7 results about "Probability matching" patented technology

The present disclosure provides a resource recommendation method, a recommendation prediction model training method, an apparatus and a device, and belongs to the technical field of multimedia. The resource recommendation method comprises: obtaining historical recommendation information and historical search information of an object; obtaining recommendation interest features and search interest features of the object based on a resource to be recommended, the historical recommendation information and the historical search information; performing dimension reduction processing on object features of the object, resource features of the resource, the recommendation interest features and the search interest features to obtain a recommendation probability of the resource; and recommending a target resource in a plurality of resources to the object based on the recommendation probability of the plurality of resources to be recommended. In the scheme provided by the present disclosure, it is ensured that the indication of the recommendation probability matches the interest of the object, the accuracy of the recommendation probability is ensured, and then the resource recommended to the object is the resource of interest to the object, so as to ensure the accuracy of the resource recommendation and then ensure the resource recommendation effect.

The application discloses a data processing method and device, equipment and a storage medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: obtaining a noise measurement index of a first candidate task based on an initial neural network model; obtaining a sampling probability of the first candidate task based on the noise measurement index of the first candidate task; selecting a reference task meeting a first selection condition from the first candidate task according to the sampling probability; updating the parameters of the initial neural network model based on the reference task; learning a target task based on the neural network model after the parameter update, obtaining a target neural network model, and using the target neural network model for data processing. The sampling probability obtained by considering the noise measurement index is a sampling probability matched with the interference degree of the candidate task on the parameter update of the model. The reliability of the reference task selected from the candidate task based on the sampling probability is high, which is beneficial to improving the update quality of the parameters of the model, thereby improving the accuracy of data processing.

The invention discloses a sales agent voice conversation method. The method comprises the following steps: firstly, carrying out time sequence context frame association analysis and emotion dynamic spectrum quantitative modeling on a real-time voice data stream to generate a user demand dynamic feature conformation; then, through hierarchical denoising reconstruction and intention posterior probability matching, a core demand feature base vector is determined, and a scenarized demand positioning feature topology is generated; calling the commodity attribute knowledge graph to execute weighted association mapping and recommendation semantic logic generation to obtain a customized recommendation response text; and finally, fusing context and emotion features to execute text-to-speech conversion and emotion speech adaptation modulation, and generating a real-time response speech stream. According to the invention, through context-emotion two-dimensional cooperative processing, accurate capture of user demands and emotion adaptive response are realized, and the demand identification accuracy and the user satisfaction are improved.

The invention relates to the technical field of lossless video coding, and provides a lossless compression method for eliminating inter-frame redundancy based on partitioning and probability matching, which comprises the following steps of: dividing a current frame into a plurality of sub-blocks, and classifying each sub-block by calculating the similarity between a current block and a matching block; the classified sub-blocks are subjected to matched coding operation through a differentiation processing strategy, and compressed data are generated; and carrying out entropy coding on the compressed data, and outputting a lossless compressed code stream. The video frame is divided into a plurality of sub-blocks through the technical means of combining block processing, probability matching, multi-level classification and reversible integer predictive coding, the sub-blocks are classified according to the inter-frame similarity, differentiated processing strategies are adopted for different types, and finally residual and motion information is compressed through entropy coding. The inter-frame redundancy is effectively eliminated, the compression efficiency is remarkably improved, and meanwhile, the robustness of fast motion and scene switching is kept.

Embodiments of the present disclosure disclose a device association method and device based on behavior blocking and probability matching. A specific implementation of the method comprises: obtaining a set of device behavior records; combining each device behavior record in the set of device behavior records that meets a preset similarity condition into a similar device record group to obtain a set of similar device record groups; for each similar device record group in the set of similar device record groups, determining a matching probability value between each two similar device records in the similar device record group to obtain a group of matching probability values; generating a device behavior association graph according to each group of matching probability values obtained; identifying connected nodes of the device behavior association graph to obtain a set of connected node groups; and for each connected node group in the set of connected node groups, assigning each connected node in the connected node group with a same identifier to complete device association. The implementation can improve the accuracy of device association.

A computer-implemented method for improving entity matching in a probabilistic matching engine may train a graph neural network (GNN) model according to an output of the probabilistic matching engine to perform entity matching and determine an anti-fact interpretation for non-matching of entities. A data transformation list may be identified by operable traces using a GNN model. The data transformation lists may be ranked based on computational overhead and an improvement in entity matching within an estimated probability matching engine using a GNN model.

The present application relates to the technical field of precipitation forecast, in particular to a kind of hourly time lag ensemble precipitation forecast correction method based on weight coefficient, it is based on the time lag ensemble forecast method of Taylor test result and the forecast-observation probability matching method based on GAMMA cumulative probability analysis, and the fine gridding display of precipitation correction product is realized, aims at reducing precipitation forecast error, improve hourly precipitation forecast and 24 hours precipitation forecast accuracy, provide scientific reference for hourly precipitation, especially short-time heavy precipitation forecast;It includes the following steps: using new type of ensemble weight coefficient Wi based on Taylor test result is developed;Using the time lag ensemble forecast method based on new type of weight coefficient, the forecast member of different time starting is collected;Using the forecast-observation probability matching method based on GAMMA cumulative probability analysis, corrects hourly precipitation change and 24 hours precipitation order of magnitude;Precipitation forecast test;Its beneficial effects are: the present application improves hourly, 24 hours sunny and heavy rain forecast capability.