web query classification / search engines using clickthrough data / aTVT algorithm / search relevance / search technology / topical web query classification / search experience / search process / Web users / overall search relevance / travel domain / search system / search log / search engines / search intentions / efficient boosting algorithm / different ranking algorithms / Web Search / learning-to-rank algorithms / search intent / Web search relevance / web page collection / web document retrieval / search engine / text processing / search results / Web queries / search intention / search engine dataset / /
Organization
Royal Statistical Society / International World Wide Web Conference Committee / MIT / Georgia Institute of Technology / IDF / /
Person
Addison Wesley / T. Liu / M. Tsai / W. Xiong / Table / T. Qin / Y. Liu / H. Li / Z. Cao / J. Xu / Jiang Bian / /
Position
rst author / dedicated model for them / ranking model for each query category separately / single model / ranking model for each query class/cluster / corresponding model for ranking / ranking model for each ranking-sensitive query topic / respective ranking model for each query topic / Machine learning General / dedicated ranking model for each ranking-sensitive query topic / Cao / /
Ranking Specialization for Web Search: A Divide-and-Conquer Approach by Using Topical RankSVM Jiang Bian ∗