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:
this https URL

本文提出 Ada-Retrieval，一种适应性多轮检索范式，用于推荐系统，通过迭代地改进用户表示以更好地捕捉完整物品空间中的潜在候选物，实验结果表明 Ada-Retrieval 显著提升了各种基础模型的性能。