Cross-Dataset Propensity Estimation for Debiasing Recommender Systems

Autor:	Li, Fengyu, Dean, Sarah
Rok vydání:	2022
Předmět:	Computer Science - Information Retrieval Computer Science - Machine Learning Statistics - Methodology
Druh dokumentu:	Working Paper
Popis:	Datasets for training recommender systems are often subject to distribution shift induced by users' and recommenders' selection biases. In this paper, we study the impact of selection bias on datasets with different quantization. We then leverage two differently quantized datasets from different source distributions to mitigate distribution shift by applying the inverse probability scoring method from causal inference. Empirically, our approach gains significant performance improvement over single-dataset methods and alternative ways of combining two datasets. Comment: In Workshop on Distribution Shifts, 36th Conference on Neural Information Processing Systems (NeurIPS 2022)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2212.13892 Zobrazit plný text záznamu View this record from Arxiv