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February, 16th 2009 [ Slideshare ]


#1: Long Tail navigation using audio content-based similarity [ AVI ]

#2: Searchsounds demo [ AVI ]

#3: FOAFing the Music demo 1: New music releases [ MOV ]

#4: FOAFing the Music demo 2: Recommended artists [ MOV ]


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) 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, (ii) a user-centric evaluation that measures the user's relevance and novelty of the recommendations, and (iii) 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.


[ download (tar.gz) ] R code to fit a heavy--tailed distribution with the Long Tail model F(x) (Kilkki, 2007)

Thesis committee


Dr. Ricardo Baeza Yates (Yahoo! Research Barcelona)


Dr. Rafael Ramirez (Department of Technology, Universitat Pompeu Fabra, Barcelona)

Thesis reader

Dr. Stephan Baumann (DFKI, Germany)

Thesis reader

Dr. Marc Torrens (Strands.com)

Thesis reader

Dr. Josep Lluis Arcos (IIIA/CSIC)

External Reviewer

Paul Lamere (Sun Labs)


My list of publications

Some stats about the Thesis document

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