Abstract: The disclosure discloses a conversation-based recommending method. A directed graph corresponding to a current conversation is obtained. The current conversation includes clicked items, the directed graph includes nodes and directed edges between the nodes, each node corresponds to a clicked item, and each directed edge indicates relationship data between the nodes. For each node of the directed graph, an attention weight is determined for each directed edge corresponding to the node based on a feature vector of the node and the relationship data for each node of the directed graph. A new feature vector of the node is determined based on the relationship data and the attention weight of each directed edge. A feature vector of the current conversation is determined based on the new feature vector of each node. An item is recommended based on the feature vector of the current conversation.

Abstract: A session recommendation method, a device and an electronic device are provided, related to the field of graph neural network technology. The session recommendation method includes: acquiring a session control sequence, and acquiring a first embedding vector matrix based on an embedding vector of each of items in the session control sequence; generating a position information sequence based on an arrangement sequence of the items in the session control sequence, and acquiring a second embedding vector matrix based on an embedding vector of each piece of position information in the position information sequence; determining a target embedding vector matrix based on the first embedding vector matrix and the second embedding vector matrix; and determining a recommended item, based on the target embedding vector matrix and through a Session-based Recommendation Graph Neural Network.

Abstract: The present disclosure discloses a method, an apparatus and a storage medium for training a deep learning framework, and relates to the artificial intelligence field such as deep learning and big data processing. The specific implementation solution is: acquiring at least one task node in a current task node cluster, that meets a preset opening condition when a target task meets a training start condition; judging whether a number of nodes of the at least one task node is greater than or equal to a preset number; synchronously training the deep learning framework of the target task by the at least one task node according to sample data if the number of nodes is greater than the preset number; and acquiring a synchronously trained target deep learning framework when the target task meets a training completion condition.

Abstract: The disclosure discloses a conversation-based recommending method. A directed graph corresponding to a current conversation is obtained. The current conversation includes clicked items, the directed graph includes nodes and directed edges between the nodes, each node corresponds to a clicked item, and each directed edge indicates relationship data between the nodes. For each node of the directed graph, an attention weight is determined for each directed edge corresponding to the node based on a feature vector of the node and the relationship data for each node of the directed graph. A new feature vector of the node is determined based on the relationship data and the attention weight of each directed edge. A feature vector of the current conversation is determined based on the new feature vector of each node. An item is recommended based on the feature vector of the current conversation.

Abstract: A method for training a deep learning model may include: acquiring model description information and configuration information of a deep learning model; segmenting the model description information into at least two sections based on segmentation point variable in the configuration information, and loading the model description information to a corresponding resource to run; inputting a batch of training samples into a resource corresponding to a first section of model description information, then starting training and using obtained context information as an input of a resource corresponding to a subsequent section of model description information; and so on until an operation result of a resource corresponding to a final section of model description information is obtained; if a training completion condition is met, outputting a trained deep learning model; and otherwise, keeping on acquiring a subsequent batch of training samples and performing the above training steps until the condition is met.