PPT-Managing Search Relevance

Author : natalia-silvester | Published Date : 2016-04-23

in SharePoint 2013 and O365 Mikael Svenson Principal Consultant Puzzlepart SPC382 David Hollembaek Senior Consultant Microsoft Agenda The Problem The Plan The

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Managing Search Relevance: Transcript

in SharePoint 2013 and O365 Mikael Svenson Principal Consultant Puzzlepart SPC382 David Hollembaek Senior Consultant Microsoft Agenda The Problem The Plan The Tools The Problem Is this a good search result. Dynamic Domain Track. Grace Hui Yang, Georgetown University. John Frank, MIT/. Diffeo. Ian . S. oboroff. , NIST. 1. Motivation. Underexplored subsets of Web content . Limited scope and richness of indexed content, which may not include relevant components of the deep web. Clickthroughs. for News Search. Hongning. Wang. +. , . Anlei. Dong. *. , . Lihong. Li. *. , Yi Chang. *. , . Evgeniy. . Gabrilovich. *. +. CS@UIUC . *. Yahoo! Labs. Relevance . v.s. . Freshness. 2011-11709. Seo. . Seok. . Jun. Abstract. Video information retrieval. Finding info. relevant to query. Approach. Pseudo-relevance feedback. Negative PRF. Questions. How this paper approach to content-based video retrieval. Clickthroughs. for News Search. Hongning. Wang. +. , . Anlei. Dong. *. , . Lihong. Li. *. , Yi Chang. *. , . Evgeniy. . Gabrilovich. *. +. CS@UIUC . *. Yahoo! Labs. Relevance . v.s. . Freshness. vs.. Weak Induction. Homework. Study Fallacies 1-18. Review pp. 103-132. Fallacies (definition § 4.1). § 4.2 Fallacies of Relevance (1 – 8). § 4.3 Fallacies of Weak Induction (9 – 14). For Next Class: pp. 139-152. Hang Xiao. Background. Feature. a . feature. is an individual . measurable heuristic property of a phenomenon being observed. In character recognition: . horizontal and vertical . profiles, . number of internal holes, stroke . Resistance to Computer Training . Suzanne Stear. BTST 656 – May 8, 2013. Area of Focus. The purpose of this study is to:. Describe and identify the areas of resistance to computer training in a corporate setting. J. Max Wawrik. Nancy Rosado Colon. Law 16. Spring 2017. Law of Evidence. . . Key Terms. Adversary System (U.S.). A system of justice where the parties work in opposition to each . other, and each party tries to win a favorable result for itself. The . Paul N. Bennett, Microsoft Research. Joint with. Ece Kamar, Microsoft Research. Gabriella Kazai, Microsoft Research Cambridge. Motivation for Consensus Task. Recover actual . relevance of . a topic-document . for Pseudo–Relevance Feedback . Yuanhua . Lv. . & . ChengXiang. . Zhai. Department of Computer Science, UIUC. Presented by Bo Man . 2014/11/18. Positional Relevance Model . for Pseudo–Relevance Feedback . Clickthroughs. for News Search. Hongning. Wang. . , . Anlei. Dong. *. , . Lihong. Li. *. , Yi Chang. *. , . Evgeniy. . Gabrilovich. *. . CS@UIUC . *. Yahoo! Labs. Relevance . v.s. . Freshness. Community Question . Answering. Date. ： . 2014/09/25. Author . ： . Haocheng. Wu, Wei Wu, Ming Zhou, . Enhong. Chen, Lei . Duan. , . Heung-Yeung Shum. Source. ： . WSDM’14. Advisor: . 1. 2. Take-. away. . today. Interactive relevance feedback:. improve initial retrieval results by telling the IR system which docs are relevant / . nonrelevant. Best known relevance feedback method: . Tom Burton-West. Information Retrieval Programmer. Digital Library Production Service. University of Michigan Library. www.hathitrust.org/blogs/large-scale-search. Code4lib . February 12, 2013. w. www.hathitrust.org.

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