At the heart of contemporary recommender systems (RSs) are latent factor models that provide quality recommendation experience to users. These models use embedding vectors, which are typically of a uniform and fixed size, to represent users and items. As the number of users and items continues to grow, this design becomes inefficient and hard to scale. Recent lightweight embedding methods have enabled different users and items to have diverse embedding sizes, but are commonly subject to two major drawbacks. Firstly, they limit the embedding size search to optimizing a heuristic balancing the recommendation quality and the memory complexity, where the trade-off coefficient needs to be manually tuned for every memory budget requested. The implicitly enforced memory complexity term can even fail to cap the parameter usage, making the resultant embedding table fail to meet the memory budget strictly. Secondly, most solutions, especially reinforcement learning based ones derive and optimize the embedding size for each each user/item on an instance-by-instance basis, which impedes the search efficiency. In this paper, we propose Budgeted Embedding Table (BET), a novel method that generates table-level actions (i.e., embedding sizes for all users and items) that is guaranteed to meet pre-specified memory budgets. Furthermore, by leveraging a set-based action formulation and engaging set representation learning, we present an innovative action search strategy powered by an action fitness predictor that efficiently evaluates each table-level action. Experiments have shown state-of-the-art performance on two real-world datasets when BET is paired with three popular recommender models under different memory budgets.

翻译：当代推荐系统的核心是潜在因子模型，这些模型通过嵌入向量为用户提供高质量的推荐体验。这些嵌入向量通常采用统一且固定的大小来表示用户和物品。然而，随着用户和物品数量的持续增长，这种设计变得低效且难以扩展。近期提出的轻量级嵌入方法虽能支持不同用户和物品使用不同大小的嵌入向量，但普遍存在两个主要缺陷：其一，这些方法将嵌入尺寸搜索限制为优化一个权衡推荐质量与内存复杂度的启发式目标，其中权衡系数需针对每个内存预算手动调整。其隐式强加的内存复杂度项甚至无法严格限制参数使用量，导致最终生成的嵌入表无法切实满足内存预算约束。其二，多数方法（尤其是基于强化学习的方法）需逐个为用户/物品推导并优化嵌入尺寸，这严重阻碍了搜索效率。本文提出预算化嵌入表（BET），这是一种能够生成满足预设内存预算的表级动作（即所有用户和物品的嵌入尺寸）的新方法。通过引入基于集合的动作公式化表示和集合表示学习，我们设计了一种创新性的动作搜索策略，借助动作适应度预测器高效评估每个表级动作。实验表明，当BET与三种主流推荐模型在不同内存预算下配合使用时，在两个真实世界数据集上均达到了当前最优性能。