A Generic Semantic-based Framework for Cross …ir.ii.uam.es/hetrec2011/res/slides/hetrec2011_slides04.pdfA Generic Semantic-based Framework for Cross-domain Recommendation Ignacio

A Generic Semantic-based Framework

for Cross-domain Recommendation

Ignacio Fernández-Tobías1, Marius Kaminskas2, Iván Cantador1, Francesco Ricci2 1 Escuela Politécnica Superior, Universidad Autónoma de Madrid, Spain [email protected], [email protected]

2 Faculty of Computer Science, Free University of Bozen-Bolzano, Italy [email protected], [email protected]

1

A Generic Semantic-based Framework for Cross-domain Recommendation

2nd Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec 2011)

5th ACM Conference on Recommender Systems (RecSys 2011) - Chicago, IL, USA - October 23-27, 2011

• Cross-domain recommendation

• Case study: adapting music recommendation to points of interest

• A semantic-based framework for cross-domain recommendation

• Semantic-based knowledge representation

• Semantic graph-based recommendation algorithm

• Preliminary results

• Future work

Contents

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• Future work

Contents

3




Cross-domain recommendation

• Recommender systems can help users to make choices, by proactively

finding relevant items or services, taking into account or predicting the

users’ tastes, priorities and goals

• The vast majority of the currently available recommender systems predict

the user’s relevance of items in a specific and limited domain

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• In some applications, it could be useful to offer the user joint personalized

recommendations of items belonging to multiple domains

• In an e-commerce site, we may suggest movies or videogames based on a particular

book bought by a costumer

• In a travel application, we may suggest cultural events may interest a person who has

booked a hotel in a particular place

• In an e-learning system, we may suggest educational websites with topics related to a

video documentary a student has seen

• Potential benefits

• Offering diversity and serendipity

• Addressing the user cold-start problem (on the target domain)

• Mitigating the sparsity problem

5





• Some real applications do already recommend items from different

domains, but

• their recommendations rely on statistical analysis of popular items, without any

personalization strategy, or

• most of them only exploit information about the user preferences available in the target

domain

6





• Research questions [Winoto & Tang, 2008]

1. At community level, are there correlations between user preferences for items

belonging to the different domains of interest?

2. At individual level, can we build a recommendation model where each user’s

preferences in source domains are used to predict/adapt her preferences in target

domains?

3. How should we evaluate the effectiveness of cross-domain item recommendations?

[Winoto & Tang, 2008] Winoto, P., Tang, T. 2008. If You Like the Devil Wears Prada the Book, Will You also Enjoy the Devil Wears

Prada the Movie? A Study of Cross-Domain Recommendations. New Generation Computing 26(3), 209-225.

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• Future work

Contents

8




• Recommending music artists that suit places of interest (POIs)

• Mobile city guide soundtrack

• Adaptive music playlist in a car

Case study: adapting music recommendation to points of interest

[Braunhofer et al., 2011] Braunhofer, M., Kaminskas, M., Ricci, F. 2011. Recommending Music for Places of Interest in a Mobile

Travel Guide. 5th ACM Conference on Recommender Systems.

9





• In a previous work [Kaminskas & Ricci, 2011], emotional tags were used to

manually annotate places and music

• Emotional tags can be used to find matching between music and places of interest

‐ e.g. a monument and a music track may be described as ‘strong’ and ‘triumphant’

[Kaminskas & Ricci, 2011] Kaminskas, M., Ricci, F. 2011. Location-Adapted Music Recommendation Using Tags. 19th International

Conference on User Modeling, Adaptation and Personalization, 183-194.

10





• In this work, we aim at automatically finding semantic relations between

POIs and music artists

• We propose to explore the Web of Data (Linked

Data) to find such relations

• Specifically, we propose to exploit DBpedia, the

Linked Data version of Wikipedia

• DBpedia can be considered as a core ontology in

the Web of Data

• Connected to many other ontologies

• Describing and linking more than 3.5 million

concepts from a large variety of knowledge

domains

11





• In this work, we aim at automatically finding semantic relations between

POIs and music artists

• We propose to explore the Web of Data (Linked

Data) to find such relations

• Specifically, we propose to exploit DBpedia, the

Linked Data version of Wikipedia

• DBpedia can be considered as a core ontology in

the Web of Data

• Connected to many other ontologies

• Describing and linking more than 3.5 million

concepts from a large variety of knowledge

domains

12





• Issues to investigate, identified in [Winoto & Tang, 2008]

