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FairRec: Fairness-aware News Recommendation with Decomposed Adversarial Learning
Chuhan Wu, Fangzhao Wu, Xiting Wang, Yongfeng Huang, Xing Xie
TL;DR
Click-based news recommendation can encode sensitive-attribute patterns, creating biased and less diverse results. FairRec decomposes user representations and combines adversarial learning with orthogonality regularization to produce bias-free embeddings for ranking; experiments report improved fairness, with the paper noting unresolved separation of bias-independent interests and an assumption of categorical attributes.
Problem
Existing click-based news recommendation models can capture behavior patterns associated with sensitive attributes, producing biased recommendations and limiting news diversity.
Method
FairRec decomposes user modeling into bias-aware and bias-free embeddings, strengthens bias modeling with attribute prediction, removes sensitive-attribute information adversarially, and applies orthogonality regularization before bias-free ranking.
Results
Experiments on real-world data report that FairRec effectively improves fairness in news recommendation.
Takeaways & Limitations
Using only the bias-free embedding for personalized ranking provides the paper’s fairness-aware recommendation strategy.
Takeaways & Limitations
The paper leaves open how to ensure that bias-independent user interests are maximally captured by the bias-free embedding, and assumes the sensitive attribute is categorical.
Abstract
from arXiv · showhide
News recommendation is important for online news services. Existing news recommendation models are usually learned from users' news click behaviors. Usually the behaviors of users with the same sensitive attributes (e.g., genders) have similar patterns and news recommendation models can easily capture these patterns. It may lead to some biases related to sensitive user attributes in the recommendation results, e.g., always recommending sports news to male users, which is unfair since users may not receive diverse news information. In this paper, we propose a fairness-aware news recommendation approach with decomposed adversarial learning and orthogonality regularization, which can alleviate unfairness in news recommendation brought by the biases of sensitive user attributes. In our approach, we propose to decompose the user interest model into two components. One component aims to learn a bias-aware user embedding that captures the bias information on sensitive user attributes, and the other aims to learn a bias-free user embedding that only encodes attribute-independent user interest information for fairness-aware news recommendation. In addition, we propose to apply an attribute prediction task to the bias-aware user embedding to enhance its ability on bias modeling, and we apply adversarial learning to the bias-free user embedding to remove the bias information from it. Moreover, we propose an orthogonality regularization method to encourage the bias-free user embeddings to be orthogonal to the bias-aware one to better distinguish the bias-free user embedding from the bias-aware one. For fairness-aware news ranking, we only use the bias-free user embedding. Extensive experiments on benchmark dataset show that our approach can effectively improve fairness in news recommendation with minor performance loss.
Introduction
Existing click-based news recommendation models can encode sensitive-attribute patterns, producing biased and less diverse recommendations. FairRec separates bias-aware and bias-free user representations, using adversarial learning and orthogonality regularization to improve fairness.
- Motivation: Click-based user-interest models can capture gender-linked behavior patterns and recommend fashion news to female users and NBA news to male users.Users interested in both categories may consequently receive less diverse news information.
- Approach: FairRec decomposes user modeling into bias-aware and bias-free embeddings for fairness-aware news recommendation.The bias-aware embedding captures sensitive-attribute bias, while the bias-free embedding encodes attribute-independent interest information.
- Approach: The method combines sensitive-attribute prediction, adversarial learning, and orthogonality regularization to separate bias information from recommendation interest.Attribute prediction strengthens bias modeling, while adversarial learning and orthogonality regularization remove sensitive-attribute information from the bias-free embedding.
- Approach: FairRec uses only the bias-free user embedding for personalized news ranking.This design targets fairness-aware recommendation rather than ranking with the bias-aware representation.
- Evidence: Extensive experiments on a real-world dataset show that FairRec improves fairness in news recommendation.The supplied contribution statements report improved fairness without providing a quantitative value.
Related Work
News recommendation models commonly learn user interests from click behavior, but shared behavioral patterns among users with the same sensitive attributes can produce biased results. Related fairness research includes provider-side and customer-side fairness, with customer-side studies often using ratings in e-commerce settings.
- News recommendation: Many news recommendation methods learn user representations from users’ clicked news through recurrent, relevance-based, or self-attention models.These approaches model user interest from observed news-click behavior.
