Oct 27, 2011 ... [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 ...
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]
Contents
1
• 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
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
Contents
2
• 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
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
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
3
Cross-domain recommendation
4
• 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
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
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
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
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. 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
Contents
7
• 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
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 • Recommending music artists that suit places of interest (POIs) • Mobile city guide soundtrack • Adaptive music playlist in a car
[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. 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
8
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. 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
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Case study: adapting music recommendation to points of interest 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
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 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
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 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
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
Contents
13
• 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
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 • Goal: finding semantic relations between a given POI and music artists • Example: music artists related to the ‘Vienna State Opera’
Vienna State Opera
Wolfgang Amadeus Mozart
• 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 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
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A Semantic-based framework for cross-domain recommendation • A directed Acyclic Graph (DAG) representing semantic relations between concepts in two domains Vienna Austria
instance POI State Opera of Vienna
Arnold Schoenberg
MUSIC ARTIST
CITY Mozart
class 19th century
Brahms
Bizet
TIME
Opera houses
opera
Ballet venues
ballet
ARCHITECTURE CATEGORY
KEYWORD
Opera composers
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
Ballet composers
MUSIC CATEGORY
15
A Semantic-based framework for cross-domain recommendation
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 CITY was born, died, lived in
located in
was built
POI
TIME
was born, died, lived in
belongs to ARCHITECTURE CATEGORY
has keyword
keyword of KEYWORD
subcategory of
MUSIC CATEGORY
subcategory of
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
MUSIC ARTIST
A Semantic-based framework for cross-domain recommendation
17
• As a proof of concept, we have built our approach by exploiting DBpedia ontology in two stages: 1. 2.
Manually identifying DBpedia classes and relations belonging to the domains of interest to define the semantic-based knowledge representation Automatically obtaining related DBpedia instances according to the classes and relations identified in the first stage 1
Semantic framework
2
Semantic network Wolfgang Amadeus Mozart
Vienna State Opera POI
MUSIC ARTIST
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
Contents
18
• 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
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
19
• 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 1
Vienna Austria
Arnold Schoenberg
1
1
Mozart
0.3
1 State Opera of Vienna
0.3
19th century
0.3
Brahms
Bizet
0.6 0.6 0.6
0.5
0.5
0.6
Opera houses
0.4
Ballet venues
0.4
opera
ballet
0.4
Opera composers
0.4
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
Ballet composers
Semantic graph-based recommendation algorithm
20
1·1=1 1
Vienna Austria
Arnold Schoenberg
1 Mozart
1 0.3
1
Bizet
1·0.3=0.3
State Opera of Vienna
0.3
0.3
19th century
0.6 Brahms
0.6 0.6
0.6 0.5
1·0.5=0.5
0.5
Opera houses
0.4
0.4
Opera composers
opera
1·0.5=0.5 Ballet venues
0.4
0.4 ballet
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
Ballet composers
Semantic graph-based recommendation algorithm
21
1 1
Vienna Austria
Arnold Schoenberg
1 Mozart
1 0.3
1
Bizet
0.3
State Opera of Vienna
0.3
0.3
19th century
0.6 Brahms
0.6 0.6
0.6 0.5
0.5
0.5
Opera houses
0.5·0.4=0.2 0.4
Opera composers
0.5·0.4=0.2
0.5 Ballet venues
0.4 opera
0.4
0.4 ballet
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
Ballet composers
Semantic graph-based recommendation algorithm
22
1 1
Vienna Austria
Arnold Schoenberg
1 Mozart
1 0.3
1
Bizet
0.3
State Opera of Vienna
0.3
0.3
19th century
0.6 Brahms
0.6 0.6
0.6 0.5
0.5
0.5
Opera houses
0.2 0.4
0.5 Ballet venues
0.4 opera
Opera composers
0.2
0.2·0.4=0.08
0.4
0.4 ballet
Ballet composers
