PPT-Search Query Log Analysis

Kristina Lerman What can we learn from web search queries Characteristics Length has steadily grown over the years 1990s lt 2 terms 2001 24 terms 2014 long search queries eg where is the nearest coffee shop. Speaker: . Rui-Rui. Li. Supervisor: Prof. Ben Kao. 1. Outline. Introduction. Motivation. Methods. -using click-through data. -using session data. -context aware query suggestion. -other methods. Conclusion. Sai . Vallurupalli. What are query logs useful for?. In Social Sciences, Medical & Health, Advertising & Marketing, Law Enforcement etc. . Understanding Search Behavior – Trends and Hot Trends. Paul Olenick. Strategic Consultant. AvePoint . SPC265. Session agenda. Who am I?. Who are you?. Intro to and demo of new search . experience. [. DEMO]. Search experience backbone . 6 ways to get the most out of . Ryan . McIntyre. MCITP. , MCPD. Director. , Portals & Collaboration. SharePoint 2013 Search Overview. Changes from 2010. Search Solutions. OOTB Web . Parts Review. API. Server side. Client side. Leave Now…Or…. a TASTIER Approach. Guoliang. Li. 1. , . Shengyue. Ji. 2. , Chen Li. 2. , . Jianhua. Feng. 1. 1 . Tsinghua. University, Beijing, China. 2 . University of California, Irvine, CA, USA. Traditional Keyword Search. Tips. -. prevents a search term to show in results. for example searching for . doughnut -cream. can help you to avoid creamy . doughnuts. “ “. using quotes forces . google. to search for the exact phrase (including stop words). Jaime . Teevan. , Microsoft . Reseach. dub 2013. David Foster Wallace. Mark Twain. Cowards die many times . before their deaths. . Annotated by Nelson Mandela. I . have discovered a truly marvelous proof . Ann . Arbor/Detroit . NLPers. (A2D-NLP. ) Meetup. Alex Dorman, CTO. alex. at . magnetic. dot com. . Michal . Laclav. í. k, Sr. Data Scientist. m. ichal.laclavik. at . magnetic. dot com. Who We . With People. Jaime Teevan, Microsoft Research, @. jteevan. Microsoft: Kevyn Collins-Thompson. , . Susan Dumais. , Eric Horvitz. ,. Adam Kalai, Ece Kamar,. . Dan Liebling, . Merrie. Morris, Ryen . White. Zi Yang. , Eric Nyberg. Language Technologies Institute. School of Computer Science. Carnegie Mellon University. {. ziy. , . ehn. }@. cs.cmu.edu. Outline. Background. Problem Definition. Proposed Approach. Henry Feild. James Allan. Joshua . Glatt. Center for Intelligent Information Retrieval. University of Massachusetts Amherst. July 26, 2011. Centralized search logging and mining. Search:. Server-side logging. Você gosta de emagrecer? Ou de perder 5kg ou 10kg? Independentemente da sua resposta, esse
é um objetivo que pode ser alcançado com, pelo menos, um exercício básico de autocontrole.
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Eudeslima08@gmail.com Information Retrieval – Spring 2015. Indian Statistical Institute Kolkata. Search engines. 2. User needs some information. Assumption: the required information is present somewhere. A search engine tries to bridge this gap. Course Goal. The goal of this course is to teach end users of ctcLink PeopleSoft to find and retrieve Queries and Reports in the most effective manner.. Course Learning Objectives. At the end of this course users will:.