1.
Recommender Systems
meet Linked Open Data
Tommaso Di Noia
16th International Conference on Web Engineering June 7th, 2016
tommaso.dinoia@poliba.it
@TommasoDiNoia

2.
Agenda
• Linked Open Data
• What is a Recommender System and how
does it work?
• Evaluating a Recommender System
• Recommender Systems and Linked Open Data

3.
LINKED OPEN DATA
A quick introduction to

4.
Linking Open Data cloud diagram2014, by Max Schmachtenberg, Christian Bizer, Anja Jentzsch and Richard Cyganiak. http://lod-cloud.net/

5.
Linked (Open) Data
Some definitions:
– A method of publishing data on the Web
– (An instance of) the Web of Data
– A huge database distributed in the Web
– Linked Data is the Semantic Web done right

6.
Web vs Linked Data
Web Linked Data
Analogy File System Database
Designed for Men Machines
(Software Agents)
Main elements Documents Things
Links between Documents Things
Semantics Implicit Explicit
Courtesy of Prof. Enrico Motta, The Open University, Milton Keynes – Uk – Semantic Web: Technologies and Applications.

7.
LOD is the Web

8.
Which technologies?

9.
Which technologies?
Data
Language
Query
Language
Schema
Languages

10.
URI
• Every resource/entity/thing/relation is
identified by a (unique) URI
– URI: <http://dbpedia.org/resource/Lugano>
– CURIE: dbr:Lugano
– URI: <http://purl.org/dc/terms/subject>
– CURIE: dct:subject

11.
Which vocabularies/ontologies?
• Most popular on http://prefix.cc (June 6, 2016)
– YAGO: http://yago-knowledge.org/resource/
– FOAF: http://xmlns.com/foaf/0.1/
– DBpedia Ontology: http://dbpedia.org/ontology/
– DBpedia Properties:
http://dbpedia.org/property/
– Dublin Core: http://dublincore.org/

12.
Which vocabularies/ontologies?
• Most popular on http://lov.okfn.org (June 6,
2016)
– VANN: http://purl.org/vocab/vann/
– SKOS: http://www.w3.org/2004/02/skos/core
– FOAF
– DCTERMS
– DCE: http://purl.org/dc/elements/1.1/

13.
RDF – Resource Description Framework
• Basic element: triple
[subject] [predicate] [object]
URI URI
URI | Literal
"string"@lang| "string"^^datatype

14.
RDF – Resource Description Framework
dbr:Lugano dbo:country dbr:Switzerland .
dbr:Lugano rdfs:label "Lugano"@en .
dbr:Lugano rdfs:label "Lugano"@it .
dbr:Lugano dbo:populationTotal "67201"^^xsd:integer .
dbr:Lugano dct:subject dbc:Cities_in_Switzerland .
dbr:Lugano rdf:type yago:PopulatedPlacesOnLakeLugano.
dbr:Switzerland dbo:leaderParty dbr:Ticino_League .
dbr:Switzerland dbp:neighboringMunicipalities dbr:Melide,_Switzerland .

15.
RDF – Resource Description Framework
Switzerland Lugano
Melide,_Switzerland
Ticino_League
Cities_in_Switzerland
PopulatedPlacesOnLakeLu
gano
"Lugano"@en
"Lugano"@it
"67201"^^xsd:integer
country
leaderParty
neighboringMunicipalities
type
subject
label
populationTotal

16.
RDFS and OWL in two statements
dbo:country rdfs:range dbo:Country .
dbr:Lugano owl:sameAs wikidata:Lugano .

17.
SPARQL
PREFIX dbo: <http://dbpedia.org/ontology/>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX dct: <http://purl.org/dc/terms/>
PREFIX dbc: <http://dbpedia.org/resource/Category:>
SELECT DISTINCT ?city ?name
WHERE {
?city dct:subject dbc:Cities_in_Switzerland.
?city rdfs:label?name.
?city dbo:populationTotal?population .
FILTER (?population < 70000) .
FILTER (lang(?name) = 'en')
}

18.
SPARQL
curl -g -H 'Accept: application/json' 'http://dbpedia.org/sparql?default-graph-
uri=http%3A%2F%2Fdbpedia.org&query=PREFIX+dbo%3A%3Chttp%3A%2F%2Fdbpedia.org%2Fontology%2F%3E+PREFIX+rdfs%3A%3Chttp%
3A%2F%2Fwww.w3.org%2F2000%2F01%2Frdfschema%23%3E+PREFIX+dct%3A%3Chttp%3A%2F%2Fpurl.org%2Fdc%2Fterms%2F%3E+PREFI
X+dbc%3A%3Chttp%3A%2F%2Fdbpedia.org%2Fresource%2FCategory%3A%3E+SELECT+DISTINCT+%3Fcity+%3Fname+WHERE%7B%3Fcity+d
ct%3Asubject+dbc%3ACities_in_Switzerland.%3Fcity+rdfs%3Alabel+%3Fname.%3Fcity+dbo%3ApopulationTotal+%3Fpopulation.FILTER%28%
3Fpopulation+%3C+70000%29.FILTER+%28lang%28%3Fname%29%3D%27en%27%29%7D'

19.
RECOMMENDER SYSTEMS
The information overload problem

20.
60 seconds in the Web

21.
Personalized Information Access
• Help the user in finding the information they
might be interestedin
• Consider their preferences/pastbehaviour
• Filter irrelevant information

22.
Recommender Systems
• Help users in dealing with Information/Choice Overload
• Help to match users with items

23.
Some definitions
– In its most common formulation, the recommendation problem is
reduced to the problem of estimating ratings for the items that have
not been seen by a user.
[G. Adomavicius and A. Tuzhilin. Toward the Next Generation of Recommender Systems: A survey of the State-of-the-Art and
Possible Extension. TKDE, 2005.]
– Recommender Systems (RSs) are software tools and techniques
providing suggestions for items to be of use to a user.
[F. Ricci, L. Rokach, B. Shapira, and P. B. Kantor, editors. Recommender Systems Handbook. Springer, 2015.]