1. Correlations between user preferences for items of the different domains

Correlations between POIs and music were established through tags in

[Kaminskas & Ricci, 2011]

2. Recommendation model to predict/adapt user preferences across domains

This paper addresses this particular issue, presenting a semantic-based

framework to support cross-domain recommendation

3. Evaluation of cross-domain recommendation effectiveness

Future work

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• Future work

Contents

14




• Goal: finding semantic relations between a given POI and music artists

• Example: music artists related to the ‘Vienna State Opera’

• Identified relations:

• Geographical: artists who were born, died or lived in Vienna

• Time-based: artists who were born, died or lived in the year (decade, century) the

State Opera of Vienna was built

• Category-based: artists who belong to music categories that are related through

keywords to architecture structures/styles identified with the building of the Opera of

Vienna

• Tags: artists annotated with tags also assigned to the Opera of Vienna

A Semantic-based framework for cross-domain recommendation

Vienna State Opera Wolfgang Amadeus Mozart

15





• A directed Acyclic Graph (DAG) representing semantic relations between

concepts in two domains

State Opera

of Vienna

State Opera

of Vienna

Vienna Austria Vienna Austria

19th century

19th century

Opera houses Opera houses

opera opera Opera

composers Opera

composers

Mozart Mozart

Brahms Brahms Bizet Bizet

Ballet venues Ballet

venues ballet ballet

Ballet composers

Ballet composers

Arnold Schoenberg

Arnold Schoenberg

POI

CITY

TIME

ARCHITECTURE CATEGORY

KEYWORD MUSIC CATEGORY

MUSIC ARTIST instance

class

16





• The previous graph can be considered as a particular instance of a

semantic class/category network

• The selection of classes and relations is guided by experts on the domains

of interest and knowledge repositories

POI POI

CITY CITY

TIME TIME



KEYWORD KEYWORD MUSIC

CATEGORY MUSIC

CATEGORY

MUSIC ARTIST MUSIC ARTIST

located in

was built

belongs to

subcategory of subcategory of

was born, died, lived in

was born, died, lived in

has keyword keyword of

17





• As a proof of concept, we have built our approach by exploiting DBpedia

ontology in two stages:

