Can I read Towards Amortized Ranking-Critical Training for Collaborative Filtering on EtoBox?

Towards Amortized Ranking-Critical Training for Collaborative Filtering by Lobel, Sam; Li, Chunyuan; Gao, Jianfeng; Carin, Lawrence is a scholarly article available to read on EtoBox.

What is Towards Amortized Ranking-Critical Training for Collaborative Filtering about?

Collaborative filtering is widely used in modern recommender systems. Recent research shows that variational autoencoders (VAEs) yield state-of-the-art performance by integrating flexible representations from deep neural networks into latent variable models, mitigating limitations of traditional linear factor models. VAEs are typically trained by maximizing the likelihood (MLE) of users interacting with ground-truth items. While simple and often effective, MLE-based training does not directly maximize the recommendation-quality metrics one typically cares about, such as top-N ranking. In this paper we investigate new methods for training collaborative filtering models based on actor-critic reinforcement learning, to directly optimize the non-differentiable quality metrics of interest. Specifically, we train a critic network to approximate ranking-based metrics, and then update the actor network (represented here by a VAE) to directly optimize against the learned metrics. In contrast to traditional learning-to-rank methods that require to re-run the optimization procedure for new lists, our critic-based method amortizes the scoring process with a neural network, and can directly pro

Author

Lobel, Sam; Li, Chunyuan; Gao, Jianfeng; Carin, Lawrence

Published

2019

Language

EN