Auralist: introducing serendipity into music recommendation
Proceedings of the fifth ACM international conference on Web search and data …, 2012•dl.acm.org
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 …
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 …
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.
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