A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
A scalable system detects price anomalies in online marketplaces to improve customer experience.
GCF estimates heterogeneous treatment effects for continuous treatments in online marketplaces.
Study fair team formation in online labor marketplaces.
Recommendations are broadly used in marketplaces to match users with items relevant to their interests and needs. To understand user intent and tailor recommendations to their needs, we use deep learning to explore various heterogeneous data available in marketplaces. This paper focuses on the challenge of measuring re…
Optimizes bidding strategies for LinkedIn ads across multiple platforms.
Study fairness in intervention to maximize outcomes.
This paper presents approaches to determine a network based pricing for 3D printing services in the context of a two-sided manufacturing-as-a-service marketplace. The intent is to provide cost analytics to enable service bureaus to better compete in the market by moving away from setting ad-hoc and subjective prices. A…
A new dataset tracks user interactions and click responses in online marketplaces.
The paper offers algorithms for managing freelancers and in-house workers in online labor markets.
A new mechanism optimizes data marketplace pricing efficiently.
Sample-Rank simplifies MO recommendations by sampling and ranking, improving revenue with stable conversion rates.
Optimizes Airbnb pricing to increase revenue and reduce booking regret.
We study an online multi-task learning setting, in which instances of related tasks arrive sequentially, and are handled by task-specific online learners. We consider an algorithmic framework to model the relationship of these tasks via a set of convex constraints. To exploit this relationship, we design a novel algori…
MARS-Gym framework for marketplaces to train and evaluate recommender systems.
In today's day and age when almost every industry has an online presence with users interacting in online marketplaces, personalized recommendations have become quite important. Traditionally, the problem of collaborative filtering has been tackled using Matrix Factorization which is linear in nature. We extend the wor…
Proposes a framework for fairness in two-sided marketplaces.
Recommendation algorithms are widely adopted in marketplaces to help users find the items they are looking for. The sparsity of the items by user matrix and the cold-start issue in marketplaces pose challenges for the off-the-shelf matrix factorization based recommender systems. To understand user intent and tailor rec…
Uber optimizes marketplace levers using machine learning to improve resource allocation efficiency.
Study of repeated principal-agent bandit game with self-interested and exploratory learning agents.
BAT benchmark for autobidding tasks in RTB auctions.
Framework for optimizing search engine rankings using observational data.
This paper introduces a reinforcement learning framework for A/B testing with dynamic causal effects.
Reinforcement learning improves online matching by combining expert policies.
Kiva is an online non-profit crowdsouring microfinance platform that raises funds for the poor in the third world. The borrowers on Kiva are small business owners and individuals in urgent need of money. To raise funds as fast as possible, they have the option to form groups and post loan requests in the name of their …
NFTs revolutionize art sales by providing proof of ownership.
In this paper, we propose an offline counterfactual policy estimation framework called Genie to optimize Sponsored Search Marketplace. Genie employs an open box simulation engine with click calibration model to compute the KPI impact of any modification to the system. From the experimental results on Bing traffic, we s…
Deep models predict missing product attributes from text and images.
We consider the problem of multi-product dynamic pricing, in a contextual setting, for a seller of differentiated products. In this environment, the customers arrive over time and products are described by high-dimensional feature vectors. Each customer chooses a product according to the widely used Multinomial Logit (…
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…
This paper examines how wash traders exploit market conditions in Bitcoin, finding strategic timing and spillover effects.
Conventional Learning-to-Rank (LTR) methods optimize the utility of the rankings to the users, but they are oblivious to their impact on the ranked items. However, there has been a growing understanding that the latter is important to consider for a wide range of ranking applications (e.g. online marketplaces, job plac…
New estimator reduces bias in interference studies on content marketplaces.
Method prevents model divergence in rapidly changing ad markets.
Automatic classification of scientific articles based on common characteristics is an interesting problem with many applications in digital library and information retrieval systems. Properly organized articles can be useful for automatic generation of taxonomies in scientific writings, textual summarization, efficient…
Method constructs prediction intervals for time-varying individual treatment effects.
Two-sided marketplaces such as eBay, Etsy and Taobao have two distinct groups of customers: buyers who use the platform to seek the most relevant and interesting item to purchase and sellers who view the same platform as a tool to reach out to their audience and grow their business. Additionally, platforms have their o…
This paper studies the problems of vehicle make & model classification. Some of the main challenges are reaching high classification accuracy and reducing the annotation time of the images. To address these problems, we have created a fine-grained database using online vehicle marketplaces of Turkey. A pipeline is prop…
Market incentivizes parties to share high-quality data for collaborative machine learning tasks.
New algorithm optimizes interventions under network interference, scaling to large networks.
Alternative app data improves credit scoring for underserved borrowers.
Efficiently projects points onto polytopes, especially useful in web-scale applications.
New method estimates treatment effects in complex interference settings.
Sponsored search in E-commerce platforms such as Amazon, Taobao and Tmall provides sellers an effective way to reach potential buyers with most relevant purpose. In this paper, we study the auction mechanism optimization problem in sponsored search on Alibaba's mobile E-commerce platform. Besides generating revenue, we…
We study the pricing problem faced by a firm that sells a large number of products, described via a wide range of features, to customers that arrive over time. Customers independently make purchasing decisions according to a general choice model that includes products features and customers' characteristics, encoded as…
Improved model accuracy can reduce overall user accuracy in competitive markets.
Generating accurate and reliable sales forecasts is crucial in the E-commerce business. The current state-of-the-art techniques are typically univariate methods, which produce forecasts considering only the historical sales data of a single product. However, in a situation where large quantities of related time series …
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…
New algorithm minimizes regret in multi-armed bandits with network interference.