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A Generic Semantic-based Framework for Cross-domain Recommendation - - PowerPoint PPT Presentation

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A Generic Semantic-based Framework for Cross-domain Recommendation Ignacio Fernndez-Tobas 1 , Marius Kaminskas 2 , Ivn Cantador 1 , Francesco Ricci 2 1 Escuela Politcnica Superior, Universidad Autnoma de Madrid, Spain

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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

ign.fernandez01@estudiante.uam.es, ivan.cantador@uam.es

2 Faculty of Computer Science, Free University of Bozen-Bolzano, Italy

mkaminskas@unibz.it, fricci@unibz.it

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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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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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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

  • 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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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

  • 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
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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

  • 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

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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

  • 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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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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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

  • 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.

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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

Case study: adapting music recommendation to points of interest

  • 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.

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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

Case study: adapting music recommendation to points of interest

  • 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

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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

Case study: adapting music recommendation to points of interest

  • 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

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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

Case study: adapting music recommendation to points of interest

  • 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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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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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

  • 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

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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

A Semantic-based framework for cross-domain recommendation

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

concepts in two domains

State Opera

  • f

Vienna State Opera

  • f

Vienna

Vienna Austria Vienna Austria

19th century 19th century

Opera houses Opera houses

  • pera
  • pera

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

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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

A Semantic-based framework for cross-domain recommendation

  • 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
  • f interest and knowledge repositories

POI POI CITY CITY TIME TIME

ARCHITECTURE CATEGORY ARCHITECTURE CATEGORY

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

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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

A Semantic-based framework for cross-domain recommendation

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

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

  • f

Vienna State Opera

  • f

Vienna

Vienna Austria Vienna Austria

19th century 19th century

Opera houses Opera houses

  • pera
  • pera

Opera composers Opera composers

Mozart Mozart

Brahms Brahms Bizet Bizet Ballet venues Ballet venues ballet ballet

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

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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

Semantic graph-based recommendation algorithm

State Opera

  • f

Vienna State Opera

  • f

Vienna Vienna Austria Vienna Austria 19th century 19th century Opera houses Opera houses

  • pera
  • pera

Opera composers Opera composers

Mozart Mozart Brahms Brahms Bizet Bizet Ballet venues Ballet venues ballet ballet

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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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

Semantic graph-based recommendation algorithm

State Opera

  • f

Vienna State Opera

  • f

Vienna Vienna Austria Vienna Austria 19th century 19th century Opera houses Opera houses

  • pera
  • pera

Opera composers Opera composers

Mozart Mozart Brahms Brahms Bizet Bizet Ballet venues Ballet venues ballet ballet

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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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

Semantic graph-based recommendation algorithm

0.2·0.4=0.08

State Opera

  • f

Vienna State Opera

  • f

Vienna Vienna Austria Vienna Austria 19th century 19th century Opera houses Opera houses

  • pera
  • pera

Opera composers Opera composers

Mozart Mozart Brahms Brahms Bizet Bizet Ballet venues Ballet venues ballet ballet

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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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

Semantic graph-based recommendation algorithm

0.08

State Opera

  • f

Vienna State Opera

  • f

Vienna Vienna Austria Vienna Austria 19th century 19th century Opera houses Opera houses

  • pera
  • pera

Opera composers Opera composers

Mozart Mozart Brahms Brahms Bizet Bizet Ballet venues Ballet venues ballet ballet

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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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

Semantic graph-based recommendation algorithm

0.08

State Opera

  • f

Vienna State Opera

  • f

Vienna Vienna Austria Vienna Austria 19th century 19th century Opera houses Opera houses

  • pera
  • pera

Opera composers Opera composers

Mozart Mozart Brahms Brahms Bizet Bizet Ballet venues Ballet venues ballet ballet

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

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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

Semantic graph-based recommendation algorithm

  • 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 I I r

C C I I f I I V 

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

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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

Semantic graph-based recommendation algorithm

  • 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

k

I I , ,

1 

 

) , ( , ), , ( ); ( , ), ( ); ( rel ), ( rel ) (

1 1 e e

I I V I I V I W I W C I g I W

k k I

  

] 1 , [ ), , ( rel ) 1 ( ) ' , ( rel ) ' , (

' r r

        

I I C

C I I I I V

      

k p I p p

C I I V I W I W

1 e

] 1 , [ ), ( rel ) 1 ( ) , ( ) ( ) (   

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

algorithm computing weights by linear combination

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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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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

Preliminary results

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

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 Austria 20th century Ludwig van Beethoven Classical Instrumental Germany Austria 19th century Antonio Vivaldi Classical Baroque Italy Austria 18th century Giovanni Felice Sances Classical Baroque Italy Austria 17th century Fritz Kreisler Classical Violin Austria USA 20th century Georg Christoph Wagenseil Classical Baroque Austria Austria 18th century Antonio Salieri Classical Italian Italy Austria 19th century

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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

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 MUSIC ARTIST: Wolfgang Amadeus Mozart TAG: energetic MUSIC CATEGORY: Opera composers MUSIC ARTIST: Wolfgang Amadeus Mozart TAG: sentimental MUSIC CATEGORY: Opera composers MUSIC ARTIST: Wolfgang Amadeus Mozart MUSIC GENRE: classical MUSIC ARTIST: Wolfgang Amadeus Mozart ARCHITECTURE CATEGORY: Theatres TAG: animated MUSIC GENRE: classical MUSIC ARTIST: Wolfgang Amadeus Mozart

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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

Preliminary results

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

Preliminary results

PLACE OF INTEREST: Wembley Stadium CITY: London, United Kingdom MUSIC ARTIST: Beady Eye TIME: 2007 MUSIC ARTIST: Beady Eye ARCHITECTURE CATEGORY: Music venues ARCHITECTURE CATEGORY: Rock music venues KEYWORD: rock MUSIC CATEGORY: Indie rock MUSIC ARTIST: Beady Eye MUSIC CATEGORY: Rock music MUSIC ARTIST: Beady Eye TAG: strong MUSIC CATEGORY: Rock music MUSIC ARTIST: Beady Eye

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

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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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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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

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

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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

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

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SLIDE 39

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

ign.fernandez01@estudiante.uam.es, ivan.cantador@uam.es

2 Faculty of Computer Science, Free University of Bozen-Bolzano, Italy

mkaminskas@unibz.it, fricci@unibz.it