@inproceedings{
zhang2024generalization,
title={Generalization Error Bounds for Two-stage Recommender Systems with Tree Structure},
author={Jin Zhang and Ze Liu and Defu Lian and Enhong Chen},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=m1a4CrRJR7}
}
Two-stage recommender systems play a crucial role in efficiently identifying relevant items and personalizing recommendations from a vast array of options. This paper, based on an error decomposition framework, analyzes the generalization error for two-stage recommender systems with a tree structure, which consist of an efficient tree-based retriever and a more precise yet time-consuming ranker. We use the Rademacher complexity to establish the generalization upper bound for various tree-based retrievers using beam search, as well as for different ranker models under a shifted training distribution. Both theoretical insights and practical experiments on real-world datasets indicate that increasing the branches in tree-based retrievers and harmonizing distributions across stages can enhance the generalization performance of two-stage recommender systems.
Generalization Error Bounds for Two-stage Recommender Systems with Tree Structure
Jin Zhang Ze Liu Defu Lian Enhong Chen