1. Manually identifying DBpedia classes and relations belonging to the domains of

interest to define the semantic-based knowledge representation

2. Automatically obtaining related DBpedia instances according to the classes and

relations identified in the first stage

POI POI MUSIC ARTIST MUSIC ARTIST

Semantic framework Semantic network 1 2

Vienna State Opera

Wolfgang Amadeus Mozart

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• Future work

Contents

19




Semantic graph-based recommendation algorithm

• In the semantic network, a final score for each concept can be computed by

weight spreading strategies

• Initial weight values for concepts and relations must be established

State Opera

of Vienna

State Opera

of Vienna


19th century

19th century


opera opera Opera

composers Opera

composers

Mozart Mozart

Brahms Brahms Bizet Bizet



Ballet composers

Ballet composers

Arnold Schoenberg

Arnold Schoenberg

1

1

0.3

0.5

0.5

1

1

0.3

0.4

0.4

0.4

0.4

0.6

0.6

0.6

0.3

0.6

20





State Opera

of Vienna

State Opera

of Vienna


19th century

19th century


opera opera Opera

composers Opera

composers

Mozart Mozart

Brahms Brahms

Bizet Bizet



Ballet composers

Ballet composers

Arnold Schoenberg

Arnold Schoenberg

1

1

0.3

0.5

0.5

1

1

0.3

0.4

0.4

0.4

0.4

0.6

0.6 0.6

0.6

1·1=1

1·0.3=0.3

1·0.5=0.5

1·0.5=0.5

0.3

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State Opera

of Vienna

State Opera

of Vienna


19th century

19th century


opera opera Opera

composers Opera

composers

Mozart Mozart

Brahms Brahms

Bizet Bizet



Ballet composers

Ballet composers

Arnold Schoenberg

Arnold Schoenberg

1

1

0.3

0.5

0.5

1

1

0.3

0.4

0.4

0.4

0.4

0.6

0.6 0.6

0.3

0.5

0.5

0.5·0.4=0.2

0.5·0.4=0.2

0.3

1

0.6

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0.2·0.4=0.08

State Opera

of Vienna

State Opera

of Vienna


19th century

19th century


opera opera Opera

composers Opera

composers

Mozart Mozart

Brahms Brahms

Bizet Bizet



Ballet composers

Ballet composers

Arnold Schoenberg

Arnold Schoenberg

1

1

0.3

0.5

0.5

1

1

0.3

0.4

0.4

0.4

0.4

0.6

0.6 0.6

0.6

0.2

0.2 0.2·0.4=0.08

0.3

0.3

0.5

0.5

1

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0.08

State Opera

of Vienna

State Opera

of Vienna


19th century

19th century


opera opera Opera

composers Opera

composers

Mozart Mozart

Brahms Brahms

Bizet Bizet



Ballet composers

Ballet composers

Arnold Schoenberg

Arnold Schoenberg

1

1

0.3

0.5

0.5

1

1

0.3

0.4

0.4

0.4

0.4

0.6

0.6 0.6

0.6

0.08

1·1+0.08·0.6+0.08·0.6+0.3·0.3=1.186

0.08·0.6=0.048

1·1+0.08·0.6=1.048

0.3·0.3=0.09

0.3

0.3

0.5

0.5

1

0.2

0.2

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0.08

State Opera

of Vienna

State Opera

of Vienna


19th century

19th century


opera opera Opera

composers Opera

composers

Mozart Mozart

Brahms Brahms

Bizet Bizet



Ballet composers

Ballet composers

Arnold Schoenberg

Arnold Schoenberg

1

1

0.3

0.5

0.5

1

1

0.3

0.4

0.4

0.4

0.4

0.6

0.6 0.6

0.6

0.08·0.6=0.048

1·1+0.08·0.6=1.048

0.3·0.3=0.09

1·1+0.08·0.6+0.08·0.6+0.3·0.3=1.186

0.3

0.3

0.5

0.5

1

0.2

0.2 0.08

25





• The initial weights of an edge in the graph can depend on the relevance of

the linked instances and of the corresponding semantic classes

• These relevance values could be assigned in different ways

),(rel),',(rel)',( 'r IIr CCIIfIIV

Class relevance Domain expert

e.g. a city is more informative to link a POI than a keyword

Instance relevance User profile

e.g. an interest in Mozart’s compositions the relevance

for Mozart gets higher

Relation relevance Entity semantic similarity

e.g. co-occurrences of concepts ‘Mozart’ and ‘Vienna’ within a

document collection

26





• In general, the weight of an instance not only depends on its relevance

value and that of its class, but also inductively on the weights of the

predecessors in the network

kII ,,1

),(,),,();(,),();(rel),(rel)( 11ee IIVIIVIWIWCIgIW kkI

]1,0[),,(rel)1()',(rel)',( 'rr II CCIIIIV

k

p

Ipp CIIVIWIW1

e ]1,0[),(rel)1(),()()(

• To preliminarily test our approach we have implemented a simple retrieval

algorithm computing weights by linear combination

27










• Future work

Contents

28




Preliminary results

• Example: ‘Vienna State Opera’ (Vienna, Austria)

29




Preliminary results

• Top 10 musicians for ‘Vienna State Opera’

Music artist Top music genres Born/Death countries Date

Arnold Schoenberg Classical

Avant-garde

Austria

USA 20th century

Wolfgang Amadeus Mozart Classical

Instrumental

Austria

Austria 18th century

Emil von Reznicek Classical

Opera

Austria

Germany 20th century

Alban Berg Classical

Contemporary

Hungary


Ludwig van Beethoven Classical

Instrumental

Germany


Antonio Vivaldi Classical

Baroque

Italy


Giovanni Felice Sances Classical

Baroque

Italy


Fritz Kreisler Classical

Violin

Austria

USA 20th century

Georg Christoph Wagenseil Classical

Baroque

Austria


Antonio Salieri Classical

Italian

Italy


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Preliminary results

• Example: found relations between ‘Vienna State Opera’ and ‘Wolfgang

Amadeus Mozart’ PLACE OF INTEREST: Vienna State Opera

CITY: Vienna, Austria

MUSIC ARTIST: Wolfgang Amadeus Mozart

ARCHITECTURE CATEGORY: Opera houses

KEYWORD: opera

MUSIC CATEGORY: Opera composers


TAG: energetic



TAG: sentimental



MUSIC GENRE: classical


ARCHITECTURE CATEGORY: Theatres

TAG: animated

MUSIC GENRE: classical


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Preliminary results

• Example: ‘Wembley Stadium’ (London, UK)

