15results about How to "Achieve recommendation" patented technology

The invention discloses a consumption coupon issuing control method and system based on big data, and the method comprises the steps: collecting the time sequence information of the consumption behavior data of a target user after obtaining the authorization of the target user; a data processing and semantic comprehension algorithm based on artificial intelligence and natural language analysis is introduced at the rear end to analyze time sequence information of the consumption behavior data of the target users, so that feature dynamic aggregation information of the consumption behavior data semantics of the target users in the time sequence is captured; therefore, consumption coupon type recommendation is performed for the target user, and a detailed user portrait can be constructed based on the preference and consumption habit of the consumer so as to predict the consumption behavior, thereby realizing personalized consumption coupon recommendation and issuing.

The invention discloses a scientific research partner recommendation method based on an attention mechanism and a graph neural network, and provides an academic partner recommendation model AGNNRec based on the graph neural network and the attention mechanism. According to the method, a recommendation model is constructed based on a graph neural network, a context path is introduced to capture a high-order relationship between nodes, a multi-head relationship attention mechanism is introduced to learn embedded vectors of node high-order neighbors in parallel, and finally, recommendation of partners is completed by using a cosine similarity algorithm. Experiments are carried out on the model on a self-built data set and a public data set DBLP respectively, in the experiment using the DBLP data set, the effects of the model on Precision (at) 5 and NDGG (at) 5 are improved by 19.07% and 1.17% respectively, and the effectiveness of AGNNRec in science and technology partner recommendation tasks is shown.

The invention relates to a user portrait construction and updating method of an information system based on intelligent IVR. The method comprises the following steps: step 1, collecting user voice data through an IVR system; step 2, performing voice analysis on the recorded voice interaction data; 3, associating the historical behavior data of the user with the voice data, and establishing a user behavior model; step 4, constructing a user portrait; and 5, updating the user portrait. According to the method, real-time voice interaction between the user and the IVR system is fully utilized, and the emotion and demand of the user can be captured more accurately, so that a more refined user portrait is constructed.

The invention belongs to the technical field of data processing, particularly relates to an information recommendation method and system based on brain science knowledge extraction and a graph network, and aims at solving the problems that an existing retrieval recommendation method is insufficient in operation mechanization, convenience and accuracy. The method comprises the following steps: extracting a plurality of keywords in text information and a text relationship among the keywords through a semantic analysis method based on brain science; constructing a knowledge graph based on the plurality of keywords and the text relationship among the keywords; constructing a knowledge relationship network based on the knowledge graph, and training through a graph data learning algorithm to obtain a graph neural network; the graph neural network comprises a representation vector of each keyword; and according to a retrieval instruction input by a user, determining the recommended content by calculating the semantic similarity between the retrieval instruction and the representation vector of the keyword. According to the method, the knowledge engine is automatically constructed from the text, and integration, recommendation and prediction of knowledge and automatic processing from the text information to the knowledge engine are realized.

The present invention provides a label-based cross-modal resource recommendation method and system, and relates to the technical field of data resource recommendation. The method comprises the steps of: S1, constructing a user and resource feature vector; S2, constructing a resource and label feature vector; S3, performing an inner product operation on the user and resource feature vector and the resource and label feature vector to obtain a user and label preference matrix; S4, the elements in the user and label preference matrix are predicted preference values, the predicted preference values ​​represent the degree of preference of a certain user for a certain label, the same row of predicted preference values ​​represent the degree of preference of the same user for different labels, and the predicted preference values ​​in the same row are used to construct a predicted preference vector; S5, obtaining the predicted preference vector from the user and label preference matrix, and recommending resources to the user according to the predicted preference vector. The whole process is simple in calculation, and can recommend multi-modal resources, making it convenient for users to obtain various types of resources of interest.

The present invention is applicable to the field of grassland measurement technology, and provides a variable-size sample square frame based on a grassland sample plot species identification and recommendation device, including a sample square frame and an electronic control component used therewith; the sample square frame includes a fixing part; the fixing part includes a horizontal plate and a vertical plate arranged perpendicular to each other; an extension plate is fixed between the horizontal plate and the vertical plate; a sliding groove is provided on the surface of the extension plate; a first sliding part parallel to the horizontal plate is slidingly arranged on the vertical plate; a second sliding part slidingly cooperates with the horizontal plate is slidingly arranged on the first sliding part; the second sliding part slides in cooperation with the sliding groove; an ear plate is fixed on the side of the horizontal plate; the device drives the first rotating rod to rotate by controlling the start-up brake motor, drives the first sliding part to slide along the vertical plate, and drives the second sliding part to slide along the extension plate during the sliding process of the first sliding part, so that the second sliding part slides synchronously along the horizontal plate, thereby realizing the size adjustment of the sample square frame and meeting the sample square specification requirements in different scenarios.

The present invention proposes a maintenance solution matching and recommendation method based on causality and equipment structure enhancement, which belongs to the field of equipment maintenance, including: S1: constructing a synonym library in the maintenance field; training an unsupervised SimCSE model to obtain a standardized knowledge graph IDS-EMKG-QA; optimizing the question sentence to obtain a standardized question sentence; S2: performing template-based intent recognition on the standardized question sentence, if the question is in the intent recognition template, then generating and matching the answer based on the query statement, if not, go to step S3; S3: using a graph search method, according to the standardized question sentence, inferring and screening candidate answers based on the fault causal link and the equipment composition architecture in IDS-EMKG-QA; using the RotatE graph embedding model to match and sort the candidate answers and the standardized question sentence to obtain the recommendation result. The method of the present invention solves the diversity and complexity problems in the maintenance process of complex equipment.

