Search Results for author:
Found 4 papers, 1 papers with code
Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search
Although a few studies use multi-agent reinforcement learning to set up a cooperative game, they still suffer the following drawbacks: (1) They fail to avoid collusion solutions where all the advertisers involved in an auction collude to bid an extremely low price on purpose.
IntentGC: a Scalable Graph Convolution Framework Fusing Heterogeneous Information for Recommendation
In this work, we collect abundant relationships from common user behaviors and item information, and propose a novel framework named IntentGC to leverage both explicit preferences and heterogeneous relationships by graph convolutional networks.
Personalized Attraction Enhanced Sponsored Search with Multi-task Learning
(2) It is difficult to obtain users' explicit feedback of their preference in product features.
Deep Reinforcement Learning for Sponsored Search Real-time Bidding
In this paper, we consider the RTB problem in sponsored search auction, named SS-RTB.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
