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.
@InProceedings{Zhang:EtAl:12,
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}
}