Training Recommenders Over Large Item Corpus With Importance Sampling

By predicting a personalized ranking on a set of items, item recommendation helps users determine the information they need. While optimizing a ranking-focused loss is more in line with the objectives of item recommendation, previous studies have indicated that current sampling-based ranking methods don't always surpass non-sampling ones. This is because it is either inefficient to sample a pool of representative negatives for better generalization or challenging to gauge their contributions to ranking-focused losses accurately. To this end, we propose a novel weighted ranking loss, which weights each negative with the softmax probability based on model's predictive score. Our theoretical analysis suggests that optimizing this loss boosts the normalized discounted cumulative gain. Furthermore, it appears that this loss acts as an approximate analytic solution for adversarial training of personalized ranking. To improve optimization efficiency, we approximate the weighted ranking loss with self-normalized importance sampling and show that the loss has good generalization properties. To improve generalization, we further develop efficient cluster-based negative samplers based on clustering over item vectors, to decrease approximation error caused by the divergence between the proposal and the target distribution. Comprehensive evaluations on real-world datasets show that our methods remarkably outperform leading item recommendation algorithms.