PPT-Temporal Query Log Profiling to Improve Web Search Ranking

Alexander Kotov UIUC Pranam Kolari Yi Chang Yahoo Lei Duan Microsoft Motivation Improvements in ranking can be achieved in two ways Better featuresmethods for promoting highquality result pages. Information Retrieval in Practice. All slides ©Addison Wesley, 2008. Search Engine Architecture. A software architecture consists . of . software . components, the interfaces provided by those . components, and . Miguel Costa. , Daniel Gomes (speaker). Portuguese Web . Archive. Information Retrieval. is the activity of obtaining . information resources relevant. . to an . information need. from a . collection. . Schütze. and Christina . Lioma. Lecture 7: Scores in a Complete Search System. 1. Overview. Recap . . Why rank? . More on cosine. Implementation of ranking . The complete search system. 2. Search Relevance in SharePoint 2013 . Victor Poznanski . Principal Program . Manager. Jan Inge Bergseth . Architect. SPC145. When You Leave, You Should Know…. What: . …our view of relevance is. Vice President, Experience, Search, and Platforms. @. hughewilliams. , . hugh@hughwilliams.com. Challenges in Commerce Search. eBay Today. Of data in our . Hadoop. and Teradata clusters. Page views each day. Miguel Costa. , Daniel Gomes (speaker). Portuguese Web . Archive. Information Retrieval. is the activity of obtaining . information resources relevant. . to an . information need. from a . collection. CSE 538. MRS BOOK – CHAPTER VII. 1. Overview. Recap . . Why rank? . More on cosine. Implementation of ranking . The complete search system. 2. Outline. Recap . . Why rank? . More on cosine. Identifying Relevant Websites. from User Activity Data. Misha Bilenko and Ryen White. presented by Matt Richardson. Microsoft Research. Search = Modeling User Behavior. Retrieval functions estimate relevance from behavior of several user groups:. Auto-Completion. Sandesh Sanjay Gade. Milad . Shokouhi. PRESENTATION. Query auto-completion (QAC). QAC is one of the most prominent features of modern search engines.. List of query candidates generated according to the . User Behavior Modeling and Search Personalization. *. Hongning . Wang. Department of Computer Science. University of Illinois at Urbana-Champaign. Urbana IL, 61801 USA. wang296@illinois.edu. *work is done when visiting Microsoft Research. Christopher . Manning and . Pandu . Nayak. Lecture 14: Learning to Rank. Machine learning for IR ranking?. We’ve looked at methods for ranking documents in IR. Cosine similarity, inverse document frequency, . All slides ©Addison Wesley, 2008. Retrieval Models. Provide a mathematical framework for defining the search process. includes explanation of assumptions. basis of many ranking algorithms. can be implicit. All slides ©Addison Wesley, 2008. Search Engine Architecture. A software architecture consists . of . software . components, the interfaces provided by those . components, and . the relationships between . CS276 – Information Retrieval and Web Search. Situation. Thanks to your stellar performance in CS276, you quickly rise to VP of Search at internet retail giant nozama.com. Your boss brings in her nephew Sergey, who claims to have built a better search engine for nozama. Do you.