1882 results about "User group" patented technology

The invention discloses an advertisement putting real-time optimization method and system based on user behavior prediction, and particularly relates to the technical field of advertisement putting. Real-time behavior data and historical behavior data of a target user group are collected, a dynamic behavior sequence is generated through time window division, the historical behavior data are classified to construct a user behavior feature library, potential behavior prediction features of users are extracted, and a unified prediction feature vector set is generated; comparing and analyzing with a preset advertisement content label library, and outputting a user interest association degree data set in combination with an advertisement inventory state; a dynamic optimization parameter set is generated in combination with putting strategy constraint conditions; finally, the advertisement putting content, the advertisement putting frequency and the display position are adjusted in real time according to the dynamic optimization parameter set, an optimization result is output, and an advertisement display queue is updated, so that accurate capture and intelligent matching of user interests are achieved, and the advertisement putting effect and the user experience are improved.

The invention belongs to the technical field of artificial intelligence, and discloses a government information consultation system based on a large language model, which comprises a user interaction module, a large language model core engine, a knowledge base integration module, a multi-level authority management module, a feedback optimization mechanism and a risk control module. The comprehensive intelligent government affair service system is constructed through the six core modules, remarkable advantages are shown in government affair service digital transformation, the system innovatively adopts multi-mode interactive design, multiple input and output modes of voice, text and images are supported, an intelligent authority management mechanism is matched, and the intelligent authority management mechanism is matched with the intelligent authority management mechanism. According to the technical scheme, the accessibility and convenience of government affair services are greatly improved, precise services for different user groups are achieved, it is guaranteed that sensitive data are safe and controllable while wide spreading of government affair information is guaranteed, and a large language model of a system core is subjected to professional government affair scene optimization training and is combined with a dynamically-updated knowledge graph technology.

A computerized system receives an original prompt that a querying user sends to a Large Language Model (LLM) that is operably connected to organizational data sources of an organization. Instead of executing the original prompt by the LLM, the system obtains user-related organizational context that pertains to characteristics of the querying user, obtains data-related organizational context that pertains to data from which the LLM is expected to obtain information for responding to the original query, and obtains pre-defined organizational policy rules, that indicate which type of users are authorized to access which type of organizational data. Based on the obtained data, the system modifies the original prompt into an adapted prompt. The system sends the adapted prompt, and not the original prompt, to the LLM for processing. The system obtains LLM-generated output from the LLM in response to the adapted prompt, and provides that LLM-generated output to the querying user.

The present invention relates to the technical field of electricity applications. Disclosed are a supply-demand interaction game method based on electricity consumption behaviors of user groups, and a system. The method comprises: collecting and preprocessing historical electricity consumption data of smart terminals of user groups; using a clustering analysis method to perform classification on the types of electricity users; by means of the central-limit theorem and Monte-Carlo method, constructing a user electricity consumption behavior analysis model; creating an electricity user group evolutionary game model, so as to analyze long-term evolution patterns of electricity consumption strategies of the user groups; and power supply entities adjusting and formulating, on the basis of the analysis result, an electricity price incentive plan that maximizes benefits, and transmitting, via power lines, the updated plan to smart electricity consumption terminals of the users. By means of using electricity consumption data of user groups collected by a large number of smart terminal devices and combining the data with the evolutionary game theory, the present invention successfully performs mathematical modeling and analysis on electricity consumption behaviors of user groups, thus achieving mutual benefit, win-win outcomes and stable equilibrium between supply-demand parties in long-term supply-demand interaction.

The invention relates to the technical field of data processing, and provides a trade big data processing and user behavior prediction method and system.The trade big data processing and user behavior prediction method comprises the steps that transaction data, browsing behavior data and feedback evaluation data of a user on a cross-border e-commerce platform are obtained to generate a user behavior pattern sequence, and then a user interaction graph is constructed; and carrying out edge weight updating and node label classification to obtain a high-dimensional relation graph, carrying out similarity partition processing to obtain a predicted popularity index graph, and predicting the future cross-border commodity interaction behavior of the target user group by using the predicted popularity index graph. By acquiring transaction data, browsing behavior data and feedback evaluation data of users and generating a prediction popularity index graph, multi-dimensional data are effectively and comprehensively processed, accurate prediction of cross-border commodity interaction behaviors of a target user group is realized, and the user experience is improved when a user intention deep relationship is processed and cross-time-period behavior evolution analysis is performed. The problems of low model dimension and insufficient prediction precision exist.