0.2·0.4=0.08 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
23 1·1+0.08·0.6+0.08·0.6+0.3·0.3=1.186
1 1
Vienna Austria
Arnold Schoenberg
1·1+0.08·0.6=1.048 1 Mozart
1
0.08·0.6=0.048 0.3
1
Bizet
0.3
State Opera of Vienna
0.3
0.3·0.3=0.09 0.3
19th century
0.6 Brahms
0.6 0.6
0.6 0.5
0.5
0.5
Opera houses
0.2 0.4
0.5 Ballet venues
0.4 opera
Opera composers
0.2
0.08
0.4
0.4 ballet
Ballet composers
0.08 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
24 1·1+0.08·0.6+0.08·0.6+0.3·0.3=1.186
1 1
Vienna Austria
Arnold Schoenberg
1·1+0.08·0.6=1.048 1 Mozart
1 0.08·0.6=0.048 0.3
1
Bizet
0.3
State Opera of Vienna
0.3
0.3·0.3=0.09 0.3
19th century
0.6 Brahms
0.6 0.6
0.6 0.5
0.5
0.5
Opera houses
0.2 0.4
0.5 Ballet venues
0.4 opera
Opera composers
0.2
0.08
0.4
0.4 ballet
Ballet composers
0.08 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
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
V ( I , I ' ) f rel r ( I , I ' ), rel r (CI , CI ' ) • These relevance values could be assigned in different ways Class relevance Domain expert e.g. a city is more informative to link a POI than a keyword
Relation relevance Entity semantic similarity e.g. co-occurrences of concepts ‘Mozart’ and ‘Vienna’ within a document collection
Instance relevance User profile e.g. an interest in Mozart’s compositions the relevance for Mozart gets higher
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
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 I1 ,, I k W ( I ) g rele ( I ), rel e (CI );W ( I1 ),,W ( I k );V ( I1 , I ),,V ( I k , I )
• To preliminarily test our approach we have implemented a simple retrieval algorithm computing weights by linear combination
V ( I , I ' ) rel r ( I , I ' ) (1 ) rel r (CI , CI ' ), [0,1] k
W ( I ) W ( I p ) V ( I p , I ) (1 ) rele (C I ), [0,1] p 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
Contents
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• 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
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)
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
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Preliminary results
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• Top 10 musicians for ‘Vienna State Opera’ Music artist Arnold Schoenberg Wolfgang Amadeus Mozart
Emil von Reznicek Alban Berg Ludwig van Beethoven Antonio Vivaldi Giovanni Felice Sances Fritz Kreisler Georg Christoph Wagenseil Antonio Salieri
Top music genres
Born/Death countries
Classical Avant-garde Classical Instrumental Classical Opera Classical Contemporary Classical Instrumental Classical Baroque Classical Baroque Classical Violin Classical Baroque Classical Italian
Austria USA Austria Austria Austria Germany Hungary Austria Germany Austria Italy Austria Italy Austria Austria USA Austria Austria Italy Austria
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
Date 20th century 18th century
20th century 20th century 19th century 18th century 17th century 20th century 18th century 19th century
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 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
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Preliminary results • Example: ‘Wembley Stadium’ (London, UK)
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
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Preliminary results
32
• Top 10 musicians for ‘Wembley Stadium’ Music artist Beady Eye (Oasis band members) Operahouse The Woe Betides
Skunk Anansie The Fallen Leaves Ivyrise Plastic Ono Band (John Lennon & Yoko Ono) We Are Balboa Goldhawks Teddy Thompson
Top music genres
Born/Death Countries
Rock British Indie Rock British Rock Grunge Rock Female vocalist Garage Acoustic Rock Alternative Experimental Avant-garde Indie Rock Female vocalist Rock British Folk British
UK (origin) UK (origin) UK (origin) UK (origin) UK (origin) UK (origin) UK (origin) Spain-UK (origin) UK (origin) UK USA
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
Date 2009 2006 2008
1994 2004 2007 1969 2003 2009 1976
Preliminary results • Example: found relations between ‘Wembley Stadium’ and ‘Beady Eye’
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
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
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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 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
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Contents
35
• 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
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
36
• 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 nonadapted 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
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 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
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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
[email protected],
[email protected]
2
Faculty of Computer Science, Free University of Bozen-Bolzano, Italy
[email protected],
[email protected]