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引用次数: 1

摘要

网络搜索相关性是一个数十亿美元的挑战,而在网络搜索竞争中存在着落后的劣势。垂直搜索结果可以用来丰富网页搜索内容,因此垂直搜索相关性对于提供差异化的搜索结果至关重要。基于机器学习的排名算法在网络搜索和垂直搜索任务中都显示出了它们的有效性。在这次演讲中,演讲者不仅会介绍最先进的网络搜索排名算法,还会介绍提高各种垂直搜索引擎相关性的挑战:本地搜索、购物搜索、新闻搜索等。
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From Web Search Relevance to Vertical Search Relevance
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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