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• W2133156844 abstract "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." @default.
• W2133156844 created "2016-06-24" @default.
• W2133156844 creator A5043706888 @default.
• W2133156844 creator A5076259865 @default.
• W2133156844 date "2008-04-21" @default.
• W2133156844 modified "2023-10-16" @default.
• W2133156844 title "Mining the search trails of surfing crowds" @default.
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• W2133156844 doi "https://doi.org/10.1145/1367497.1367505" @default.
• W2133156844 hasPublicationYear "2008" @default.
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