FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2162396670> ?p ?o ?g. }

• W2162396670 endingPage "268" @default.
• W2162396670 startingPage "237" @default.
• W2162396670 abstract "Although there has been significant research on modelling and learning user preferences for various types of objects, there has been relatively little work on the problem of representing and learning preferences over sets of objects. We introduce a representation language, DD-PREF, that balances preferences for particular objects with preferences about the properties of the set. Specifically, we focus on the depth of objects (i.e. preferences for specific attribute values over others) and on the diversity of sets (i.e. preferences for broad vs. narrow distributions of attribute values). The DD-PREF framework is general and can incorporate additional object- and set-based preferences. We describe a greedy algorithm, DD-Select, for selecting satisfying sets from a collection of new objects, given a preference in this language. We show how preferences represented in DD-PREF can be learned from training data. Experimental results are given for three domains: a blocks world domain with several different task-based preferences, a real-world music playlist collection, and rover image data gathered in desert training exercises." @default.
• W2162396670 created "2016-06-24" @default.
• W2162396670 creator A5020691490 @default.
• W2162396670 creator A5026172631 @default.
• W2162396670 creator A5069430593 @default.
• W2162396670 date "2010-09-01" @default.
• W2162396670 modified "2023-09-30" @default.
• W2162396670 title "Modelling and learning user preferences over sets" @default.
• W2162396670 cites W135283217 @default.
• W2162396670 cites W1527781030 @default.
• W2162396670 cites W1566572578 @default.
• W2162396670 cites W1570448133 @default.
• W2162396670 cites W1586707338 @default.
• W2162396670 cites W1590219394 @default.
• W2162396670 cites W1621677511 @default.
• W2162396670 cites W179852215 @default.
• W2162396670 cites W1965520710 @default.
• W2162396670 cites W1980896222 @default.
• W2162396670 cites W2058475745 @default.
• W2162396670 cites W2071347005 @default.
• W2162396670 cites W2076064295 @default.
• W2162396670 cites W2082691840 @default.
• W2162396670 cites W2083305840 @default.
• W2162396670 cites W2107890099 @default.
• W2162396670 cites W2110362180 @default.
• W2162396670 cites W2114660707 @default.
• W2162396670 cites W2118826155 @default.
• W2162396670 cites W2143331230 @default.
• W2162396670 cites W2145365639 @default.
• W2162396670 cites W2154962395 @default.
• W2162396670 cites W2155912844 @default.
• W2162396670 cites W2163288162 @default.
• W2162396670 cites W2164569331 @default.
• W2162396670 cites W2171541062 @default.
• W2162396670 cites W2197919320 @default.
• W2162396670 cites W2478914150 @default.
• W2162396670 cites W2612690410 @default.
• W2162396670 cites W2799061466 @default.
• W2162396670 cites W281665770 @default.
• W2162396670 cites W2966207845 @default.
• W2162396670 cites W3173138228 @default.
• W2162396670 cites W3215150661 @default.
• W2162396670 cites W54250652 @default.
• W2162396670 cites W98729111 @default.
• W2162396670 doi "https://doi.org/10.1080/09528130903119336" @default.
• W2162396670 hasPublicationYear "2010" @default.
• W2162396670 type Work @default.
• W2162396670 sameAs 2162396670 @default.
• W2162396670 citedByCount "9" @default.
• W2162396670 countsByYear W21623966702012 @default.
• W2162396670 countsByYear W21623966702014 @default.
• W2162396670 countsByYear W21623966702015 @default.
• W2162396670 countsByYear W21623966702016 @default.
• W2162396670 countsByYear W21623966702018 @default.
• W2162396670 crossrefType "journal-article" @default.
• W2162396670 hasAuthorship W2162396670A5020691490 @default.
• W2162396670 hasAuthorship W2162396670A5026172631 @default.
• W2162396670 hasAuthorship W2162396670A5069430593 @default.
• W2162396670 hasConcept C105795698 @default.
• W2162396670 hasConcept C107457646 @default.
• W2162396670 hasConcept C11413529 @default.
• W2162396670 hasConcept C119857082 @default.
• W2162396670 hasConcept C120665830 @default.
• W2162396670 hasConcept C121332964 @default.
• W2162396670 hasConcept C134306372 @default.
• W2162396670 hasConcept C154945302 @default.
• W2162396670 hasConcept C162324750 @default.
• W2162396670 hasConcept C177264268 @default.
• W2162396670 hasConcept C17744445 @default.
• W2162396670 hasConcept C181204326 @default.
• W2162396670 hasConcept C187736073 @default.
• W2162396670 hasConcept C192209626 @default.
• W2162396670 hasConcept C199360897 @default.
• W2162396670 hasConcept C199539241 @default.
• W2162396670 hasConcept C23123220 @default.
• W2162396670 hasConcept C2776359362 @default.
• W2162396670 hasConcept C2780451532 @default.
• W2162396670 hasConcept C2781238097 @default.
• W2162396670 hasConcept C2781249084 @default.
• W2162396670 hasConcept C33923547 @default.
• W2162396670 hasConcept C36503486 @default.
• W2162396670 hasConcept C41008148 @default.
• W2162396670 hasConcept C51823790 @default.
• W2162396670 hasConcept C94625758 @default.
• W2162396670 hasConceptScore W2162396670C105795698 @default.
• W2162396670 hasConceptScore W2162396670C107457646 @default.
• W2162396670 hasConceptScore W2162396670C11413529 @default.
• W2162396670 hasConceptScore W2162396670C119857082 @default.
• W2162396670 hasConceptScore W2162396670C120665830 @default.
• W2162396670 hasConceptScore W2162396670C121332964 @default.
• W2162396670 hasConceptScore W2162396670C134306372 @default.
• W2162396670 hasConceptScore W2162396670C154945302 @default.
• W2162396670 hasConceptScore W2162396670C162324750 @default.
• W2162396670 hasConceptScore W2162396670C177264268 @default.
• W2162396670 hasConceptScore W2162396670C17744445 @default.
• W2162396670 hasConceptScore W2162396670C181204326 @default.
• W2162396670 hasConceptScore W2162396670C187736073 @default.
• W2162396670 hasConceptScore W2162396670C192209626 @default.

• next