Optimal Ranking for Video Recommendation

Zeno Gantner; Christoph Freudenthaler; Steffen Rendle; Lars Schmidt-Thieme

Personalization in Media Delivery Platforms

Research Article

Optimal Ranking for Video Recommendation

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@INPROCEEDINGS{10.1007/978-3-642-12630-7_30,
    author={Zeno Gantner and Christoph Freudenthaler and Steffen Rendle and Lars Schmidt-Thieme},
    title={Optimal Ranking for Video Recommendation},
    proceedings={Personalization in Media Delivery Platforms},
    proceedings_a={PERMED},
    year={2012},
    month={10},
    keywords={},
    doi={10.1007/978-3-642-12630-7_30}
}

Zeno Gantner
Christoph Freudenthaler
Steffen Rendle
Lars Schmidt-Thieme
Year: 2012
Optimal Ranking for Video Recommendation
PERMED
Springer
DOI: 10.1007/978-3-642-12630-7_30

Zeno Gantner¹^,*, Christoph Freudenthaler¹^,*, Steffen Rendle¹^,*, Lars Schmidt-Thieme¹^,*

1: University of Hildesheim

*Contact email: gantner@ismll.de, freudenthaler@ismll.de, srendle@ismll.de, schmidt-thieme@ismll.de

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.

Published: 2012-10-23

: http://dx.doi.org/10.1007/978-3-642-12630-7_30