Abstract
Item recommendation from implicit feedback is the task of predicting a personalized ranking on a set of items (e.g. movies, products, video clips) from user feedback like clicks or product purchases. We evaluate the performance of a matrix factorization model optimized for the new ranking criterion BPR-Opt on data from a BBC video web application. The experimental results indicate that our approach is superior to state-of-the-art models not directly optimized for personalized ranking.
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© 2010 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Gantner, Z., Freudenthaler, C., Rendle, S., Schmidt-Thieme, L. (2010). Optimal Ranking for Video Recommendation. In: Daras, P., Ibarra, O.M. (eds) User Centric Media. UCMEDIA 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 40. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12630-7_30
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DOI: https://doi.org/10.1007/978-3-642-12630-7_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12629-1
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