PPT-User Modeling in Search Logs via A Non-parametric Bayesian Approach

Hongning Wang 1 ChengXiang Zhai 1 Feng Liang 2 1 Department of Computer Science 2 Department of Statistics University of Illinois at UrbanaChampaign Urbana IL 61801 USA wang296czhailiangfIllinoisedu. 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. Author: David Heckerman. . Presented By:. Yan Zhang - 2006. Jeremy Gould – 2013. 1. Outline. Bayesian Approach. Bayesian vs. classical probability methods. Examples. Bayesian Network. Structure. Normally, users perform search tasks using multiple applications in concert: a search engine interface presents lists of potentially relevant documents; a document reader displays their contents; and a third tool—a text editor or personal information management application—is used to record notes and assessments (MS Word and MS PowerPoint). . from the Web. Writers: . Immanuel Trummer, Alon Halevy, Hongrae Lee, . Sunita . Sarawagi. , Rahul . Gupta. Presenting: . Amir Taubenfeld. Outline for Today’s Lecture. Motivation: Search future is in structured data. A CHI 2011 course. v11. Susan . Dumais. , Robin Jeffries, Daniel M. Russell, Diane Tang, Jaime . Teevan. CHI Tutorial, May, 2011. 1. Introduction. Daniel M. Russell . Google. 2. What Can We (HCI) Learn from Log Analysis? . . Using . Games. Jinyoung Kim and W. Bruce Croft. 8/19 MSR HCG Group Talk. HCG / GWAP . Goal. Motivate people to solve computational problems. With guarantee that the output is correct. To collect judgments for algorithmic training. . (. Megiddo . ’. 83). Android Library for CS1. Ivaylo. . Ilinkin. Gettysburg College. Hangman Evolution. C++ Days. Java Days. J2ME Days. Android Days. Library Goals. Require no knowledge of Android. Kristina . Lerman. What can we learn from web search queries?. Characteristics. Length has steadily grown over the years. 1990’s: < 2 terms. 2001: 2.4 terms. 2014: long search queries, e.g., “where is the nearest coffee shop”. CyberGIS. : . A Demonstration with Flux Footprint Modeling. Michael E. Hodgson, April Hiscox, Shaowen Wang, Babak Behzad, Sara Flecher, . Kiumars. . Soltani. , Yan Liu and Anand Padmanabhan. Receptor . Kristina . Lerman. What can we learn from web search queries?. Characteristics. Length has steadily grown over the years. 1990’s: < 2 terms. 2001: 2.4 terms. 2014: long search queries, e.g., “where is the nearest coffee shop”. Introduction to Engineering Design. Parameters. 3D CAD programs use . parameters. to define a model of a design . solution. A parameter is . a property of a system whose value determines how the . system will . Carrie Deis. Nadine Dewdney. Phase I clinical trials. Standard Designs. Adaptive Designs. Bayesian Approach. Traditional vs. Bayesian. Hybridization. FDA Guidance. Conclusion. Overview. Conducted to determine toxicity for the dosing of the new intervention. A short tutorial…. Susan . Dumais. , Robin Jeffries, Daniel M. Russell, Diane Tang. , Jaime Teevan. HCIC Feb, 2010. What can we (HCI) learn from logs analysis? . Logs are the traces of human behavior. Jingjing Ye, PhD. BeiGene. PSI Journal Club: Bayesian Methods. Nov. 17, 2020. Outline. Background . Using a case study to illustrate potential useful Bayesian analysis. Analysis and monitoring. Design study.