recommender systems and linked open data
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Recommender Systemsand
Linked Open Data
Tommaso Di Noia
Polytechnic University of BariITALY
11th Reasoning Web Summer School – Berlin August 1, 2015
tommaso.dinoia@poliba.it@TommasoDiNoia
Agenda
• A quick introduction to Linked Open Data
• Recommender systems
• Evaluation
• Recommender Systems and Linked Open Data
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
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.
URI
• Every resource/entity/thing/relation isidentified by a (unique) URI
– URI: <http://dbpedia.org/resource/Berlin>
– CURIE: dbpedia:Berlin
– URI: <http://purl.org/dc/terms/subject>
– CURIE: dcterms:subject
Which vocabularies/ontologies?
• Most popular on http://prefix.cc (July 25, 2015)
– 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/
Which vocabularies/ontologies?
• Most popular on http://lov.okfn.org (July 25, 2015)
– 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/
RDF – Resource Description Framework
• Basic element: triple
[subject] [predicate] [object]
URI URI
URI | Literal
"string"@lang | "string"^^datatype
RDF – Resource Description Framework
dbpedia:Berlin dbo:country dbpedia:Germany .
dbpedia:Berlin rdfs:label "Berlin"@en .
dbpedia:Berlin rdfs:label "Berlino"@it .
dbpedia:Berlin dbo:populationTotal "3517424"^^xsd:integer .
dbpedia:Berlin dcterms:subject category:Capitals_in_Europe .
dbpedia:Berlin rdf:type yago:UrbanArea108675967 .
dbpedia:Germany dbo:language dbpedia:German_Language .
dbpedia:Germany dbo:firstDriverCountry dbpedia:2014_German_Grand_Prix .
RDF – Resource Description Framework
Germany Berlin
2014_German_Grand_Prix
German_Language
Capitals_in_Europe
UrbanArea108675967"Berlin"@en
"Berlin"@it
"3517424"^^xsd:integer
country
language
firstDriverCountry
type
subject
label
populationTotal
RDFS and OWL in two statements
dbo:country rdfs:range dbo:Country .
dbpedia:Berlin owl:sameAs freebase:Berlin .
SPARQL
PREFIX dbo: <http://dbpedia.org/ontology/>PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>PREFIX dcterms: <http://purl.org/dc/terms/>PREFIX category: <http://dbpedia.org/resource/Category:>
SELECT DISTINCT ?name ?city WHERE {?city dcterms:subject category:Capitals_in_Europe .?city rdfs:label ?name .?city dbo:populationTotal ?population .FILTER (?population < 30000) .}
SPARQL
curl -g -H 'Accept: application/json' 'http://dbpedia.org/sparql?query=SELECT+DISTINCT+?name+?city+WHERE+{?city+dcterms:subject+category:Capitals_in_Europe+.+?city+rdfs:label+?name+.+?city+dbpedia-owl:populationTotal+?population+.+FILTER+(?population+<+30000)+.}'
Personalized Information Access
• Help the user in finding the information theymight be interested in
• Consider their preferences/past behaviour
• Filter irrelevant information
Recommender Systems
• Help users in dealing with Information/Choice Overload• Help to match users with items
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, 2011.]
The problem
• Estimate a utility function to automatically predict how much a user will like an item which is unknown to them.
InputSet of users
Set of items
Utility function
𝑈 = {𝑢1 , … , 𝑢𝑀}
𝑋 = {𝑥1 , … , 𝑥𝑁}
𝑓: 𝑈 × 𝑋 → 𝑅
∀ 𝑢 ∈ 𝑈, 𝑥𝑢′ = arg 𝑚𝑎𝑥𝑥∈𝑋 𝑓(𝑢, 𝑥)
Output
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
Mat
rix
Tita
nic
I lo
ve s
ho
pp
ing
Arg
o
Love
Act
ual
ly
The
han
gove
r
Tommaso
Vito
Phuong
Jessica
Paolo
The rating matrix(in the real world)
5 ? ? 4 3 ?
2 4 5 ? 5 ?
