Computer Laboratory

Diarmuid Ó Séaghdha

Auralist: Introducing Serendipity into Music Recommendation


Yuan Cao Zhang, Diarmuid Ó Séaghdha, Daniele Quercia and Tamas Jambor

Recommendation systems exist to help users discover content in a large body of items. An ideal recommendation system should mimic the actions of a trusted friend or expert, producing a personalised collection of recommendations that balance between the desired goals of accuracy, diversity, novelty and serendipity. We introduce the Auralist recommendation framework, a system that in contrast to previous work attempts to balance and improve all four factors simultaneously. Using a collection of novel algorithms inspired by principles of 'serendipitous discovery', we demonstrate a method of successfully injecting serendipity, novelty and diversity into recommendations whilst limiting the impact on accuracy. We evaluate Auralist quantitatively over a broad set of metrics and, with a user study on music recommendation, show that Auralist's emphasis on serendipity indeed improves user satisfaction.

  author = 	 {Yuan Cao Zhang and Diarmuid {\'O S\'eaghdha} and Daniele Quercia and Tamas Jambor},
  title = 	 {Auralist: Introducing Serendipity into Music Recommendation},
  booktitle = 	 {Proceedings of the 5th ACM Conference on Web Search and Data Mining (WSDM-12)},
  year =	 2012,
  address =	 {Seattle, WA}