Learning to predict trending queries
Chi-Hoon Lee, Hengshuai Yao, Xu He, Su Han Chan, JieYang Chang, Farzin Maghoul · 2014
Among the many tasks driven by very large scaled web search queries, it is an interesting task to predict how likely queries about a topic become popular (a.k.a. trending or buzzing) as the news in the near future, which is known as "Detecting trending queries." This task is nontrivial since the realization of buzzing trends of queries often requires sufficient statistics through users' activities. To address this challenge, we propose a novel framework that predicts whether queries become trending in the future. In principle, our system is built on the two learners. The first is to learn dynamics of time series for queries. The second, our decision maker, is to learn a binary classifier that determines whether queries become trending. Our framework is extremely efficient to be built taking advantage of the grid architecture that allows to deal with the large volume of data. In addition, it is flexible to continuously adapt as trending patterns evolve. The experiments results show that our approach achieves high quality of accuracy (over 77.5%} true positive rate) and yet detects much earlier (on average 29 hours advanced) than that of the baseline system.