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    <identifier>10.11922/sciencedb.j00104.00026</identifier>
    <datestamp>2020-10-16T13:33:14Z</datestamp>
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  <dc:date>2020-10-16</dc:date>
  <dc:title>User Profiling for CSDN: Keyphrase Extraction, User Tagging and User Growth Value Prediction</dc:title>
  <dc:identifier>doi:10.11922/sciencedb.j00104.00026</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>Six tables, five figures and one algorithm of this paper.  Table 1 shows candidate phrases. Table 2 demonstrates performance of different types of features. Table 3 summarizes notations used in this paper. Table 4 shows the statistics of data sets. Table 5 compares scores of methods. Table 6 shows performance of models. Algorithm 1 shows semi-supervised regression algorithm. Figure 1 shows candidate phrase generation procedure. Figure 2 is the framework of a two-phase model. Figure 3 depicts the architecture of the neural network. Figure 4 shows the architecture of user growth value prediction. Figure 5 presents bagging of primary model. </dc:description>
  <dc:subject>User profiling;  Keyphrase extraction;  User tagging;  Growth value prediction;  Word embedding</dc:subject>
  <dc:creator>Guoliang Xing</dc:creator>
  <dc:creator> Hao Gao</dc:creator>
  <dc:creator>Qi Cao</dc:creator>
  <dc:creator>Xinyu Yue</dc:creator>
  <dc:creator>Bingbing Xu</dc:creator>
  <dc:creator>Keting Cen</dc:creator>
  <dc:creator>Huawei Shen</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:relation>http://www.doi.org/10.1162/dint_a_00015</dc:relation>
  <dc:publisher>Science Data Bank</dc:publisher>
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