We propose a novel semi-supervised boosting algorithm using linear programming, which explicitly maximizes the margin over both labeled and unlabeled data. Experiments conducted on a number of UCI datasets and synthetic data show that, the algorithm we propose performs better than the state-ofthe-art supervised and semi-supervised boosting algorithms, and it is more robust with noisy data.

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Listwise ranking losses have been widely studied in recommender systems. However, new paradigms of content consumption present new challenges for ranking methods. In this work we contribute an analysis of learning to rank for personalized mobile push notifications and discuss the unique challenges this presents compared to traditional ranking problems. To address these challenges, we introduce a novel ranking loss based on weighting the pairwise loss between candidates by the expected regret incurred for misordering the pair. We demonstrate that the proposed method can outperform prior methods both in a simulated environment and in a production experiment on a major social network.

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