Learning to Rank With Bregman Divergences and Monotone Retargeting
This work addresses ranking accuracy in information retrieval and recommendation systems, offering an incremental improvement with a novel method for a known bottleneck.
The paper tackles the learning to rank problem by introducing a method based on monotone retargeting and Bregman divergences, which are statistically consistent with the NDCG criterion, resulting in an algorithm with global optimum guarantees and empirical results showing it can outperform state-of-the-art NDCG consistent techniques on benchmark datasets.
This paper introduces a novel approach for learning to rank (LETOR) based on the notion of monotone retargeting. It involves minimizing a divergence between all monotonic increasing transformations of the training scores and a parameterized prediction function. The minimization is both over the transformations as well as over the parameters. It is applied to Bregman divergences, a large class of "distance like" functions that were recently shown to be the unique class that is statistically consistent with the normalized discounted gain (NDCG) criterion [19]. The algorithm uses alternating projection style updates, in which one set of simultaneous projections can be computed independent of the Bregman divergence and the other reduces to parameter estimation of a generalized linear model. This results in easily implemented, efficiently parallelizable algorithm for the LETOR task that enjoys global optimum guarantees under mild conditions. We present empirical results on benchmark datasets showing that this approach can outperform the state of the art NDCG consistent techniques.