24.
The problem
• Estimate a utility function to automatically
predict how much a user will like an item
which is unknown to them.
Input
Set of users
Set of items
Utility function
𝑈 = {𝑢%,…, 𝑢(}
𝑋 = {𝑥%,… , 𝑥,}
𝑓: 𝑈×𝑋 → 𝑅
∀ 𝑢 ∈ 𝑈, 𝑥5
6
= arg 𝑚𝑎𝑥<∈= 𝑓(𝑢, 𝑥)
Output

25.
The rating matrix
5 1 2 4 3 ?
2 4 5 3 5 2
4 3 2 4 1 3
3 5 1 5 2 4
4 4 5 3 5 2
The Matrix
Titanic
I love shopping
Argo
Love Actually
The hangover
Tommaso
Francesco
Vittoria
Jessica
Paolo

26.
The rating matrix
(in the real world)
5 ? ? 4 3 ?
2 4 5 ? 5 ?
? 3 ? 4 ? 3
3 5 ? 5 2 ?
4 ? 5 ? 5 2
The Matrix
Titanic
I love shopping
Argo
Love Actually
The hangover
Tommaso
Francesco
Vittoria
Jessica
Paolo

27.
How sparse is a rating matrix?
𝑠𝑝𝑎𝑟𝑠𝑖𝑡𝑦 = 1 −
|𝑅|
𝑋 ⋅ 𝑈

28.
Ratings
Explicit
Implicit

29.
Rating Prediction vs Ranking
Best Worst

30.
Recommendation techniques
• Content-based
• Collaborative filtering
• Demographic
• Knowledge-based
• Community-based
• Hybrid recommendersystems

31.
Collaborative RS
Collaborative RSs recommend items to a user by identifying
other users with a similar profile
Recommender
System
User profile
Users
Item7
Item15
Item11
…
Top-N Recommendations
Item1, 5
Item2, 1
Item5, 4
Item10, 5
….
….
Item1, 4
Item2, 2
Item5, 5
Item10, 3
….
Item1, 4
Item2, 2
Item5, 5
Item10, 3
….
Item1, 4
Item2, 2
Item5, 5
Item10, 3
….

32.
Content-based RS
Recommender
System
User profile
Item7
Item15
Item11
…
Top-N Recommendations
Item1, 5
Item2, 1
Item5, 4
Item10, 5
….
Items
Item1
Item2
Item100
Item’s
descriptions
….
CB-RSs recommend items to a user based on their description
and on the profile of the user’s interests

33.
Knowledge-based RS
Recommender
System
Item7
Item15
Item11
…
Top-N Recommendations
Items
Item1
Item2
Item100Item’s
descriptions
….
KB-RSs recommend items to a user based on their description
and domain knowledge encoded in a knowledge base
Knowledge-base

34.
Collaborative Filtering
• Memory-based
– Mainly based on k-NN
– Does not requireany preliminary model building
phase
• Model-based
– Learn a predictive model beforecomputing
recommendations

35.
User-based Collaborative Recommendation
5 1 2 4 3 ?
2 4 5 3 5 2
4 3 2 4 1 3
3 5 1 5 2 4
4 4 5 3 5 2
The Matrix
Titanic
I love shopping
Argo
Love Actually
The hangover
Tommaso
Francesco
Vittoria
Jessica
Paolo
𝑠𝑖𝑚 𝑢J, 𝑢K =
∑ 𝑟5M,< − 𝑟5M
∗ 𝑟K,< − 𝑟5O<∈=
∑ 𝑟5M,< − 𝑟5M
Q
<∈= ∗ ∑ 𝑟5O,< − 𝑟5O
Q
<∈=
Pearson’s correlation coefficient
Rate prediction
𝑟̃ 𝑢J , 𝑥6
= 𝑟5M
+
∑ 𝑠𝑖𝑚 𝑢J, 𝑢K ∗ 𝑟5O,<T − 𝑟5O5O∈,
∑ 𝑠𝑖𝑚(𝑢J, 𝑢K)5O∈,
= 𝑋

36.
k-Nearest Neighbors
k = 5
N
A neighborhood of 20 to 50 neighbors is a reasonable choice
[Herlocker et al. An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms, Information
Retrieval 5 (2002), no. 4, 287–310.]

