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Jul, 2019
大规模推荐模型的神经输入搜索
Neural Input Search for Large Scale Recommendation Models
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Manas R. Joglekar, Cong Li, Jay K. Adams, Pranav Khaitan, Quoc V. Le
TL;DR
该研究提出Neural Input Search技术,它通过强化学习自动学习在记忆限制下最优的分类特征嵌入维度和词汇大小,相较于传统的Single-size Embedding,采用一种新型的嵌入方法——Multi-size Embedding,它能让特征值的嵌入维度有所不同,在两种类型的推荐问题上实现了较好的表现。
Abstract
recommendation problems
with large numbers of discrete items, such as products, webpages, or videos, are ubiquitous in the technology industry. Deep
neural networks
are being increasingly used for these
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