
u1
i1
u2 i2
u3 i3u4
i4
Graph-based RecSysRecommendations
are obtained by identifying the most relevant
(item) nodes for a target user,
according to the graph topology.
25Cataldo 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
i4
u1
i1
u2 i2
u3 i3u4
i4
Graph-based RecSysRecommendations
are obtained by identifying the most relevant
(item) nodes for a target user,
according to the graph topology.
How can we obtain such information?
26Cataldo 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-based Recommendations
PageRank calculates the ‘importance’ of a node according to the quality and the number of its connections
PageRank
27Cataldo 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
i4
u1
i1
u2 i2
u3 i3u4
i4
Graph-based RecSysIt is likely that i4 is
suggested to u3, since it is more (and better)
connected in the graph
28Cataldo 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
i4
u1
i1
u2 i2
u3 i3u4
i4
Graph-based RecSysIt is likely that i4 is
suggested to u3, since it is more (and better)
connected in the graph
Issue: Classic PageRank
is not personalized!
29Cataldo 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
i4
Graph-based RecSysIt is likely that i4 is
suggested to u3, since it is more (and better)
connected in the graph
Issue: Classic PageRank
is not personalized!
1/8
1/8
1/8
1/81/8
1/8
1/8 1/8
When PageRank is run, all the nodes
are provided with an even distributed
probability
30Cataldo 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
i4
Graph-based RecSysIt is likely that i4 is
suggested to u3, since it is more (and better)
connected in the graph1/8
1/8
1/8
1/81/8
1/8
1/8 1/8
When PageRank is run, all the nodes
are provided with an even distributed
probability
Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: bringing order to
the Web.
Reference
31Cataldo 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
i4
Graph-based RecSysIt is likely that i4 is
suggested to u3, since it is more (and better)
connected in the graph1/8
1/8
1/8
1/81/8
1/8
1/8 1/8
When PageRank is run, all the nodes
are provided with an even distributed
probability
Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: bringing order to
the Web.
ReferenceAll the users are provided with the same ranking, which it is independent from user preferences
32Cataldo 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
i4
Graph-based RecSysIt is likely that i4 is
suggested to u3, since it is more (and better)
connected in the graph1/8
1/8
1/8
1/81/8
1/8
1/8 1/8
When PageRank is run, all the nodes
are provided with an even distributed
probability
Page, L., Brin, S., Motwani, R., & Winograd, T. (1999). The PageRank citation ranking: bringing order to
the Web.
ReferenceSolution: PageRank with
Priors
33Cataldo 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-based Recommendations
Rationale: 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
Random Walks have to be influenced by previous users behaviors (preferences!). Probability cannot be even distributed.
34Cataldo 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
PageRank with PriorsA large probability
(e.g. 80%) is assigned a priori to
specific items
0.33/8
0.33/8
0.33/8
0.33/8
0.33/8
0.33/8
3/8
3/8
35Cataldo 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
PageRank with PriorsA large probability
(e.g. 80%) is assigned a priori to
specific items
0.33/8
0.33/8
0.33/8
0.33/8
0.33/8
0.33/8
3/8
3/8
e.g., the items a user liked!
36Cataldo 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
PageRank with PriorsA large probability
(e.g. 80%) is assigned a priori to
specific items
0.33/8
0.33/8
0.33/8
0.33/8
0.33/8
0.33/8
3/8
3/8
e.g., the items a user liked!
PageRank is run, and calculations
are thus influenced by such a different
distribution of a priori probabilities.
37Cataldo 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
PageRank with Priors is a very good recommendation model.
Musto, C., Basile, P., Lops, P., de Gemmis, M., & Semeraro, G. (2014).
Linked Open Data-enabled Strategies for Top-N Recommendations.
CBRecSys 2014
Reference
38Cataldo 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
PageRank with Priors is a very good recommendation model.
Musto, C., Basile, P., Lops, P., de Gemmis, M., & Semeraro, G. (2014).
Linked Open Data-enabled Strategies for Top-N Recommendations.
CBRecSys 2014
Reference
We want to exploit Linked Open Data to further improve it.
