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Found 5 papers, 1 papers with code
Behavior-Contextualized Item Preference Modeling for Multi-Behavior Recommendation
Our proposed Behavior-Contextualized Item Preference Network discerns and learns users' specific item preferences within each behavior.
MB-HGCN: A Hierarchical Graph Convolutional Network for Multi-behavior Recommendation
In this paper, we propose MB-HGCN, a novel multi-behavior recommendation model that uses a hierarchical graph convolutional network to learn user and item embeddings from coarse-grained on the global level to fine-grained on the behavior-specific level.
Cascading Residual Graph Convolutional Network for Multi-Behavior Recommendation
In particular, we design a cascading residual graph convolutional network structure, which enables our model to learn user preferences by continuously refining user embeddings across different types of behaviors.
Sparsely-Labeled Source Assisted Domain Adaptation
However, there are usually a large number of unlabeled data but only a few labeled data in the source domain, and how to transfer knowledge from this sparsely-labeled source domain to the target domain is still a challenge, which greatly limits their application in the wild.
Importance Filtered Cross-Domain Adaptation
Firstly, an importance filtered mechanism is devised to generate filtered soft labels to mitigate negative transfer desirably.
