Two-Sided Fairness for Repeated Matchings in Two-Sided Markets

Sühr, Tom; Biega, Asia J.; Zehlike, Meike; Gummadi, Krishna P.; Chakraborty, Abhijnan

doi:10.1145/3292500.3330793

Cited by 120 publications

(94 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As such, randomization can allow us to balance exposure across heterogeneous intents. Exposure balancing may also prevent churn caused by starvation of producers in two-sided economy systems such as ride-sharing platforms [42].…”

Section: Discussionmentioning

confidence: 99%

Evaluating Stochastic Rankings with Expected Exposure

Dı́az

Mitra

Ekstrand

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

Self Cite

131

140

View full text Add to dashboard Cite

We introduce the concept of expected exposure as the average attention ranked items receive from users over repeated samples of the same query. Furthermore, we advocate for the adoption of the principle of equal expected exposure: given a fixed information need, no item should receive more or less expected exposure than any other item of the same relevance grade. We argue that this principle is desirable for many retrieval objectives and scenarios, including topical diversity and fair ranking. Leveraging user models from existing retrieval metrics, we propose a general evaluation methodology based on expected exposure and draw connections to related metrics in information retrieval evaluation. Importantly, this methodology relaxes classic information retrieval assumptions, allowing a system, in response to a query, to produce a distribution over rankings instead of a single fixed ranking. We study the behavior of the expected exposure metric and stochastic rankers across a variety of information access conditions, including ad hoc retrieval and recommendation. We believe that measuring and optimizing expected exposure metrics using randomization opens a new area for retrieval algorithm development and progress. CCS CONCEPTS • Information systems → Evaluation of retrieval results; Learning to rank.

show abstract

Section: Discussionmentioning

confidence: 99%

Evaluating Stochastic Rankings with Expected Exposure

Dı́az

Mitra

Ekstrand

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

Self Cite

131

140

View full text Add to dashboard Cite

show abstract

“…Different dispatch programs cause different energy behaviors. Among different dispatch algorithms, optimization is a hot topic [13]. However, the performance of the optimization algorithm depends heavily on current knowledge.…”

Section: Methodsmentioning

confidence: 99%

GPS Data in Urban Online Car-Hailing: Simulation on Optimization and Prediction in Reducing Void Cruising Distance

Wang

Chen

et al. 2020

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Ride-hailing, as a popular shared-transportation method, has been operated in many areas all over the world. Researchers conducted various researches based on global cases. They argued on whether car-hailing is an effective travel mode for emission reduction and drew different conclusions. The detailed emission performance of the ride-hailing system depends on the cases. Therefore, there is an urgent demand to reduce the overall picking up distance during the dispatch. In this study, we try to satisfy this demand by proposing an optimization method combined with a prediction model to minimize the global void cruising distance when solving the dispatch problem. We use Didi ride-hailing data on one day for simulation and found that our method can reduce the picking up distance by 7.51% compared with the baseline greedy algorithm. The proposed algorithm additionally makes the average waiting time of passengers more than 4 minutes shorter. The statistical results also show that the performance of our method is stable. Almost the metric in all cases can be kept in a low interval. What is more, we did a day-to-day comparison. We found that, despite the different spatial-temporal distribution of orders and drivers on different day conditions, there are little differences in the performance of the method. We also provide temporal analysis on the changing pattern of void cruising distance and quantity of orders on weekdays and weekends. Our findings show that our method can averagely reduce more void cruising distance when ride-hailing is active compared with the traditional greedy algorithm. The result also shows that the method can stably reduce void cruising distance by about 4000 to 5000 m per order across one day. We believe that our findings can improve deeper insight into the mechanism of the ride-hailing system and contribute to further studies.

show abstract

“…In this section, we introduce a series of baseline methods that are adopted from [22] and [31]. Nearest participant first (NPF).…”

Section: Baseline Methodsmentioning

confidence: 99%

Auxiliary-task Based Deep Reinforcement Learning for Participant Selection Problem in Mobile Crowdsourcing

Shen

Zhang

et al. 2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

View full text Add to dashboard Cite

In mobile crowdsourcing (MCS), the platform selects participants to complete location-aware tasks from the recruiters aiming to achieve multiple goals (e.g., profit maximization, energy efficiency, and fairness). However, different MCS systems have different goals and there are possibly conflicting goals even in one MCS system. Therefore, it is crucial to design a participant selection algorithm that applies to different MCS systems to achieve multiple goals. To deal with this issue, we formulate the participant selection problem as a reinforcement learning problem and propose to solve it with a novel method, which we call auxiliary-task based deep reinforcement learning (ADRL). We use transformers to extract representations from the context of the MCS system and a pointer network to deal with the combinatorial optimization problem. To improve the sample efficiency, we adopt an auxiliary-task training process that trains the network to predict the imminent tasks from the recruiters, which facilitates the embedding learning of the deep learning model. Additionally, we release a simulated environment on a specific MCS task, the ride-sharing task, and conduct extensive performance evaluations in this environment. The experimental results demonstrate that ADRL outperforms and improves sample efficiency over other well-recognized baselines in various settings. CCS CONCEPTS • Information systems → Crowdsourcing; • Theory of computation → Sequential decision making.

show abstract

Two-Sided Fairness for Repeated Matchings in Two-Sided Markets

Cited by 120 publications

References 22 publications

Evaluating Stochastic Rankings with Expected Exposure

Evaluating Stochastic Rankings with Expected Exposure

GPS Data in Urban Online Car-Hailing: Simulation on Optimization and Prediction in Reducing Void Cruising Distance

Auxiliary-task Based Deep Reinforcement Learning for Participant Selection Problem in Mobile Crowdsourcing

Contact Info

Product

Resources

About