57 results about "Social information" patented technology

The invention relates to the technical field of social information, in particular to a social network influence prediction method and system. According to the method, brand-related users, content and interaction data are obtained from multiple platforms, a multi-dimensional influence scoring model is constructed, and initial influence scores of the users are calculated. And in combination with a social network cascade propagation theory, constructing a propagation probability calculation model, and predicting an information propagation range and user influence changes. And finally, generating an influence prediction report containing KOL ranking, hot topic analysis and optimal intervention opportunity suggestions. According to the method and system, through organic combination of a plurality of innovation points, the accuracy, comprehensiveness and practicability of prediction are remarkably improved, the existing technical problems are effectively solved, and a powerful support tool is provided for a brand to formulate a precision marketing strategy in a complex social media environment.

The invention relates to the technical field of artificial intelligence, and discloses a multi-agent social network simulation method and system based on a cognitive inference chain, and the method comprises the steps: initializing a multi-agent system comprising a social environment engine, a user portrait engine and a cognitive inference engine, executing a multi-agent social network simulation cycle of a preset round of iteration, in each iteration round, the social environment engine pushes social information to the intelligent agent as external stimulation and activates the intelligent agent to execute an independent decision, the cognitive state of each dimension in the cognitive reasoning chain is updated through large language model reasoning, corresponding social behaviors are generated, and the social behaviors and corresponding cognitive state tracks are recorded; and periodically analyzing historical records to optimize influence coefficients among all cognitive dimensions of the cognitive inference chain, and adjusting an inference strategy of a preset large language model. The simulation of the cognitive process of the intelligent agent is a transparent and traceable evolutionary process, and the complete and understandable simulation of the'observation-cognition-behavior 'cycle is realized.

Disclosed in the present application are a social information pushing method, a related apparatus, a device and a storage medium. The method of the present application comprises: in response to a payment transaction request, acquiring a consumption feature vector corresponding to a target user account, the payment transaction request carrying the target user account and device location information; on the basis of the device location information and the consumption feature vector corresponding to the target user account, determining M user accounts; on the basis of the device location information, determining N pieces of recommended location information; and pushing social information to at least two terminals, the at least two terminals comprising a target terminal and at least one terminal, the target terminal having logged into the target user account, each of the at least one terminal having logged into one of the M user accounts, and the social information comprising the N pieces of recommended location information. The present application reduces the social cost of users and can also improve the accuracy of social matching.

This invention discloses a social denoising recommendation method based on edge confidence invariant learning, belonging to the recommender system field within machine learning. The invention constructs an edge confidence estimation mechanism that integrates preference similarity and structural priors, and based on this, performs soft pruning and confidence-weighted propagation on the social graph to suppress noise diffusion from low-quality social edges at the source. Addressing social structural perturbations and noise distribution changes, the invention introduces a multi-environment generation mechanism, constructing multiple differentiated social environments under the guidance of edge confidence priors, and learning stable and effective preference signals through cross-environment invariance constraints, thereby reducing the model's dependence on environment-specific spurious correlations. Simultaneously, the invention designs a cross-domain gating fusion mechanism to selectively forget and enhance social representations, and combines cross-domain alignment constraints to alleviate semantic shifts between the social and recommendation domains, improving the controllability of social information injection.

This invention discloses a method for recommending points of interest (POIs) based on user spatiotemporal behavior and social information. The method includes constructing a spatiotemporal knowledge graph based on user POI visit sequences and social relationships; learning POI representations in the spatiotemporal knowledge graph and constructing a POI transfer matrix; updating POI features through graph convolution using the POI transfer matrix as weights; calculating a user preference representation based on the user POI visit sequence and POI features; calculating the weights of historically visited POIs based on the spatiotemporal distances between POIs and the distances between POIs and user preferences; inputting the POI sequence into a recurrent neural network and updating a hidden representation using the weights of historically visited POIs; and connecting the hidden representation with the user preference representation, inputting the result into a recommendation model to generate the next POI that matches the user's preferences. This method can effectively model user spatiotemporal behavior, capturing behavioral patterns and user preferences, and making POI recommendations more accurate.