37.
Item-based Collaborative Recommendation
5 1 2 4 3 ?
2 4 5 3 5 2
4 3 2 4 1 3
3 5 1 5 2 4
4 4 5 3 5 2
The Matrix
Titanic
I love shopping
Argo
Love Actually
The hangover
𝑠𝑖𝑚 𝑥J, 𝑥K =
𝑥J ⋅ 𝑥K
|𝑥J| ∗ |𝑥K|
=
∑ 𝑟5,<M
∗ 𝑟5,<O5
∑ 𝑟5,<M
Q
5 ∗ ∑ 𝑟5,<
Q
5
Cosine Similarity
Rate prediction
𝑟̃ 𝑢J, 𝑥6
=
∑ 𝑠𝑖𝑚 𝑥⃗, 𝑥⃗′ ∗ 𝑟<,5M<∈=WM
∑ 𝑠𝑖𝑚 𝑥⃗, 𝑥⃗′<∈=WM
𝑠𝑖𝑚 𝑥J, 𝑥K =
∑ 𝑟5,<M
− 𝑟5X ∗ 𝑟5,<O
− 𝑟5X5
∑ 𝑟5,<M
− 𝑟5X
Q
5 ∗ ∑ 𝑟5,<O
− 𝑟5X
Q
5
Adjusted Cosine Similarity
= 𝑋5M
Tommaso
Francesco
Vittoria
Jessica
Paolo

38.
CF drawbacks
• Sparsity / Cold-start
– New user
– New item
• Grey sheep problem

39.
Content-Based RS
• Items are described in terms of
attributes/features
• A finite set of values is associated to each
feature
• Item representation is a (Boolean) vector

40.
Content-based
CB-RSs try to recommend items similar* to
those a given user has liked in the past
[M. de Gemmis et al. Recommender Systems Handbook. Springer. 2015]
• Heuristic-based
– Usually adopt techniques borrowed from IR
• Model-based
– Often we have a model for each user
(*) similar from a content-based perspective

41.
CB drawbacks
• Content overspecialization
• Portfolio effect
• Sparsity / Cold-start
– New user

42.
Knowledge-based RS
• Conversational approaches
• Reasoning techniques
– Case-based reasoning
– Constraint reasoning

43.
Hybrid recommender systems
[Robin D. Burke. Hybrid recommender systems: Survey and experiments. User Model. User-Adapt. Interact., 12(4):331–370, 2002.]
Weighted
The scores (or votes) of several recommendation
techniques are combined together to produce a single
recommendation.
Switching
The system switches between recommendation
techniques depending on the current situation.
Mixed
Recommendations from several different
recommenders are presented at the same time
Feature combination
Features from different recommendation data sources
are thrown together into a single recommendation
algorithm.
Cascade One recommender refines the recommendations
given by another.
Feature augmentation Output from one technique is used as an input feature
to another.
Meta-level
The model learned by one recommender is used as
input to another.

44.
EVALUATION

45.
Dataset split
20%80%
…
hold-out
k-fold cross-validation
Training Set
Test Set (TS)

46.
Protocols
• Rated test-items
• All unrated items: compute a score for every
item not rated by the user (also items not
appearing in the user test set)

47.
Accuracy metrics for rating prediction
𝑀𝑒𝑎𝑛 𝐴𝑏𝑠𝑜𝑙𝑢𝑡𝑒 𝐸𝑟𝑟𝑜𝑟
𝑀𝐴𝐸 =
1
|𝑇𝑆|
c d |𝑟̃5,<M
− 𝑟5,<M
|
5,<M ∈ef
𝑅𝑜𝑜𝑡 𝑀𝑒𝑎𝑛 𝑆𝑞𝑢𝑎𝑟𝑒𝑑 𝐸𝑟𝑟𝑜𝑟
𝑅𝑀𝑆𝐸 =
1
|𝑇𝑆|
c d (𝑟̃5,<M
− 𝑟5,<M
)Q
5,<M ∈ef

48.
MAE and RMSE drawback
• Not very suitable for top-N recommendation
– Errorsin the highest part of the recommendation
list are considered in the same way as the ones in
the lowest part

49.
Accuracy metrics for top-N
recommendation
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 @ 𝑁
𝑃5@𝑁 =
|𝐿5 𝑁 ∩ 𝑇𝑆5
o
|
𝑁
𝑅𝑒𝑐𝑎𝑙𝑙 @ 𝑁
𝑅5@𝑁 =
|𝐿5 𝑁 ∩ 𝑇𝑆5
o
|
|𝑇𝑆5
o
|
𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 𝐷𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝐺𝑎𝑖𝑛 @ 𝑁
𝑛𝐷𝐶𝐺5@𝑁 =
1
𝐼𝐷𝐶𝐺@𝑁
d
2wW,x − 1
logQ(1 + 𝑘)
,
|}%
𝐿5 𝑁 is the recommendation list
up to the N-th element
𝑇𝑆5
o
is the set of relevant test
items for 𝑢
𝐼𝐷𝐶𝐺@𝑁 indicates the score
Obtained by an ideal ranking of 𝐿5 𝑁

50.
Is all about precision?

51.
Is all about precision?
• Novelty
– Recommend items in the long tail
• Diversity
– Avoid to recommend only items in a small subset
of the catalog
– Suggest diverse items in the recommendation list
• Serendipity
– Suggest unexpected but interesting items