? 3 ? 4 ? 3
3 5 ? 5 2 ?
4 4 5 ? 5 2
The
Mat
rix
Tita
nic
I lo
ve s
ho
pp
ing
Arg
o
Love
Act
ual
ly
The
han
gove
r
Tommaso
Vito
Phuong
Jessica
Paolo
Recommendation techniques
• Content-based
• Collaborative filtering
• Demographic
• Knowledge-based
• Community-based
• Hybrid recommender systems
Collaborative Recommender Systems
Collaborative RSs recommend items to a user by identifying other users with a similar profile
Recommender System
User profile
Users
Item7
Item15Item11…
Top-N Recommendations
Item1, 5Item2, 1Item5, 4Item10, 5….
….
Item1, 4Item2, 2Item5, 5Item10, 3….
Item1, 4Item2, 2Item5, 5Item10, 3….
Item1, 4Item2, 2Item5, 5Item10, 3….
Content-based Recommender Systems
Recommender System
User profile
Item7
Item15Item11…
Top-N Recommendations
Item1, 5Item2, 1Item5, 4Item10, 5….
Items
Item1Item2
Item100Item’s
descriptions
….
CB-RSs recommend items to a user based on their description and on the profile of the user’s interests
Knowledge-based Recommender Systems
Recommender System
Item7
Item15Item11…
Top-N Recommendations
Items
Item1Item2
Item100Item’s descriptions
….
KB-RSs recommend items to a user based on their description and domain knowledge encoded in a knowledge base
Knowledge-base
Collaborative Filtering
• Memory-based
– Mainly based on k-NN
– Does not require any preliminary model building phase
• Model-based
– Learn a predictive model before computingrecommendations
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
Mat
rix
Tita
nic
I lo
ve s
ho
pp
ing
Arg
o
Love
Act
ual
ly
The
han
gove
r
Tommaso
Vito
Phuong
Jessica
Paolo
Pearson’s correlation coefficient
Rate prediction
= 𝑋
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
Mat
rix
Tita
nic
I lo
ve s
ho
pp
ing
Arg
o
Love
Act
ual
ly
The
han
gove
r
𝑠𝑖𝑚 𝑥𝑖 , 𝑥𝑗 = 𝑥𝑖 ⋅ 𝑥𝑗
|𝑥𝑖| ∗ |𝑥𝑗 |=
σ 𝑟𝑢,𝑥𝑖∗ 𝑟𝑢,𝑥𝑗
𝑢
σ 𝑟𝑢,𝑥𝑖2
𝑢
∗ σ 𝑟𝑢,𝑥2
𝑢
Cosine Similarity
Rate prediction
𝑟ǁ 𝑢𝑖 , 𝑥′ = σ 𝑠𝑖𝑚 𝑥Ԧ, 𝑥Ԧ′ ∗ 𝑟𝑥,𝑢𝑖
𝑥∈𝑋𝑢𝑖
σ 𝑠𝑖𝑚 𝑥Ԧ, 𝑥Ԧ′ 𝑥∈𝑋𝑢𝑖
Adjusted Cosine Similarity
= 𝑋𝑢𝑖
Tommaso
Vito
Phuong
Jessica
Paolo
Content-Based Recommender Systems
• Items are described in terms of attributes/features
• A finite set of values is associated to eachfeature
• Item representation is a (Boolean) vector
Content-based
CB-RSs try to recommend items similar* to those a given user has liked in the past
[P. Lops, M. de Gemmis, G. Semeraro. Content-based Recommender Systems: State of the Art and Trends. Recommender Systems Handbook. 2011]
• Heuristic-based
– Usually adopt techniques borrowed from IR
• Model-based
– Often we have a model for each user
(*) similar from a content-based perspective
Knowledge-basedRecommender Systems
• Conversational approaches
• Reasoning techniques
– Case-based reasoning
– Constraint reasoning
Hybrid recommender systems
• Weighted
• Switching
• Mixed
• Feature combination
• Cascade
• Feature augmentation
• Meta-level
Robin D. Burke. Hybrid recommender systems: Survey and experiments. User Model. User-Adapt. Interact., 12(4):331–370, 2002.
