Source-linked AI summary
AutoDebias: Learning to Debias for Recommendation
Jiawei Chen, Hande Dong, Yang Qiu, Xiangnan He, Xin Xin, Liang Chen, Guli Lin, Keping Yang
TL;DR
Recommender systems learn from observational behavior data containing multiple biases, while existing debiasing methods have limited coverage. AutoDebias unifies debiasing through risk discrepancy and learns its configurations from uniform data; theoretical and empirical analyses validate the proposal, with reported improvements across evaluated settings.
Problem
Existing recommendation debiasing methods often target one or two specific biases, lacking universal capacity for mixed or unknown biases.
Method
AutoDebias uses a general debiasing framework and meta-learning to optimize debiasing parameters with a separate set of uniform data.
Results
AutoDebias outperforms all compared methods on all datasets and metrics; on Yahoo!R3, improvements are 5.6% in NLL and 11.2% in NDCG@5.
Takeaways & Limitations
The framework provides a universal debiasing approach that accounts for multiple bias combinations while reducing manual bias identification and configuration tuning.
Takeaways & Limitations
Future work should address more complex meta-models and dynamic real-world biases, which are usually not static.
Abstract
from arXiv · showhide
Recommender systems rely on user behavior data like ratings and clicks to build personalization model. However, the collected data is observational rather than experimental, causing various biases in the data which significantly affect the learned model. Most existing work for recommendation debiasing, such as the inverse propensity scoring and imputation approaches, focuses on one or two specific biases, lacking the universal capacity that can account for mixed or even unknown biases in the data. Towards this research gap, we first analyze the origin of biases from the perspective of \textit{risk discrepancy} that represents the difference between the expectation empirical risk and the true risk. Remarkably, we derive a general learning framework that well summarizes most existing debiasing strategies by specifying some parameters of the general framework. This provides a valuable opportunity to develop a universal solution for debiasing, e.g., by learning the debiasing parameters from data. However, the training data lacks important signal of how the data is biased and what the unbiased data looks like. To move this idea forward, we propose \textit{AotoDebias} that leverages another (small) set of uniform data to optimize the debiasing parameters by solving the bi-level optimization problem with meta-learning. Through theoretical analyses, we derive the generalization bound for AutoDebias and prove its ability to acquire the appropriate debiasing strategy. Extensive experiments on two real datasets and a simulated dataset demonstrated effectiveness of AutoDebias. The code is available at \url{https://github.com/DongHande/AutoDebias}.
1 INTRODUCTION
Recommendation data contains multiple observational biases, while existing debiasing methods typically address only limited bias types. AutoDebias develops a general framework and learns debiasing configurations from uniform data.
- Motivation: Recommendation behavior data is affected by selection, exposure, conformity, and position biases.These biases arise from user choice, system exposure, public opinions, and display position.
- Prior work: Existing debiasing methods include imputation, inverse propensity scoring, and generative modeling, but address limited bias settings.Imputation assigns pseudo-labels, whereas IPS reweights collected data for expectation-unbiased learning.
- Framework: A general risk-discrepancy framework unifies most debiasing strategies by recovering them through parameter specifications.The framework is intended to handle multiple or combined biases while reducing manual bias identification and configuration tuning.
- AutoDebias: AutoDebias uses uniform data to supervise learning of debiasing parameters through a meta-learning optimization process.Uniform data is assumed to be collected by a random logging policy and is used to optimize debiasing parameters.
- Evaluation: The paper evaluates AutoDebias on explicit feedback, implicit feedback, and simulated list-feedback data.The experiments are designed to validate effectiveness across three data types.
2 PRELIMINARY
Recommendation learning seeks low true risk under an ideal unbiased distribution, but collected training data can induce a risk discrepancy. The paper characterizes several biases as distribution mismatches that can make empirical optima suboptimal.
- Task formulation: Recommendation learns a model from user-item-feedback triplets to minimize expected prediction error under an ideal unbiased distribution.The feedback may include ratings, clicks, or retention time, and the ideal distribution is used for model testing.