52.
Novelty
𝐸𝑛𝑡𝑟𝑜𝑝𝑦 − 𝐵𝑎𝑠𝑒𝑑 𝑁𝑜𝑣𝑒𝑙𝑡𝑦
𝐸𝐵𝑁5@𝑁 = − d 𝑝J ⋅ logQ 𝑝J
<∈•W(,)
𝑝J =
| 𝑢 ∈ 𝑈 𝑥 𝑖𝑠 𝑟𝑒𝑙𝑒𝑣𝑎𝑛𝑡 𝑡𝑜 𝑢 }|
|𝑈|

53.
Diversity
𝐼𝑛𝑡𝑟𝑎 − 𝐿𝑖𝑠𝑡 𝐷𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦
𝐼𝐿𝐷5@𝑁 =
1
2
⋅ d d 1 − 𝑠𝑖𝑚 𝑥J, 𝑥K
<O∈•W ,<M∈•W €
𝐼𝐿𝐷@𝑁 =
1
|𝑈|
⋅ d 𝐼𝐿𝐷5@𝑁
5∈•
𝐴𝑔𝑔𝑟𝑒𝑔𝑎𝑡𝑒 𝐷𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦
𝐴𝐷𝑖𝑛@𝑁 =
| ⋃ 𝐿5(𝑁)5∈• |
|𝑋|

54.
RECOMMENDER SYSTEMS AND
LINKED OPEN DATA

55.
Content-Based Recommender Systems
P. Lops, M. de Gemmis, G. Semeraro. Content-based recommender Systems: State of the Art and Trends. In: P. Kantor, F. Ricci, L. Rokach, B. Shapira,
editors, Recommender Systems Hankbook: A complete Guide for Research Scientists & Practitioners

56.
Content-Based Recommender Systems
P. Lops, M. de Gemmis, G. Semeraro. Content-based recommender Systems: State of the Art and Trends. In: P. Kantor, F. Ricci, L. Rokach, B. Shapira,
editors, Recommender Systems Hankbook: A complete Guide for Research Scientists & Practitioners

57.
Need of domain knowledge!
We need rich descriptions of the items!
No suggestion is available if the analyzed content does not contain enough
information to discriminate items the user might like from items the user
might not like.*
(*) M. de Gemmis et al. Recommender Systems Handbook. Springer. 2015
The quality of CB recommendations are correlated with the quality of the
features that are explicitly associated with the items.
Limited Content Analysis

58.
Traditional Content-based RSs
• Base on keyword/attribute -based item
representations
• Rely on the quality of the content-analyzer to
extract expressive item features
• Lack of knowledge about the items

59.
Semantics-aware approaches
Traditional Ontological/Semantic
Recommender Systems
make use of limited
domain
ontologies;

60.
What about Linked Data?
Use Linked Data to mitigate
the limited content analysis
issue
• Plenty of structured data
available
• No Content Analyzer
required
Linking Open Data cloud diagram2014, by Max Schmachtenberg, Christian Bizer, Anja Jentzsch and Richard Cyganiak. http://lod-cloud.net/

61.
Why RS + LOD
• Multi-Domain knowledge

62.
Why RS + LOD
• Standardized (distributed) access to data
PREFIX dbpedia: <http://dbpedia.org/resource/>
PREFIX dbo: <http://dbpedia.org/ontology/>
SELECT ?actor WHERE {
dbpedia:Pulp_Fiction dbo:starring ?actor .
}
PREFIX yago: <http://yago-knowledge.org/resource/>
PREFIX owl: <http://www.w3.org/2002/07/owl#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX dbpedia-owl: <http://dbpedia.org/ontology/>
CONSTRUCT{
?book ?p ?o .
?book yago:linksTo ?yagolink .
}
WHERE{
SERVICE <http://live.dbpedia.org/sparql> {
?book rdf:type dbpedia-owl:Book .
?book ?p ?o .
?book owl:sameAs ?yago .
FILTER(regex(str(?yago),"http://yago-knowledge.org/resource/"))
.
}
SERVICE <http://lod2.openlinksw.com/sparql> {
?yago yago:linksTo ?yagolink .
}
}

63.
Why RS + LOD
• Semantic Analysis

64.
A high level architecture
V. C. Ostuni et al., Sound and Music Recommendation with Knowledge Graphs. ACM Transactions on Intelligent Systems and Technology (TIST)
– 2016 – http://sisinflab.poliba.it/publications/2016/OODSD16/

65.
Item Linker
• Direct Item Linking
• Item Description Linking

66.
Direct item Linking
dbr:I_Am_Legend_(film)

67.
Direct item Linking
dbr:Troy_(film)
dbr:Troy
dbr:I_Am_Legend_(film)

68.
Direct item Linking
dbr:Scarface_(1983_film)
dbr:Scarface:_The_World_Is_Yours
dbr:Troy_(film)
dbr:Troy
dbr:I_Am_Legend_(film)

69.
Direct Item Linking
dbr:Divine_Comedy

70.
Direct Item Linking
dbr:The_Da_Vinci_Code
dbr:Divine_Comedy

71.
Direct Item Linking
???
dbr:The_Da_Vinci_Code
dbr:Divine_Comedy

72.
Direct Item Linking
• The easy way
SELECT DISTINCT ?uri, ?title WHERE {
?uri rdf:type dbpedia-owl:Film.
?uri rdfs:label ?title.
FILTER langMatches(lang(?title), "EN") .
FILTER regex(?title, "matrix", "i")
}

73.
Direct item Linking
• Other approaches
– DBpedia Lookup
https://github.com/dbpedia/lookup
– Silk Framework
http://silk-framework.com/

74.
Direct Item Linking

75.
Item Description Linking

76.
Item Description Linking

77.
Item Description Linking

78.
Item Description Linking

79.
Item Graph Analyzer
• Build your own knowledge graph
– Select relevant properties. Possible solutions:
• Ontologicalproperties
• Categorical properties
• Frequent properties
• Feature selection techniques
– Explore the graph up to a limited depth