Protocols
• Rated test-items
• All unrated items: compute a score for everyitem not rated by the user (also items notappearing in the user test set)
MAE and RMSE drawback
• Not very suitable for top-N recommendation
– Errors in the highest part of the recommendationlist are considered in the same way as the ones in the lowest part
Accuracy metrics for top-N recommendation
𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛 @ 𝑁
𝑃𝑢@𝑁 = |𝐿𝑢 𝑁 ∩ 𝑇𝑆𝑢
+|
𝑁
𝑅𝑒𝑐𝑎𝑙𝑙 @ 𝑁
𝑅𝑢@𝑁 = |𝐿𝑢 𝑁 ∩ 𝑇𝑆𝑢
+|
|𝑇𝑆𝑢+|
𝑛𝑜𝑟𝑚𝑎𝑙𝑖𝑧𝑒𝑑 𝐷𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝐶𝑢𝑚𝑢𝑙𝑎𝑡𝑖𝑣𝑒 𝐺𝑎𝑖𝑛 @ 𝑁
𝐿𝑢 𝑁 is the recommendation list up to the N-th element
𝑇𝑆𝑢+ is the set of relevant test
items for 𝑢
𝐼𝐷𝐶𝐺@𝑁 indicates the score Obtained by an ideal ranking of 𝐿𝑢 𝑁
Is all about precision?
• Diversity
– Avoid to recommend only items in a small subset of the catalog
– Suggest diverse items in the recommendation list
• Novelty
– Recommend items in the long tail
• Serendipity
– Suggest unexpected but interesting items
Diversity
𝐼𝑛𝑡𝑟𝑎 − 𝐿𝑖𝑠𝑡 𝐷𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦
𝐼𝐿𝐷𝑢@𝑁 = 1
2⋅ 1 − 𝑠𝑖𝑚 𝑥𝑖 , 𝑥𝑗
𝑥𝑗∈𝐿𝑢 𝑁
𝑥𝑖∈𝐿𝑢 𝑛
𝐼𝐿𝐷@𝑁 = 1
|𝑈|⋅ 𝐼𝐿𝐷𝑢@𝑁
𝑢∈𝑈
𝐴𝑔𝑔𝑟𝑒𝑔𝑎𝑡𝑒 𝐷𝑖𝑣𝑒𝑟𝑠𝑖𝑡𝑦
𝐴𝐷𝑖𝑛@𝑁 = | ڂ 𝐿𝑢(𝑁)
𝑢∈𝑈 |
|𝑋|
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
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
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.*
(*) P. Lops, M. de Gemmis, G. Semeraro. Content-based Recommender Systems: State of the Art and Trends. In: P. Kantor, F. Ricci, L. Rokach and B. Shapira, editors, Recommender Systems Handbook: A Complete Guide for Research Scientists & Practitioners
The quality of CB recommendations are correlated with the quality of the features that are explicitly associated with the items.
Limited Content Analysis
Traditional Content-based RecSys
• 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
Semantic-aware approaches
Traditional Ontological/Semantic
Recommender Systems
make use of limited
domain
ontologies;
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 diagram 2014, by Max Schmachtenberg, Christian Bizer, Anja Jentzsch and Richard Cyganiak. http://lod-cloud.net/
Why RS + LOD
• Standardized access to dataPREFIX 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 .
