Meta Recommendation With Robustness Improvement

Meta learning has been recognized as an effective remedy for solving the cold-start problem in the recommendation domain. Existing models aim to learn how to generalize from the user behaviors in the training set to testing set. However, in the cold start settings, with only a small number of training samples, the testing distribution may easily deviate from the training one, which may invalidate the learned generalization patterns, and lower the recommendation performance. For alleviating this problem, in this paper, we propose a robust meta recommender framework to address the distribution shift problem. In specific, we argue that the distribution shift may exist on both the user- and interaction-levels, and in order to mitigate them simultaneously, we design a novel distributionally robust model by hierarchically reweighing the training samples. Different sample weights correspond to different training distributions, and we minimize the largest loss induced by the sample weights in a simplex, which essentially optimizes the upper bound of the testing loss. In addition, we analyze our framework on the convergence rates and generalization error bound to provide more theoretical insights. Empirically, we conduct extensive experiments based on different meta recommender models and real-world datasets to verify the generality and effectiveness of our framework.