7 results about "Preference relation" patented technology

The invention belongs to the field of natural language processing, and relates to an intelligent memory dynamic evolution method and system based on metadata and two channels, and the method comprises the steps: extracting an entity in an instruction input by a user, and obtaining an instruction entity; querying a metadata index database based on the instruction entity to obtain an entity state; when the entity state is a known state or a non-input state, updating the relational vector database and the graph database through a two-channel mechanism; a user input instruction is matched to the new relational vector database and the new graph database for mixed retrieval, and a preference relation and a hard constraint relation are obtained; constructing a context vector based on the user core portrait, the preference relationship and the hard constraint relationship; processing the user input instruction and the context vector to obtain generated content, and outputting feedback; and the interaction response capability and the engineering landing effect of the large language model intelligent agent in a long-period and complex interaction scene are greatly improved.

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 invention discloses a man-machine cooperation method for solving human deviation. The man-machine cooperation method comprises the steps that initialization is carried out; iteratively executing batch Thompson sampling, batch Thompson sampling, preference query and data updating and Gaussian process posteriori updating until the maximum number of iterations is reached; and after iteration is finished, returning an optimal action corresponding to the maximum potential function value in the action space. According to the embodiment, the long-tail preference relationship problem is fundamentally solved. In the batch Thompson sampling stage, diversified candidate preference pairs are generated through an adaptive covariance scaling factor and a double-independent sampling mechanism; in the sub-mode marginal gain evaluation stage, the dominant effect of head preference is effectively inhibited by utilizing the profit decreasing characteristic and marginal gain maximization of a sub-mode function; and in a Gaussian process posteriori updating stage, the observed preferences are integrated into a Bayesian framework, and posteriori distribution is refined to guide subsequent sampling. By optimizing the preference learning process, the preference query times are remarkably reduced, and the learning efficiency and accuracy are improved.

The invention discloses an inspection robot three-branch decision-making method for a complex electromagnetic environment of a transformer substation, and belongs to the technical field of intelligent inspection and intelligent decision-making. And multiple measurement results are constructed into assembly type uncertainty evaluation data to represent the volatility and uncertainty of information in a complex environment. On the basis, performing utility modeling on the decision-making object, and introducing an adjustment mechanism reflecting and comparing psychological effects to obtain a comprehensive evaluation result fusing objective utility and subjective cognitive factors; furthermore, weights are adaptively determined based on attribute information features, and multi-attribute comprehensive evaluation is realized. Through comparative analysis of preference relations or distance relations among objects, decision-making objects are divided into three kinds of decision-making areas including an acceptance area, an observation area and a rejection area, and data complementary measurement or parameter adjustment is triggered for objects in the observation area, so that a closed-loop self-adaptive decision-making mechanism is formed. According to the invention, the decision reliability and the intelligent level of the inspection robot in a complex electromagnetic interference environment can be improved.

The invention relates to a recommendation system fusing fuzzy neighborhood and mixed negative sampling technology, and belongs to the technical field of recommendation. The method comprises the steps of obtaining and cleaning data information from a database; designing a similarity measurement method based on fuzzy preference consistency, and describing a preference relationship between users or items from multiple dimensions of consistency, inconsistency and uncertainty; obtaining an interaction matrix C of the users and the items by using the original scoring matrix R of the users and the items, and performing similarity calculation between the users and the items to obtain nearest reciprocal neighbors of the users and farthest reciprocal neighbors of the items; constructing a neighborhood information guided double-tower generation method, enhancing generation process supervision by aggregating neighborhood semantics, and realizing one-step denoising of a diffusion method by using a generative adversarial network; designing a mixed negative sampling strategy combining strong and weak negative samples, and introducing negative samples with rich information to assist denoising training; calculating a predicted value of the target user u on an unscored item by using a score prediction method; and sorting the prediction sets, and selecting the first k items with the highest prediction values to recommend to the target user u.

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.