Collaborative Filtering with Localised Ranking


  • Charanpal Dhanjal Télécom ParisTech
  • Romaric Gaudel University of Lille
  • Stéphan Clémençon Télécom ParisTech



Recommender Sytems, Matrix Factorization, AUC, Local AUC


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.




How to Cite

Dhanjal, C., Gaudel, R., & Clémençon, S. (2015). Collaborative Filtering with Localised Ranking. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1).



Main Track: Novel Machine Learning Algorithms