document: Mining the search trails of surfing crowds : identifying relevant websites from user activity The paper proposes identifying relevant information sources from the history of combined searching and browsing behavior of many Web users . While it has been previously shown that user interactions with search engines can be employed to improve document ranking , browsing behavior that occurs beyond search result pages has been largely overlooked in prior work . The paper demonstrates that users ' post-search browsing activity strongly reflects implicit endorsement of visited pages , which allows estimating topical relevance of Web resources by mining large-scale datasets of search trails . We present heuristic and probabilistic algorithms that rely on such datasets for suggesting authoritative websites for search queries . Experimental evaluation shows that exploiting complete post-search browsing trails outperforms alternatives in isolation ( e.g. , clickthrough logs ) , and yields accuracy improvements when employed as a feature in learning to rank for Web search .
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