Assessing and Predicting Vertical Intent for Web Queries

Ke Zhou, Ronan Cummins, Martin Halvey, Mounia Lalmas, Joemon M. Jose

Abstract: Aggregating search results from a variety of heterogeneous sources, i.e. so-called verticals [1], such as news, image, video and blog, into a single interface has become a popular paradigm in web search. In this paper, we present the results of a user study that collected more than 1,500 assessments of vertical intent over 320 web topics. Firstly, we show that users prefer diverse vertical content for many queries and that the level of inter-assessor agreement for the task is fair [2]. Secondly, we propose a methodology to predict the vertical intent of a query using a search engine log by exploiting click-through data, and show that it outperforms traditional approaches.