Uber optimizes marketplace levers using machine learning to improve resource allocation efficiency.
problem Optimizing budget allocation for drivers and riders to maximize business value.
method End-to-end machine learning and optimization procedure using feature store, model training, and ADMM.
result Substantially improved Uber's resource allocation efficiency through high-dimensional optimization.
Optimizes bidding strategies for LinkedIn ads across multiple platforms.
problem Optimizing automated bidding agents for dynamic online marketplaces.
method Developed a general optimization framework for buyer's interest, agnostic to auction mechanisms.
result Automatically guarantees the optimality of budget allocation across ad units and platforms.
Genie optimizes search marketplaces by estimating policy impacts without risky experiments.
problem Optimizing search marketplaces with frequent policy changes and limited randomized experiments.
method Genie uses an open box simulation engine and click calibration model to estimate KPI impacts.
result Genie outperforms existing approaches in optimizing Bing Ads Marketplace.
Deep neural networks improve recommendation accuracy in marketplaces.
problem Measuring and optimizing recommender performance in marketplaces.
method Hybrid item representation models, sequence-based models, and multi-armed bandit models.
result Promising deep neural network recommenders are currently in production at FINN.no.
A scalable system detects price anomalies in online marketplaces to improve customer experience.
problem Inaccurate prices on online marketplaces lead to poor customer experience and revenue loss.
method MoatPlus uses unsupervised statistical features and an ensemble of models to generate upper price bounds.
result Our approach improves precise anchor coverage by up to 46.6% in high-vulnerability item subsets.
Proposes a framework for fairness in two-sided marketplaces.
problem Achieving fairness in two-sided marketplaces.
method Developed an end-to-end framework for fairness constraints from both sides of the marketplace, including dynamic aspects.
result Efficacy of the proposed framework demonstrated through simulations.
MARS-Gym framework for marketplaces to train and evaluate recommender systems.
problem Challenges in designing, training, and evaluating recommender systems in marketplaces.
method Open-source framework for Reinforcement Learning agents in marketplaces.
result Empowers researchers and engineers to quickly build and evaluate agents for recommendations.
Framework for optimizing search engine rankings using observational data.
problem Optimizing ranking policies for search engines using limited observational data.
method Formulated expected reward optimization problem, estimated context value distribution, trained ranking policy via Bayesian inference.
result Demonstrated trade-offs in ranking policies trained on empirical reward estimates.
A new mechanism optimizes data marketplace pricing efficiently.
problem Designing fair and efficient pricing mechanisms for data marketplaces.
method Two-stage approach: auctions to estimate value distributions, then optimal posted prices.
result MAPP achieves optimal revenue with minimal price discrimination.
This paper optimizes search experiences in two-sided marketplaces by balancing multiple conflicting metrics.
problem Balancing conflicting business metrics in two-sided marketplaces like eBay and Etsy.
method Joint optimization of market-level metrics using Evolutionary Strategies.
result Demonstrated effectiveness of the proposed method on Etsy data.
Deep learning improves recommendation systems for marketplaces.
problem Challenges in matrix factorization for marketplaces.
method Hybrid recommender system combining user-generated contents and user behavior data.
result Five lessons learned from deep learning experiments.
Paper proposes machine learning for pricing 3D printing services in marketplaces.
problem Inefficient pricing methods for 3D printing services in marketplaces.
method Data mining and machine learning methods to estimate price ranges based on supplier and customer characteristics.
result Machine learning model achieves 65% accuracy for US suppliers and 59% for Europe suppliers in classifying 3D printer listings.
Optimizes Airbnb pricing to increase revenue and reduce booking regret.
problem Maximizing revenue in an online marketplace with competing products.
method Two-stage model to price retrieved items based on learned distributions of their values.
result Improves revenue and booking regret by at least +20% and +55% respectively.
Study fair team formation in online labor marketplaces.
problem Design fair algorithms for team formation in online labor marketplaces.
method Define and analyze the Fair Team Formation problem, provide inapproximability results, and develop four algorithms.
result Developed four algorithms for fair team formation in online labor marketplaces.
GCF estimates heterogeneous treatment effects for continuous treatments in online marketplaces.
problem Estimating heterogeneous treatment effects for continuous treatments in online marketplaces.
method Kernel-based doubly robust estimator and distance-based splitting criterion.
result GCF estimates heterogeneous treatment effects for continuous treatments effectively.
