(Not Too) Personalized Learning to Rank for Contextual Suggestion Andrew Yates1, Dave DeBoer1, Hui Yang1, Nazli Goharian1, Steve Kunath2, Ophir Frieder1 1 Department of Computer Science, Georgetown University 2

First Page		Document Content
Date: 2013-02-12 08:11:17 Bing Google Search Web search engine Learning to rank Web query classification Web search query Information science Information retrieval Internet search engines		(Not Too) Personalized Learning to Rank for Contextual Suggestion Andrew Yates1, Dave DeBoer1, Hui Yang1, Nazli Goharian1, Steve Kunath2, Ophir Frieder1 1 Department of Computer Science, Georgetown University 2 Add to Reading List Source URL: trec.nist.gov Download Document from Source Website File Size: 426,64 KB Share Document on Facebook

	Senior Java Developer (m/f) SYMPTOMA GmbH is looking for dedicated software developers (Java, web development) in Salzburg and Attersee. SYMPTOMA is an innovative startup currently developing a search engine for diseases DocID: 1u79c - View Document
	Guide – Finding your website Suggestions to help get your website found on the web Just by creating a website doesn’t mean it will automatically be found through an Internet Search Engine tool such as Google. There a DocID: 1stQu - View Document
	Website Design Nigeria – Contract with Customer Page 1 This document sets out to explain the contractual commitments by both Website Design Nigeria (WDN) and its Customer when designing and maintaining a website. DocID: 1rs0x - View Document
	Digital prediction software is featured in international market Synaption platform is able to analyze large data to anticipate important scenarios Search engine innovation. This has been the main goal of most technology DocID: 1rrsG - View Document
	Information Retrieval Based on Extraction of Domain Specific Significant Keywords and Other Relevant Phrases from a Conceptual Semantic Network Structure Mohammad Moinul Hoque University of Evora, Portugal moincse@yahoo. DocID: 1rqAR - View Document