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：现代推荐系统的核心在于潜在因子模型，这些模型为用户提供高质量的推荐体验。此类模型通常采用统一固定维度的嵌入向量来表示用户与物品。随着用户与物品数量的持续增长，这种设计模式逐渐显现出效率低下且难以扩展的缺陷。近期出现的轻量化嵌入方法虽能实现用户与物品的差异化嵌入维度配置，但普遍存在两大局限性：其一，现有方法将嵌入维度搜索问题简化为推荐质量与内存复杂度的启发式平衡优化，且每次调整内存预算时均需手动校准权衡系数。其隐式约束的内存复杂度项甚至可能无法有效控制参数量，导致生成的嵌入表无法严格满足内存预算要求。其二，多数解决方案（特别是基于强化学习的方法）采用逐实例方式推导和优化每个用户/物品的嵌入维度，严重制约了搜索效率。本文提出预算化嵌入表（BET）这一创新方法，该方法能生成严格满足预设内存预算的表级操作（即所有用户与物品的嵌入维度配置）。通过采用集合化操作表述机制并引入集合表示学习技术，我们设计出由操作适应度预测器驱动的高效行动搜索策略，可对每个表级操作进行快速评估。实验表明，当BET与三种主流推荐模型在不同内存预算下结合使用时，在两个真实世界数据集上均取得了最先进的性能表现。