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Found 3 papers, 3 papers with code
CaSe4SR: Using category sequence graph to augment session-based recommendation
In CaSe4SR, we build an item graph and a category graph, from user behavior sequence and item category sequence.
SemSeq4FD: Integrating global semantic relationship and local sequential order to enhance text representation for fake news detection
In this paper, we propose a novel graph-based neural network model named SemSeq4FD for early fake news detection based on enhanced text representations.
Neural Att entive Session-based Recommendation
Specifically, we explore a hybrid encoder with an attention mechanism to model the user’s sequential behavior and capture the user’s main purpose in the current session, which are combined as a unified session representation later.
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