Personalized Ranking for Non-Uniformly Sampled Items

  title={Personalized Ranking for Non-Uniformly Sampled Items},
  author={Zeno Gantner and Lucas Drumond and Christoph Freudenthaler and Lars Schmidt-Thieme},
  booktitle={KDD Cup},
We develop an adapted version of the Bayesian Personalized Ranking (BPR) optimization criterion (Rendle et al., 2009) that takes the non-uniform sampling of negative test items — as in track 2 of the KDD Cup 2011 — into account. Furthermore, we present a modified version of the generic BPR learning algorithm that maximizes the new criterion. We use it to train ranking matrix factorization models as components of an ensemble. Additionally, we combine the ranking predictions with rating… CONTINUE READING

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SeBPR: Semantics Enhanced Bayesian Personalized Ranking with Comparable Item Pairs

2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) • 2016
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