8 citations · 9 across the 5 of their papers we have counts for
5 papers
RL-MPCA: A Reinforcement Learning Based Multi-Phase Computation Allocation Approach for Recommender Systems
Jiahong Zhou, Shunhui Mao, Guoliang Yang +5
Recommender systems aim to recommend the most suitable items to users from a large number of candidates. Their computation cost grows as the number of user requests and the complex…
MDDL: A Framework for Reinforcement Learning-based Position Allocation in Multi-Channel Feed
Xiaowen Shi, Ze Wang, Yuanying Cai +6
Nowadays, the mainstream approach in position allocation system is to utilize a reinforcement learning model to allocate appropriate locations for items in various channels and the…
NMA: Neural Multi-slot Auctions with Externalities for Online Advertising
Guogang Liao, Xuejian Li, Ze Wang +6
Online advertising driven by auctions brings billions of dollars in revenue for social networking services and e-commerce platforms. GSP auctions, which are simple and easy to unde…
Hybrid Transfer in Deep Reinforcement Learning for Ads Allocation
Ze Wang, Guogang Liao, Xiaowen Shi +6
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. Notice that…
Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks
Ze Wang, Guogang Liao, Xiaowen Shi +5
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…