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Collaborative Filtering with Localised Ranking


In recommendation systems, one is interested in the ranking of the predicted items as opposed to other losses such as the mean squared error. Although a variety of ways to evaluate rankings exist in the literature, here we focus on the Area Under the ROC Curve (AUC) as it widely used and has a strong theoretical underpinning. In practical recommendation, only items at the top of the ranked list are presented to the users. With this in mind we propose a class of objective functions which primarily represent a smooth surrogate for the real AUC, and in a special case we show how to prioritise the top of the list. This loss is differentiable and is optimised through a carefully designed stochastic gradient-descent-based algorithm which scales linearly with the size of the data. We mitigate sample bias present in the data by sampling observations according to a certain power-law based distribution. In addition, we provide computation results as to the efficacy of the proposed method using synthetic and real data.
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hal-01255890 , version 1 (14-01-2016)


  • HAL Id : hal-01255890 , version 1


Charanpal Dhanjal, Romaric Gaudel, Stéphan Clémençon. Collaborative Filtering with Localised Ranking. Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI'15), Jan 2015, Austin, United States. pp.7. ⟨hal-01255890⟩
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