3 results about "Preference relation" patented technology

The application belongs to the field of natural language processing, and relates to an intelligent memory dynamic evolution method and system based on metadata and a double-channel, comprising the following steps: extracting entities in a user input instruction to obtain instruction entities; querying a metadata index library based on the instruction entities to obtain entity states; when the entity states are known states or unrecorded states, updating a relational vector database and a graph database through a double-channel mechanism; matching the user input instruction to the new relational vector database and the new graph database respectively for hybrid retrieval to obtain preference relations and hard constraint relations; constructing a context vector based on a user core image, the preference relations and the hard constraint relations; processing the user input instruction and the context vector to obtain generated content and output feedback; and greatly improving the interactive response capability and engineering landing effect of a large language model agent in a long cycle and a complex interactive scene.

The application discloses a knowledge graph enhanced recommendation method and system based on a variational graph autoencoder, and a device, relates to the technical field of knowledge graphs, and comprises the following steps: obtaining interaction data between users and items, social network data between users, and external knowledge data; constructing a knowledge graph based on the external knowledge data; performing path enhancement processing on the knowledge graph to mine multi-hop semantic association relationships between entities; extracting entity interaction features using the knowledge graph; extracting social relationship features of users according to the social network data; performing feature fusion on the entity interaction features and the social relationship features to obtain fused features; introducing a variational graph autoencoder to probabilistically model the fused features to learn latent vectors of the users; and predicting preference relationships between the users and the items according to the latent vectors to generate personalized recommendation results for the users. Ultimately, the application can stably depict the latent preferences of the users in a data sparse and cold start scenario, and realize accurate recommendation.

The application relates to the technical field of group decision-making, and discloses a group decision-making model based on enterprise management and preference relation relative projection, which comprises a data acquisition and initialization module, a group synthesis and consensus evaluation module, a double-drive dynamic weight calculation module, an expert mutual evaluation relation updating module and an iterative convergence and decision generation module, and can acquire an initial preference relation matrix and a mutual evaluation matrix; a group synthesis preference matrix is constructed based on current expert weights, and a global consensus level is analyzed; the objective consensus contribution degree and the subjective social trust degree of the experts are calculated, then the expert weights are updated by fusion, and the mutual evaluation matrix is updated; iteration is performed until convergence, a decision scheme ranking and an expert weight distribution report are generated based on the final group synthesis preference matrix; and the group decision-making efficiency based on enterprise management and preference relation relative projection can be improved.