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To solve this problem, we propose a semantic collaborative filtering model that represents the semantics of users\u2019 preferences and items with their corresponding concepts. In this work, we extend the Bayesian belief network (BBN)-based model because it provides a clear formalism for representing users\u2019 preferences and items with concepts. Because the conventional BBN-based model regards the index terms derived from items as concepts, it does not exploit domain knowledge. We have therefore extended this conventional model to exploit concepts derived from domain knowledge. A practical approach to exploiting domain knowledge is to use world knowledge such as the Open Directory Project web directory or the Wikipedia encyclopaedia. Through experiments, we show that our model outperforms other conventional collaborative filtering models while comparing the recommendation quality when using different world knowledge.<\/jats:p>","DOI":"10.1177\/0165551510392318","type":"journal-article","created":{"date-parts":[[2010,12,31]],"date-time":"2010-12-31T20:58:37Z","timestamp":1293829117000},"page":"49-66","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":15,"title":["A probabilistic approach to semantic collaborative filtering using world knowledge"],"prefix":"10.1177","volume":"37","author":[{"given":"Jae-won","family":"Lee","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Seoul National University,"}]},{"given":"Sang-goo","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Seoul National University"}]},{"given":"Han-joon","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Electrical and Computer Engineering, University of Seoul"}]}],"member":"179","published-online":{"date-parts":[[2010,12,31]]},"reference":[{"key":"atypb1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-76993-4_55"},{"key":"atypb2","volume-title":"Proceedings of the 28th International Conference on Very Large Databases","author":"D. 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