Data Driven Domain Appraisal: Extracting Information from Short Dense Texts

摘要

Domain names can be traded via online market places and auctions. Speculation can be lucrative, with high value transactions reaching into tens of millions of dollars. This paper proposes a framework for automated domain name appraisal and evaluates several formulations of the problem with real world data. A dynamic nonlinear valuation modelling process is defined using machine learning techniques. Attributes or value factors are derived from short domain name text strings as well as a variety of other contextual data able to be obtained online from open sources. A data set of 9.975 million domains is used for evaluation and results show that search engine query data is a primary driver of value but using extracted text features can facilitate higher performance in distinguishing high value domains particularly when used with ensemble learning.