Modeling content creator incentives on algorithm-curated platforms
J Hron, K Krauth, MI Jordan, N Kilbertus… - ar** new features. While user-side experiments are common, seller …
Tackling Interference Induced by Data Training Loops in A/B Tests: A Weighted Training Approach
N Si - arxiv preprint arxiv:2310.17496, 2023 - arxiv.org
In modern recommendation systems, the standard pipeline involves training machine
learning models on historical data to predict user behaviors and improve recommendations …
learning models on historical data to predict user behaviors and improve recommendations …
The value of external data for digital platforms: Evidence from a field experiment on search suggestions
Firms increasingly leverage external data with an aim to unlock improvements in their
offerings, but it is challenging to measure the value of external data. Collaborating with a …
offerings, but it is challenging to measure the value of external data. Collaborating with a …
Random Isn't Always Fair: Candidate Set Imbalance and Exposure Inequality in Recommender Systems
Traditionally, recommender systems operate by returning a user a set of items, ranked in
order of estimated relevance to that user. In recent years, methods relying on stochastic …
order of estimated relevance to that user. In recent years, methods relying on stochastic …
Matching theory-based recommender systems in online dating
Online dating platforms provide people with the opportunity to find a partner. Recommender
systems in online dating platforms suggest one side of users to the other side of users. This …
systems in online dating platforms suggest one side of users to the other side of users. This …
Experimental design for multi-channel imaging via task-driven feature selection
This paper presents a data-driven, task-specific paradigm for experimental design, to
shorten acquisition time, reduce costs, and accelerate the deployment of imaging devices …
shorten acquisition time, reduce costs, and accelerate the deployment of imaging devices …