DimensionRank: Personal Neural Representations for Personalized General Search

TL;DR

DimensionRank introduces a novel neural network-based algorithm for personalized general web search, modeling each user with a unique neural representation vector, enabling more tailored search results and immunity to brigading.

Contribution

It is the first to model individual users with personal neural vectors and to apply personalization to general web search using neural network techniques.

Findings

First implementation of user-specific neural representations for web search

Demonstrates immunity to brigading in social media algorithms

Proposes Deep Revelations, a new personalized search and social network platform

Abstract

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.