80.
Which LOD RSs?
• Content-based
– Heuristic-based
– Model based
• Hybrid
• Knowledge-based

81.
Common features

82.
Linked Data as a structured
information source for item descriptions
Rich item descriptions

83.
Different item features
representations
• Direct properties
• Property paths
• Node paths
• Neighborhoods
• …

84.
Datasets
Subset of Movielensmapped to DBpedia
Subset of Last.fm mapped to DBpedia
Subset of The Library Thing mapped to DBpedia
Mappings
https://github.com/sisinflab/LODrecsys-datasets

85.
Direct properties

86.
Jaccard similarity
𝑠𝑖𝑚K„……„w† 𝑥J, 𝑥K =
|𝑁† 𝑥J ∩ 𝑁† 𝑥K |
|𝑁† 𝑥J ∪ 𝑁†(𝑥K)|

87.
Content-based prediction
𝑟ˆ 𝑢, 𝑥K =
∑ 𝑟 𝑢, 𝑥J ⋅ 𝑠𝑖𝑚(𝑥J, 𝑥K)<M∈,∩‰wŠ‹JŒ•(5)
∑ 𝑠𝑖𝑚(𝑥J, 𝑥K)<M∈,∩‰wŠ‹JŒ•(5)

88.
Vector Space Model for LOD
Righteous Kill
starring
director
subject/broader
genre
Heat
RobertDe Niro
John Avnet
Serial killer films
Drama
Al Pacino
Brian Dennehy
Heist films
Crimefilms
starring
RobertDe Niro
Al Pacino
Brian Dennehy
Righteous Kill
Heat
… …

89.
Vector Space Model for LOD
Righteous Kill
STARRING
Al Pacino
(v1)
Robert
De Niro
(v2)
Brian
Dennehy
(v3)
Righteous
Kill (m1)
X X X
Heat (m2) X X
Heat
Righteous Kill (x1) wv1,x1 wv2,x1 wv3,x1
Heat (x2) wv1,x2 wv2,x2 0
𝑤•Œ‰„…J€Š,••„‘ = 𝑡𝑓•Œ‰„…J€Š,••„‘ ∗ 𝑖𝑑𝑓•Œ‰„…J€Š

90.
Vector Space Model for LOD
Righteous Kill
STARRING
Al Pacino
(v1)
Robert
De Niro
(v2)
Brian
Dennehy
(v3)
Righteous
Kill (m1)
X X X
Heat (m2) X X
Heat
Righteous Kill (x1) wv1,x1 wv2,x1 wv3,x1
Heat (x2) wv1,x2 wv2,x2 0
𝑤•Œ‰„…J€Š,••„‘ = 𝑡𝑓•Œ‰„…J€Š,••„‘ ∗ 𝑖𝑑𝑓•Œ‰„…J€Š
𝑡𝑓 ∈ {0,1}

91.
Vector Space Model for LOD
+
+
+
… =
𝒔𝒊𝒎 𝒔𝒕𝒂𝒓𝒓𝒊𝒏𝒈(𝒙𝒊, 𝒙𝒋) =
𝒘 𝒗 𝟏,𝒙𝒊
∗ 𝒘 𝒗 𝟏,𝒙𝒋
+ 𝒘 𝒗 𝟐,𝒙𝒊
∗ 𝒘 𝒗 𝟐,𝒙𝒋
+ 𝒘 𝒗 𝟑,𝒙𝒊
∗ 𝒘 𝒗 𝟑,𝒙𝒋
𝒘 𝒗 𝟏,𝒙𝒊
𝟐 + 𝒘 𝒗 𝟐,𝒙𝒊
𝟐 + 𝒘 𝒗 𝟑,𝒙𝒊
𝟐 ∗ 𝒘 𝒗 𝟏,𝒙𝒋
𝟐 + 𝒘 𝒗 𝟐,𝒙𝒋
𝟐 + 𝒘 𝒗 𝟑,𝒙𝒋
𝟐
𝜶 𝒔𝒕𝒂𝒓𝒓𝒊𝒏𝒈 ∗ 𝒔𝒊𝒎 𝒔𝒕𝒂𝒓𝒓𝒊𝒏𝒈(𝒙𝒊, 𝒙𝒋)
𝜶 𝒅𝒊𝒓𝒆𝒄𝒕𝒐𝒓 ∗ 𝒔𝒊𝒎 𝒅𝒊𝒓𝒆𝒄𝒕𝒐𝒓 (𝒙𝒊, 𝒙𝒋)
𝜶 𝒔𝒖𝒃𝒋𝒆𝒄𝒕 ∗ 𝒔𝒊𝒎 𝒔𝒖𝒃𝒋𝒆𝒄𝒕(𝒙𝒊, 𝒙𝒋)
𝒔𝒊𝒎 (𝒙𝒊, 𝒙𝒋)

92.
VSM Content-based Recommender
Predict the rating using a Nearest Neighbor Classifier wherein the similarity
measure is a linear combination of local property similarities
𝑟̃ 𝑢, 𝑥K =
∑ 𝑟 𝑢, 𝑥J ⋅
∑ 𝛼ª ⋅ 𝑠𝑖𝑚ª(𝑥J, 𝑥K)ª∈‰
|𝑃|<M∈‰wŠ‹JŒ•(5)
|𝑝𝑟𝑜𝑓𝑖𝑙𝑒(𝑢)|
Tommaso Di Noia, Roberto Mirizzi, Vito Claudio Ostuni, Davide Romito, Markus Zanker. Linked Open Data to supportContent-based Recommender Systems. 8th
International Conference on SemanticSystems (I-SEMANTICS) -2012 (Best Paper Award)

