A growing trend in commercial search engines is the display of specialized content such as news, products, etc. interleaved with web search results. Ideally, this content should be displayed only when highly relevant to the search query, as it competes for space with “regular” results and advertisements. One measure of the relevance to the search query is the click-through rate the specialized content achieves when displayed; hence, if we can predict this click-through rate accurately, we can use this as the basis for selecting when to show specialized content.

In this paper, we consider the problem of estimating the click-through rate for dedicated news search results. For queries for which news results have been displayed repeatedly, the click-through rate can be tracked online; however, the key challenge for which “new” queries to display news results remains. In this paper we propose a supervised model that offers accurate prediction of news click-through rates and satisfies the requirement of adapting quickly to emerging news events. We evaluate our technique using a large corpus of real news click-through data obtained from a major commercial search engine.