The invention provides a customer management method and system based on intelligent marketing and a medium, and belongs to the technical field of artificial intelligence and big data. The method comprises the steps of obtaining customer basic information, customer social information and customer behavior preference information, constructing a customer portrait, and extracting customer value information and customer loss risk information; client value evaluation data can be obtained according to the client value information; and obtaining customer loss probability data according to the customer loss risk information. And obtaining a market trend influence coefficient according to the market trend dynamic information. And processing according to the customer value evaluation data and the customer loss probability data in combination with the market trend influence coefficient, obtaining and correcting a customer priority index, obtaining a customer priority correction index, querying through a preset marketing database, and obtaining a customer management strategy scheme. According to the method and the system, a comprehensive customer portrait is constructed, clear guidance is provided for marketing and customer service of enterprises, and the method and the system have very high practicability.

A video recommendation method fusing social information comprises the steps that firstly, a user-video interaction graph and a social graph are constructed through video media platform data, different combination strategies of a diffusion model and graph convolution operation are applied in a denoising priority path and a structure priority path respectively, and collaborative perception and fusion of user embedding and structure information are achieved; secondly, aligning user embedding output by the double-track denoising path by adopting comparative learning, and enhancing robustness; secondly, performing deep coding on the user-video interaction graph and the social graph by applying a double-graph neural network, comprehensively capturing user preferences and embedding videos; and finally, for noise possibly introduced by multi-module fusion, deep denoising is executed to purify user embedding again. According to the method, guidance of structural information is introduced in the diffusion denoising process, and more robust user embedding is obtained by using a comparative learning strategy, so that a structural perception high-fidelity social denoising task is completed, and personalized video recommendation is realized on a video media platform.

This invention relates to the field of artificial intelligence technology and discloses a multi-agent social network simulation method and system based on a cognitive reasoning chain. The method includes: initializing a multi-agent system comprising a social environment engine, a user profile engine, and a cognitive reasoning engine; executing a multi-agent social network simulation loop with a preset number of iterations; in each iteration, the social environment engine pushes social information to the agents as external stimuli and activates the agents to make independent decisions; updating the cognitive states of each dimension in the cognitive reasoning chain and generating corresponding social behaviors through reasoning using a large-scale language model; recording the social behaviors and their corresponding cognitive state trajectories; periodically analyzing historical records to optimize the influence coefficients between the cognitive dimensions of the cognitive reasoning chain; and adjusting the reasoning strategy of the preset large-scale language model. This makes the simulation of the agent's cognitive process a transparent and traceable evolutionary process, achieving a complete and understandable simulation of the "observation-cognition-behavior" cycle.

The specification discloses a data detection method and device, electronic equipment and storage medium, which can combine multimedia data sent by a user in various different forms through a to-be-detected object in a business platform for display to other users and portrait data of the user, such as basic information of the user, device information used by the user, and social information of the user in the business platform, directly detect the to-be-detected object, detect whether the to-be-detected object is an object with abnormal behavior, detect all business behaviors related to the to-be-detected object according to the detection result of whether the to-be-detected object has abnormal behavior, and avoid the situation that the user adopts various different countermeasures to evade detection due to detection of only the business behaviors of the to-be-detected object, thereby improving the accuracy of abnormal detection of the business behaviors of the to-be-detected object.

A social method, apparatus, computer device, and storage medium based on a three-dimensional virtual environment are provided, which belong to the field of virtual environments. The method includes the steps of: displaying (210) a first virtual character located at a first location in the three-dimensional virtual environment; receiving (220) a check-in operation, the check-in operation including an operation of editing first social information for a first event; and displaying (230) a first check-in marker created by the first virtual character at a second location in the three-dimensional virtual environment, the first check-in marker being used to present the first social information edited by the first virtual character for the first event to a virtual character who has reached the second location. According to the present invention, by placing the check-in marker in the three-dimensional virtual environment, social information can be shared with other virtual characters in the three-dimensional virtual environment.

The invention belongs to the technical field of recommendation systems, and particularly relates to a recommendation method based on two-channel fusion of graph convolution and hypergraph convolution. According to the method, a two-channel architecture of graph convolution and hypergraph convolution is constructed and is respectively used for capturing explicit interaction information between users and merchants and high-order social information between the users, so that noise interference caused by hyperedge redundancy is effectively relieved; a gating attention fusion mechanism is designed, dynamic aggregation of double-channel embedding is achieved by means of a learnable weight distribution strategy, and recommendation accuracy and personalized expression ability are improved; merchant static features are introduced, category labels, geographical location information and the like are used as semantic vectors to improve recommendation performance, hyperedge noise propagation can be inhibited by means of an information bottleneck principle, and recommendation accuracy is improved.

