[Paper Review] Toward Amortized Ranking-Critical Training For Collaborative Filtering

Sam Lobel, Chunyuan Li+2 more|arXiv (Cornell University)|Apr 30, 2020

Data Stream Mining TechniquesComputer Science61 references9 citations

TL;DR

This paper proposes an actor-critic reinforcement learning framework for collaborative filtering that directly optimizes ranking-based objectives by training a critic network to approximate ranking metrics and using it to guide actor network updates. The method amortizes ranking computation via neural networks, enabling efficient inference on new item lists and achieving state-of-the-art or superior performance on three large-scale datasets.

ABSTRACT

We investigate new methods for training collaborative filtering models based on actor-critic reinforcement learning, to more directly maximize ranking-based objective functions. Specifically, we train a critic network to approximate ranking-based metrics, and then update the actor network to directly optimize against the learned metrics. In contrast to traditional learning-to-rank methods that require re-running the optimization procedure for new lists, our critic-based method amortizes the scoring process with a neural network, and can directly provide the (approximate) ranking scores for new lists. We demonstrate the actor-critic's ability to significantly improve the performance of a variety of prediction models, and achieve better or comparable performance to the state-of-the-art on three large-scale datasets.

Motivation & Objective

• To address the inefficiency of traditional learning-to-rank methods that require re-optimization for each new list by amortizing the ranking scoring process.
• To directly optimize collaborative filtering models using ranking-based objectives instead of surrogate losses.
• To improve recommendation performance by training a critic network to approximate complex ranking metrics and using it to guide policy updates in the actor network.
• To enable efficient inference on new item lists by leveraging a learned critic for approximate ranking scores.
• To achieve state-of-the-art or competitive performance on large-scale recommendation datasets using direct ranking optimization.

Proposed method

• Train a critic neural network to approximate ranking-based metrics such as NDCG or MAP, enabling differentiable estimation of ranking quality.
• Use the critic's output as a reward signal to update the actor network via policy gradient methods, directly optimizing for ranking performance.
• Amortize the ranking computation by using the critic network to provide approximate scores for new item lists without re-running optimization.
• Formulate the training objective as a reinforcement learning problem where the actor learns a policy to generate item rankings.
• Integrate the critic and actor networks end-to-end, allowing joint training with backpropagation through both components.
• Use off-policy updates and experience replay to stabilize training and improve sample efficiency.

Experimental results

Research questions

• RQ1 Can a critic network effectively approximate complex ranking metrics to serve as a surrogate reward in reinforcement learning for collaborative filtering?
• RQ2 Does amortizing the ranking computation via a neural critic lead to faster and more scalable inference on new item lists?
• RQ3 Can direct optimization using the critic's reward signal improve recommendation performance compared to standard collaborative filtering with surrogate losses?
• RQ4 How does the proposed method compare to state-of-the-art learning-to-rank baselines in terms of ranking metrics and scalability?
• RQ5 To what extent can the critic network generalize to unseen item lists without retraining?

Key findings

• The proposed actor-critic framework achieves better or comparable performance to state-of-the-art methods on three large-scale recommendation datasets.
• The critic network effectively approximates ranking metrics, enabling accurate and differentiable reward signals for policy optimization.
• Amortized inference using the critic allows efficient scoring of new item lists without re-running optimization procedures.
• The method demonstrates improved ranking performance by directly optimizing for metrics like NDCG and MAP, rather than relying on proxy losses.
• The framework is scalable and generalizes well to unseen item lists due to the learned critic's inductive bias.
• Empirical results show consistent gains across multiple evaluation metrics, confirming the effectiveness of direct ranking optimization.