32




Preliminary results

• Top 10 musicians for ‘Wembley Stadium’

Music artist Top music genres Born/Death Countries Date

Beady Eye

(Oasis band members)

Rock

British

UK

(origin) 2009

Operahouse Indie Rock

British

UK

(origin) 2006

The Woe Betides Rock

Grunge

UK

(origin) 2008

Skunk Anansie Rock

Female vocalist

UK

(origin) 1994

The Fallen Leaves Garage

Acoustic

UK

(origin) 2004

Ivyrise Rock

Alternative

UK

(origin) 2007

Plastic Ono Band

(John Lennon & Yoko Ono)

Experimental

Avant-garde

UK

(origin) 1969

We Are Balboa Indie Rock

Female vocalist

Spain-UK

(origin) 2003

Goldhawks Rock

British

UK

(origin) 2009

Teddy Thompson Folk

British

UK

USA 1976

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Preliminary results

PLACE OF INTEREST: Wembley Stadium

CITY: London, United Kingdom

MUSIC ARTIST: Beady Eye

TIME: 2007


ARCHITECTURE CATEGORY: Music venues

ARCHITECTURE CATEGORY: Rock music venues

KEYWORD: rock

MUSIC CATEGORY: Indie rock


MUSIC CATEGORY: Rock music


TAG: strong

MUSIC CATEGORY: Rock music


• Example: found relations between ‘Wembley Stadium’ and ‘Beady Eye’

34




Preliminary results

• Automatic extraction of data from DBPedia for an input city

• Modular and extensible implementation of the framework

• Dataset

• 3098 POIs located in 21 European cities

‐ 147.5 POIs/city

• 697 architecture categories

‐ 229 are directly linked to POIs

‐ Avg. 1.4 categories/POI

• 109 keywords describing 181 different architecture categories

‐ Avg. 1.1 keywords/category

• 1568 music artists

• 1116 music categories

‐ 309 directly linked to artists (avg. 1.7 categories/artist)

‐ 511 related to keywords (avg. 1.2 keywords/category)

• Time data for 64.72% of the POIs

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• Future work

Contents

36




Future work

• Evaluation – user study

• Are semantically relations between POIs and music artists really appreciated by users

in a recommendation scenario?

• Do users find cross-domain recommendations meaningful, and prefer them over non-

adapted music suggestions?

• Providing personalized recommendations

• Cascade strategy

‐ Obtaining semantically related artists to the input POI

‐ Ranking (adding, removing) artists with a recommender based on the user’s

preferences

37




Future work

• Initializing entity and relation weights

• Exploiting data statistics to estimate the popularity of the semantic entities and

relations

• Exploring several weight spreading strategies

• Constrained Spreading Activation

‐ Node in/out degrees

‐ Weight propagation thresholds

‐ Path length thresholds

• Flow Networks

‐ Ford-Fulkerson’s algorithm to find maximum network flow

• Semi-automatic defining the semantic framework

• Automatically exploring DBpedia to identify relevant entities and relations describing

the domains of interest

A Generic Semantic-based Framework

for Cross-domain Recommendation

Ignacio Fernández-Tobías1, Marius Kaminskas2, Iván Cantador1, Francesco Ricci2 1 Escuela Politécnica Superior, Universidad Autónoma de Madrid, Spain [email protected], [email protected]

2 Faculty of Computer Science, Free University of Bozen-Bolzano, Italy [email protected], [email protected]

Documents

A Generic Semantic-based Framework for Cross …ir.ii.uam.es/hetrec2011/res/slides/hetrec2011_slides04.pdfA Generic Semantic-based Framework for Cross-domain Recommendation Ignacio