Embodiments of the present application disclose a motion trajectory generation method and device, electronic equipment and storage medium. Applied to the field of automated testing. Including: obtaining historical motion trajectory data of at least one reference vehicle and a target vehicle. Using a trajectory generation model to determine a social information vector corresponding to the target vehicle according to the historical motion trajectory data of all reference vehicles. Using the trajectory generation model to determine a first historical trajectory feature vector corresponding to the target vehicle according to the historical motion trajectory data of the target vehicle. Using the trajectory generation model to generate a motion planning trajectory of the target vehicle according to the social information vector corresponding to the target vehicle and the trajectory feature vector corresponding to the target vehicle. Embodiments of the present application can quickly generate motion trajectories of multiple traffic participants using a trajectory generation model. In this way, multiple traffic participants can be added to the test scene to increase the complexity of the test scene.

The application discloses a social recommendation method and device based on hyperbolic space contrast learning, and the method comprises the following steps: step S1, mapping the nodes of a user-item interaction graph and a social network graph into a hyperbolic space, and reconstructing the user-item interaction graph to obtain a reconstructed graph; step S2, combining the user-item interaction graph, the social network graph and the reconstructed graph, and performing hyperbolic graph learning by using a graph neural network to obtain preliminary fusion embedding of each node; step S3, based on the preliminary fusion embedding, performing social information conversion fusion for each user to obtain personalized fusion embedding; step S4, taking the hyperbolic distance between the user and the item as a prediction score to construct a hyperbolic space model, setting a contrast learning strategy of positive sample weighting and negative sample screening, and setting a contrast learning loss of social awareness and interaction awareness; and using the hyperbolic space model to score the correlation between the user and the item. The application is significantly improved in recommendation effect.

The invention discloses an Internet of Vehicles trust management method and system based on block chain and social information driving. According to the method, node credibility data are stored in a decentralized mode through a block chain, an intelligent contract is deployed to automatically execute trust evaluation, and network loads are reduced through edge nodes; quantifying social relations such as node ownership and friendship to generate a reference list, dynamically updating a direct trust value, calculating an inter-cluster relative trust value, weighting and calculating an indirect trust value in combination with the reference list, and fusing the three types of trust values to identify malicious nodes; the system comprises a block chain storage module, a smart contract module, an edge node module and the like, and stores data through an MPT structure; the implementation verification shows that the malicious node detection success rate is greatly improved, singing attenuation and DDoS attacks can be resisted, the storage and calculation overhead is reduced, and the security and robustness of Internet of Vehicles trust management are improved.

The application provides a kind of interest social relation recommendation method, device, system and storage medium, belong to social recommendation technical field, method includes: import user ID data, original project interaction data and original social data;User ID data and original project interaction data are vectorized to obtain initial user node vector and original project interaction vector;Original project interaction vector and original social data are cleaned to obtain interest social data;Training model is constructed, and training model is analyzed by initial user node vector, original project interaction vector and original interest social data to obtain social relation recommendation model.The application can dynamically filter and remove meaningless social in interest social space, effectively filter social information that can interfere or mislead user, improve the calculation efficiency of recommendation algorithm, also can improve the accuracy of recommendation, has great practical value in recommendation method, greatly meets the needs of user.

This application discloses a method, system, server, medium, device, and program for generating social information in multiplayer games. The method includes: constructing a user's player profile based on the user's player information; when the spatial distance between a first character and a second character in the game scene is less than or equal to the social detection distance, determining similar characteristics between the first user and the second user based on the first user's player profile and the second user's player profile, wherein the first character is the character controlled by the first user and the second character is the character controlled by the second user; generating first social information based on the first character's character information and similar characteristics, and / or generating second social information based on the second character's character information and similar characteristics, wherein the first social information is suitable for being sent to the second user in response to the first user's social interaction operation, and the second social information is suitable for being sent to the first user in response to the second user's social interaction operation.

The invention discloses a vehicle-mounted equipment control method based on multiple agents, and belongs to the technical field of intelligent control. According to the method, identity information of a target user using a vehicle is obtained, user real-time information of the target user is collected, the user real-time information comprises operation information, physiological information and social contact information, a corresponding user database is determined according to the identity information, and the user database is used for storing the vehicle information. And screening a target data set in the user database according to the social information and the physiological information, determining equipment control data according to the operation information, the target data set and a large language model, and controlling the vehicle-mounted equipment according to the equipment control data, thereby achieving the beneficial effect of improving the intelligent degree of the vehicle-mounted equipment.

