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：潜在因子模型因其卓越性能而成为当今推荐系统最主流的骨干架构。潜在因子模型将用户和物品表示为用于配对相似度计算的实数嵌入向量，且传统上所有嵌入均被限制为相对较大的统一维度（例如256维）。随着当代电子商务中用户基础与商品目录的指数级扩张，这种设计已明显导致内存效率低下。为促进轻量化推荐，强化学习近年来为不同用户/物品识别差异化嵌入维度提供了新的可能性。然而，受搜索效率与最优RL策略学习的挑战，现有基于RL的方法局限于高度离散化的预定义嵌入维度选择，导致在给定内存预算下通过引入更精细粒度获取更优推荐效果的巨大潜力被大幅忽视。本文提出连续输入嵌入维度搜索（CIESS），一种创新的基于RL的方法，可在包含任意嵌入维度的连续搜索空间中运行。在CIESS中，我们进一步提出创新的基于随机游走的探索策略，使RL策略能高效探索更多候选嵌入维度并收敛至更优决策。CIESS同时具备模型无关性，因此可泛化至各类潜在因子推荐系统。在两个真实世界数据集上的实验表明，当与三种主流推荐模型配合时，CIESS在不同内存预算下均展现出最先进的性能。