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.
MARS-Gym framework for marketplaces to train and evaluate recommender systems.
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…
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…
A scalable system detects price anomalies in online marketplaces to improve customer experience.
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…
Framework for optimizing search engine rankings using observational data.
Study fair team formation in online labor marketplaces.
A new mechanism optimizes data marketplace pricing efficiently.
Proposes a framework for fairness in two-sided marketplaces.
Optimizes bidding strategies for LinkedIn ads across multiple platforms.
Uber optimizes marketplace levers using machine learning to improve resource allocation efficiency.
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…
GCF estimates heterogeneous treatment effects for continuous treatments in online marketplaces.
Correctly pricing products or services in an online marketplace presents a challenging problem and one of the critical factors for the success of the business. When users are looking to buy an item they typically search for it. Query relevance models are used at this stage to retrieve and rank the items on the search p…
A new dataset tracks user interactions and click responses in online marketplaces.
NFTs revolutionize art sales by providing proof of ownership.
Study fairness in intervention to maximize outcomes.
Alternative app data improves credit scoring for underserved borrowers.
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…
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…
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 …
Improved model accuracy can reduce overall user accuracy in competitive markets.
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 breaks down graphs into structural and featural components for task-agnostic data valuation.
New estimator reduces bias in interference studies on content marketplaces.
The paper introduces Absolute Shapley Value to handle negative contributions in machine learning model training.
Sample-Rank simplifies MO recommendations by sampling and ranking, improving revenue with stable conversion rates.
Efficiently projects points onto polytopes, especially useful in web-scale applications.
A new method reduces data valuation variance for more trustworthy data trading.
Deep models predict missing product attributes from text and images.
We study the problem of collaborative machine learning markets where multiple parties can achieve improved performance on their machine learning tasks by combining their training data. We discuss desired properties for these machine learning markets in terms of fair revenue distribution and potential threats, including…
Model predicts competition between similar products in sales.
The paper addresses Qini curve estimation under clustered network interference.
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…
Method prevents model divergence in rapidly changing ad markets.
Financial asset markets are sociotechnical systems whose constituent agents are subject to evolutionary pressure as unprofitable agents exit the marketplace and more profitable agents continue to trade assets. Using a population of evolving zero-intelligence agents and a frequent batch auction price-discovery mechanism…
Study enhances cryptocurrency sentiment analysis using TikTok and Twitter data.
The paper offers algorithms for managing freelancers and in-house workers in online labor markets.
Machine learning as a service (MLaaS), and algorithm marketplaces are on a rise. Data holders can easily train complex models on their data using third party provided learning codes. Training accurate ML models requires massive labeled data and advanced learning algorithms. The resulting models are considered as intell…
Data analytics using machine learning (ML) has become ubiquitous in science, business intelligence, journalism and many other domains. While a lot of work focuses on reducing the training cost, inference runtime and storage cost of ML models, little work studies how to reduce the cost of data acquisition, which potenti…
Using the most comprehensive source of commercially available data on the US National Market System, we analyze all quotes and trades associated with Dow 30 stocks in 2016 from the vantage point of a single and fixed frame of reference. We find that inefficiencies created in part by the fragmentation of the equity mark…
The original research question here is given by marketers in general, i.e., how to explain the changes in the desired timescale of the market. Tangled String, a sequence visualization tool based on the metaphor where contexts in a sequence are compared to tangled pills in a string, is here extended and diverted to dete…
Study of repeated principal-agent bandit game with self-interested and exploratory learning agents.
Method constructs prediction intervals for time-varying individual treatment effects.
Debt-financed collateral in DeFi increases stability risks.
Deep semi-supervised anomaly detection improves fraud detection in financial markets.
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…
Study copyright's impact on creative industries using AI-generated fonts.