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.
Description
Oscar Celma's PhD thesis
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