… capturing users’ preferences, because users usually … user-item representationlearning 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 representationlearning and text … -enhanced Network RepresentationLearning (CNRL). CNRL …
W Wang, H Yin, X Du, W Hua, Y Li… - Proceedings of the 42nd …, 2019 - dl.acm.org
… the general correlation among users. In this paper, we propose a general userrepresentation learning framework to jointly and automatically learn and fuse users’ related information …
… art network representationlearning 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 (representationlearning). … designed to integrate node attributes and labels into representationlearning like R-GCN, HAN, …
… end-to-end userrepresentationlearning 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 userrepresentationlearning with both macro and micro interaction data of user behaviors on mobile apps. To be specific, macro interaction …
S Luo, Y Xiao, L Song - Proceedings of the 31st ACM international …, 2022 - dl.acm.org
… learn the representation through a federated GNN. Based on these learned representations, we cluster users into different user groups and learn … a joint representationlearning, user …
… state learning, and imitation based on user profiles as policy learning. Along these lines, we propose a Reinforcement Imitative RepresentationLearning (RIRL) method for user profiling…