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Intell. Syst. Technol."],"published-print":{"date-parts":[[2018,1,31]]},"abstract":"<jats:p>Topic modelling methods such as Latent Dirichlet Allocation (LDA) have been successfully applied to various fields, since these methods can effectively characterize document collections by using a mixture of semantically rich topics. So far, many models have been proposed. However, the existing models typically outperform on full analysis on the whole collection to find all topics but difficult to capture coherent and specifically meaningful topic representations. Furthermore, it is very challenging to incorporate user preferences into existing topic modelling methods to extract relevant topics. To address these problems, we develop a novel personalized Association-based Topic Selection (ATS) model, which can identify semantically valid and relevant topics from a set of raw topics based on the semantical relatedness between users\u2019 preferences and the structured patterns captured in topics. The advantage of the proposed ATS model is that it enables an interactive topic modelling process driven by users\u2019 specific interests. Based on three benchmark datasets, namely, RCV1, R8, and WT10G under the context of information filtering (IF) and information retrieval (IR), our rigorous experiments show that the proposed ATS model can effectively identify relevant topics with respect to users\u2019 specific interests, and hence to improve the performance of IF and IR.<\/jats:p>","DOI":"10.1145\/3094786","type":"journal-article","created":{"date-parts":[[2017,8,24]],"date-time":"2017-08-24T11:49:04Z","timestamp":1503575344000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":11,"title":["Finding Semantically Valid and Relevant Topics by Association-Based Topic Selection Model"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2422-0548","authenticated-orcid":false,"given":"Yang","family":"Gao","sequence":"first","affiliation":[{"name":"Beijing Engineering Research Center of Massive Language Information Processing and Cloud Computing Application; Beijing Institute of Technology; and Beijing Advanced Innovation Center for Imaging Technology"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuefeng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, Queensland University of Technology(QUT)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raymond Y. 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