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Stable Matrix Approximation for Top-N Recommendation on Implicit Feedback Data

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Item Summary Li, Dongsheng Miao, Changyu Chu, Stephen Mallen, Jason Yoshioka, Tomomi Srivastava, Pankaj 2017-12-28T00:50:35Z 2017-12-28T00:50:35Z 2018-01-03
dc.identifier.isbn 978-0-9981331-1-9
dc.description.abstract Matrix approximation (MA) methods are popular in recommendation tasks on explicit feedback data. However, in many real-world applications, only positive feedbacks are explicitly given whereas negative feedbacks are missing or unknown, i.e., implicit feedback data, and standard MA methods will be unstable due to incomplete positive feedbacks and inaccurate negative feedbacks. This paper proposes a stable matrix approximation method, namely StaMA, which can improve the recommendation accuracy of matrix approximation methods on implicit feedback data through dynamic weighting during model learning. We theoretically prove that StaMA can achieve sharper uniform stability bound, i.e., better generalization performance, on implicit feedback data than MA methods without weighting. Meanwhile, experimental study on real-world datasets demonstrate that StaMA can achieve better recommendation accuracy compared with five baseline MA methods in top-N recommendation task.
dc.format.extent 10 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 51st Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Service Analytics
dc.subject collaborative filtering, matrix approximation, top-N recommendation
dc.title Stable Matrix Approximation for Top-N Recommendation on Implicit Feedback Data
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2018.195
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