Fighting Fire with Fire

Rastegarpanah, Bashir; Gummadi, Krishna P.; Crovella, Mark

doi:10.1145/3289600.3291002

Cited by 76 publications

(16 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Pre-processing for fairness in recommender systems has been considered in the context of consumer fairness. Rastegarpanah et al (Rastegarpanah et al 2019) proposed to add new fake users who provide ratings on existing items, to minimize the losses of all user's groups, computed as the mean squared estimation error over all known ratings in each group. Despite working on the provider side, our upsampling extends the interactions of the real users and items and aims at adjusting interactions involving minority providers.…”

Section: Treatments For Provider Fairnessmentioning

confidence: 99%

Interplay between upsampling and regularization for provider fairness in recommender systems

Boratto

Fenu

Marras

2021

User Model User-Adap Inter

View full text Add to dashboard Cite

Considering the impact of recommendations on item providers is one of the duties of multi-sided recommender systems. Item providers are key stakeholders in online platforms, and their earnings and plans are influenced by the exposure their items receive in recommended lists. Prior work showed that certain minority groups of providers, characterized by a common sensitive attribute (e.g., gender or race), are being disproportionately affected by indirect and unintentional discrimination. Our study in this paper handles a situation where (i) the same provider is associated with multiple items of a list suggested to a user, (ii) an item is created by more than one provider jointly, and (iii) predicted user–item relevance scores are biasedly estimated for items of provider groups. Under this scenario, we assess disparities in relevance, visibility, and exposure, by simulating diverse representations of the minority group in the catalog and the interactions. Based on emerged unfair outcomes, we devise a treatment that combines observation upsampling and loss regularization, while learning user–item relevance scores. Experiments on real-world data demonstrate that our treatment leads to lower disparate relevance. The resulting recommended lists show fairer visibility and exposure, higher minority item coverage, and negligible loss in recommendation utility.

show abstract

Section: Treatments For Provider Fairnessmentioning

confidence: 99%

Interplay between upsampling and regularization for provider fairness in recommender systems

Boratto

Fenu

Marras

2021

User Model User-Adap Inter

View full text Add to dashboard Cite

show abstract

“…Wang and Joachims [36] proposed an uncertainty quantification approach to control the threshold of each retrieval channel in one retrieval process. Hao et al [13], Rastegarpanah et al [29] proposed a fairnessrelated matrix factorization method to adjust the weight of the retrieval model. In resource allocation, Balseiro et al [1], Cheung et al [7] proposed a mirror-descent method to solve in the dual space.…”

Section: Related Workmentioning

confidence: 99%

FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation Retrieval

Xu,

Ding

et al. 2024

Proceedings of the ACM Web Conference 2024

View full text Add to dashboard Cite

In pursuit of fairness and balanced development, recommender systems (RS) often prioritize group fairness, ensuring that specific groups maintain a minimum level of exposure over a given period. For example, RS platforms aim to ensure adequate exposure for new providers or specific categories of items according to their needs. Modern industry RS usually adopts a two-stage pipeline: stage-1 (retrieval stage) retrieves hundreds of candidates from millions of items distributed across various servers, and stage-2 (ranking stage) focuses on presenting a small-size but accurate selection from items chosen in stage-1. Existing efforts for ensuring amortized group exposures focus on stage-2, however, stage-1 is also critical for the task. Without a high-quality set of candidates, the stage-2 ranker cannot ensure the required exposure of groups. Previous fairnessaware works designed for stage-2 typically require accessing and traversing all items. In stage-1, however, millions of items are distributively stored in servers, making it infeasible to traverse all of them. How to ensure group exposures in the distributed retrieval process is a challenging question. To address this issue, we introduce a model named FairSync, which transforms the problem into a constrained distributed optimization problem. Specifically, FairSync resolves the issue by moving it to the dual space, where a central node aggregates historical fairness data into a vector and distributes it to all servers. To trade off the efficiency and accuracy, the gradient descent technique is used to periodically update the parameter of the dual vector. The experiment results on two public recommender retrieval datasets showcased that FairSync outperformed all the baselines, achieving the desired minimum level of exposures while maintaining a high level of retrieval accuracy.

show abstract

“…User fairness seeks equal treatment, in recommendation accuracy, explainability, etc., among different (groups of) users [9,10]. Item fairness concerns whether the system treats items fairly [5,29], such as equal prediction errors or resource allocations. They mainly treated item utility as static and have not modeled its utility changes over time, namely timeliness.…”

Section: Related Work 21 Fairness-aware Recommendationmentioning

confidence: 99%

Measuring Item Global Residual Value for Fair Recommendation

Wang

Jiang

et al. 2023

Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval

View full text Add to dashboard Cite

In the era of information explosion, numerous items emerge every day, especially in feed scenarios. Due to the limited system display slots and user browsing attention, various recommendation systems are designed not only to satisfy users' personalized information needs but also to allocate items' exposure. However, recent recommendation studies mainly focus on modeling user preferences to present satisfying results and maximize user interactions, while paying little attention to developing item-side fair exposure mechanisms for rational information delivery. This may lead to serious resource allocation problems on the item side, such as the Snowball Effect. Furthermore, unfair exposure mechanisms may hurt recommendation performance. In this paper, we call for a shift of attention from modeling user preferences to developing fair exposure mechanisms for items. We first conduct empirical analyses of feed scenarios to explore exposure problems between items with distinct uploaded times. This points out that unfair exposure caused by the time factor may be the major cause of the Snowball Effect. Then, we propose to explicitly model item-level customized timeliness distribution, Global Residual Value (GRV), for fair resource allocation. This GRV module is introduced into recommendations with the designed Timeliness-aware Fair Recommendation Framework (TaFR). Extensive experiments on two datasets demonstrate that TaFR achieves consistent improvements with various backbone recommendation models. By modeling item-side customized Global Residual Value, we achieve a fairer distribution of resources and, at the same time, improve recommendation performance. CCS CONCEPTS• Information systems → Recommender systems.

show abstract

Fighting Fire with Fire

Cited by 76 publications

References 30 publications

Interplay between upsampling and regularization for provider fairness in recommender systems

Interplay between upsampling and regularization for provider fairness in recommender systems

FairSync: Ensuring Amortized Group Exposure in Distributed Recommendation Retrieval

Measuring Item Global Residual Value for Fair Recommendation

Contact Info

Product

Resources

About