39Cataldo 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
Methodology
Graphs provide a uniform and solid representation to model collaborative (user preferences) data points
as well as LOD-based ones
40Cataldo 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
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
Methodology
41Cataldo 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
i4
u1
u2
u3
u4
Methodologyexample - original representation
42Cataldo 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
i4
u1
u2
u3
u4
dcterms:subject
http://dbpedia.org/resource/Films_About_Rebellions
Films about Rebellions
dbprop:director
Quentin Tarantino
dbprop:director
Methodologyexample - LOD-boosted representation
1999 films
http://dbpedia.org/resource/1999_films
dcterms:subject
dcterms:subject
43Cataldo 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
i4
u1
u2
u3
u4
dcterms:subject
http://dbpedia.org/resource/Films_About_Rebellions
Films about Rebellions
dbprop:director
Quentin Tarantino
dbprop:director
Methodologyexample - LOD-boosted representation
1999 films
http://dbpedia.org/resource/1999_films
dcterms:subject
dcterms:subject
Many new information can be injected in the
graph
44Cataldo 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
i4
u1
u2
u3
u4
dcterms:subject
http://dbpedia.org/resource/Films_About_Rebellions
Films about Rebellions
dbprop:director
Quentin Tarantino
dbprop:director
Methodologyexample - LOD-boosted representation
1999 films
http://dbpedia.org/resource/1999_films
dcterms:subject
dcterms:subject
PageRank with Priors can be run again against this novel representation
45Cataldo 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
i4
u1
u2
u3
u4
dcterms:subject
http://dbpedia.org/resource/Films_About_Rebellions
Films about Rebellions
dbprop:director
Quentin Tarantino
dbprop:director
Methodologyexample - LOD-boosted representation
1999 films
http://dbpedia.org/resource/1999_films
dcterms:subject
dcterms:subject
How do the recommendations
change with such a new topology?
46Cataldo 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
i4
u1
u2
u3
u4
dcterms:subject
http://dbpedia.org/resource/Films_About_Rebellions
Films about Rebellions
dbprop:director
Quentin Tarantino
dbprop:director
Methodologyexample - LOD-boosted representation
1999 films
http://dbpedia.org/resource/1999_films
dcterms:subject
dcterms:subject
Is there any significant increase in terms of
computational load?
47Cataldo 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
i4
u1
u2
u3
u4
dcterms:subject
http://dbpedia.org/resource/Films_About_Rebellions
Films about Rebellions
dbprop:director
Quentin Tarantino
dbprop:director
Methodologyexample - LOD-boosted representation
1999 films
http://dbpedia.org/resource/1999_films
dcterms:subject
dcterms:subject
Is it necessary to inject all of the properties
available in the LOD cloud?
48Cataldo 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
i4
u1
u2
u3
u4
dcterms:subject
http://dbpedia.org/resource/Films_About_Rebellions
Films about Rebellions
dbprop:director
Quentin Tarantino
dbprop:director
Methodologyexample - LOD-boosted representation
1999 films
http://dbpedia.org/resource/1999_films
dcterms:subject
dcterms:subject
XX
49Cataldo 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
i4
u1
u2
u3
u4
dcterms:subject
http://dbpedia.org/resource/Films_About_Rebellions
Films about Rebellions
dbprop:director
Quentin Tarantino
dbprop:director
Methodologyexample - LOD-boosted representation
1999 films
http://dbpedia.org/resource/1999_films
dcterms:subject
dcterms:subjectXX
X
50Cataldo 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
i4
u1
u2
u3
u4
dcterms:subject
http://dbpedia.org/resource/Films_About_Rebellions
Films about Rebellions
dbprop:director
Quentin Tarantino
dbprop:director
Methodologyexample - LOD-boosted representation
1999 films
http://dbpedia.org/resource/1999_films
dcterms:subject
dcterms:subject
Is it possibile to automatically select the most promising
properties?
51
Experiments
Cataldo 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
52Cataldo 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
Research QuestionsDo 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.
53Cataldo 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
Research Questions3.
4.
2/2Is 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?
54Cataldo 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
Experimental EvaluationDescription of the dataset
MovieLens 100k983 users1,682 movies100,000 ratings55.17% positive ratings84.43 ratings/user (avg.)48.48 ratings/item (avg.)93.7% sparsity
55Cataldo 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
Experimental EvaluationDBpedia mapping
1,600 movies (95%) were successfully mapped to DBpedia by querying a SPARQL endpoint with the title of the movie.
