论文标题
DimensionRank：个性化一般搜索的个人神经表示
DimensionRank: Personal Neural Representations for Personalized General Search
论文作者
论文摘要
网络搜索和社交媒体一直是Internet上最重要的两个应用程序。我们首先提供一个统一的框架，称为一般搜索，其中所有搜索和社交媒体产品都可以看作是实例。 DimensionRank是我们的主要贡献。这是基于神经网络的个性化一般搜索的算法。 DimensionRank的大胆创新是使用自己独特的个人神经代表矢量对每个用户进行建模和代表，这是一个实用值的多维矢量空间中的一种博学的表示。这是我们意识到的第一项Internet服务，将每个用户用自己的独立表示向量建模。这也是我们意识到的第一项尝试为一般Web搜索进行个性化的服务。同样，神经表示使我们能够介绍第一种Reddit风格的算法，该算法不受“准行为”的问题。我们认为，个性化的一般搜索将比Google的一定型Web搜索算法产生的搜索产品数量级。 最后，我们宣布了基于DimensionRank的新搜索和社交网络Internet应用程序。
Web Search and Social Media have always been two of the most important applications on the internet. We begin by giving a unified framework, called general search, of which which all search and social media products can be seen as instances. DimensionRank is our main contribution. This is an algorithm for personalized general search, based on neural networks. DimensionRank's bold innovation is to model and represent each user using their own unique personal neural representation vector, a learned representation in a real-valued multidimensional vector space. This is the first internet service we are aware of that to model each user with their own independent representation vector. This is also the first service we are aware of to attempt personalization for general web search. Also, neural representations allows us to present the first Reddit-style algorithm, that is immune to the problem of "brigading". We believe personalized general search will yield a search product orders of magnitude better than Google's one-size-fits-all web search algorithm. Finally, we announce Deep Revelations, a new search and social network internet application based on DimensionRank.
