Online ranking combination

As a task of high importance for recommender systems, we consider the problem of learning the convex combination of ranking algorithms by online machine learning. In the case of two base rankers, we show that the exponentially weighted combination achieves near optimal performance. However, the number of required points to be evaluated may be prohibitive with more base models in a real application. We propose a gradient based stochastic optimization algorithm that uses finite differences. Our new algorithm achieves similar empirical performance for two base rankers, while scaling well with an increased number of models. In our experiments with five real-world recommendation data sets, we show that the combination offers significant improvement over previously known stochastic optimization techniques. Our algorithm is the first effective stochastic optimization method for combining ranked recommendation lists by online machine learning.