56Cataldo 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
Experimental EvaluationDBpedia mapping
60 properties were extracted from the LOD cloud
57Cataldo 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
Experimental EvaluationMost Popular LOD properties
Top-10 properties in the Movie domaindcterms:subjectdbprop:starring
dbprop:producerdbprop:title
dbprop:writerdbprop:country
dbprop:distributordbprop:music
dbprop:directordbprop:runtime
58Cataldo 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
Experimental EvaluationExperimental Protocol
Algorithm PageRank with Priors
Data Split 5-fold Cross Validation
Graph Representation G, GLOD, GLOD+FS
Feature Selection Techniques PageRank, Chi-Square, Information Gain, Gain Ratio, mRMR, PCA, SVM
#Selected Features 10, 30, 50 (out of 60 overall features)
Evaluation Metrics F1, Intra-List Diversity, Run Time
59Cataldo 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
Experimental EvaluationGraph Representations :: Recap
GBasic Graph with collaborative data points
60Cataldo 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
Experimental Evaluation
GLOD Graph extended with all the properties gathered from the LOD cloud
Graph Representations :: Recap
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
Experimental Evaluation
GLOD+FS Graph encoding only the most relevant properties selected by a feature selection technique FS
Graph Representations :: Recap
Experiment 1
62
Impact of LOD-based features.
Cataldo 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
F1@5
F1@10
F1@15
G
G_LOD
G
G_LOD
G
G_LOD
53 55 57 59 61
59,63
60,83
54,24
59,41
60,23
53,89
Experiment 1
63
Impact of LOD-based features :: F1-measure
Cataldo 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
Significant improvement in all the metrics (Wilcoxon test)
Run Time (min.)
G
G_LOD
50 262,5 475 687,5 900
880
72
Experiment 1
64Cataldo 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
Tremendous increase in the run time, as well
Impact of LOD-based features :: Run Time
Experiment 1
65Cataldo 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
Impact of LOD-based features :: Graph Representation
Nodes Edges
G 2,625 100,000
GLOD 53,794 178,020
Tremendous increase in the run timedepending on the amount of information encoded in the graph
Experiment 1
66
Impact of LOD-based features :: LESSONS LEARNED
F1 RunTime
LOD features can have a good impact on the overall F1
Tremendous Run Time increase
1.2.
Cataldo 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
Experiment 2
67
Impact of Feature Selection techniques
Cataldo 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
PageRank
mRMR
Chi-Square
SVM
Gain Ratio
Inf. Gain
PCA
103050103050103050103050103050103050103050
53 53,5 54 54,5 5554,31
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
68
Impact of Feature Selection techniques :: F1@5
Typically, the larger the number of features, the better the F1
Cataldo 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
PageRank
mRMR
Chi-Square
SVM
Gain Ratio
Inf. Gain
PCA
103050103050103050103050103050103050103050
53 53,5 54 54,5 5554,31
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
69
Impact of Feature Selection techniques :: F1@5
Typically, the larger the number of features, the better the F1
#50
#50
#50
#50
#50
#30
#30
(best)
Cataldo 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
PageRank
mRMR
Chi-Square
SVM
Gain Ratio
Inf. Gain
PCA
103050103050103050103050103050103050103050
53 53,5 54 54,5 5554,31
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
70
Impact of Feature Selection techniques :: F1@5
#50
#50
#50
#50
#50
#30
#30
(best)
VERY IMPORTANT: we noted a dataset-dependant behavior
Cataldo 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
PageRank
mRMR
Chi-Square
SVM
Gain Ratio
Inf. Gain
PCA
103050103050103050103050103050103050103050
53 53,5 54 54,5 5554,31
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
71
Impact of Feature Selection techniques :: F1@5
How does it perform with respect to the baseline?Cataldo 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
Experiment 2
72
Impact of Feature Selection techniques :: F1@5
GLOD (baseline) = 54,24
PageRank
mRMR
Chi-Square
SVM
Gain Ratio
Inf. Gain
PCA
103050103050103050103050103050103050103050
53 53,5 54 54,5 5554,31
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
Cataldo 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
PageRank
mRMR
Chi-Square
SVM
Gain Ratio
Inf. Gain
PCA
103050103050103050103050103050103050103050
53 53,5 54 54,5 5554,31
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
73
Impact of Feature Selection techniques :: F1@5
Only three out of seven techniques (and only with 50 features) overcome the baseline
Cataldo 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
PageRank
mRMR
Chi-Square
SVM
Gain Ratio
Inf. Gain
PCA
103050103050103050103050103050103050103050
53 53,5 54 54,5 5554,31
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
74
Impact of Feature Selection techniques :: F1@5
Cataldo 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
Overall, PCA is the best-performing feature selection technique
Run Time (min.)
