DimensionRank: Personal Neural Representations for Personalized General Search
This addresses the need for personalized search and social media experiences for internet users, though it appears incremental as it builds on existing neural network methods for personalization.
The paper tackles the problem of one-size-fits-all web search by introducing DimensionRank, an algorithm that models each user with a unique personal neural representation vector for personalized general search, claiming it will yield a search product orders of magnitude better than Google's algorithm.
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.