93.
VSM Content-based Recommender
We predict the rating using a Nearest Neighbor Classifier wherein the similarity
measure is a linear combination of local property similarities
𝑟̃ 𝑢, 𝑥K =
∑ 𝑟 𝑢, 𝑥J ⋅
∑ 𝛼ª ⋅ 𝑠𝑖𝑚ª(𝑥J, 𝑥K)ª∈‰
|𝑃|<M∈‰wŠ‹JŒ•(5)
|𝑝𝑟𝑜𝑓𝑖𝑙𝑒(𝑢)|
Selected properties

94.
VSM Content-based Recommender
We predict the rating using a Nearest Neighbor Classifier wherein the similarity
measure is a linear combination of local property similarities
𝑟̃ 𝑢, 𝑥K =
∑ 𝑟 𝑢, 𝑥J ⋅
∑ 𝛼ª ⋅ 𝑠𝑖𝑚ª(𝑥J, 𝑥K)ª∈‰
|𝑃|<M∈‰wŠ‹JŒ•(5)
|𝑝𝑟𝑜𝑓𝑖𝑙𝑒(𝑢)|
heuristic-based → model-based

95.
Property subset evaluation
The subject+broader
solution is better than only
subject or subject+more
broaders.
The best solution is
achieved with
subject+broader+
genres.
Too many broaders
introduce noise.
Ratedtest items protocol

96.
Evaluation against other
content-based approaches
Ratedtest items protocol

97.
Evaluation against other approaches
Ratedtest items protocol

98.
Property paths

99.
Path-based features
Analysis of complex relations between the user preferences and the
target item
T. Di Noia et al., SPRank: Semantic Path-based Ranking for Top-N Recommendations using Linked Open Data. ACM Transactions on Intelligent Systems and
Technology (TIST) – 2016 -http://sisinflab.poliba.it/publications/2016/DOTD16/

100.
Data model
I1 i2 i3 i4
u1 1 1 0 0
u2 1 0 1 0
u3 0 1 1 0
u4 0 1 0 1
Implicit Feedback Matrix Knowledge Graph
^
S =

101.
Data model
Implicit Feedback Matrix Knowledge Graph
^
S =
I1 i2 i3 i4
u1 1 1 0 0
u2 1 0 1 0
u3 0 1 1 0
u4 0 1 0 1

102.
Data model
Implicit Feedback Matrix Knowledge Graph
^
S =
I1 i2 i3 i4
u1 1 1 0 0
u2 1 0 1 0
u3 0 1 1 0
u4 0 1 0 1

103.
Path-based features
Path: acyclic sequence of relations ( s , .. rl , .. rL )
Frequency of j-th path in the sub-graph
related to u and x
• The more the paths, the more the relevance of the item.
• Different paths have different meaning.
• Not all types of paths are relevant.
u3 s i2 p2 e1 p1 i1 à (s, p2 ,p1)
𝑤5<(𝑗) =
#𝑝𝑎𝑡ℎ5<(𝑗)
∑ #𝑝𝑎𝑡ℎ5<(𝑗)K

104.
Problem formulation
Feature vector
Set of irrelevant items for u
Set of relevant items for u
Training Set
Sample of irrelevant items for u
𝑋5
o
= 𝑥 ∈ 𝑋 𝑠̂5< = 1}
𝑋5
¯
= 𝑥 ∈ 𝑋 𝑠̂5< = 0}
𝑋5
¯∗
⊆ 𝑋5
¯
𝑤5< ∈ ℝ²
TR = ⋃ < 𝑤5<, 𝑠̂5< > 𝑥 ∈ (𝑋5
o
∪ 𝑋5
¯∗
)}5

105.
u1
x1
u2
u3
x2
x3
e1
e3
e4
e2
e5
u4
x4
Path-based features
wu3x1
?

106.
u1
u2
u3
e1
e3
e4
e2
e5
u4
Path-based features
path(1) (s, s, s) : 1
x1
x2
x3
x4

107.
u1
u2
u3
e1
e3
e4
e2
e5
u4
Path-based features
path(1) (s, s, s) : 2
x1
x2
x3
x4

108.
u1
u2
u3
e1
e3
e4
e2
e5
u4
Path-based features
path(1) (s, s, s) : 2
path(2) (s, p2, p1) : 1
x1
x2
x3
x4

109.
u1
u2
u3
e1
e3
e4
e2
e5
u4
Path-based features
path(1) (s, s, s) : 2
path(2) (s, p2, p1) : 2
x1
x2
x3
x4

110.
u1
u2
u3
e1
e3
e4
e2
e5
u4
Path-based features
path(1) (s, s, s) : 2
path(2) (s, p2, p1) : 2
path(3) (s, p2, p3,p1) : 1
x1
x2
x3
x4

111.
Path-based features
path(1) (s, s, s) : 2
path(2) (s, p2, p1) : 2
path(3) (s, p2, p3,p1) : 1
u1
u2
u3
e1
e3
e4
e2
e5
u4
x1
x2
x3
x4
𝑤5µ<¶
1 =
2
5
𝑤5µ<¶
2 =
2
5
𝑤5µ<¶
3 =
1
5

