Combining content-based and collaborative filtering for job recommendation system: A cost-sensitive Statistical Relational Learning approach

Recommendation systems usually involve exploiting the relations among known features and content that describe items (content-based filtering) or the overlap of similar users who interacted with or rated the target item (collaborative filtering). To combine these two filtering approaches, current mo...

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Vydané v:	Knowledge-based systems Ročník 136; s. 37 - 45
Hlavní autori:	Yang, Shuo, Korayem, Mohammed, AlJadda, Khalifeh, Grainger, Trey, Natarajan, Sriraam
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Amsterdam Elsevier B.V 15.11.2017 Elsevier Science Ltd
Predmet:	Artificial intelligence Collaboration Collaborative filtering Content-based filtering Cost-sensitive learning Data management Filtration Human-computer interaction Hybrid systems Job hunting Learning Measures Recommendation system Recommender systems Relational learning Software engineering Statistical analysis Statistical Relational Learning Cost-sensitive learning Collaborative filtering Statistical Relational Learning Content-based filtering Recommendation system
ISSN:	0950-7051, 1872-7409
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