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：现代推荐系统（RSs）的核心是隐因子模型，它们为用户提供高质量的推荐体验。这些模型使用嵌入向量（通常具有统一且固定的维度）来表示用户和物品。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。近期的轻量化嵌入方法允许不同用户和物品拥有多样化的嵌入维度，但通常存在两个主要缺陷。首先，它们将嵌入维度搜索限制在优化推荐质量与内存复杂度之间的启发式平衡上，其中权衡系数需要针对每个给定的内存预算进行手动调整。隐含强制施加的内存复杂度项甚至可能无法限制参数使用量，导致生成的嵌入表无法严格满足内存预算要求。其次，大多数解决方案（尤其是基于强化学习的方法）以逐实例为基础推导和优化每个用户/物品的嵌入维度，这降低了搜索效率。本文提出预算化嵌入表（BET），这是一种新颖的方法，能够生成保证满足预设内存预算的表级动作（即所有用户和物品的嵌入维度）。此外，通过利用基于集合的动作表述并引入集合表示学习，我们提出了一种创新的动作搜索策略，该策略由动作适应度预测器驱动，可高效评估每个表级动作。实验表明，当BET与三种流行的推荐模型在不同内存预算下结合时，在两个真实世界数据集上取得了最先进的性能。