}
}
Direct item Linking
dbpedia:I_Am_Legend_(film)
dbpedia:Troy_(film)
dbpedia:Scarface_(1983_film)
dbpedia:Scarface:_The_World_Is_Yours
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")
}
Direct item Linking
• Other approaches
– DBpedia Lookup
https://github.com/dbpedia/lookup
– Silk Framework
http://silk-framework.com/
Item Graph Analyzer
• Build your own knowledge graph
– Select relevant properties. Possible solutions:
• Ontological properties
• Categorical properties
• Frequent properties
– Explore the graph up to a limited depth
Different item featuresrepresentations
• Direct properties
• Property paths
• Node paths
• Neighborhoods
• …
Datasets
Subset of Movielens mapped to DBpedia
Subset of Last.fm mapped to DBpedia
Subset of The Library Thing mapped to DBpedia
Mappings
http://sisinflab.poliba.it/semanticweb/lod/recsys/datasets/
Vector Space Model for LOD
Righteous Kill
starringdirector
subject/broadergenre
Heat
Ro
ber
tD
e N
iro
Joh
n A
vne
t
Seri
al k
ille
r fi
lms
Dra
ma
Al P
acin
oB
rian
Den
neh
y
He
ist
film
sC
rim
efi
lms
starring
Ro
be
rtD
e N
iro
Al P
acin
o
Bri
an D
en
ne
hy
Righteous KillHeat
… …
Vector Space Model for LOD
Righteous Kill
STARRINGAl Pacino
(v1)
Robert De Niro
(v2)
BrianDennehy
(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
𝑤𝐴𝑙𝑃𝑎𝑐𝑖𝑛𝑜,𝐻𝑒𝑎𝑡 = 𝑡𝑓𝐴𝑙𝑃𝑎𝑐𝑖𝑛𝑜,𝐻𝑒𝑎𝑡 ∗ 𝑖𝑑𝑓𝐴𝑙𝑃𝑎𝑐𝑖𝑛𝑜
Vector Space Model for LOD
Righteous Kill
STARRINGAl Pacino
(v1)
Robert De Niro
(v2)
BrianDennehy
(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
𝑤𝐴𝑙𝑃𝑎𝑐𝑖𝑛𝑜,𝐻𝑒𝑎𝑡 = 𝑡𝑓𝐴𝑙𝑃𝑎𝑐𝑖𝑛𝑜,𝐻𝑒𝑎𝑡 ∗ 𝑖𝑑𝑓𝐴𝑙𝑃𝑎𝑐𝑖𝑛𝑜
𝑡𝑓 ∈ {0,1}
Vector Space Model for LOD
+
+
+
… =
𝒔𝒊𝒎𝒔𝒕𝒂𝒓𝒓𝒊𝒏𝒈(𝒙𝒊 , 𝒙𝒋) = 𝒘𝒗𝟏,𝒙𝒊
∗ 𝒘𝒗𝟏,𝒙𝒋+ 𝒘𝒗𝟐,𝒙𝒊
∗ 𝒘𝒗𝟐,𝒙𝒋+ 𝒘𝒗𝟑,𝒙𝒊
∗ 𝒘𝒗𝟑,𝒙𝒋
𝒘𝒗𝟏,𝒙𝒊𝟐 + 𝒘𝒗𝟐,𝒙𝒊
𝟐 + 𝒘𝒗𝟑,𝒙𝒊𝟐
∗ 𝒘𝒗𝟏,𝒙𝒋
𝟐 + 𝒘𝒗𝟐,𝒙𝒋𝟐 + 𝒘𝒗𝟑,𝒙𝒋
𝟐
𝜶𝒔𝒕𝒂𝒓𝒓𝒊𝒏𝒈 ∗ 𝒔𝒊𝒎𝒔𝒕𝒂𝒓𝒓𝒊𝒏𝒈(𝒙𝒊, 𝒙𝒋)
𝜶𝒅𝒊𝒓𝒆𝒄𝒕𝒐𝒓 ∗ 𝒔𝒊𝒎𝒅𝒊𝒓𝒆𝒄𝒕𝒐𝒓(𝒙𝒊, 𝒙𝒋)
𝜶𝒔𝒖𝒃𝒋𝒆𝒄𝒕 ∗ 𝒔𝒊𝒎𝒔𝒖𝒃𝒋𝒆𝒄𝒕(𝒙𝒊, 𝒙𝒋)
𝒔𝒊𝒎 (𝒙𝒊, 𝒙𝒋)
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
If this similarity is greater or equal to 0, we suggest the movie m to the user u.
