A language model approach for tag recommendation |
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Authors: | Ke Sun Xiaolong Wang Chengjie Sun Lei Lin |
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Affiliation: | 1. Anhui Province Key Laboratory of Big Data Analysis and Application, University of Science and Technology of China, China;2. School of Computer Science and Technology, University of Science and Technology of China, China;1. INCAS3, Dr. Nassaulaan 9, 9401 HJ Assen, The Netherlands;2. Department of Electrical Engineering, Eindhoven University of Technology, P.O. Box 513, 5600MB Eindhoven, The Netherlands |
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Abstract: | Tags are user-generated keywords for entities. Recently tags have been used as a popular way to allow users to contribute metadata to large corpora on the web. However, tagging style websites lack the function of guaranteeing the quality of tags for other usages, like collaboration/community, clustering, and search, etc. Thus, as a remedy function, automatic tag recommendation which recommends a set of candidate tags for user to choice while tagging a certain document has recently drawn many attentions. In this paper, we introduce the statistical language model theory into tag recommendation problem named as language model for tag recommendation (LMTR), by converting the tag recommendation problem into a ranking problem and then modeling the correlation between tag and document with the language model framework. Furthermore, we leverage two different methods based on both keywords extraction and keywords expansion to collect candidate tag before ranking with LMTR to improve the performance of LMTR. Experiments on large-scale tagging datasets of both scientific and web documents indicate that our proposals are capable of making tag recommendation efficiently and effectively. |
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