… by jointly optimizing user association and power control. The joint user association and power
… This paper proposes a novel idea for resolving this question: the optimal user association …

… capturing users’ preferences, because users usually … user-item representation learning
model for rating prediction, named CARL. CARL derives a joint representation for a given user-…

… Most existing NRL methods focus on learning representations … analogy between network
representation learning and text … -enhanced Network Representation Learning (CNRL). CNRL …

… the general correlation among users. In this paper, we propose a general user representation
learning framework to jointly and automatically learn and fuse users’ related information …

… art network representation learning techniques according to the underlying learning
mechanisms… We summarize evaluation protocols used for validating network representation …

… Next, we introduce the problem of general network embedding (representation learning). …
designed to integrate node attributes and labels into representation learning like R-GCN, HAN, …

… end-to-end user representation learning framework with GCN and neural random forest
as main building blocks, namely GraphR , to capture and represent both user preference and …

… Following this direction, we study the task of user representation learning with both macro
and micro interaction data of user behaviors on mobile apps. To be specific, macro interaction …

… learn the representation through a federated GNN. Based on these learned representations,
we cluster users into different user groups and learn … a joint representation learning, user …

… state learning, and imitation based on user profiles as policy learning. Along these lines, we
propose a Reinforcement Imitative Representation Learning (RIRL) method for user profiling…