112.
Evaluation of different ranking
functions
0
0,1
0,2
0,3
0,4
0,5
0,6
given 5 given 10 given 20 given 30 given 50 given All
recall@5
user profile size
Movielens
BagBoo
GBRT
Sum

113.
Evaluation of different ranking
functions
0
0,1
0,2
0,3
0,4
0,5
0,6
given 5 given 10 given 20 given All
recall@5
user profile size
Last.fm
BagBoo
GBRT
Sum

114.
Comparative approaches
• BPRMF, Bayesian Personalized Ranking for Matrix Factorization
• BPRLin, Linear Modeloptimized for BPR (Hybrid alg.)
• SLIM, Sparse Linear Methodsfor Top-N Recommender Systems
• SMRMF, Soft Margin Ranking Matrix Factorization
MyMediaLite

115.
Comparison with other
approaches
0
0,1
0,2
0,3
0,4
0,5
0,6
given 5 given 10 given 20 given 30 given 50 given All
user profile size
Movielens
SPrank
BPRMF
SLIM
BPRLin
SMRMF
precision@5

116.
Comparison with other
approaches
0
0,1
0,2
0,3
0,4
0,5
0,6
given 5 given 10 given 20 given All
user profile size
Last.fm
SPrank
BPRMF
SLIM
BPRLin
SMRMF
precision@5

117.
Neighborhoods

118.
Graph-based Item Representation
The Godfather
Mafia_films
Gangster_films
American
Gangster
Films_about_organized_crime
_in_the_United_States
Best_Picture_Academy
_Award_winners
Best_Thriller_Empire
_Award_winners
Films_shot_in_New_York_City
subject
subject
subject
subject
subject
subject
subject
V. C. Ostuni et al., Sound and Music Recommendation with Knowledge Graphs. ACM Transactions on Intelligent Systems and Technology (TIST)
– 2016 – http://sisinflab.poliba.it/publications/2016/OODSD16/

119.
Graph-based Item Representation
The Godfather
Mafia_films Films_about_organized_crime
Gangster_films
American
Gangster
Films_about_organized_crime
_in_the_United_States
Films_about_organized_
crime_by_country
Best_Picture_Academy
_Award_winners
Best_Thriller_Empire
_Award_winners
Awards_for_best_film
Films_shot_in_New_York_City
subject
subject
subject
broader
broader
broader
broader
broader
subject
subject
subject
subject

120.
Graph-based Item Representation
The Godfather
Mafia_films Films_about_organized_crime
Gangster_films
American
Gangster
Films_about_organized_crime
_in_the_United_States
Films_about_organized_
crime_by_country
Best_Picture_Academy
_Award_winners
Best_Thriller_Empire
_Award_winners
Awards_for_best_film
Films_shot_in_New_York_City
subject
subject
subject
broader
broader
broader
broader
broader
broader
subject
subject
subject
subject

121.
Graph-based Item Representation
The Godfather
Mafia_films Films_about_organized_crime
Gangster_films
American
Gangster
Films_about_organized_crime
_in_the_United_States
Films_about_organized_
crime_by_country
Best_Picture_Academy
_Award_winners
Best_Thriller_Empire
_Award_winners
Awards_for_best_film
Films_shot_in_New_York_City
subject
subject
subject
broader
broader
broader
broader
broader
broader
subject
subject
subject
subject
Exploit entities descriptions

122.
h-hop Item Neighborhood Graph
The Godfather
Mafia_films Films_about_organized_crime
Gangster_films
Best_Picture_Academy
_Award_winners Awards_for_best_film
Films_shot_in_New_York_City
subject
subject
subject
broader
broader
broader

123.
Kernel Methods
Work by embedding data in a vector space and looking for linear
patterns in such space
𝑥 → 𝜙(𝑥)
[Kernel Methods for General Pattern Analysis. Nello Cristianini . http://www.kernel-methods.net/tutorials/KMtalk.pdf]
𝜙(𝑥)
𝜙
𝑥Input space Feature space
We can work in the new space F by specifying an inner product
function between points in it
𝑘 𝑥𝑖, 𝑥𝑗 = < 𝜙(𝑥𝑖), 𝜙(𝑥𝑗)>

124.
h-hop Item Entity-based
Neighborhood Graph Kernel
Explicit computation of the feature map
Importance of the entity 𝑒º in the neighborhood
graph for the item 𝑥J
𝑘»¼ 𝑥J, 𝑥K = 𝜙»¼ 𝑥J , 𝜙»¼ 𝑥K
𝜙»¼ 𝑥J = (𝑤<M,•¶
, 𝑤<M,•½
, …, 𝑤<M,•¾
,… , 𝑤<M,•¿
)

125.
Explicit computation of the feature map
# edges involving 𝑒º at l hops from 𝑥J
a.k.a. frequency of the entityin the
item neighborhood graph
factor takinginto account at which hop the entity appears
h-hop Item Entity-based
Neighborhood Graph Kernel
𝑤<M,•¾
= d 𝛼Œ ⋅ 𝑐‰ÀÁ
<M ,•¾
Â
Œ}%
𝑘»¼ 𝑥J, 𝑥K = 𝜙»¼ 𝑥J , 𝜙»¼ 𝑥K
𝜙»¼ 𝑥J = (𝑤<M,•¶
, 𝑤<M,•½
, …, 𝑤<M,•¾
,… , 𝑤<M,•¿
)