Efficiently projects points onto polytopes, especially useful in web-scale applications.
problem Efficiently projecting points onto polytopes in large-scale applications.
method Developed a vertex-oriented incremental algorithm for polytope projection, tailored for simplex and unit-box cut polytopes.
result Majority of projections lie on vertices of polytopes, leading to significant performance improvements.
Sample-Rank simplifies MO recommendations by sampling and ranking, improving revenue with stable conversion rates.
problem Multi-objective recommendations in online food ordering systems.
method Multi-goal sampling followed by ranking, reducing MO problem to LTR model.
result Significant lift in revenue (2.64%) with stable conversion rates, no drop in last-mile traversal.
NFTs revolutionize art sales by providing proof of ownership.
problem Lack of provenance and authenticity in digital art.
method Analysis of major art NFT marketplaces.
result NFTs reduce the need for intermediaries in the art trade.
In crowd labeling, a large amount of unlabeled data instances are outsourced to a crowd of workers. Workers will be paid for each label they provide, but the labeling requester usually has only a limited amount of the budget. Since data instances have different levels of labeling difficulty and workers have different r…
Study fairness in intervention to maximize outcomes.
problem Fairness in intervention on a given node.
method Counterfactual estimation with partial causal model knowledge.
result Theoretical guarantees on error probability and effectiveness of algorithm.
Suppliers (including companies and individual prosumers) may wish to protect their private information when selling items they have in stock. A market is envisaged where private information can be protected through the use of differential privacy and option contracts, while privacy-aware suppliers deliver their stock a…
A new dataset tracks user interactions and click responses in online marketplaces.
problem Lack of exposure data in recommender systems datasets.
method Proposes a novel dataset including slates and click responses, allowing more accurate likelihood models.
result Models using exposure data show more natural likelihood, reducing bias towards previously exposed items.
The theory of rational choice assumes that when people make decisions they do so in order to maximize their utility. In order to achieve this goal they ought to use all the information available and consider all the choices available to choose an optimal choice. This paper investigates what happens when decisions are m…
New estimator reduces bias in interference studies on content marketplaces.
problem Interference bias in experiments on content marketplaces like Douyin.
method Developed a Monte-Carlo estimator based on DQ techniques.
result Achieved bias second-order in treatment effect with low variance.
Market incentivizes parties to share high-quality data for collaborative machine learning tasks.
problem Fair revenue distribution and data replication threats in collaborative machine learning markets.
method Introduces a novel payment division function robust to replication and customized output models.
result Validated assumptions and showed approximate satisfaction for commonly used models.
Method prevents model divergence in rapidly changing ad markets.
problem Model divergence due to rapid ad turnover and discontinuity.
method Dual ascent optimization with latent vector constraints.
result Significant reduction in diverging instances and improved user experience/revenue.
BAT benchmark for autobidding tasks in RTB auctions.
problem Lack of comprehensive datasets and benchmarks for autobidding.
method Developed a benchmark for two auction formats, implemented robust baselines.
result Provides a framework for developing and refining autobidding algorithms.
Alternative app data improves credit scoring for underserved borrowers.
problem Improving credit scoring for low-wealth and young individuals.
method Use of alternative data from app-based marketplaces, validated with TreeSHAP method.
result Alternative data sources predict financial behavior better than traditional bureau data.
Improved model accuracy can reduce overall user accuracy in competitive markets.
problem The impact of model competition on overall user accuracy.
method Defined a model of competition for classification tasks and used data representations to study the effect of scale.
result Improving data representation quality can decrease overall predictive accuracy across users (social welfare) in a competitive market.
The paper optimizes A/B tests by balancing lift and cost in large-scale settings.
problem Balancing lift and cost in A/B tests for large-scale experimentation.
method Empirical Bayes approach using a greedy knapsack algorithm to rank experiments based on lift-to-cost ratio, incorporating local false discovery rate (lfdr).
result The proposed method maximizes expected profit while controlling false discovery rate, demonstrating superior performance in large-scale settings.
New framework optimizes rankings for fairness in various applications.
problem Ranking systems can unfairly prioritize certain items over others.
method Proposes a general LTR framework that optimizes fairness constraints while maximizing utility.
result Demonstrates effectiveness in individual and group-fairness settings.
Tangled String visualizes stock market changes at various timescales.
problem Explaining multi-scale contextual shifts in stock market prices.
method Sequence visualization tool based on metaphor of tangled strings.
result Change points in stock prices coincide with real changes.
The paper addresses Qini curve estimation under clustered network interference.
problem Qini curves can be biased when interference is ignored in clustered network settings.
method Proposes three estimation strategies for clustered network interference.
result Identifies the most appropriate approach based on bias-variance trade-offs.
