<rdf:RDF xmlns:community="http://www.bibsonomy.org/ontologies/2008/05/community#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:owl="http://www.w3.org/2002/07/owl#" xmlns:admin="http://webns.net/mvcb/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:syn="http://purl.org/rss/1.0/modules/syndication/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:taxo="http://purl.org/rss/1.0/modules/taxonomy/" xmlns:cc="http://web.resource.org/cc/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" xmlns:swrc="http://swrc.ontoware.org/ontology#" xmlns:rdfs="http://www.w3.org/2000/01/rdf-schema#" xmlns="http://purl.org/rss/1.0/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xml:base="http://www.bibsonomy.org/user/jaeschke/mode+clustering"><owl:Ontology rdf:about=""><rdfs:comment>BibSonomy publications for /user/jaeschke/mode+clustering</rdfs:comment><owl:imports rdf:resource="http://swrc.ontoware.org/ontology/portal"/></owl:Ontology><rdf:Description rdf:about="http://www.bibsonomy.org/bibtex/2ae7ce7b8d1a31e81f9aa8b8367039506/jaeschke"><owl:sameAs rdf:resource="http://www.bibsonomy.org/uri/bibtex/2ae7ce7b8d1a31e81f9aa8b8367039506/jaeschke"/><rdf:type rdf:resource="http://swrc.ontoware.org/ontology#InProceedings"/><owl:sameAs rdf:resource="http://citeseer.ist.psu.edu/popescul01probabilistic.html"/><swrc:date>Sun Jul 16 14:21:19 CEST 2006</swrc:date><swrc:address>Seattle, Washington</swrc:address><swrc:booktitle>17th Conference on Uncertainty in Artificial Intelligence</swrc:booktitle><swrc:month>August 2--5</swrc:month><swrc:pages>437--444</swrc:pages><swrc:title>Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments</swrc:title><swrc:year>2001</swrc:year><swrc:keywords>clustering network todo three 3mode mode </swrc:keywords><swrc:abstract>Recommender systems leverage product and community information to target products to consumers. Researchers have developed collaborative recommenders, content-based recommenders, and a few hybrid systems. We propose a unified probabilistic framework for merging collaborative and content-based recommendations. We extend Hofmann&#039;s aspect model to incorporate three-way co-occurrence data among users, items, and item content. The relative influence of collaboration data versus content data is not...</swrc:abstract><swrc:author><rdf:Seq><rdf:_1><swrc:Person swrc:name="Alexandrin Popescul"/></rdf:_1><rdf:_2><swrc:Person swrc:name="Lyle Ungar"/></rdf:_2><rdf:_3><swrc:Person swrc:name="David Pennock"/></rdf:_3><rdf:_4><swrc:Person swrc:name="Steve Lawrence"/></rdf:_4></rdf:Seq></swrc:author></rdf:Description></rdf:RDF>