- News recommendation: Click-based models can encode similar behavior patterns among users sharing sensitive attributes, leading to biased recommendations and reduced news diversity.The related-work discussion connects this issue to the fairness motivation for FairRec.
- Fairness in recommendation: Recommendation fairness research covers provider-side fairness and customer-side fairness.Provider-side methods concern recommendation opportunities across providers, while customer-side methods concern recommendations for users with different sensitive attributes.
- Fairness in recommendation: Several customer-side fairness methods in e-commerce use ratings to indicate fairness.The paper later distinguishes its news-recommendation fairness setting from rating-based evaluation.
Methodology
FairRec separates user representations into bias-aware and bias-free components, then uses adversarial learning and orthogonality regularization to reduce sensitive-attribute information in ranking representations.
- FairRec computes fairness-aware ranking scores from candidate-news embeddings and a bias-free user embedding.The framework ranks candidate news using the bias-free representation rather than the bias-aware component.
- The user-interest model is decomposed into bias-aware and bias-free embeddings to separate sensitive-attribute information from attribute-independent interests.The bias-aware component captures sensitive-attribute bias, while the bias-free component is intended to encode attribute-independent user interests.
- An attribute-prediction task strengthens bias modeling in the bias-aware embedding, while adversarial discrimination penalizes sensitive-attribute information in the bias-free embedding.The bias-aware embedding predicts user attributes, whereas negative discriminator gradients are used to discourage attribute inference from the bias-free embedding.
- Orthogonality regularization encourages bias-aware and bias-free embeddings to be orthogonal, addressing residual sensitive-attribute information that adversarial learning may leave behind.The method motivates this regularizer because the discriminator may not perfectly recover the sensitive attribute or match its real decision boundary.
- During training, both embeddings are added for recommendation-model learning, while the final ranking uses only the bias-free embedding.The unified training representation is u = ub + ud, and recommendation uses the bias-free embedding for fairness-aware ranking.
Experiments
Experiments evaluate FairRec on a gender-labeled news dataset using fairness and recommendation-performance measures, ablations, hyperparameter analyses, and case studies. FairRec improves fairness while retaining competitive recommendation performance, and its components contribute to the fairness gains.
- Dataset and Experimental Settings: The experiments use a benchmark news dataset with gender labels to evaluate fairness and recommendation performance.The dataset contains 10,000 users, with 4,228 users providing gender labels; the last week is reserved for testing.
- Evaluation Metrics: Fairness is measured through sensitive-attribute prediction performance on the top K ranked candidate news, rather than user-rating metrics.Lower fairness scores indicate better fairness in the reported comparisons.
- Performance Evaluation: FairRec achieves better recommendation fairness than non-fairness-aware methods and outperforms adversarial-learning baselines with a substantial margin.The experiments compare FairRec with methods including LibFM, EBNR, DKN, DAN, NPA, and other fairness-aware baselines.
- Performance Evaluation: FairRec improves fairness with only a minor loss in recommendation performance compared with its basic model, NRMS.The reported performance results use higher scores as better outcomes.
- Effectiveness of Decomposed Adversarial Learning: Removing attribute prediction, adversarial learning, or orthogonality regularization reduces fairness under K = 10.The ablation results identify all three components as contributing to FairRec's fairness improvements.
- Hyperparameter Analysis: Moderate loss-coefficient values provide better fairness–performance trade-offs than excessively large values.The analysis recommends λG = 0.5, a λD range of 0.3–0.6, and λA = λG = λD = 0.5 for convenience.
- Case Study: Case studies show FairRec ranking fashion news for a male user and NFL news for a female user when those items may also match their interests.The comparisons are made against NRMS recommendation results for one male and one female user.
Conclusion
FairRec improves news recommendation fairness by separating bias-aware and bias-free user representations, then ranking news with the bias-free embedding. Extensive experiments report substantially improved fairness with minor performance sacrifice.
- FairRec decomposes user modeling into bias-aware and bias-free embeddings for fairness-aware news ranking.Attribute prediction strengthens bias modeling, while adversarial learning and orthogonality regularization reduce sensitive-attribute information in the bias-free embedding.
- Extensive experiments show substantially improved news recommendation fairness with minor performance sacrifice.