Retrieval models aim at selecting a small set of item candidates which match the preference of a given user. They play a vital role in large-scale recommender systems since subsequent models such as rankers highly depend on the quality of item candidates. However, most existing retrieval models employ a single-round inference paradigm, which may not adequately capture the dynamic nature of user preferences and stuck in one area in the item space. In this paper, we propose Ada-Retrieval, an adaptive multi-round retrieval paradigm for recommender systems that iteratively refines user representations to better capture potential candidates in the full item space. Ada-Retrieval comprises two key modules: the item representation adapter and the user representation adapter, designed to inject context information into items' and users' representations. The framework maintains a model-agnostic design, allowing seamless integration with various backbone models such as RNNs or Transformers. We perform experiments on three widely used public datasets, incorporating five powerful sequential recommenders as backbone models. Our results demonstrate that Ada-Retrieval significantly enhances the performance of various base models, with consistent improvements observed across different datasets. Our code and data are publicly available at: https://github.com/ll0ruc/Ada-Retrieval.

翻译：检索模型旨在选取少量与给定用户偏好匹配的物品候选项。由于后续模型（如排序器）高度依赖物品候选项的质量，检索模型在大规模推荐系统中起着关键作用。然而，现有大多数检索模型采用单轮推理范式，可能无法充分捕捉用户偏好的动态特性，并容易陷入物品空间的某一区域。本文提出Ada-Retrieval，一种面向推荐系统的自适应多轮检索范式，通过迭代优化用户表征以更好地捕捉整个物品空间中的潜在候选项。Ada-Retrieval包含两个关键模块：物品表征适配器与用户表征适配器，用于将上下文信息注入物品与用户表征。该框架采用模型无关设计，可无缝集成各类骨干模型（如RNN或Transformer）。我们在三个广泛使用的公开数据集上开展实验，并纳入五种强大的序列推荐模型作为骨干模型。结果表明，Ada-Retrieval能显著提升多种基模型的性能，且在不同数据集上观察到了一致的改进效果。我们的代码和数据已开源：https://github.com/ll0ruc/Ada-Retrieval。