DeepFair: Deep Learning for Improving Fairness in Recommender Systems

Bobadilla, Jesús; Lara-Cabrera, Raúl; González-Prieto, Ángel; Ortega, Fernando

doi:10.9781/ijimai.2020.11.001

Cited by 23 publications

(13 citation statements)

References 20 publications

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“…• Gender [35,36,37,38,39,40,41,42,43,44,45,46,47,48,49] • Race [50,40,51,52,49] • Age [35,53,54,55,38] • Nationality [56] Target: Merit-based fairness -attained through a user's merit over time.…”

Section: Notions Of Fairnessmentioning

confidence: 99%

Fairness in Recommender Systems: Research Landscape and Future Directions

Deldjoo¹,

Jannach²,

Bellogín³

et al. 2022

Preprint

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Recommender systems can strongly influence which information we see online, e.g, on social media, and thus impact our beliefs, decisions, and actions. At the same time, these systems can create substantial business value for different stakeholders. Given the growing potential impact of such AI-based systems on individuals, organizations, and society, questions of fairness have gained increased attention in recent years. However, research on fairness in recommender systems is still a developing area. In this survey, we first review the fundamental concepts and notions of fairness that were put forward in the area in the recent past. Afterwards, we provide a survey of how research in this area is currently operationalized, for example, in terms of the general research methodology, fairness metrics, and algorithmic approaches. Overall, our analysis of recent works points to certain research gaps. In particular, we find that in many research works in computer science very abstract problem operationalizations are prevalent, which circumvent the fundamental and important question of what represents a fair recommendation in the context of a given application.

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Section: Notions Of Fairnessmentioning

confidence: 99%

Fairness in Recommender Systems: Research Landscape and Future Directions

Deldjoo¹,

Jannach²,

Bellogín³

et al. 2022

Preprint

View full text Add to dashboard Cite

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“…In Table 1, for each reproducible paper, we identified the recommendation task (RP : Rating Prediction; TR : Top-N Recommendation), the notion of consumer fairness (EQ : equity of the error/utility score across demographic groups; IND : independence of the predicted relevance scores or recommendations from the demographic group), the consumers' grouping (G : Gender, A : Age, O : Occupation, B : Behavioral), the mitigation type (PRE-, IN-or POST-Processing), the evaluation data sets (ML : MovieLens 1M or 10M, LFM : LastFM 1K or 360K, AM: Amazon, SS: Sushi, SY: Synthetic), the utility/accuracy metrics (NDCG : Normalized Discounted Cumulative Gain; F1 : F1 Score; AUC: Area Under Curve; MRR : Mean Reciprocal Rank; RMSE : Root Mean-Square Error; MAE : Mean Ab- We identified [26,27,20,25] and [4,17,14] as non-reproducible procedures according to our criteria for top-n recommendation and rating prediction, respectively.…”

Section: Mitigation Procedures Collectionmentioning

confidence: 99%

Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations

Boratto¹,

Fenu²,

Marras³

et al. 2022

Preprint

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Enabling non-discrimination for end-users of recommender systems by introducing consumer fairness is a key problem, widely studied in both academia and industry. Current research has led to a variety of notions, metrics, and unfairness mitigation procedures. The evaluation of each procedure has been heterogeneous and limited to a mere comparison with models not accounting for fairness. It is hence hard to contextualize the impact of each mitigation procedure w.r.t. the others. In this paper, we conduct a systematic analysis of mitigation procedures against consumer unfairness in rating prediction and top-n recommendation tasks. To this end, we collected 15 procedures proposed in recent top-tier conferences and journals. Only 8 of them could be reproduced. Under a common evaluation protocol, based on two public data sets, we then studied the extent to which recommendation utility and consumer fairness are impacted by these procedures, the interplay between two primary fairness notions based on equity and independence, and the demographic groups harmed by the disparate impact. Our study finally highlights open challenges and future directions in this field.

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“…It is expected to be characterized by simple design, excellent adaptability, high robustness, and the high compatibility with a non-linear PEMFC system. As a member of the deep reinforcement learning (DRL) family, deep deterministic policy gradient (DDPG) is a model-free algorithm [18][19][20]. Capable of direct control based on the input data, the DDPG algorithm is characterized by the simplicity of structure and outstanding adaptability.…”

Section: Introductionmentioning

confidence: 99%

Distributed deep reinforcement learning for optimal voltage control of PEMFC

2021

IET Renewable Power Gen

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Proton exchange membrane fuel cells (PEMFCs) are promising components in the renewable energy field due to their high energy efficiency and low pollution output. However, these cells are also characterized by considerable nonlinearity, which in turn adversely affects the PEMFC output voltage. Conventional control algorithms cannot guarantee sufficient output voltage control, as they lack the robust adaptive ability required for adapting to these fluctuations in PEMFCs. In this paper, an optimal output voltage controller based on distributed deep reinforcement learning, which controls the output voltage by regulating the fuel input of the PEMFC, is proposed. In addition, an ensemble intelligence exploration multi-delay deep deterministic policy gradient (EIM-DDPG) algorithm is proposed for this controller. An ensemble intelligence exploration policy plus a classification experience replay mechanism are included within the EIM-DDPG algorithm to improve the exploration ability of the algorithm and thus increase the robustness and adaptive capability of the controller. As a result, the model-free optimal output voltage controller offers high robustness and adaptability. The simulation results in this paper demonstrate that the proposed optimal controller can realize the effective control of PEMFC output voltage.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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DeepFair: Deep Learning for Improving Fairness in Recommender Systems

Cited by 23 publications

References 20 publications

Fairness in Recommender Systems: Research Landscape and Future Directions

Fairness in Recommender Systems: Research Landscape and Future Directions

Consumer Fairness in Recommender Systems: Contextualizing Definitions and Mitigations

Distributed deep reinforcement learning for optimal voltage control of PEMFC

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