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公开(公告)号：US20230195809A1
公开(公告)日：2023-06-22
申请号：US17882922
申请日：2022-08-08
申请人： NAVER CORPORATION
IPC分类号： G06F16/9535 , G06N3/04 , G06K9/62
CPC分类号： G06F16/9535 , G06N3/0454 , G06K9/6259
摘要： A method of training a hypergraph convolutional network (HGCN) includes: receiving training data including search instances and recommendation instances; constructing a hypergraph from the training data, where each node of the hypergraph represents one of a user profile, a query term, and a content item, and where the hypergraph represents each of the search instances and each of the recommendation instances as a hyperedge linking corresponding ones of the nodes; initializing base embeddings associated with the hypergraph nodes; propagating the base embeddings through one or more convolutional layers of the HGCN to obtain, for each of the convolutional layers, respective embeddings of the nodes of the hypergraph; computing, based on the base embeddings and the respective embeddings obtained from each of the one or more convolutional layers: a first loss; and a second loss; and selectively updating ones of the base embeddings based on the first and second losses.
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公开(公告)号：US20240289631A1
公开(公告)日：2024-08-29
申请号：US18524235
申请日：2023-11-30
申请人： NAVER CORPORATION
IPC分类号： G06N3/092 , G06N3/0455 , G06N3/0475
CPC分类号： G06N3/092 , G06N3/0455 , G06N3/0475
摘要： Methods and systems for training a recommender system implemented by a processor and memory to recommend a slate of items from a collection to a user. A neural network-based decoder is pretrained to generate a slate of items from a representation in a continuous low-dimensional latent space. A reinforcement learning agent in the recommender system is trained to determine an action in the latent space, where the action represents a recommended slate of items from the collection based on a state. The recommendation system comprises the pretrained decoder for generating the recommended slate of items from the action determined by the agent.
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