PDF-query result ranking over e commerce web databases

Given an unspecified attribute and the user query is evaluated by the difference between the distribution. Yi Chen. Wei Wang. Ziyang. Liu. University of New South . Wales, . Australia. Arizona State University, USA. Arizona State University, USA. Traditional Access Methods for Databases. Advantages: high-quality results. : . Data Harvesting Detection on Hidden Databases. Shiyuan. Wang, . Divyakant. . Agrawal. , . Amr. El . Abbadi. University of California, Santa Barbara. CCSW 2010. DSL. Data Security Concern: Back-End Databases of Web-based Applications. Alexander . Kotov. (UIUC). . Pranam. . Kolari. , Yi . Chang (Yahoo!). Lei . Duan. (Microsoft). Motivation. Improvements in ranking can be achieved in two ways:. Better features/methods for promoting high-quality result pages. Weifeng Su Dept. of Computer Science & Engineering HKUST Hong Kong weifeng@cse.ust.hk Jiying Wang Computer Science Dept. City University Hong Kong wangjy@cityu.edu.hk Qiong Huang Dept. o . 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. Raju . Balakrishnan. . rajub@asu.edu. (PhD Dissertation Defense). Committee: Subbarao Kambhampati (chair). Yi Chen. . AnHai. Doan. . Huan. Liu.. Agenda. Part 1: Ranking the Deep Web. SourceRank: Ranking Sources.. INST 734. Module 3. Doug . Oard. Agenda. Ranked retrieval. Similarity-based ranking. Probability-based ranking. Boolean Retrieval. Strong points. Accurate, . if you know the right strategies. Efficient for the computer. 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. : Ranking Tweets by Exploiting the Tweet/User/Web Ecosystem . and . Inter-Tweet Agreement . Srijith. . Ravikumar. Master’s Thesis Defense. Committee Members. Dr. . Subbarao. . Kambhampati. (Chair). Weifeng Su Dept. of Computer Science & Engineering HKUST Hong Kong weifeng@cse.ust.hk Jiying Wang Computer Science Dept. City University Hong Kong wangjy@cityu.edu.hk Qiong Huang Dept. o 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:. . over. . Incomplete. Databases. Werner . Nutt. j. oint. . work. . with. Marco . Montali. , Sergey . Paramonov. , Simon . Razniewski. , . Ognjen. . Savkovic. , . Alex . Tomasi. , . Fariz. . Darari. Raju . Balakrishnan. . rajub@asu.edu. (PhD Dissertation Defense). Committee: Subbarao Kambhampati (chair). Yi Chen. . AnHai. Doan. . Huan. Liu.. Agenda. Part 1: Ranking the Deep Web. SourceRank: Ranking Sources.. with Boolean Query Interfaces . without Ranking Support. Vagelis . Hristidis. , Florida International University. Yuheng. . Hu. , Arizona State University. Panos. . Ipeirotis. , New York University.