- Task formulation: Because true risk is inaccessible, training optimizes empirical risk on the collected behavior data.Standard learning guarantees rely on empirical risk being an unbiased estimator of true risk.
- Risk discrepancy: When training and ideal distributions differ, even a sufficiently large training set can leave a gap between empirical and true-risk optima.The resulting risk discrepancy can produce inferior recommendation performance despite empirical optimization.
- Bias types: Selection bias skews observed user-item pairs, conformity bias distorts feedback labels, and exposure and position biases affect observed distributions.Exposure bias also produces positive-only implicit feedback, while position bias can affect both exposure and judgments.
- Bias types: These biases can be summarized as deviations between the training distribution and the ideal unbiased distribution.This common formulation motivates methods that directly address mixtures of known or unknown biases.
3 A GENERAL DEBIASING FRAMEWORK
The framework treats debiasing as redesigning empirical risk so its expectation under biased training data matches true risk. By parameterizing reweighting, imputation, and related corrections, it can represent many existing strategies and support automatically learned debiasing parameters.
- General framework: The framework accounts for various recommendation biases by redesigning empirical risk to correct discrepancies between biased and true distributions.The discrepancy arises because data contributions are skewed under the training distribution.
- General framework: Missing support regions mean reweighting observed data alone cannot recover the true risk when training data covers only part of the unbiased distribution.Learning only on S1 leaves S0 blank and is problematic when their patterns differ.
- General framework: The debiasing parameter set 𝜙 = {w^(1), w^(2), m} combines data weights and imputed labels into a generic empirical risk.The expectation over the loss can be absorbed into pseudo-labels for L2, L1, and cross-entropy losses.
- General framework: Properly specified parameters make the empirical risk an unbiased estimator of true risk, enabling automatic debiasing by learning 𝜙 from data.The framework may have multiple valid parameter solutions, including one that satisfies the unbiasedness condition.
- Connections to existing methods: The framework subsumes selection-, exposure-, position-, and conformity-bias methods as special cases through suitable parameter settings.These include IPS, data imputation, negative weighting, IPS variants, and conformity corrections.
- Connections to existing methods: Variant IPS requires the training distribution to cover the whole support of the unbiased distribution, a condition that seldom holds in practice.
4 AUTODEBIAS ALGORITHM
AutoDebias learns debiasing parameters with a meta learner supervised by small uniform data, while a base recommender trains on biased data. Alternating updates approximate the bi-level optimization that evaluates tentative model updates on uniform data before applying corrected parameters.
- Algorithm design: Uniform data collected by a random logging policy supervises debiasing parameters because biased training data lacks signals about its bias and the unbiased target.
- Algorithm design: The base learner optimizes the recommendation model on training data using current debiasing parameters 𝜙, which act as its hyper-parameters.
- Algorithm design: The meta learner optimizes 𝜙 toward better recommendation performance on uniform data using the trained base model.A concise meta model re-parameterizes 𝜙 to reduce overfitting when uniform data is small.
- Meta model: The meta model uses user, item, feedback, and observation features to parameterize debiasing weights and imputation values.The implementation uses a linear model with an activation controlling imputation scale.
- Training procedure: AutoDebias alternates tentative θ updates on training data, 𝜙 updates from uniform-data feedback, and actual θ updates with the revised 𝜙.The uniform-data gradient is back-propagated through the tentative update chain.
- Training procedure: The alternating optimization strategy is not guaranteed to find the global optimum, although it empirically works well for bi-level optimization.
5 THEORETICAL ANALYSIS
Theoretical analysis establishes generalization bounds for AutoDebias and characterizes when it can recover an approximately optimal debiasing strategy. It also shows that restricting the meta hypothesis space introduces bounded error and can reduce variance.
- Analysis goals: The analysis asks whether AutoDebias can learn debiasing parameters that make expected biased empirical risk consistent with true risk, and how meta-model inductive bias affects performance.
