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**Valentin Grouès, Yannick Naudet, Odej Kao**

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

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**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

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**Need for Semantics pref(d1,u)=pref(d2,u)=0.19**

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

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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)

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**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

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**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: 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’’

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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

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**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

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**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)

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**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

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**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”.

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**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; ];

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**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 [ fusor:hasHighBoundary 20;

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**Application to knowledge-based recommender systems**

𝑹( 𝑰 𝒋 , 𝑷 𝒖 ): aggregation function to compute the recommendation score of an item 𝑰 𝒋 regarding the user preferences 𝑷 𝒖 𝑰 𝒋 = 𝒙 𝒊 , 𝝁 𝒙 𝒊 𝑰 𝒋 ,𝒊=𝟏,…,𝑵 : an item having 𝑵 characteristics 𝒙 𝒊 𝑷 𝒖 : the set of fuzzy sets 𝑨 𝒊 , 𝒊=𝟏,…,𝑵 representing the preferences of the user 𝒖 for each respective characteristic 𝐱 𝒊 of the items 𝝁 𝒙 𝒊 𝑰 𝒋 : the membership degree of the characteristic 𝐱 𝒊 of an item 𝑰 𝒋 to the fuzzy set 𝐀 𝒊

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**Application to knowledge-based recommender systems**

Intuitive heuristics for 𝑹( 𝑰 𝒋 , 𝑷 𝒖 ) : ∀𝒊, 𝝁 𝒙 𝒊 𝑰 𝒋 > 𝝁 𝒙 𝒊 𝑰 𝒌 → 𝑹 𝑰 𝒋 , 𝑷 𝒖 >𝑹 𝑰 𝒌 , 𝑷 𝒖 ∄𝒉, 𝝁 𝒙 𝒉 𝑰 𝒌 > 𝝁 𝒙 𝒉 𝑰 𝒋 ∧(∃𝒎, 𝝁 𝒙 𝒎 𝑰 𝒋 > 𝝁 𝒙 𝒎 𝑰 𝒌 ) → 𝑹 𝑰 𝒋 , 𝑷 𝒖 >𝑹 𝑰 𝒌 , 𝑷 𝒖 ( 𝜇 𝑥 𝑖 𝑰 𝒋 = 𝜇 𝑥 𝑖 ( 𝑰 𝒌 ) )∧𝒎𝒊 𝒏 𝒊 𝜇 𝑥 𝑖 𝐼 𝑗 >𝑚𝑖 𝑛 𝑖 𝜇 𝑥 𝑖 𝐼 𝒌 → 𝑅 𝐼 𝑗 , 𝑃 𝑢 >𝑅 𝐼 𝑘 , 𝑃 𝑢 𝝁 𝒙 𝒊 𝑰 𝒋 − 𝝁 𝒙 𝒊 𝑰 𝒌 >𝜶→ 𝑹 𝑰 𝒋 , 𝑷 𝒖 >𝑹 𝑰 𝒌 , 𝑷 𝒖 If there are no characteristics of the item 𝑰 𝒌 having a membership value higher than the corresponding one of 𝑰 𝒋 and at least one characteristic of 𝑰 𝒋 having a membership value higher than the corresponding one of 𝑰 𝒌 then 𝑰 𝒋 should get a higher recommendation score If an item 𝑰 𝒋 has a higher membership degree than an other item 𝑰 𝒌 for each of their characteristics then 𝑰 𝒋 should get a higher recommendation score 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 two items 𝑰 𝒋 and 𝑰 𝒌 have the same average of their characteristics membership values, then the item having the highest minimum membership value should get a higher recommendation

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Example 𝑹 𝒂𝒗𝒈𝒎𝒊𝒏 𝑰 𝒋 , 𝑷 𝒖 = 𝟏 𝟐 (𝒎𝒊 𝒏 𝒊 𝝁 𝒙 𝒊 𝑰 𝒋 + 𝟏 𝑵 𝒊=𝟏 𝑵 𝝁 𝒙 𝒊 𝑰 𝒋 ) A user looking for a restaurant with moderate prices and close to his position

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**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 𝑹 𝒂𝒗𝒈𝒎𝒊𝒏 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

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