@conference {
	title = {Deal Sites{\textquoteright} Characteristics},
	author = {Pouli, Vasiliki., Baras, John S.},
	year = {2012},
	month = {03/2012},
	abstract = {Deal sites like Groupon (www.groupon.com) , Travelzoo (www.travelzoo.com ), LivingSocial (www.livingsocial.com ) and others are social network sites that offer daily deals to users for discounted prices. The daily deals consist of trips, restaurants, gifts, theaters, and many others, in various countries across the world. Users by buying a deal create a link with the seller and follow him. Users can also refer friends and earn money. Groupon is the number one daily deal site and as of January 2011 it has more than 50 million subscribers, it has sold more than 22 million Groupon deals in North America and has saved in this way more than $980 millions only in North America. Our target is to examine deal sites with millions of users such as Groupon and compute various statistics. More specifically in this project we want to gather data from the Groupon deal site and compute statistical results like: what kind of deals are mostly sold out (e.g. trips, gifts, food?) and in which areas across USA and Canada. Also we want to find out the range of prices per category of deals that is more attractive to the users. One other thing of interest is the seasons (time periods) that most deals are sold out (for example Christmas, Valentines day etc). Last but not least, it is also worth seeing which companies offer more than one deals, which company had the most deals and in which category.The Groupon site gives you an API to request: deals for specific categories, information about the deals, how many deals were sold out, in which area, by which company etc. By the use of this API we can gather information on the deals and then via the use of Hadoop (http://hadoop.apache.org/ ) we can process all this information to compute the statistical results mentioned above and infer useful conclusions. The outcomes of this project can be useful for further improvements; development or extensions to these kinds of deal sites that can make both buyers and sellers even more satisfied and save people from lots of money.},
	address = {Redondo Beach, CA},
	booktitle = {The Social Network Big Data Hackathon at the INSNA Sunbelt Conference},
	pages = {pp. no. 2712-2717},
	organization = {Crowne Plaza Hotel}
}
