Peacock：学习工业应用的长尾主题特征
Peacock: Learning Long-Tail Topic Features for Industrial Applications
摘要：
潜在狄利克雷分配（LDA）是一种在学术界流行的主题建模技术，但在工业界，特别是在涉及搜索引擎和在线广告系统的大规模应用中，并不多见。一个主要的潜在原因是，所使用的主题模型规模太小，是有用的，例如，在文献中报道的一些最大的LDA模型有多达$10^3$的主题，这很难覆盖长尾语义词集。在本文中，我们表明，主题的数量是一个关键因素，可以显着提高效用的主题建模系统。特别是，我们表明，一个“大”LDA模型与至少$10^5$主题推断从$10^9$搜索查询可以实现工业搜索引擎和在线广告系统，这两个服务于数亿用户的显着改善。我们开发了一个名为Peacock的分布式系统，从大数据中学习大LDA模型。Peacock的主要特点包括分层分布式结构、实时预测和主题去重。我们的经验表明，孔雀系统是能够提供显着的好处，通过高度可扩展的LDA主题模型的几个工业应用。
英文摘要：
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 cover difficultly the long-tail semantic word sets. In this paper, 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 serving 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
