Multiple Robust Learning for Recommendation

• URL: http://arxiv.org/abs/2207.10796v1
• Date: Sat, 9 Jul 2022 13:15:56 GMT
• Title: Multiple Robust Learning for Recommendation
• Authors: Haoxuan Li, Quanyu Dai, Yuru Li, Yan Lyu, Zhenhua Dong, Peng Wu, Xiao-Hua Zhou
• Abstract summary: In recommender systems, a common problem is the presence of various biases in the collected data. We propose a multiple robust (MR) estimator that can take the advantage of multiple candidate imputation and propensity models to achieve unbiasedness.
• Score: 13.06593469196849
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In recommender systems, a common problem is the presence of various biases in the collected data, which deteriorates the generalization ability of the recommendation models and leads to inaccurate predictions. Doubly robust (DR) learning has been studied in many tasks in RS, with the advantage that unbiased learning can be achieved when either a single imputation or a single propensity model is accurate. In this paper, we propose a multiple robust (MR) estimator that can take the advantage of multiple candidate imputation and propensity models to achieve unbiasedness. Specifically, the MR estimator is unbiased when any of the imputation or propensity models, or a linear combination of these models is accurate. Theoretical analysis shows that the proposed MR is an enhanced version of DR when only having a single imputation and propensity model, and has a smaller bias. Inspired by the generalization error bound of MR, we further propose a novel multiple robust learning approach with stabilization. We conduct extensive experiments on real-world and semi-synthetic datasets, which demonstrates the superiority of the proposed approach over state-of-the-art methods.

Related papers

• Debiased Recommendation with Noisy Feedback [41.38490962524047]
  We study intersectional threats to the unbiased learning of the prediction model from data MNAR and OME in the collected data. First, we design OME-EIB, OME-IPS, and OME-DR estimators, which largely extend the existing estimators to combat OME in real-world recommendation scenarios.
  arXiv  Detail & Related papers  (2024-06-24T23:42:18Z)
• Addressing Bias Through Ensemble Learning and Regularized Fine-Tuning [0.2812395851874055]
  This paper proposes a comprehensive approach using multiple methods to remove bias in AI models. We train multiple models with the counter-bias of the pre-trained model through data splitting, local training, and regularized fine-tuning. We conclude our solution with knowledge distillation that results in a single unbiased neural network.
  arXiv  Detail & Related papers  (2024-02-01T09:24:36Z)
• Debiasing Multimodal Models via Causal Information Minimization [65.23982806840182]
  We study bias arising from confounders in a causal graph for multimodal data. Robust predictive features contain diverse information that helps a model generalize to out-of-distribution data. We use these features as confounder representations and use them via methods motivated by causal theory to remove bias from models.
  arXiv  Detail & Related papers  (2023-11-28T16:46:14Z)
• A Generalized Doubly Robust Learning Framework for Debiasing Post-Click Conversion Rate Prediction [23.340584290411208]
  Post-click conversion rate (CVR) prediction is an essential task for discovering user interests and increasing platform revenues. Currently, doubly robust (DR) learning approaches achieve the state-of-the-art performance for debiasing CVR prediction. We propose two new DR methods, namely DR-BIAS and DR-MSE, which control the bias of DR loss and balance the bias and variance flexibly.
  arXiv  Detail & Related papers  (2022-11-12T15:09:23Z)
• MRCLens: an MRC Dataset Bias Detection Toolkit [82.44296974850639]
  We introduce MRCLens, a toolkit that detects whether biases exist before users train the full model. For the convenience of introducing the toolkit, we also provide a categorization of common biases in MRC.
  arXiv  Detail & Related papers  (2022-07-18T21:05:39Z)
• Cross Pairwise Ranking for Unbiased Item Recommendation [57.71258289870123]
  We develop a new learning paradigm named Cross Pairwise Ranking (CPR) CPR achieves unbiased recommendation without knowing the exposure mechanism. We prove in theory that this way offsets the influence of user/item propensity on the learning.
  arXiv  Detail & Related papers  (2022-04-26T09:20:27Z)
• Distributionally Robust Models with Parametric Likelihood Ratios [123.05074253513935]
  Three simple ideas allow us to train models with DRO using a broader class of parametric likelihood ratios. We find that models trained with the resulting parametric adversaries are consistently more robust to subpopulation shifts when compared to other DRO approaches.
  arXiv  Detail & Related papers  (2022-04-13T12:43:12Z)
• Doubly Robust Collaborative Targeted Learning for Recommendation on Data Missing Not at Random [6.563595953273317]
  In recommender systems, the feedback data received is always missing not at random (MNAR) We propose bf DR-TMLE that effectively captures the merits of both error imputation-based (EIB) and doubly robust (DR) methods. We also propose a novel RCT-free collaborative targeted learning algorithm for DR-TMLE, called bf DR-TMLE-TL
  arXiv  Detail & Related papers  (2022-03-19T06:48:50Z)
• Enhanced Doubly Robust Learning for Debiasing Post-click Conversion Rate Estimation [29.27760413892272]
  Post-click conversion, as a strong signal indicating the user preference, is salutary for building recommender systems. Currently, most existing methods utilize counterfactual learning to debias recommender systems. We propose a novel double learning approach for the MRDR estimator, which can convert the error imputation into the general CVR estimation.
  arXiv  Detail & Related papers  (2021-05-28T06:59:49Z)
• Learning the Truth From Only One Side of the Story [58.65439277460011]
  We focus on generalized linear models and show that without adjusting for this sampling bias, the model may converge suboptimally or even fail to converge to the optimal solution. We propose an adaptive approach that comes with theoretical guarantees and show that it outperforms several existing methods empirically.
  arXiv  Detail & Related papers  (2020-06-08T18:20:28Z)
• Machine learning for causal inference: on the use of cross-fit estimators [77.34726150561087]
  Doubly-robust cross-fit estimators have been proposed to yield better statistical properties. We conducted a simulation study to assess the performance of several estimators for the average causal effect (ACE) When used with machine learning, the doubly-robust cross-fit estimators substantially outperformed all of the other estimators in terms of bias, variance, and confidence interval coverage.
  arXiv  Detail & Related papers  (2020-04-21T23:09:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.