3 research outputs found
Ada-Retrieval: An Adaptive Multi-Round Retrieval Paradigm for Sequential Recommendations
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.Comment: 9 pages, Accepted to AAAI202