Tommaso Di Noia, Roberto Mirizzi, Vito Claudio Ostuni, Davide Romito, Markus Zanker. Linked Open Data to support Content-based Recommender Systems. 8th International Conference on Semantic Systems (I-SEMANTICS) - 2012
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
If this similarity is greater or equal to 0, we suggest the movie m to the user u.
Selected properties
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
If this similarity is greater or equal to 0, we suggest the movie m to the user u.
heuristic-based → model-based
Property subset evaluation
The subject+broadersolution is better than only subject or subject+morebroaders.
The best solution is achieved with subject+broader+genres.
Too many broadersintroduce noise.
Rated test items protocol
Path-based features
Analysis of complex relations between the user preferences and the target item
Vito Claudio Ostuni, Tommaso Di Noia, Eugenio Di Sciascio, Roberto Mirizzi. Top-N Recommendations from Implicit Feedback leveraging Linked Open Data.
7th Conference on Recommender Systems (RecSys ) – 2013
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
Data modelImplicit 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
Data modelImplicit 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
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 (s, p2 , p1)
Problem formulation
Feature vector
Set of irrelevant items for u
Set of relevant items for u
Training Set
Sample of irrelevant items for u
𝑋𝑢+ = 𝑥 ∈ 𝑋 𝑠Ƹ𝑢𝑥 = 1}
𝑋𝑢− = 𝑥 ∈ 𝑋 𝑠Ƹ𝑢𝑥 = 0}
𝑋𝑢−∗ ⊆ 𝑋𝑢
−
𝑤𝑢𝑥 ∈ ℝ𝐷
TR = ڂ < 𝑤𝑢𝑥 , 𝑠Ƹ𝑢𝑥 > 𝑥 ∈ (𝑋𝑢+ ∪ 𝑋𝑢
−∗)} 𝑢
u1
u2
u3
e1
e3
e4
e2
e5
u4
Path-based features
path(1) (s, s, s) : 2path(2) (s, p2, p1) : 1
x1
x2
x3
x4
u1
u2
u3
e1
e3
e4
e2
e5
u4
Path-based features
path(1) (s, s, s) : 2path(2) (s, p2, p1) : 2
x1
x2
x3
x4
u1
u2
u3
e1
e3
e4
e2
e5
u4
Path-based features
path(1) (s, s, s) : 2path(2) (s, p2, p1) : 2path(3) (s, p2, p3, p1) : 1
x1
x2
x3
x4
Path-based features
path(1) (s, s, s) : 2path(2) (s, p2, p1) : 2path(3) (s, p2, p3, p1) : 1
u1
u2
u3
e1
e3
e4
e2
e5
u4
x1
x2
x3
x4
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
reca
ll@5
user profile size
Movielens
BagBoo
GBRT
Sum
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
reca
ll@5
user profile size
Last.fm
BagBoo
GBRT
Sum
Comparative approaches
• 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
MyMediaLite
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
pre
cisi
on
@5
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
pre
cisi
on
@5
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
subjectsubject
subject
subject
subject
subject
Vito Claudio Ostuni, Tommaso Di Noia, Roberto Mirizzi, Eugenio Di Sciascio. A Linked Data Recommender System using a Neighborhood-based Graph Kernel. The 15th International Conference on Electronic Commerce and Web Technologies – 2014
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
subjectsubject
broader
broader
broader
broader
broader
subject
subject
subject
subject
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
subjectsubject
broader
broader
broader
broader
broader
subject
subject
subject
subject
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
subjectsubject
broader
broader
broader
broader
broader
broader
subject
subject
subject
subject
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
