Knowledge graph (KG) embedding encodes the entities and relations from a KG into low-dimensional vector spaces to support various applications such as KG completion, question answering, and recommender systems. In real world, knowledge graphs (KGs) are dynamic and evolve over time with addition or...
Each character is indexed into a vocabulary and then fed through an embedding layer. At this point, multiple recurrent layers can be stacked. The hidden states of the special beginning token (\({\mathtt {}})\) are extracted from the last layer and ran through a dense layer that outputs...
GCE-GNN aggregates global contextual information about sessions using a global graph while retaining the session graph for more comprehensive session embedding [23]. DHCN transforms sessions into a hypergraph, capturing the complex transitions between items using a hypergraph convolutional network [24]....