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Knowl. Discov. Data"],"published-print":{"date-parts":[[2010,12]]},"abstract":"<jats:p>Collaborative tagging systems, such as<jats:italic>Delicious, CiteULike<\/jats:italic>, and others, allow users to annotate resources, for example, Web pages or scientific papers, with descriptive labels called<jats:italic>tags<\/jats:italic>. The social annotations contributed by thousands of users can potentially be used to infer categorical knowledge, classify documents, or recommend new relevant information. Traditional text inference methods do not make the best use of social annotation, since they do not take into account variations in individual users\u2019 perspectives and vocabulary. In a previous work, we introduced a simple probabilistic model that takes the interests of individual annotators into account in order to find hidden topics of annotated resources. Unfortunately, that approach had one major shortcoming: the number of topics and interests must be specified a priori. To address this drawback, we extend the model to a fully Bayesian framework, which offers a way to automatically estimate these numbers. In particular, the model allows the number of interests and topics to change as suggested by the structure of the data. We evaluate the proposed model in detail on the synthetic and real-world data by comparing its performance to Latent Dirichlet Allocation on the topic extraction task. For the latter evaluation, we apply the model to infer topics of Web resources from social annotations obtained from<jats:italic>Delicious<\/jats:italic>in order to discover new resources similar to a specified one. Our empirical results demonstrate that the proposed model is a promising method for exploiting social knowledge contained in user-generated annotations.<\/jats:p>","DOI":"10.1145\/1870096.1870100","type":"journal-article","created":{"date-parts":[[2010,12,22]],"date-time":"2010-12-22T14:41:31Z","timestamp":1293028891000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Modeling Social Annotation"],"prefix":"10.1145","volume":"5","author":[{"given":"Anon","family":"Plangprasopchok","sequence":"first","affiliation":[{"name":"National Electronics and Computer Technology Center"}]},{"given":"Kristina","family":"Lerman","sequence":"additional","affiliation":[{"name":"USC Information Sciences Institute"}]}],"member":"320","published-online":{"date-parts":[[2010,12]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-04930-9_2"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.5555\/944919.944937"},{"volume-title":"Proceedings of the ECML Workshop on Statistical Approaches to Web Mining.","author":"Buntine W.","key":"e_1_2_1_3_1"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1613\/jair.62"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1995.10476550"},{"key":"e_1_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Gilks W. Richardson S. and Spiegelhalter D. 1996. Markov Chain Monte Carlo in Practice. Interdisciplinary Statistics. Chapman &amp; Hall London U.K. Gilks W. Richardson S. and Spiegelhalter D. 1996. Markov Chain Monte Carlo in Practice . Interdisciplinary Statistics. 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E.","key":"e_1_2_1_22_1"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/1277741.1277762"},{"key":"e_1_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1080\/01621459.1992.10475289"},{"volume-title":"Proceedings of the 20th Conference on Uncertainty in Artificial Intelligence. 487--494","author":"Rosen-Zvi M.","key":"e_1_2_1_25_1"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008814227332"},{"volume-title":"Proceedings of the WWW Workshop on Collaborative Web Tagging.","year":"2006","author":"Schmitz P.","key":"e_1_2_1_27_1"},{"key":"e_1_2_1_28_1","unstructured":"Steyvers M. and Griffiths T. 2006. Probabilistic topic models. In Latent Semantic Analysis: A Road to Meaning T. Landauer D. Mcnamar S. Dennis and W. Kintsch Eds. Lawrence Earlbaum Mahwah NJ. Steyvers M. and Griffiths T. 2006. Probabilistic topic models. In Latent Semantic Analysis: A Road to Meaning T. Landauer D. Mcnamar S. Dennis and W. Kintsch Eds. Lawrence Earlbaum Mahwah NJ."},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214506000000302"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1145\/1135777.1135839"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/1367497.1367594"}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1870096.1870100","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/1870096.1870100","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T10:59:47Z","timestamp":1750244387000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/1870096.1870100"}},"subtitle":["A Bayesian Approach"],"short-title":[],"issued":{"date-parts":[[2010,12]]},"references-count":30,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2010,12]]}},"alternative-id":["10.1145\/1870096.1870100"],"URL":"https:\/\/doi.org\/10.1145\/1870096.1870100","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"type":"print","value":"1556-4681"},{"type":"electronic","value":"1556-472X"}],"subject":[],"published":{"date-parts":[[2010,12]]},"assertion":[{"value":"2009-12-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2010-03-01","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2010-12-01","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}