The invention discloses an international propagation effect accurate evaluation method and system based on a large language model, and relates to the field of international propagation effect evaluation, and the method comprises the steps: obtaining a behavior log data set of a user, and dividing the user to a preset life cycle stage; extracting an interest label set of the user in a continuous time window, calculating a weight change rate of the same semantic keyword, and generating a user interest drift trend feature; extracting a propagation content feature vector of the to-be-evaluated propagation content, and calculating a basic matching degree with the interest label set; dynamically correcting the basic matching degree in combination with the life cycle stage and interest drift trend characteristics to obtain a final matching degree; extracting a path mode feature vector of the user, performing parameter matching with a propagation path mode library, and determining a propagation effect evaluation level; dividing the users into user groups according to the propagation effect evaluation levels, and implementing an intervention strategy on the user groups; according to the invention, refined grading evaluation of the propagation effect can be realized.

The invention provides an information resource distribution method and system applied to a dynamic network environment, and the method comprises the steps: firstly obtaining a real-time state feature set of a target distribution network, determining a resource distribution constraint condition set in a current network environment based on the real-time state feature set, carrying out the priority analysis of a real-time resource request sequence of a target user group, and obtaining a real-time resource distribution constraint condition set; obtaining resource timeliness grade distribution and a request path optimization feature set, generating a dynamic distribution strategy parameter set matched with a resource distribution constraint condition, triggering a resource redirection operation, and enabling the resource request response rate of the target user group to be improved to a preset threshold interval; and the information resource distribution efficiency and stability in the dynamic network are improved.

The invention relates to the technical field of network advertisement promotion, in particular to a network advertisement promotion method and system based on the Internet, and the method comprises the following steps: obtaining user behavior data, analyzing a browsing track, a staying duration, a click frequency and an interactive behavior, calculating the activity of a user in real time, and recognizing the interest point of the user. And updating user tags in real time, classifying user groups, and generating a user tag data set. According to the method, the interest point of the user is identified, the user label is updated in real time, it is ensured that personalized recommendation of the advertisement conforms to the real-time behavior of the user, the active time period of the user is analyzed and predicted, resource waste caused by putting in a fixed time period is avoided, budget allocation is dynamically adjusted according to the adaptation degree between the behavior of the user and the platform, and the user experience is improved. The advertisement putting efficiency is improved, user behavior changes before and after advertisement display are compared, the advertisement putting effect is accurately evaluated, data support is provided for adjustment of advertisement strategies, and the advertisement putting effect and resource configuration are optimized.

The invention provides a right content delivery strategy generation method and device based on AI big data, and relates to the technical field of strategy generation, and the right content delivery strategy generation method comprises the steps: obtaining user behavior data corresponding to a content platform type, so as to construct a corresponding target user portrait; dividing the to-be-put right and interest content into corresponding right and interest types according to content platform types; based on the target user portrait, analyzing preference degrees and response rates of user groups of the content platforms to different right and interest types; generating an adaptation relation matrix corresponding to the preference degree and the response rate; and distributing a corresponding right content type to each content platform according to the adaptation relation matrix, and determining a delivery frequency weight corresponding to each content platform. The cross-platform user portrait integration degree is improved, a right and platform dynamic matching mechanism is optimized, and the real-time adjustment capability of the delivery strategy is enhanced by constructing the cross-platform user portrait, dynamically generating the adaptation relation matrix and intelligently distributing the delivery strategy.

Action and / or process determination and recommendations for Robotic Process Automation (RPA) using semantic action graphs is disclosed. Semantic action graphs are graphs that store individual actions, and potentially graphical elements and / or text associated with the actions, as nodes, as well as the relationships between nodes as edges. Metadata to develop the semantic action graphs may be derived from task mining applications that can monitor the interactions of users with computing systems, workforce intelligence, etc. The semantic action graphs may be for a user, an organization, an industry, product-wide, etc. At their lowest level of granularity, the recommendations may be for mouse clicks, key presses, Application Programming Interface (API) calls, system events, etc. At higher levels of granularity, the recommendations may be for opening an order, creating a lead, approving a work item, etc.

