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Found 7 papers, 2 papers with code
MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel Feed
There are two types of data employed to train reinforcement learning (RL) model for position allocation, named strategy data and random data.
PIER: Permutation-Level Interest-Based End-to-End Re-ranking Framework in E-commerce
Then we design a novel omnidirectional attention mechanism in OCPM to capture the context information in the permutation.
NMA: Neural Multi-slot Auctions with Externalities for Online Advertising
Although VCG-based multi-slot auctions (e. g., VCG, WVCG) make it theoretically possible to model global externalities (e. g., the order and positions of ads and so on), they lack an efficient balance of both revenue and social welfare.
Hybrid Transfer in Deep Reinforcement Learning for Ads Allocation
Ads allocation, which involves allocating ads and organic items to limited slots in feed with the purpose of maximizing platform revenue, has become a research hotspot.
Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks
With the recent prevalence of reinforcement learning (RL), there have been tremendous interests in utilizing RL for ads allocation in recommendation platforms (e. g., e-commerce and news feed sites).
Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation
A mixed list of ads and organic items is usually displayed in feed and how to allocate the limited slots to maximize the overall revenue is a key problem.
Cross DQN: Cross Deep Q Network for Ads Allocation in Feed
Our model results in higher revenue and better user experience than state-of-the-art baselines in offline experiments.
