@PHDTHESIS{Celma:Thesis2008,
  author = {Celma, O.},
  title = {Music Recommendation and Discovery in the Long Tail},
  school = {Universitat Pompeu Fabra},
  year = {2008},
  address = {Barcelona, Spain},
  abstract = {Music consumption is biased towards a few popular artists. For instance,
	in 2007 only 1% of all digital tracks accounted for 80% of all sales.
	Similarly, 1,000 albums accounted for 50% of all album sales, and
	80% of all albums sold were purchased less than 100 times. There
	is a need to assist people to filter, discover, personalise and recommend
	from the huge amount of music content available along the Long Tail.
	
	Current music recommendation algorithms try to accurately predict
	what people demand to listen to. However, quite often these algorithms
	tend to recommend popular -or well-known to the user- music, decreasing
	the effectiveness of the recommendations. These approaches focus
	on improving the accuracy of the recommendations. That is, try to
	make accurate predictions about what a user could listen to, or buy
	next, independently of how useful to the user could be the provided
	recommendations.
	
	In this Thesis we stress the importance of the user's perceived quality
	of the recommendations. We model the Long Tail curve of artist popularity
	to predict -potentially-interesting and unknown music, hidden in
	the tail of the popularity curve. Effective recommendation systems
	should promote novel and relevant material (non-obvious recommendations),
	taken primarily from the tail of a popularity distribution.
	
	The main contributions of this Thesis are: <i>(i)</i> a novel network-based
	approach for recommender systems, based on the analysis of the item
	(or user) similarity graph, and the popularity of the items, <i>(ii)</i>
	a user-centric evaluation that measures the user's relevance and
	novelty of the recommendations, and <i>(iii)</i> two prototype systems
	that implement the ideas derived from the theoretical work. Our findings
	have significant implications for recommender systems that assist
	users to explore the Long Tail, digging for content they might like.},
  url = {http://mtg.upf.edu/~ocelma/PhD/doc/ocelma-thesis.pdf}
}