Auralist

Yuan Cao Zhang, Diarmuid Ó Séaghdha, Daniele Quercia, Tamas Jambor
<span title="">2012</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/puezkhxc3rggrgb456avsvxi34" style="color: black;">Proceedings of the fifth ACM international conference on Web search and data mining - WSDM &#39;12</a> </i> &nbsp;
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
more &raquo; ... 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.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/2124295.2124300">doi:10.1145/2124295.2124300</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/wsdm/ZhangSQJ12.html">dblp:conf/wsdm/ZhangSQJ12</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ajzweapzfvaqvfbsqbxcxm763e">fatcat:ajzweapzfvaqvfbsqbxcxm763e</a> </span>
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