We discuss several uses of blockchain (and, more generally, distributed ledger) technologies outside of cryptocurrencies with a pragmatic view. We mostly focus on three areas: the role of coin economies for what we refer to as data malls (specialized data marketplaces); data provenance (a historical record of data and …
Study optimizes crowdfunding platform offerings based on customer behavior.
problem Maximizing crowdfunding platform revenue through optimal product assortment.
method Multinomial logit model and machine learning methods (multivariate regression, classification) for revenue prediction.
result Optimal assortments can significantly increase platform revenue.
New algorithm optimizes interventions under network interference, scaling to large networks.
problem Optimal policy learning under network interference where one individual's treatment affects others.
method Developed a scalable Thompson sampling algorithm for dynamic networks.
result Proved a Bayesian regret bound that is sublinear in network size and rounds.
Study breaks down graphs into structural and featural components for task-agnostic data valuation.
problem Lack of methods to assess the value of graphs in data marketplaces.
method Introduces blind message passing framework to evaluate graphs without specific task metrics.
result Demonstrates effectiveness in capturing structural disparities, relevance, and diversity of seller data for buyers.
Optimizes auction mechanisms in e-commerce search ads to balance revenue and user experience.
problem Optimizing auction mechanisms in e-commerce search ads while maintaining quality users and ROI.
method Developed a practical convex optimization formulation and auction simulation system to estimate business indicators.
result Proper entropy regularization can maximize revenue while constraining other business indicators.
Contextual bandit framework improves revenue optimization in securities lending market.
problem Optimizing revenue for agent lenders in a dynamic securities lending market.
method Utilized contextual bandit frameworks to address dynamic pricing problems in an e-commerce-like securities lending market.
result Contextual bandit approach consistently outperforms traditional methods by at least 15% in total revenue generated.
The paper introduces Absolute Shapley Value to handle negative contributions in machine learning model training.
problem Negative marginal contributions in machine learning model training.
method Investigates three philosophies: Original Shapley Value, Zero Shapley Value, and Absolute Shapley Value.
result Absolute Shapley Value significantly outperforms other definitions in evaluating data importance.
The paper offers algorithms for managing freelancers and in-house workers in online labor markets.
problem Managing freelancers and in-house workers in online labor markets efficiently.
method Developed algorithms for team formation with outsourcing in an online setting.
result Efficient online algorithms for minimizing costs in hiring and outsourcing.
New algorithms learn stable matchings from uncertain user preferences.
problem Learning stable matchings from uncertain user preferences.
method Stochastic multi-armed bandit problem, incentive-aware learning objective, primal-dual formulation.
result Near-optimal regret bounds for learning stable matchings.
Optimizes package types for e-commerce to reduce damage and costs.
problem Sub-optimal package types lead to damaged shipments and high costs.
method Multi-stage approach that balances shipment and damage costs using a scalable algorithm.
result Significant cost savings of tens of millions of dollars achieved.
Deep models predict missing product attributes from text and images.
problem Incomplete or missing product attributes in e-commerce catalogs.
method Combining textual and visual data with a novel modality-merging method.
result Our approach improves attribute prediction on Rakuten-Ichiba and other datasets.
Study of repeated principal-agent bandit game with self-interested and exploratory learning agents.
problem Interaction between principal and agent in unknown environments with learning and exploration behaviors.
method Developed algorithms for self-interested and exploratory learning agents with bandit feedback, achieving regret bounds.
result Achieved O ~ ( T 2 / 3 ) \widetilde{O}(T^{2/3}) O ( T 2/3 ) regret bound for exploratory learning agent in i.i.d. reward setup. Debt-financed collateral in DeFi increases stability risks.
problem Financial stability risks in DeFi ecosystems due to debt-financed collateral.
method Categorization and classification algorithm to measure debt-financed collateral.
result Wide-spread use of stablecoins as debt-financed collateral increases financial stability risks.
Study copyright's impact on creative industries using AI-generated fonts.
problem Estimating supply and demand in creative industries with AI-generated content.
method Neural network embeddings, spatial regression, event-study analyses, structural model of supply and demand.
result Copyright can raise consumer welfare by encouraging product relocation.
Model predicts competition between similar products in sales.
problem Predicting cannibalization between similar products in sales.
method Developed a neural network model that computes a 'competitiveness' function based on product features.
result The model outperforms traditional methods in predicting market share.