Expediting search trend detection via prediction of query counts.

ABSTRACTThe massive volume of queries submitted to major Web search engines reflects human interest at a global scale. While the popularity of many search queries is stable over time or fluctuates with periodic regularity, some queries experience a sudden and ephemeral rise in popularity that is unexplained by their past volumes. Typically the popularity surge is precipitated by some real-life event in the news cycle. Such queries form what are known as search trends. All major search engines, using query log analysis and other signals, invest in detecting such trends. The goal is to surface trends accurately, with low latency relative to the actual event that sparked the trend. This work formally defines precision, recall and latency metrics related to top-k search trend detection. Then, observing that many trend detection algorithms rely on query counts, we develop a linear auto-regression model to predict future query counts. Subsequently, we tap the predicted counts to expedite search trend detection by plugging them into an existing trend detection scheme. Experimenting with query logs from a major Web search engine, we report both the stand-alone accuracy of our query count predictions, as well as the task-oriented effects of the prediction on the emitted trends. We show an average reduction in trend detection latency of roughly twenty minutes, with a negligible impact on the precision and recall metrics.