Combining Linguistic Values and Semantics to Represent User Preferences

1. CombiningLinguistic 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) island programming language • pref(d1,u)=pref(d2,u)=0.19 • Distinction between the two concepts is essential for not producing undesirable recommendations Iván Cantador. "Exploiting the Conceptual Space in Hybrid Recommender Systems: a Semantic-based Approach". 2008, Madrid

3. Need for Semantics • 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) island island • pref(d1,u)=pref(d2,u)=0.19 Semantic relations between concepts have to be considered Iván Cantador. "Exploiting the Conceptual Space in Hybrid Recommender Systems: a Semantic-based Approach". 2008, Madrid

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 200 000 triples)

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

6. Weighted Interests Ontology 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 ]; • URI: http://purl.org/ontology/wi/core# • 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’’

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

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

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)

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 Yannick Naudet, Valentin Groues, Muriel Foulonneau, Introduction to Fuzzy-Ontological Context-Aware Recommendations in Mobile Environments, APRESW 2010, Heraklion, Greece

11. Ex: Describing interest boundaries 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”.

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; ]; ];

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; ]; ];

14. Application to knowledge-based recommender systems • : aggregation function to compute the recommendation score of an item regarding the user preferences • : an item having characteristics • : the set of fuzzy sets representing the preferences of the user for each respective characteristic of the items • : the membership degree of the characteristic of an item to the fuzzy set

15. Application to knowledge-based recommender systems • Intuitive heuristics for : • ( 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 If an item has a higher membership degree than an other item for each of their characteristics then should get a higher recommendation score If there are no characteristics of the item having a membership value higher than the corresponding one of and at least one characteristic of having a membership value higher than the corresponding one of then should get a higher recommendation score If two items and have the same average of their characteristics membership values, then the item having the highest minimum membership value should get a higher recommendation

16. Example • A user looking for a restaurant with moderate prices and close to his position

17. Conclusions and perspectives • Propositions: • eFoaf: representation of weighted interests, user relationships, abilities, etc. • A method to use linguistic values to describe user interests • A list of intuitive heuristics to determine an aggregation method • Future work: • Evaluations of the added value of using linguistic values to describe user interests, empirical comparison of different aggregation functions • Integration with semantic similarity measures • Semantic implicit profiling

18. Thank you for your attention Any questions ?