Detecting Seasonal Queries Using Time Series and Content Features

Abstract

Many user information needs are strongly influenced by time. Some of these intents are expressed by users in queries issued indistinctively over time. Others follow a seasonal pattern. Examples of the latter are the queries "Golden Globe Award", "September 11th" or "Halloween", which refer to seasonal events that occur or have occurred at a specific occasion and for which, people often search in a planned and cyclic manner. Understanding this seasonal behavior, may help search engines to provide better ranking approaches and to respond with temporally relevant results leading into user's satisfaction. Detecting the diverse types of seasonal queries is therefore a key step for any search engine looking to present accurate results. In this paper, we categorize web search queries by their seasonality into 4 different categories: Non-Seasonal (NS, e.g., "Secure passwords"), Seasonal-related to ongoing events (SOE, "Golden Globe Award"), Seasonal-related to historical events (SHE, e.g., "September 11th") and Seasonal-related to special days and traditions (SSD, e.g., "Halloween"). To classify a given query we extract both time series (using the document publish date) and content features from its relevant documents. A Random Forest classifier is then used to classify web queries by their seasonality. Our experimental results show that they can be categorized with high accuracy. © 2017 Copyright held by the owner/author(s).