The invention discloses an AI search recommendation method, system and device combined with commodity semantic understanding and a medium, and belongs to the technical field of commodity recommendation. The method comprises the steps that semantic vectors of commodity titles and descriptions are generated; generating a feature vector of the commodity image; fusing the semantic vector of the commodity title and description with the feature vector of the commodity image to generate a comprehensive semantic vector of the commodity; generating a user interest vector; updating the user interest vector by using a recurrent neural network based on the user interest vector; and carrying out joint modeling on the comprehensive semantic vector of the commodity and the updated user interest vector to generate a recommendation result. According to the method, a content semantic driving modeling mode is adopted, recommendation judgment can still be conducted through the deep matching relation between commodity image-text semantics and user behavior preferences even under the condition that user historical behaviors are limited or new commodities are online, good cold start adaptability is achieved, and the method is suitable for popularization and application. And meanwhile, high recommendation difference and content diversity are shown for different user groups.

The invention discloses a dynamic self-adaptive recommendation strategy optimization method for a business handling failure scene, and relates to the technical field of business handling recommendation and intelligent decision making, and the method comprises the steps: firstly collecting various types of data of a whole business handling process, and guaranteeing the integrity and real-time performance at a frequency of 100 milliseconds per time; a decision tree and Bayesian network fusion algorithm is used for attribution, and direct and indirect reasons are clarified; integrating data to construct a user portrait, and mining potential and subsequent demands; generating a recommendation scheme set based on attribution and portraits, and adjusting priorities and forms in combination with scene features; feedback data is introduced, a strategy weight is optimized by using a gradient descent algorithm, and the scheme is updated regularly; a multi-dimensional index weighted evaluation effect is set, and emergency optimization is carried out if the evaluation result does not reach the standard; and establishing a distributed strategy library, and reusing the optimal strategy of the similar scene by using a K-nearest neighbor algorithm. According to the method, failure reason accurate positioning and personalized recommendation are realized, the recommendation effect is continuously optimized along with data accumulation, and the method is adaptive to multiple service types and user groups.

The invention relates to the technical field of digital marketing, in particular to an intelligent marketing data analysis system and method. According to the invention, through deep analysis of the user interaction data, the user behavior offset is accurately identified, so that the marketing strategy can be dynamically adjusted, and the putting accuracy is improved. By means of the change trend of the residence time sequence, the relative change rate is calculated in combination with the access preference, user interest hotspot distribution information is effectively extracted, and the pertinence of content recommendation is improved. Based on access path stability analysis of interest hotspots, a stable mode of user behaviors is extracted, and an advertisement putting interval is optimized, so that user loss caused by frequent putting is reduced. And user groups with fluency decline or obvious jumping behaviors are screened by utilizing change calculation of interaction fluency, so that the advertisement pushing rhythm is reasonably adjusted, and the advertisement experience is improved. The user access data is synthesized to analyze the conversion change of the user behavior after the advertisement is displayed, and the influence of different advertisement intervals on the user behavior is accurately evaluated.

The invention relates to the technical field of data processing, and provides a social network privacy data protection method and system, and the method comprises the steps: obtaining to-be-encrypted privacy data, and obtaining a keyword popularity coefficient and a sensitivity weight coefficient according to the privacy data; acquiring behavior characteristics and basic information of a user group, and acquiring a group portrait index according to the behavior characteristics and the basic information; acquiring network behavior activeness, equipment characteristics and historical behavior information of the target user, and acquiring personal characteristics of the target user according to the network behavior activeness, the equipment characteristics and the historical behavior information; according to the keyword popularity coefficient, the sensitivity weight coefficient, the group portrait index and the personal characteristics of the target user, obtaining the encryption level of the privacy data; and calling different encryption algorithms to encrypt the privacy data according to the encryption level. According to the method, different encryption algorithms are called for encryption for the privacy data of different encryption levels, and the dynamic adaptability of encryption protection of the privacy data of the social network is improved.