126.
Weights computation
i
e1
e2
p3
p2
e4
e5
p3
p3
h=2
𝑐‰À¶ <M ,•¶
= 2
𝑐‰À¶ <M ,•½
= 1
𝑐‰À½ <M ,•Ã
= 1
𝑐‰À½ <M ,•Ä
= 2

127.
Weights computation example
i
e1
e2
p3
p2
e4
e5
p3
p3
h=2
𝑐‰À¶ <M ,•¶
= 2
𝑐‰À¶ <M ,•½
= 1
𝑐‰À½ <M ,•Ã
= 1
𝑐‰À½ <M ,•Ä
= 2
Informative entity about the item even if not directly related to it

128.
Experimental Settings
• Trained a SVM Regression model for each user
• Accuracy Evaluation: Precision, Recall
• Novelty Evaluation: Entropy-based Novelty (All
Items protocol) [the lower the better]

129.
Comparative approaches
•NB: 1-hop item neigh. + Naive Bayes classifier
•VSM: 1-hop item neigh. Vector Space Model (tf-idf) +
SVM regr
•WK: 2-hop item neigh. Walk-based kernel + SVM regr

130.
Comparison with other
approaches (i)
0
0,1
0,2
0,3
0,4
0,5
0,6
0,7
0,8
Prec@10 [20/80] Prec@10 [40/60] Prec@10 [80/20]
NK-bestPrec
NK-bestEntr
NB
VSM
WK
Ratedtest items protocol

131.
Comparison with other
approaches (ii)
0
0,2
0,4
0,6
0,8
1
1,2
1,4
1,6
1,8
EBN@10 [20/80] EBN@10 [40/60] EBN@10 [80/20]
NK-bestPrec
NK-bestEntr
NB
VSM
WK

132.
Neighborhoods (path-based)

133.
The FreeSound case study
Vito Claudio Ostuni, Sergio Oramas, Tommaso Di Noia, Xavier Serra, Eugenio Di Sciascio. A Semantic Hybrid Approach for Sound Recommendation. 24th
World Wide Web Conference - 2015

134.
FreeSound Knowledge Graph
Item textual descriptionsenrichment: EntityLinking tools can be used
to enrich item textual descriptions with LOD

135.
Explicit computation of the feature map
# sequences and subsequences of nodes
from 𝑥J to em
Normalization factor
h-hop Item Node-Based
Neighborhood Graph Kernel
𝜙»¼ 𝑥J = (𝑤<M,ª∗¶
, …, 𝑤<M,ª∗¾
,… , 𝑤<M,ª∗¿
)
𝑘»¼ 𝑥J, 𝑥K = 𝜙»¼ 𝑥J , 𝜙»¼ 𝑥K
𝑤<M,ª∗¾
=
#𝑝 ∗º (𝑥J)
𝑝º − 𝑝 ∗º

136.
Hybrid Recommendation via
Feature Combination
The hybridizations is based on the combination of different data
sources
Final approach: collaborative + LOD + textual description + tags
Users who ratedthe item
u1 u2 u3 …. entity1 entity2 …. keyw1 keyw2 … tag1 …
entities from the knowledge
graph (explicit feature mapping)
Keywords extractedfrom
the textual description
tags associated to the item
Item Feature Vector

137.
Accuracy
All items protocol

138.
Long Tail

139.
Aggregate Diversity

140.
Implementation
• LODreclib – a Java library to build a LOD based
recommendersystem
https://github.com/sisinflab/lodreclib
• Cinemappy (currently for iOS only) – a
context-awaremobile recommender system
https://itunes.apple.com/it/app/cinemappy/id6
81762350?mt=8

141.
Implementation
V. C. Ostuni et al., Mobile Movie Recommendations with Linked Data. CD-ARES 2013: 400-415

142.
Dataset selection

143.
Select the domain(s) of your RS
SELECT count(?i) AS ?num ?c
WHERE {
?i a ?c .
FILTER(regex(?c, "^http://dbpedia.org/ontology")) .
}
ORDER BY DESC(?num)

144.
Open issues
• Generalize to graph pattern extraction to represent
features
• Automatically select the triples related to the domain
of interest
• Automatically select meaningful properties to
represent items
• Analysis with respect to «knowledge coverage» of the
dataset
– What is the best approach?
• Cross-domain recommendation
• More graph-based similarity/relatedness metrics

145.
Does the LOD dataset selection
matter?
Phuong Nguyen, Paolo Tomeo, Tommaso Di Noia, Eugenio Di Sciascio. Content-based recommendations via DBpedia and Freebase: a case study
in the music domain. The 14th International Semantic Web Conference - ISWC 2015

146.
Conclusions
• Linked Open Data to enrich the content descriptions of
item
• Exploit different characteristcs of the semantic network
to represent/learn features
• Improved accuracy
• Improved novelty
• Improved Aggregate Diversity
• Entity linking for a better expoitation of text-based data
• Select the right approach, dataset, set of properties to
build your RS

147.
Not covered here
• User profile
• Preferences
• Context-aware
• Knowledge-based approaches
• Cross-domain
• Feature selection
• …

148.
Q & A
Tommaso Di Noia
tommaso.dinoia@poliba.it
@TommasoDiNoia