Latent factor models are the most popular backbones for today's recommender systems owing to their prominent performance. Latent factor models represent users and items as real-valued embedding vectors for pairwise similarity computation, and all embeddings are traditionally restricted to a uniform size that is relatively large (e.g., 256-dimensional). With the exponentially expanding user base and item catalog in contemporary e-commerce, this design is admittedly becoming memory-inefficient. To facilitate lightweight recommendation, reinforcement learning (RL) has recently opened up opportunities for identifying varying embedding sizes for different users/items. However, challenged by search efficiency and learning an optimal RL policy, existing RL-based methods are restricted to highly discrete, predefined embedding size choices. This leads to a largely overlooked potential of introducing finer granularity into embedding sizes to obtain better recommendation effectiveness under a given memory budget. In this paper, we propose continuous input embedding size search (CIESS), a novel RL-based method that operates on a continuous search space with arbitrary embedding sizes to choose from. In CIESS, we further present an innovative random walk-based exploration strategy to allow the RL policy to efficiently explore more candidate embedding sizes and converge to a better decision. CIESS is also model-agnostic and hence generalizable to a variety of latent factor RSs, whilst experiments on two real-world datasets have shown state-of-the-art performance of CIESS under different memory budgets when paired with three popular recommendation models.

翻译：潜在因子模型凭借其卓越性能已成为当今推荐系统最主流的基础架构。这类模型将用户和物品表示为实值嵌入向量以计算成对相似度，传统上所有嵌入被限制为统一且较大的维度（例如256维）。随着当代电子商务中用户基数和物品目录的指数级增长，这种设计正日益显露出内存效率低下的问题。为促进轻量级推荐，强化学习最近为区分不同用户/物品的嵌入维度提供了可能。然而，受搜索效率和最优强化学习策略学习的制约，现有基于强化学习的方法被局限于高度离散且预设的嵌入维度选项。这导致在给定内存预算下，通过引入更细粒度的嵌入维度来提升推荐效果的可能性被严重忽视。本文提出连续输入嵌入维度搜索，这是一种创新的基于强化学习的方法，可在包含任意可选维度的连续搜索空间中运行。在CIESS中，我们进一步提出基于随机游走的创新探索策略，使强化学习策略能够高效探索更多候选嵌入维度并收敛至更优决策。CIESS同时具有模型无关性，可泛化至多种潜在因子推荐系统。在两个真实数据集上的实验表明，当与三种流行推荐模型配合使用时，CIESS在不同内存预算下均展现出最先进的性能。