Search Results for author:
Found 8 papers, 2 papers with code
Unsupervised Representation Learning of Player Behavioral Data with Confidence Guided Masking
Players of online games generate rich behavioral data during gaming.
Collaborative filtering via heterogeneous neural networks
These methods may fail to capture the personalized informativeness of each vertex.
PPPNE: Personalized proximity preserved network embedding
These methods may fail to capture the personalized informativeness of each vertex.
Ranked #1 on
Link Prediction
on PPI
Personalized Bundle Recommendation in Online Games
In business domains, \textit{bundling} is one of the most important marketing strategies to conduct product promotions, which is commonly used in online e-commerce and offline retailers.
Reinforcement Learning with a Disentangled Universal Value Function for Item Recommendation
As a part of the value function, free from the sparse and high-variance reward signals, a high-capacity reward-independent world model is trained to simulate complex environmental dynamics under a certain goal.
Model-based Reinforcement Learning
Recommendation Systems
+2
GraphFederator: Federated Visual Analysis for Multi-party Graphs
This paper presents GraphFederator, a novel approach to construct joint representations of multi-party graphs and supports privacy-preserving visual analysis of graphs.
Human-Computer Interaction Cryptography and Security Graphics
OptMatch: Optimized Matchmaking via Modeling the High-Order Interactions on the Arena
OptMatch contains an offline learning stage and an online planning stage.
Stable Prediction via Leveraging Seed Variable
By assuming the relationships between causal variables and response variable are invariant across data, to address this problem, we propose a conditional independence test based algorithm to separate those causal variables with a seed variable as priori, and adopt them for stable prediction.