The invention provides a grouping federal recommendation method based on bilateral additive article embedding. A central server constructs a double-layer article embedding characterization structure under a federated learning framework: a client locally maintains user personalized embedding and an article local personalized embedding matrix, and a server generates global group shared article embedding through a dynamic clustering grouping mechanism; superposing global sharing embedding and local personalized embedding by adopting an additive fusion strategy to generate user side personalized article characterization; a progressive course learning scheme is designed, and smooth transition from complete personalization to additive representation is realized by dynamically adjusting a regularization weight coefficient; and a grouping and clustering process is optimized in combination with a knowledge migration strategy, and collaborative knowledge sharing across user groups is promoted. In a client local training stage, a personalized recommendation loss function based on binary cross entropy is constructed, global shared embedding and local embedding parameters are synchronously updated, and a server side updates a global model through grouping federation aggregation. On the premise of protecting user privacy, the problems that in traditional federated recommendation, article characterization is simplified, and personalized perception is insufficient are effectively solved, the accuracy of a recommendation system is remarkably improved, communication overhead is reduced, and the method is suitable for personalized recommendation services of privacy sensitive scenes such as e-commerce and content platforms.

The invention discloses a personalized content recommendation and ROI (Region of Interest) improvement method and system based on a multi-dimensional user portrait, and the method comprises the following steps: collecting user data, and generating the multi-dimensional portrait of a user through feature extraction and weighted fusion; taking the advertisement platform content feature vectors and the multi-dimensional portraits as nodes of a graph structure, taking the interactive behavior data of the users as edges of the graph structure, and constructing an association graph; analyzing the association map and the multi-dimensional portrait by using a causal reasoning model, and identifying invalid correlation variables and key variables which influence a user decision; carrying out recommendation decision making based on a causal reasoning result under a multi-objective optimization framework, and generating a personalized recommendation list of each user; and calculating potential conversion values of different user groups based on a time sequence prediction model, and optimizing an advertisement budget allocation strategy. According to the invention, the personalized degree of advertisement recommendation and the ROI of advertisement putting can be improved.

A processor may gather raw data comprising a plurality of characteristic data samples of a target user group. The processor may categorize the characteristic data samples into a plurality of user-related classes and triggers. The processor may build an input property graph for each characteristic data sample. The processor may augment the input property graph by a concept of hierarchies. The processor may determine a modification vector from the augmented input property graph. The processor may train an encoder / decoder combination machine-learning system. An embedding vector and a modification vector are used as input for the decoder to build a trained machine-learning generative model.

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to a law and regulation question answering system and method based on an industry large model, and the system comprises a data layer, a model layer, a service layer and an application layer. The data layer is responsible for collecting, storing and managing law and regulation data; the model layer comprises an industry large model and an RAG module; the service layer provides question and answer generation, multi-round dialogue management and security control core services; the application layer provides diversified application scenes for different user groups; the method has the beneficial effects that the general large model is finely adjusted in a targeted manner by using massive professional data in the legal field, so that the model can deeply learn logic association between connotation and extension of legal concepts and provisions and judgment goals of cases. Through the fine adjustment, the model can accurately grasp the core of the question when facing a complex legal question, provides an answer meeting a legal professional standard, and ensures the accuracy and preciseness of the answer, thereby effectively solving the problem of understanding deviation of professional knowledge.

The invention provides an intelligent message pushing method and system, and the method comprises the steps: collecting the social behavior data and personal attribute data of a user in a social network; dividing the users into a plurality of groups by using the social behavior data and the personal attribute data based on an ant colony algorithm; analyzing a social relation and an interaction mode among users in the group; collecting message resources in the social network, and classifying and labeling messages; performing collaborative filtering processing on the to-be-pushed message, determining a target user group, and generating a message recommendation list; and pushing the recommended message to users in the target user group according to a preset pushing strategy. The user social behavior data and the personal attribute data are converted into the ant feature vectors based on the ant colony algorithm, similar feature vector ants are gathered through pheromone updating and path selection mechanisms, different user groups are formed, the user group division accuracy is improved, and user requirements are more accurately grasped.

This disclosure provides methods, devices and systems for provisioning resources for wireless communication. Some implementations more specifically relate to provisioning resources based on link state discriminants (LSDs). In some aspects, an LSD may indicate an average amount of time a respective station (STA) is in an awake state. In such aspects, an access point (AP) may prioritize communication with specific STAs, or over specific links, based on the LSDs. In some other aspects, an LSD may indicate an average amount of time all STAs associated with an AP are in an awake state. In such aspects, the AP may dynamically reduce power consumption based on the LSDs. Still further, in some aspects, an LSD may indicate an average amount of time two or more STAs are concurrently in an awake state on a given link. In such aspects, an AP may assign STAs to multi-user groups based on the LSDs.