subjectsubject
broader
broader
broader
broader
broader
broader
subject
subject
subject
subject
Exploit entities descriptions
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
subjectsubject
broader
broader
broader
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)
𝜙𝑥Input 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)>
h-hop Item Entity-based Neighborhood Graph Kernel
Explicit computation of the feature map
entity importance in the item neighborhood graph
𝑘𝐺ℎ 𝑥𝑖, 𝑥𝑗 = 𝜙𝐺ℎ 𝑥𝑖 , 𝜙𝐺ℎ 𝑥𝑗
𝜙𝐺ℎ 𝑥𝑖 = (𝑤𝑥𝑖 ,𝑒1, 𝑤𝑥𝑖 ,𝑒2
, …, 𝑤𝑥𝑖 ,𝑒𝑚, … , 𝑤𝑥𝑖 ,𝑒𝑡
)
Explicit computation of the feature map
# edges involving 𝑒𝑚 at l hops from 𝑥𝑖
a.k.a. frequency of the entity in the
item neighborhood graph
factor taking into account at which hop the entity appears
h-hop Item Entity-based Neighborhood Graph Kernel
𝑘𝐺ℎ 𝑥𝑖, 𝑥𝑗 = 𝜙𝐺ℎ 𝑥𝑖 , 𝜙𝐺ℎ 𝑥𝑗
𝜙𝐺ℎ 𝑥𝑖 = (𝑤𝑥𝑖 ,𝑒1, 𝑤𝑥𝑖 ,𝑒2
, …, 𝑤𝑥𝑖 ,𝑒𝑚, … , 𝑤𝑥𝑖 ,𝑒𝑡
)
Weights computation example
i
e1e2
p3
p2
e4
e5
p3p3
h=2
𝑐𝑃1 𝑥𝑖 ,𝑒1= 2
𝑐𝑃1 𝑥𝑖 ,𝑒2= 1
𝑐𝑃2 𝑥𝑖 ,𝑒4= 1
𝑐𝑃2 𝑥𝑖 ,𝑒5= 2
Weights computation example
i
e1e2
p3
p2
e4
e5
p3p3
h=2
𝑐𝑃1 𝑥𝑖 ,𝑒1= 2
𝑐𝑃1 𝑥𝑖 ,𝑒2= 1
𝑐𝑃2 𝑥𝑖 ,𝑒4= 1
𝑐𝑃2 𝑥𝑖 ,𝑒5= 2
Informative entity about the item even if not directly related to it
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]
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
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
Rated test items protocol
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
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
FreeSound Knowledge Graph
Item textual descriptions enrichment: Entity Linking tools can be usedto enrich item textual descriptions with LOD
Explicit computation of the feature map
# sequences and subsequences of nodes
from 𝑥𝑖 to em
Normalization factor
h-hop Item Node-Based Neighborhood Graph Kernel
𝜙𝐺ℎ 𝑥𝑖 = (𝑤𝑥𝑖 ,𝑝∗1, …, 𝑤𝑥𝑖 ,𝑝∗𝑚
, … , 𝑤𝑥𝑖 ,𝑝∗𝑡)
𝑘𝐺ℎ 𝑥𝑖, 𝑥𝑗 = 𝜙𝐺ℎ 𝑥𝑖 , 𝜙𝐺ℎ 𝑥𝑗
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 rated the item
u1 u2 u3 …. entity1 entity2 …. keyw1 keyw2 … tag1 …
entities from the knowledgegraph (explicit feature mapping)
Keywords extracted from the textual description
tags associated to the item
Item Feature Vector
• Feature combination hybrid approach
• adding collaborative features to item content feature vectors can improveconsiderably recommendation accuracy
• Semantic Enrichment
• semantics can help in improving different performances beyond accuracysuch as novelty and catalog coverage
Hybrid approaches: some lessons learnt
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)
A comparison betweenDBpedia and Freebase
Accuracy Coverage Diversity Novelty
Freebase + + - -
DBpedia - - + +
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
A comparison betweenDBpedia and Freebase
Accuracy Coverage Diversity Novelty
1-hop - - - +
2-hop + + + -
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
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
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 «knowledgecoverage» of the dataset– What is the best approach?
Many thanks to the RecSys crew @ SisInf Lab
Roberto Mirizzi
now at Yahoo! CA
Vito Claudio Ostuni
now at
Jessica Rosati
Phd Fellowship Awardee @
Paolo Tomeo
Jindřich Mynarz
Phuong Nguyen
Sergio Oramas
Aleksandra Karpus
Visiting Students and PostDoc
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