GLOD
GLOD+PCA
50 262,5 475 687,5 900
581
880
Experiment 2
75Cataldo 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
33.9% decrease
Impact of Feature Selection techniques :: Run Time
76Cataldo 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
Nodes Edges
GLOD 53,794 178,020
GLOD+PCA 49,158 169,405
Experiment 2Impact of Feature Selection techniques :: Run Time
-8.6% nodes and -4.8% edges
Experiment 2
77Cataldo 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
Recap of the Experiment
G GLOD GLOD+PCA
F1@5 0,5406 0,5424 0,5431F1@10 0,6068 0,6083 0,6088F1@15 0,5956 0,5963 0,5970
Run Time 72 880 581LOD Properties 0 60 50
The adoption of Feature Selection Techniques improves F1 and decreases run time
Experiment 2
78
Impact of Features Selection techniques :: LESSONS LEARNED
F1 RunTime
Computational load drops down, as expected
Features Selection techniques are useful, but they do not always improve F11.
2.3. The optimal number of features is dataset-dependant
Cataldo 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
Experiment 3
79
Trade-off between F1 and diversity
Cataldo 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
Experiment 3
80
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?
Cataldo 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
Experiment 3
81
Trade-off between F1 and diversity :: F1@5
Features Selection techniques can be split into four classes
Cataldo 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
Experiment 3
82
Trade-off between F1 and diversity :: F1@5
Features Selection techniques can be split into four classes
Low Diversity Low Accuracy
Cataldo 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
Experiment 3
83
Trade-off between F1 and diversity :: F1@5
Features Selection techniques can be split into four classes
Low Diversity Low Accuracy
Low Diversity High Accuracy
Cataldo 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
Experiment 3
84
Trade-off between F1 and diversity :: F1@5
Features Selection techniques can be split into four classes
Low Diversity Low Accuracy
Low Diversity High Accuracy
High Diversity Low Accuracy
Cataldo 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
Experiment 3
85
Trade-off between F1 and diversity :: F1@5
Cataldo 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
Features Selection techniques can be split into four classes
Low Diversity Low Accuracy
Low Diversity High Accuracy
High Diversity Low Accuracy
High Diversity High Accuracy
Experiment 3
86
Trade-off between F1 and diversity :: F1@5
Cataldo 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
Features Selection techniques can be split into four classes
Low Diversity Low Accuracy
Low Diversity High Accuracy
High Diversity Low Accuracy
High Diversity High Accuracy
Experiment 3
87
Trade-off between F1 and diversity :: F1@5
Cataldo 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
mRMR, Information Gain and ChiSquare are not useful
Experiment 3
88
Trade-off between F1 and diversity :: F1@5
Cataldo 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
PCA maximizes F1, at the expense of a little diversity
Experiment 3
89
Trade-off between F1 and diversity :: F1@5
Cataldo 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
Gain Ratio and SVM sacrifice F1, to induce an higher diversity
Experiment 3
90
Trade-off between F1 and diversity :: F1@5
Cataldo 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
PageRank obtains a good compromise between F1 and Diversity
Experiment 3
91
Trade-off between F1 and Diversity :: LESSONS LEARNED
F1
Behavior needs to be generalized by analyzing different datasets
Features Selection techniques can maximize a specific evaluation metric, thus a graph-based recsys can be tuned according to the requirements of a scenario
1.2.
Cataldo 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
Experiment 4
92
Comparison to State of the artBPRMF (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.
Cataldo 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
Experiment 4
93
Comparison to State of the Art
Cataldo 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
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
94
Comparison to State of the Art
Cataldo 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
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)
PageRank with Priors boosted with LOD is the best-performing approach
Experiment 4
95
Comparison to State of the Art
Cataldo 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
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)
Even state-of-the-art approaches based on Matrix Factorization are overcame by our methodology
Conclusions
96Cataldo 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
Recap
97
Methodolology1. PageRank with Priors as base algorithm2. 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
Cataldo 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
INVESTIGATION ABOUT THE EFFECTIVENESS OF LINKED OPEN DATA INGRAPH-BASED RECOMMENDER SYSTEMS
Lessons Learned
98
Evaluation1. PageRank with Priors benefit of the injection of data points
coming from the LOD cloud2. 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.
Cataldo 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
INVESTIGATION ABOUT THE EFFECTIVENESS OF LINKED OPEN DATA INGRAPH-BASED RECOMMENDER SYSTEMS
Future Research
99
Evaluation against different datasets and stronger baselines;
Further expansion of the graph, by introducing more LOD-based data points
Evaluation of Novelty and Serendipity on LOD-based Recommendations;
Cataldo 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
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