The embodiment of the invention provides an animal social analysis method and device, electronic equipment and a medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: extracting a global video frame sequence from a real-time multi-view video of an animal group; performing real-time group behavior recognition, individual behavior recognition and animal posture recognition on the global video frame sequence, and performing social contact pointing analysis on the animal group according to group social contact behavior categories, single animal behavior categories and target animal posture data obtained through real-time recognition to obtain group social contact pointing data; and according to the group social behavior category, the single animal behavior category and the group social pointing data, social information integration is carried out to obtain target animal social data. According to the embodiment of the invention, the social directivity analysis is carried out according to the group social behavior category, the single animal behavior category and the target animal posture data, so that the social directivity of the animal group social behavior can be accurately judged in real time, and the efficiency and precision of animal social analysis are improved.

The invention relates to an interaction method and device based on promotion content, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps that a social information flow interface of a first account is displayed, the social information flow interface comprises social content sent by a second account having a social relationship with the first account and promotion content sent through a target promotion account, and a virtual article in a first state is displayed in a display area of the promotion content; in response to an interaction operation on the virtual item in the first state, displaying a sharing interface about the virtual item, and displaying a virtual item in a second state in the sharing interface; and in response to a sharing operation on the virtual item in the sharing interface, sharing the virtual item. In this way, the interaction experience is improved.

Embodiments of the present application disclose a social information issuing method and device. A specific embodiment of the method comprises: receiving a social information obtaining request sent by a first user, determining a second user to which the social information obtaining request is directed; obtaining social information uploaded by the second user, wherein the social information at least comprises locked social information; in response to the target social information requested in the social information obtaining request being the locked social information, forwarding the social information obtaining request to the second user; and in response to receiving an authorization permission returned by the second user for the social information obtaining request, sending the target social information to the first user. The embodiment can provide the user with social information published and allowed to be viewed by other users based on the request of the user, protect the privacy of the publisher of the social information, avoid providing the user with too much social information with low value, and improve the quality of the obtained social information.

The invention discloses a vehicle social contact assisting method and device, and the method comprises the steps: determining a target vehicle which is in near-field connection with a vehicle in a target range when the vehicle is in a weak network connection state, and receiving the social contact information sent by the target vehicle; obtaining driving information of the vehicle; and according to the driving information, push display processing of the social information by the vehicle is determined. Thus, even if the vehicle is in the weak network connection state, the vehicle can still stably obtain the social information, and service continuity is guaranteed. Through a driving safety strategy, social information display opportunity and an interaction mode are reasonably controlled, distraction of a driver is avoided, and driving safety and social demands are balanced.

The application discloses a social label prediction method and system for heterogeneous social network distribution external generalization. The application comprises the following steps: 1) constructing social network data into a heterogeneous social network graph; 2) performing initial clustering division on the heterogeneous social network graph; 3) inputting the clustered heterogeneous social network graph into an adaptive neighborhood propagation module based on a graph neural network, and performing one round of parameter optimization on the adaptive neighborhood propagation module under the supervision of existing social information labels in the graph; 4) using the adaptive neighborhood propagation module after a new round of parameter optimization to extract graph feature information of the heterogeneous social network graph again, and then performing clustering cluster division again using the updated graph feature information; 5) repeating iteration to complete the training of the adaptive neighborhood propagation module and use the adaptive neighborhood propagation module for prediction. The application has strong robustness and accuracy for the heterogeneous social network data outside the training set distribution, and can accurately predict user labels.

A system and method for providing a social networking service to a plurality of devices. A social networking server for facilitating social information exchange among the plurality of devices through a socially aware network. A social profiles manager is coupled to the social networking server that creates and manages individual centric profiles of the plurality of devices. The individual centric profiles of the plurality of devices are social representation of individual devices within the socially aware network. The plurality of devices are identifiable by other participants and the social networking server through their social profiles.

The invention discloses a session social recommendation model method based on a gated graph neural network, and aims to solve the problem that a current recommendation method based on a graph structure only pays attention to a single recommendation field and does not consider user behavior information and a user social relationship at the same time. In order to solve the problem that a user session is modeled by using a GGNN and a recurrent neural network (RNN), only the short-term influence of the session is concerned, and all information of the session cannot be covered, the invention provides a novel session-based social recommendation method, which comprises the following steps of: firstly, representing the session information of the user by using the GGNN; then, a GAT (Graph Attention Network) is used for aggregating social information of users and friends on the social network, user interests are simulated, a session graph is constructed from a user session sequence, the GGNN is used for processing the user session graph, complex items hidden in a graph structure are mined for interaction to obtain more accurate session embedding, and the GAT is used for aggregating the user social information in the social network graph; and richer user interest representation is obtained, so that the recommendation accuracy is improved.