The present invention discloses a method for recommending user entity groups based on knowledge graph embedding, which portraits user entities in the knowledge graph and returns the top-K user entity groups with the highest relevance to the target user entity according to the user entity portrait features. The present invention can more comprehensively use the structural information and semantic information of the knowledge graph to depict dynamic user entity portraits, enhance the accuracy of the user entity group recommendation method, provide reasons for the recommendation results, make user operations simple and efficient, and can also construct labels and provide connections for user entities in the knowledge graph.

This invention provides a method, apparatus, electronic device, and storage medium for generating product recommendation copy. The method includes acquiring fields of multiple products input by a user; using the fields of the multiple products as input to a text generation model; processing the fields of the multiple products based on the text generation model; and outputting product recommendation copy arranged in a specific order. The text generation model is trained using sample data. In this embodiment of the invention, the text generation model trained based on sample data can predict the fields of multiple products and output product recommendation copy arranged in a specific order, thereby enabling recommendations for multiple products and improving the logical consistency of product recommendation copy.

This application provides a method, apparatus, and medium for calculating the value of a business portfolio, relating to the field of information technology. The method includes: obtaining a first user characteristic factor, candidate product factors, and discount strategy factors; inputting the first user characteristic factor, candidate product factors, and discount strategy factors into a multi-factor combination optimization algorithm, which, based on a multi-level cache and / or a real-time computing engine, obtains an effective business portfolio suitable for the first user and its predicted value; and recommending, accepting, and monitoring the effective business portfolio based on the predicted value. The first user characteristic factor is obtained based on the characteristics of the first user, and the effective business portfolio is a combination of at least one candidate product and / or a combination of at least one candidate product and at least one discount strategy. This application helps improve the digital and intelligent operation level of enterprises.

A course recommendation method, device, equipment and medium are disclosed. The method comprises: acquiring actual interaction messages between a target user in real time; determining a current interest value of the target user according to the actual interaction messages and a user label of the target user; determining a corresponding target recommendation strategy according to a comparison result between the current interest value and a preset interest threshold value, and using the target recommendation strategy to recommend a course to the target user. The present application solves the problem that the course recommendation in the prior art is subjective and based on human experience, resulting in low recommendation pertinence, and achieves targeted course recommendation to the target user, improves the course recommendation efficiency, and enhances the user experience.

The present disclosure is about a data recommendation method, device, server and storage medium, and belongs to the field of computer technology. The method includes: obtaining target data features corresponding to the target multimedia data to be recommended, calling a feature conversion model, performing feature conversion processing on the target data features, obtaining the predicted operation features after the conversion, determining the matching degree between each user and the target multimedia data according to the user features and predicted operation features corresponding to multiple users, selecting at least one target user from multiple users according to the matching degrees corresponding to the multiple users, and recommending the target multimedia data to the at least one target user. The predicted operation features of the multimedia data are used to represent the operations that the user may perform on the multimedia data, determine the users matching the multimedia data, and subsequently recommend the multimedia data to the matching users, thereby realizing the accurate recommendation of the multimedia data, ensuring the accuracy of the data recommendation, and realizing the cold start of the multimedia data.

The invention is suitable for the technical field of grassland measurement, and provides a variable-size quadrat frame based on a grassland sample plot species identification and recommendation device, which comprises a quadrat frame and an electric control assembly matched with the quadrat frame for use, the quadrat frame comprises a fixing part; the fixing part comprises a transverse plate and a vertical plate which are perpendicular to each other; an extension plate is fixed between the transverse plate and the vertical plate; sliding grooves are formed in the surfaces of the extending plates. A first sliding part parallel to the transverse plate is arranged on the vertical plate in a sliding manner; a second sliding part in sliding fit with the transverse plate is arranged on the first sliding part in a sliding manner; the second sliding part is in sliding fit with the sliding groove. Lug plates are fixed on the side surfaces of the transverse plates; according to the device, the band-type brake motor is controlled to start to drive the first rotating rod to rotate, the first sliding part is driven to slide along the vertical plate, the second sliding part is driven to slide along the extending plate in the sliding process of the first sliding part, the second sliding part synchronously slides along the transverse plate, size adjustment of the quadrat frame is achieved, and the quadrat specification requirements in different scenes are met.

The present application provides a method and system for recommending media advertising slots based on user travel, a computer-readable storage medium, and a computer program product, which implement click expectation estimation of the proposed products in the media advertising slots based on the user travel portrait in the travel scenario, and then objectively and accurately implement media advertising slot marketing recommendations based on the click expectation value of the proposed products in the media advertising slots, thereby abandoning the reliance of media resource marketing recommendations on subjective experience and effectively improving the accuracy of delivery.

The invention discloses a session-based recommendation method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining first session information of a user in a first session field and second session information of a user in a second session field, the first session information and the second session information comprise item sequences generated after a user clicks different target items in a corresponding session field, and inputting the first session information and the second session information into a trained cross-domain recommendation model, and obtaining recommendation parameters of each target item in the first session field and the second session field, and generating a recommendation list for the user according to the recommendation parameters. By adopting the embodiment of the invention, the cross-domain recommendation model can learn feature knowledge in different session fields by combining the session information in the different session fields, so that the recommendation parameters of the item in the session neighborhood can be output in the different session fields, and cross-domain recommendation is realized; and meanwhile, the recommendation effects of the two fields are also improved.