Abstract
Web search relevance is a billion dollar challenge, while there is a disadvantage of backwardness in web search competition. Vertical search result can be incorporated to enrich web search content, therefore vertical search relevance is critical to provide differentiated search results. Machine learning based ranking algorithms have shown their effectiveness for both web search and vertical search tasks. In this talk, the speaker will not only introduce state-of-the-art ranking algorithms for web search, but also cover the challenges to improve relevance of various vertical search engines: local search, shopping search, news search, etc.
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