A method, computer system, and computer program product are provided for generating personalized knowledge session content using a generative machine learning model. User profile data of a plurality of users and service description data that is descriptive of a service are processed to assign the plurality of users to a plurality of user groups. A personalized message is generated for each user group based on the user profile data and the service description data, wherein the personalized message is generated using one or more machine learning models. Each personalized message is provided to each user group and feedback data is collected based on the providing of each personalized message. Based on analyzing of the feedback data, one or more of: the user profile data, the one or more machine learning models, and the service description data are updated.

The invention relates to the field of movies, and discloses a movie recommendation method and system based on big data, and the method comprises the steps: collecting the multi-dimensional behavior data of a user to form an initial behavior data set; based on the initial behavior data set, calculating preference scores of the user for different categories of short play contents to obtain a user interest distribution matrix; dividing user groups and labeling user category labels; forming an initial short episode content feature set; constructing a content semantic feature matrix; if the content semantic feature matrix and the user category label have the matching requirement, judging the integrating degree of the short episode content and the user preference, and outputting a preliminarily recommended short episode content list; and on the basis of the preliminarily recommended episode content list, sorting and optimizing the recommended episode content by using click rate and complete playing rate indexes in the historical distribution data to obtain a final recommended episode content list. The method has the following effect that the accuracy and the individuation degree of short play recommendation are improved.

The invention relates to the technical field of machine learning, and discloses a user recharging prediction method and device, equipment and a storage medium, and the method comprises the steps: associating behavior data of multiple platforms of a user through an equipment fingerprint algorithm, obtaining a user multi-dimensional feature set, determining a clustering number based on an elbow rule, and obtaining a user recharging prediction result; and performing clustering analysis on the user multi-dimensional feature set according to the clustering number, generating a user value grouping label, and inputting the user value grouping label into a random forest model to obtain recharging prediction results of different user groups. According to the method, multi-platform user behavior information is comprehensively integrated through an equipment fingerprint algorithm, data islands are broken, user value grouping labels are generated through elbow rule clustering, then the user value grouping labels are input into a random forest model to predict a recharging result, user basic features are considered, value grouping information is integrated, feature dimensions are enriched, and the recharging efficiency is improved. And users with different values can be described more accurately, so that the accuracy of recharging prediction of user groups with different values is improved.

The invention discloses a constipation prediction system based on an artificial intelligence large model, and relates to the technical field of medical health information. In order to solve the problems that complex risk factors of constipation are difficult to comprehensively capture, so that the feature coverage range of a prediction model is limited, group classification and risk level dynamic evaluation are not performed on users, so that an intervention scheme is high in universality, but accurate management requirements of different groups are difficult to meet. Multi-source data such as electronic medical records and physiological parameters are integrated through the multi-modal data processing module, refined classification of user groups is achieved, feature causal relationships are analyzed in combination with a knowledge graph technology, constipation prediction accuracy is improved, the intelligent prediction module dynamically adapts to a model according to group risk levels, and constipation prediction accuracy is improved. The probability distribution is output, a visual causal path report is generated, and the dynamic decision-making module adjusts an intervention scheme through doctor-patient cooperation based on a knowledge graph matching strategy set and optimizes a strategy in real time by using an effect feedback mechanism, so that personalized health management is realized.

The present invention provides an intelligent marketing method and system based on user portraits; first, basic information and browsing behavior data of users are collected through an authorization agreement to establish an initial user portrait. Further, by comparing the basic information in the user portrait with a pre-set similarity vector, users with similar characteristics are grouped into a similar user group and share a set of similar product tags. Finally, personalized marketing information is pushed according to the similar user group where the user belongs, and at the same time, the product tag set is dynamically adjusted and optimized according to the feedback of newly launched users to ensure the continuity and effectiveness of marketing activities. This method effectively integrates user data, optimizes marketing strategies, improves the targeting and conversion rate of advertisements, and thus significantly enhances user satisfaction and the market competitiveness of enterprises.

