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• W1546359014 abstract "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 10 3 topics, which difficultly cover the long-tail semantic word sets. In this article, we show that the number of topics is a key factor that can significantly boost the utility of topic-modeling systems. In particular, we show that a “big” LDA model with at least 10 5 topics inferred from 10 9 search queries can achieve a significant improvement on industrial search engine and online advertising systems, both of which serve hundreds of millions of users. We develop a novel distributed system called Peacock to learn big LDA models from big data. The main features of Peacock include hierarchical distributed architecture, real-time prediction, and topic de-duplication. We empirically demonstrate that the Peacock system is capable of providing significant benefits via highly scalable LDA topic models for several industrial applications." @default.
• W1546359014 created "2016-06-24" @default.
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• W1546359014 date "2015-07-15" @default.
• W1546359014 modified "2023-10-18" @default.
• W1546359014 title "Peacock" @default.
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• W1546359014 doi "https://doi.org/10.1145/2700497" @default.
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