Generalized link suggestions via web site clustering.

Proactive link suggestion leads to improved user experience by allowing users to reach relevant information with fewer clicks, fewer pages to read, or simply faster because the right pages are prefetched just in time. In this paper we tackle two new scenarios for link suggestion, which were not covered in prior work owing to scarcity of historical browsing data. In the web search scenario, we propose a method for generating quick links - additional entry points into Web sites, which are shown for top search results for navigational queries - for tail sites, for which little browsing statistics is available. Beyond Web search, we also propose a method for link suggestion in general web browsing, effectively anticipating the next link to be followed by the user. Our approach performs clustering of Web sites in order to aggregate information across multiple sites, and enables relevant link suggestion for virtually any site, including tail sites and brand new sites for which little historical data is available. Empirical evaluation confirms the validity of our method using editorially labeled data as well as real-life search and browsing data from a major US search engine.