The invention discloses a user management notification method and device for a graph database document site, and belongs to the technical field of graph databases. The method comprises the steps that a pre-configured target notification group name set is acquired, and a target notification group is associated with the access permission of the graph database document site; capturing an administrator event, and identifying whether a current event is an association creation event for adding a user into a user group; in response to the event identified as the association creation event, analyzing the event data, extracting a group identifier and a user identifier, and querying group attribute information according to the group identifier to obtain a group name; if the obtained group name belongs to the target notification group name set, querying user attribute information according to a user identifier to obtain a user name and user contact information; and constructing a notification message containing the user name and the name of the target notification group to which the user name joins, and sending the notification message to the user. The permission change notification can be automatically sent to the user when the administrator allocates or modifies the permission for the user.

The invention relates to the technical field of intelligent retail terminals, solves the problem of low commodity replenishment accuracy in an unmanned vending cabinet in the prior art, and provides a retail terminal inventory replenishment method, device and system based on multi-modal data. The method comprises the following steps: decomposing real-time video data into multiple frames of real-time images according to the real-time video data of the unmanned vending cabinet in a commodity transaction scene; performing multi-modal feature extraction on the commodity region image to determine commodity feature information; performing human body feature extraction on the user area image in the historical commodity transaction scene, and updating user group feature information; inputting the user group feature information as incremental data into the commodity demand prediction initial model, and determining a commodity demand prediction target model; and inputting the commodity feature information into a commodity demand prediction target model, and screening out a target commodity from a preset commodity list according to a prediction result, and supplementing and putting the target commodity on a shelf. According to the invention, the replenishment accuracy of the unmanned vending cabinet can be improved.

The invention discloses a marketing advertisement putting method based on media big data, and particularly relates to the technical field of advertisement putting, and the method comprises the steps: building an advertisement mapping database according to the labels of advertisements, the types of users and the advertisement effect, screening out the advertisements highly related to a target user group through similarity calculation, and carrying out the advertisement putting according to the advertisement effect. The method comprises the following steps: determining a promotion user, collecting a push success rate, a jump-out rate and a conversion rate of the promotion user, determining interested information of the promotion user, combining a knowledge graph of a constructed label with a purchase history record of the promotion user, collecting a time difference between adjacent purchase histories of the promotion user, and analyzing a consumption history behavior of the promotion user. The consumption information of the promotion user is determined, the interested information of the promotion user and the consumption information are comprehensively analyzed, the potential value of the user is quantitatively evaluated, the high-quality promotion user is determined, and the advertisement and the user can be accurately matched, the advertisement putting effect is optimized, and the advertisement conversion rate and the putting efficiency are improved.

An ecommerce messaging system described herein comprises one or more ecommerce extension apps that interact with a messaging app. These embodiments leverage the extension core to manage session apps and smart services for using tap-to-message and tap-to-update processes to generate, update, and manage order workflows and in-app stores through branded messages communicated between customer and seller session apps to share message transcripts on devices of the same user groups via the messaging host, empower ecommerce with action-key-based communication, serialize-deserialize processes, and dynamic update propagation, manage return-refund workflows with temporary tables until one-time entity data model updates can be performed for session apps, and configure multi-store shopping systems on sellers' multiple devices using a store-as-a-service model to enable distributors to distribute production copies of assigned stores to authorized publishers to add custom promotions, which will be published together as production copies to their subscribed user groups for multi-store shopping and tracking.

Techniques for providing explicit proxy solutions for 5G Service Access Service Edge (SASE) with service provider network attach are disclosed. In some embodiments, a system, a process, and / or a computer program product for providing explicit proxy solutions for 5G SASE with service provider network attach includes receiving data plane traffic associated with a User Equipment (UE) from a mobile core network at a Secure Access Service Edge (SASE) cloud network via a service provider network attach using an interconnect between the mobile core network and the SASE cloud network; enforcing a security policy on data plane traffic associated with the UE based on contextual information associated with the UE to provide secured data plane traffic using the security policy configured per user group and / or per user; and forwarding the secured data plane traffic from the SASE cloud network to its original destination if allowed by the security policy, and blocking or dropping the data plane traffic from the SASE cloud network if not allowed by the security policy.