Auralist: introducing serendipity into music recommendation

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. UCL DEPARTMENT OF COMPUTER SCIENCE 

• Type: Journal
• Year: 2012
• Where: WSDM
• Authors: Yuan Cao Zhang, Diarmuid Ó Séaghdha, Daniele Quercia, Tamas Jambor
• Keywords: Data Mining, Department Of Computer Science, Music Recommendation, Recommendation Systems, WSDM 2012