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Found 6 papers, 0 papers with code
Towards Fairness in Personalized Ads Using Impression Variance Aware Reinforcement Learning
While there are many definitions of fairness that could be applicable in the context of personalized systems, we present a framework which we call the Variance Reduction System (VRS) for achieving more equitable outcomes in Meta's ads systems.
Conditional Sequential Slate Optimization
To that end, we propose a hybrid framework extended from traditional slate optimization to solve the conditional slate optimization problem.
Addressing Purchase-Impression Gap through a Sequential Re-ranker
It is critical for eCommerce search engines to showcase in the top results the variety and selection of inventory available, specifically in the context of the various buying intents that may be associated with a search query.
Influence of Neighborhood on the Preference of an Item in eCommerce Search
Surfacing a ranked list of items for a search query to help buyers discover inventory and make purchase decisions is a critical problem in eCommerce search.
Personalized Query Auto-Completion Through a Lightweight Representation of the User Context
The ranking model with the proposed features results in a $20-30\%$ improvement over the MPC model on all metrics.
Personalized Ranking in eCommerce Search
We address the problem of personalization in the context of eCommerce search.
