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DTSTART:20180311T030000
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DTSTAMP:20180602T130902Z
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DTSTART;TZID=Canada/Eastern:20180531T180000
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DESCRIPTION:Recommender systems have attained widespread acceptance and hav
 e attracted the increased attention by the masses for over a decade. Recom
 mender systems alleviate the complexities of products and services selecti
 on tasks and are meant to overcome the issues of information overload. Jus
 t like the recommender systems’ prospects in e-commerce and several othe
 r business domains\, recommender systems have also been developed to offer
  recommendations about healthcare services and products. Considering the h
 igh volumes and dimensionality of healthcare data\, utilization of efficie
 nt techniques to manage the big data is inevitable.\n\nIn this talk\, we d
 escribe the need and rationale for using the big data enabled techniques f
 or healthcare data. As case studies\, we will detail our work on developin
 g recommendation systems for: (a) health insurance products recommendation
 \, (b) health expert recommendation from social media\, (c) identification
  of influential doctors from Twitter\, and (d) disease risk assessment ser
 vices. During the discussion on the cases studies\, we will discuss the fo
 llowing issues that are particular to the recommender systems: (a) cold st
 art\, (b) long-tail problem\, and (c) scalability.\n\nSpeaker(s): Samee U.
  Khan\, Ph.D.\, \n\nRoom: GB405\, Bldg: University of Toronto (Galbraith B
 uilding)\, 35 St. George St.\, Toronto\, Ontario\, Canada\, M5S 1A4
LOCATION:Room: GB405\, Bldg: University of Toronto (Galbraith Building)\, 3
 5 St. George St.\, Toronto\, Ontario\, Canada\, M5S 1A4
ORGANIZER:d.cecic@ieee.org
SEQUENCE:5
SUMMARY:Big Data Based Recommendation Approaches for Healthcare
URL;VALUE=URI:https://events.vtools.ieee.org/m/162924
X-ALT-DESC:Description: <br /><p>Recommender systems have attained widespre
 ad acceptance and have attracted the increased attention by the masses for
  over a decade. Recommender systems alleviate the complexities of products
  and services selection tasks and are meant to overcome the issues of info
 rmation overload. Just like the recommender systems&rsquo\; prospects in e
 -commerce and several other business domains\, recommender systems have al
 so been developed to offer recommendations about healthcare services and p
 roducts. Considering the high volumes and dimensionality of healthcare dat
 a\, utilization of efficient techniques to manage the big data is inevitab
 le.</p>\n<p>&nbsp\;</p>\n<p>In this talk\, we describe the need and ration
 ale for using the big data enabled techniques for healthcare data. As case
  studies\, we will detail our work on developing recommendation systems fo
 r: (a) health insurance products recommendation\, (b) health expert recomm
 endation from social media\, (c) identification of influential doctors fro
 m Twitter\, and (d) disease risk assessment services. During the discussio
 n on the cases studies\, we will discuss the following issues that are par
 ticular to the recommender systems: (a) cold start\, (b) long-tail problem
 \, and (c) scalability.</p>
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