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Combining Linguistic Values and Semantics to Represent User Preferences Valentin Grouès, Yannick Naudet, Odej Kao.

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Presentation on theme: "Combining Linguistic Values and Semantics to Represent User Preferences Valentin Grouès, Yannick Naudet, Odej Kao."— Presentation transcript:

1 Combining Linguistic Values and Semantics to Represent User Preferences Valentin Grouès, Yannick Naudet, Odej Kao

2 Need for Semantics Semantic ambiguity: User: u=(Indonesia=0.7;Java=0.9;island=0.2) Items: d1=(Java=0.4;hotel=0.8), d2=(Java=0.4;software=0.8)  pref(d1,u)=pref(d2,u)=0.19 Distinction between the two concepts is essential for not producing undesirable recommendations island programming language Iván Cantador. "Exploiting the Conceptual Space in Hybrid Recommender Systems: a Semantic- based Approach". 2008, Madrid 2

3 Assumption of terms independance: User: u=(Indonesia=0.7;Java=0.9;island=0.2) Items: d1=(Java=0.4;hotel=0.8), d2 =(Java=0.4;archipelago=0.8) Assumption of terms independance: User: u=(Indonesia=0.7;Java=0.9;island=0.2) Items: d1=(Java=0.4;hotel=0.8), d2 =(Java=0.4;archipelago=0.8) Need for Semantics  pref(d1,u)=pref(d2,u)=0.19 Iván Cantador. "Exploiting the Conceptual Space in Hybrid Recommender Systems: a Semantic- based Approach". 2008, Madrid island Semantic relations between concepts have to be considered 3

4 Friend Of A Friend A user model widely adopted by the Semantic Web community Personal profiles, activities and relationships Large websites and software support (Livejournal, TypePad, Foaf-o-Matic) Existing datasets (foafPub contains already more than triples) 4

5 eFoaf Cover demographic and basic user information Context aware (e.g. not only one contact address) Simple and complex interests associated with a context of validity Open to external RDF datasets Skills, abilities and handicaps 5

6 Weighted Interests Ontology URI: Authors: Dan Brickley, Libby Miller, Toby Inkster et al Description: ‘‘The Weighted Interests Vocabulary specification provides basic concepts and properties for describing describing preferences (interests) within contexts, their temporal dynamics and their origin on/ for the Semantic Web’’ ex:JohnDoe a foaf:Person ; foaf:name "John Doe" ; wi:preference [ a wi:WeightedInterest ; wi:topic dbpedia:The_Terminator ; wo:weight [ a wo:Weight ; wo:weight_value 0.5 ; wo:scale ex:aScale ; ]; wi:interest_dynamics ex:atHome ]; 6

7 Fuzzy Sets To represent imprecise information inherent to the human way of thinking Humans have a tendency to use imprecise concepts for claiming tastes: “cheap restaurant”, “long movie”, “young actor”, etc. Limitations of crisp systems: For a user willing to find a restaurant with a cost up to 20€ the system will equally discard a restaurant costing 21€ as a restaurant costing 300€.  a user would prefer having an answer proportional to the distance between his ideal preference and the recommended content 7

8 Common membership functions Trapezoidal (e.g. “moderate temperatures”) Triangular (e.g. “close to”) Left shoulder (e.g. “cheap”) Right Shoulder (e.g. “expensive”) 8 kernelsupport

9 Integrating fuzzy sets within ontologies FuSOR: A model for representing fuzzy sets and linguistic values within ontologies (Y. Naudet, V. Grouès, M. Foulonneau, Introduction to Fuzzy-Ontological Context-Aware Recommendations in Mobile Environments, APRESW 2010) 9

10 FuSor: Characteristics of the approach Can be used as an extension of an ontology without requiring any modifications, OWL DL compliant Allows using fuzzy sets and their membership functions for any datatype property Supports context and domain dependency 10 Yannick Naudet, Valentin Groues, Muriel Foulonneau, Introduction to Fuzzy-Ontological Context-Aware Recommendations in Mobile Environments, APRESW 2010, Heraklion, Greece

11 Membership functions can be used to define the way a user interest deviates from an “ideal” value. Ex: “I am looking for a restaurant with prices up to 20€ but I could accept up to 25€ even if I would be less satisfied”. 11 Ex: Describing interest boundaries

12 Combining eFoaf with Fuzzy Sets ex:JohnDoe a foaf:Person ; foaf:name "John Doe" ; wi:preference [ a wi:WeightedInterest ; wi:topic [ a ex:Restaurant ; ex:fuzzyCost ex:john_Cheap; ]; 12

13 Combining eFoaf with Fuzzy Sets ex:Cost fusor:hasFuzzyVersion ex:fuzzyCost; ; ex:john_Cheap a fusor:LinguisticValue [ fusor:hasSupport [ a fusor:Range; fusor:hasLowBoundary –INF; fusor:hasHighBoundary 25; ]; fusor:hasKernel [ a fusor:Range; fusor:hasLowBoundary –INF; fusor:hasHighBoundary 20; ]; 13

14 Application to knowledge-based recommender systems 14

15 Application to knowledge-based recommender systems 15 If the average of the membership values of an item is much higher than the average of an other item, the first one should get a higher recommendation score

16 Example 16

17 Conclusions and perspectives 17

18 Thank you for your attention 18 Any questions ?


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