Peacock: Learning Long-Tail Topic Features for Industrial Applications

@article{Wang2015PeacockLL,
  title={Peacock: Learning Long-Tail Topic Features for Industrial Applications},
  author={Yi Wang and Xuemin Zhao and Zhenlong Sun and Hao Yan and Lifeng Wang and Zhihui Jin and Liubin Wang and Yang Gao and Ching Law and Jia Zeng},
  journal={ACM TIST},
  year={2015},
  volume={6},
  pages={47:1-47:23}
}
Latent Dirichlet allocation (LDA) is a popular topic modeling technique in academia but less so in industry, especially in large-scale applications involving search engine and online advertising systems. A main underlying reason is that the topic models used have been too small in scale to be useful; for example, some of the largest LDA models reported in literature have up to 103 topics, which difficultly cover the long-tail semantic word sets. In this article, we show that the number of… CONTINUE READING
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