The invention discloses a social information display method and device, equipment and a storage medium, and belongs to the technical field of computers. The method comprises the following steps: displaying a first topic map, wherein the first topic map is used for displaying at least one piece of social information published under a first topic; a social information mark with the theme style of the first theme is displayed at the publishing position in the first theme map, and the social information mark is used for representing the social information published at the publishing position. By setting the theme map, the social information mark matched with the theme style of the theme map is displayed in the theme map according to the publishing position of the social information, so that the social information published under the theme is represented. The social information is displayed according to the geographic position, the social information can be published according to different themes, the social information marks corresponding to different theme styles are adopted for displaying, and therefore the personalized display mode of the social information is enriched.

The invention provides a data processing method and device based on an online conference, equipment and a storage medium. The method comprises the following steps: acquiring conference participation data of a target user who enters an online conference, and acquiring social information between the target user and other users; the conference participation data represents multi-modal data displayed by the user on the conference, and other users represent users except the target user in the online conference; determining credibility information between the target user and other users according to the social information between the target user and other users; the credibility information represents the social contact condition between the two users; according to the credibility information between the target user and other users, performing encryption processing on the conference participation data of the target user, and outputting the encrypted conference participation data to a server; and the server side is used for distributing the encrypted participating data to other users. According to the method provided by the invention, the protection of the online conference on the personal information of the target user is improved, and the security is improved.

The invention discloses a music recommendation system based on big data. The system comprises a data acquisition module which is responsible for collecting user behavior data, music metadata and social information; the data preprocessing module is used for preprocessing the collected data; the feature extraction module is used for extracting feature vectors related to music recommendation from the preprocessed data; the model training and optimizing module is used for constructing a GANs model which comprises a generator and a discriminator, an antagonism training strategy is adopted, so that the generator learns distribution characteristics of data, the generator is responsible for generating music data which is possibly favored by a user but not interacted yet, complementing interaction data between the user and music, and optimizing the interaction data between the user and the music; training a music recommendation model by using the complemented interaction data and the extracted feature vector features; and the recommendation generation module is used for generating a personalized music recommendation list according to the current context of the user and the trained recommendation model.

The invention provides a social information publishing method and device, a social information display method and device, electronic equipment, a storage medium and a computer program product. The method comprises the following steps: displaying multiple pieces of social information published by a target account in history, wherein the social information carries a publishing position; in response to the information fusion instruction, target social information obtained by fusing the multiple pieces of social information is displayed, the target social information comprises an action track of the target account, the action track comprises a release position of each piece of social information, and in response to the information release instruction, the target social information comprising the action track is released. According to the invention, the diversity of the social information published by the user can be improved, and the richness of social interaction can be improved.

The present disclosure relates to an image processing method, device, electronic device and storage medium. The method includes: obtaining an image to be processed and attribute information of the image to be processed, including aesthetic feature information, time information, spatial information and social information of the user to which the image belongs; calculating the attribute similarity between the image to be processed and the candidate image, and taking the candidate image with the highest attribute similarity as the reference image; extracting the features of the aesthetic feature information of the image to be processed and the features of the aesthetic feature information of the reference image, respectively obtaining the feature information to be processed and the reference feature information; performing feature alignment and feature fusion on the feature information to be processed and the reference feature information, decoding the obtained fused aesthetic feature information, and obtaining a target beautified image. In this way, by determining a reference image with a high attribute similarity with the image to be processed, and processing the image to be processed in combination with the reference image, the obtained target beautified image can better meet the aesthetic needs of the user.

The application discloses a social prompt cold start recommendation method based on an SPGCLRec framework, and comprises the following steps: step one, constructing an interaction graph and pre-training a recommendation model; step two, optimizing a social prompt based on a diffusion model; step three, enhancing a cold start user representation; and step four, generating a cold start recommendation and jointly optimizing a model. The learnable social soft prompt designed by the application embeds social information into a representation space of a pre-training model, and can realize efficient parameter adaptation without fine-tuning core weights of the model, thereby making up for the deficiency of the prior art which only relies on a single auxiliary clue and realizing efficient parameter adaptation of the pre-training model. The diffusion model is used to filter noise social relationships in a latent vector space, thereby improving user representation accuracy. By introducing a contrast learning mechanism and introducing Gaussian noise in a latent space, the semantic consistency of the optimized social prompt and the item representation is strengthened, and the robustness of the user representation is improved.