资源分类
》 资源 》论文》Canonical Trends: Detecting Trend Setters in Web Data

Canonical Trends: Detecting Trend Setters in Web Data

2020-02-28 | |  62 |   40 |   0

Abstract

Much information available on the web is copied, reused or rephrased. The phenomenon that multiple web sources pick up certain information is often called trend. A central problem in the context of web data mining is to detect those web sources that are first to publish information which will give rise to a trend. We present a simple and efficient method for finding trends dominating a pool of web sources and identifying those web sources that publish the information relevant to a trend before others. We validate our approach on real data collected from influential technology news feeds.

上一篇：Large Scale Variational Bayesian Inference for Structured Scale Mixture Models

下一篇：Continuous Inverse Optimal Control with Locally Optimal Examples

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...