- No inductive bias: When the meta hypothesis space contains optimal 𝜙*, the learned surrogate parameter yields recommendation risk within ε1 of the optimum with high probability.The bound includes |L(f*) − L(f̂(𝜙o))| < ε1 and |L(f̂(𝜙o)) − L(f̂(𝜙*))| < ε1.
- No inductive bias: Sufficient training and uniform data allow AutoDebias to acquire an almost optimal debiasing strategy and bring the recommendation model to an approximate optimum.
- Bound conditions: The generalization analysis relies on finite hypothesis spaces for recommendation and meta models and a bounded loss function.
- Inductive bias: When the meta hypothesis space excludes 𝜙*, inductive bias still produces a bounded generalization error whose deviation depends on the constrained debiasing parameters.
- Inductive bias: Constraining meta-model capacity can improve performance by reducing hypothesis-space size and lowering the variance of the surrogate optimum.
6 EXPERIMENTS
Experiments evaluate AutoDebias across explicit, implicit, and recommendation-list feedback, testing performance, component contributions, learned bias correction, and universality. AutoDebias consistently outperforms comparison methods, including with very limited uniform data and across combined biases.
- Experimental scope: Experiments cover explicit feedback, implicit feedback, and simulated recommendation-list feedback to test AutoDebias across varied bias settings.Explicit-feedback experiments use Yahoo!R3 and Coat; the simulation includes position and selection bias.
- Experimental setup: Yahoo!R3 and Coat split unbiased data into 5% uniform, 5% validation, and 90% test sets, while biased interactions form the training set.Ratings are binarized with threshold 3, assigning positive labels to ratings above 3 and negative labels otherwise.
- Performance comparison: AutoDebias outperforms all compared methods on every dataset and metric; on Yahoo!R3, improvements reach 5.6% in NLL and 11.2% in NDCG@5.The comparisons include matrix-factorization variants, IPS, doubly robust learning, and knowledge-transfer methods.
- Performance comparison: At Ratio=1% uniform data, AutoDebias still performs well, whereas KD-Label performs close to or worse than MF(biased).Performance remains consistently higher for AutoDebias as the uniform-data size varies.
- Ablation study: Ablation results show that w(1), w(2), and m contribute to debiasing, while AutoDebias-w1 still outperforms IPS without imputation.w(2) slightly harms Coat performance but substantially improves Yahoo!R3 performance.
- Exploratory analysis: Learned parameters assign lower weights to popular items and provide more exposure to unpopular items, with improvements mainly coming from unpopular items.The study defines popular items as the top 20% by positive-feedback frequency and reports fairer recommendation results.
- Universality: AutoDebias performs much better than WMF, RL-MF, and AWMF on implicit feedback, and outperforms DLA and HeckE when position and selection biases co-occur.These results support its application to exposure bias and combined biases in recommendation-list feedback.
7 RELATED WORK
Related work covers biases in recommendation outputs, including popularity and unfair discrimination, alongside meta-learning methods for tuning recommendation algorithms.
- Bias in recommendation: Popularity bias causes popular items to be recommended more frequently than their popularity warrants in long-tailed recommendation data.Prior approaches use regularizers or causal inference to promote balance or disentangle user interest from item popularity.
- Bias in recommendation: Recommendation systems can systematically and unfairly discriminate against certain individuals or groups.
- Meta learning in recommendation: Meta-learning has been used in recommendation to learn finer-grained regularization parameters and guide model retraining.
8 CONCLUSION AND FUTURE WORK
The paper proposes a universal debiasing framework based on risk discrepancy and a meta-learning algorithm that learns debiasing configurations from uniform data. Future work targets more sophisticated meta-models and dynamic real-world biases.
- Conclusion: The framework formulates multiple data biases as risk discrepancy and subsumes most existing debiasing strategies.
- Conclusion: AutoDebias adaptively learns optimal debiasing configurations from uniform data using meta-learning.The paper reports both theoretical and empirical analyses validating the proposal’s effectiveness.
- Future work: Future work should explore sophisticated meta-models for complex patterns and universal solutions for biases that evolve over time.The paper identifies dynamic rather than static real-world biases as an open direction.