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Integrating Fuzzy Sets and Semantic Values for Enhanced User Preference Representation

This study explores the integration of semantic values and fuzzy sets to represent user preferences more accurately in hybrid recommender systems. By addressing semantic ambiguity and the interdependence of terms, we aim to improve recommendation accuracy. Our approach utilizes fuzzy sets to manage imprecise user inputs, allowing for a nuanced understanding of user preferences. We also discuss the implications of the Weighted Interests Vocabulary and its applications in context-aware systems, providing a comprehensive framework for representing and inferring user interests.

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Integrating Fuzzy Sets and Semantic Values for Enhanced User Preference Representation

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  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 ?

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