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成为当今推荐系统中最流行的主干架构。这类模型将用户和物品表示为实值嵌入向量以进行成对相似度计算，且传统上所有嵌入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数和物品目录呈指数级扩展，这种设计显然正变得内存低效。为促进轻量级推荐，强化学习近期为识别不同用户/物品的差异化嵌入大小开辟了可能性。然而，受搜索效率及学习最优强化学习策略的挑战，现有基于强化学习的方法仅限于高度离散、预定义的嵌入大小选择。这导致在给定内存预算下，通过引入更细粒度嵌入大小来获得更好推荐效果的潜力被极大忽视。本文提出连续输入嵌入大小搜索（CIESS），这是一种新颖的基于强化学习的方法，可在包含任意可选嵌入大小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，使强化学习策略能高效探索更多候选嵌入大小并收敛到更优决策。CIESS具有模型无关性，因此可泛化至多种潜在因子推荐系统，而在两个真实数据集上的实验表明，当与三种主流推荐模型配合使用时，CIESS在不同内存预算下均达到了最先进的性能。