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的性能，成为当今推荐系统最主流的基础架构。潜在因子模型将用户和物品表示为实数嵌入向量以进行成对相似度计算，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群和物品目录的指数级扩张，这种设计正变得内存效率低下。为促进轻量级推荐，强化学习最近为识别不同用户/物品的差异化嵌入维度开辟了机遇。然而，受限于搜索效率和最优强化学习策略的学习，现有基于强化学习的方法被限制在高度离散且预定义的嵌入维度选择中。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维度搜索，这是一种新颖的基于强化学习的方法，可在包含任意嵌入维度的连续搜索空间中运行。在连续输入嵌入维度搜索中，我们进一步提出一种创新的基于随机游走的探索策略，使强化学习策略能够高效探索更多候选嵌入维度并收敛到更优决策。该连续输入嵌入维度搜索方法还具有模型无关性，因此可泛化至各类潜在因子推荐系统，而在两个真实数据集上的实验表明，在与三种流行推荐模型结合时，该方法在不同内存预算下均展现出最先进的性能。