RELEVANCE RANKING IN THE SUMMON SERVICE ® START

First Page		Document Content
Date: 2014-06-09 13:02:42 Information science Information retrieval Internet search engines Web analytics Information retrieval evaluation Search engine Web search engine Query expansion Relevance Full text search Recommender system Usage data		RELEVANCE RANKING IN THE SUMMON SERVICE ® START Add to Reading List Source URL: media2.proquest.com Download Document from Source Website File Size: 1,00 MB Share Document on Facebook

	Yum-Me: A Personalized Nutrient-Based Meal Recommender System LONGQI YANG, Cornell Tech, Cornell University CHENG-KANG HSIEH, University of California, Los Angeles HONGJIAN YANG and JOHN P. POLLAK, Cornell Tech, Cornell DocID: 1xVQo - View Document
	Yum-Me: A Personalized Nutrient-Based Meal Recommender System LONGQI YANG, Cornell Tech, Cornell University CHENG-KANG HSIEH, University of California, Los Angeles HONGJIAN YANG and JOHN P. POLLAK, Cornell Tech, Cornell DocID: 1tE3G - View Document
	Privacy Assessment of Data Flow Graphs for an Advanced Recommender System in the Smart Grid Fabian Knirsch1 , Dominik Engel1 , Cristian Neureiter1 , Marc Frincu2 , and Viktor Prasanna2 1 DocID: 1sXaB - View Document
	A Novel Recommender System in IoT Remo Manuel Frey1, Runhua Xu1, and Alexander Ilic2 I. ABSTRACT In Internet of Things (IoT), selling smart physical objects together with a compatible mobile app becomes an upcoming DocID: 1sIQs - View Document
	RiVal – A Toolkit to Foster Reproducibility in Recommender System Evaluation Alan Said Alejandro Bellogín DocID: 1sgiJ - View Document