29 results about "Social circle" patented technology

The invention relates to the technical field of live broadcast recommendation, in particular to an intelligent live broadcast recommendation method and system based on big data, and the method comprises the following steps: obtaining user social interaction data, recording text comments, picture sharing and like behaviors, extracting keywords and mapping the keywords to interest classifications, combining the like frequency and interest labels to update dynamic interest records, and recommending the like. And matching the time sequence interest tag set to generate a cross-platform interest map, counting distribution of watching contents of social circle members, adding recommended contents, and generating a live broadcast recommendation list. According to the invention, the social interaction data of the user is acquired and the specific behavior is recorded, the creation of the dynamic interest label record can more accurately reflect the current interest and behavior mode of the user, and the watching type and duration of the live broadcast platform and the keyword in the comment are combined to ensure that the recommended live broadcast content is highly matched with the actual interest of the user, so that the user experience is improved. The social correlation of recommendation is enhanced, and the accuracy of live broadcast recommendation and the interaction rate of users are improved.

The invention relates to the technical field of social data processing, in particular to a personalized recommendation-oriented big language model social data tag attribute generation method. Comprising the following steps: constructing a multi-modal social feature incidence matrix; generating an initial label set adapted to the social scene; and establishing a tag hierarchical relationship through an improved semantic dependency tree, calculating an aging attenuation coefficient based on a behavior occurrence time interval, and counting a proportion of users with the same tag in a core social circle as a group association value. According to the method, the complexity of the multi-modal data in the social scene can be adapted by constructing the multi-modal social feature incidence matrix, so that the generated tag system is more fit with the multivariate features of the social data. According to the method, the tag hierarchical relationship is automatically constructed through the improved semantic dependency tree, the aging attenuation coefficient is calculated, and the deep analysis capability of social semantics is improved.

The invention belongs to the technical field of insurance product pricing, and more specifically relates to a multi-factor fusion insurance product dynamic risk pricing optimization method. Collecting multi-source information data, and carrying out data preprocessing; on the basis of travel trajectory analysis of vehicle-mounted OBD data, in combination with high-risk road sections, driving behaviors and environmental factors, a space-time accumulation risk index is constructed, a graph convolutional network is used for modeling a risk transfer effect in a social circle of vehicle owners, mutual influences among the vehicle owners in the social circle are captured, and a neural network based on multi-task learning is used for establishing a social network model. Dynamic risk, social risk features and other related data are comprehensively processed, the risk probability and loss amount prediction are optimized through a shared network layer, and the insurance pricing is generated in combination with external market data, so that the comprehensiveness and accuracy of the insurance pricing are ensured; the method solves a blind area existing in a conventional pricing method, enables the insurance pricing to be more intelligent and precise, and can reflect the actual risk condition of a vehicle owner in real time.

The invention relates to the field of influence measurement, and discloses a social media-based content influence measurement method, which comprises the following steps of: constructing a social network graph according to collected user social relation data; each user is a node, interaction between the users is an edge, and features in the social network are analyzed; modeling a propagation path of the information by adopting a propagation model, and weighting the weight of the information propagation according to the social relationship between the users; establishing a weight model, endowing each edge in the propagation path with different weights according to the strength of different social relationships, and strengthening the influence of the core social group on content propagation; evaluating the propagation breadth and propagation speed of the content in the social network, analyzing the participation behavior of the user, and evaluating the propagation effects of the content in different social circles; and training the data by using a machine learning algorithm, and optimizing the propagation effect prediction model. The method has the advantage of accurately evaluating the actual propagation effect and influence of the content.

The application provides a package recommendation method and device, electronic equipment and storage medium, wherein the method comprises: determining the recommendation success rate of each user corresponding to each channel based on the package adaptation degree between the user and the to-be-recommended package, the channel adaptation degree between the user and the channel, the channel package adaptation degree between the channel and the to-be-recommended package, and the influence of the user's social circle in each channel; determining the to-be-marketed user based on the recommendation success rate of each user corresponding to each channel; determining the recommendation channel of each to-be-marketed user based on the channel adaptation degree between each user and each channel; recommending the to-be-recommended package to the corresponding to-be-marketed user based on the recommendation channel of each to-be-marketed user; wherein the package adaptation degree between any user and the to-be-recommended package is determined based on the consumption similarity between the user and the to-be-recommended package and the marginal revenue value of the to-be-recommended package. The application improves the accuracy of package marketing promotion and improves the recommendation effect of the package.

A user emotion feedback method, apparatus, and computer-readable storage medium for expressing emotions in social circle interactions through virtual figures. The method displays a first user interface containing a social circle message posted by a second account on a first client logged in with a first account. Based on a trigger operation on a user feedback control for the social circle message, feedback information of the first account's user emotion is displayed through a first virtual figure corresponding to the first account. The user feedback control is configured to provide emotion feedback on social circle messages, enabling expressive and personalized emotional responses through virtual figure representations.

The invention discloses an intelligent information pushing method and system based on cloud computing and big data and a storage medium, and the method comprises the steps: obtaining registration information of a user on a page and an input interest label, building an initialized user interest model, obtaining behavior record information of the user on the page according to the registration information and the user interest model, and pushing the behavior record information to the user. User interest feature words are obtained through classification processing, a relation neighborhood model of the user is constructed based on the user interest feature words and the behavior record information, and hotspot analysis and neighborhood authoritative user analysis are conducted on the relation neighborhood model to obtain a neighborhood chain; the method comprises the steps of obtaining trust relationships among users in a relationship neighborhood, establishing a trust model, determining an optimal push channel among the users based on a neighborhood chain and the trust model to complete intelligent information push, classifying social circles of the users in combination with the neighborhood model to obtain a propagation mode of information in the social circles, improving the push accuracy of a push system, and improving the user experience. And the timeliness and the reliability of information pushing are effectively improved.

An embodiment of the present invention provides a method, device, electronic device and storage medium for processing a user social circle portrait. The method includes: obtaining user basic data and user behavior data of a target user; inputting the user basic data into a traffic circle model to perform social circle prediction, and obtaining a family-like user group corresponding to the target user, and the family-like user group includes several associated users; dividing the associated users according to the user behavior data, and obtaining a social circle corresponding to the target user, and the social circle includes at least a colleague social circle, a friend social circle, and a family social circle.

The invention provides a social contact method based on intelligent audition equipment, which comprises the following steps: receiving a system operation instruction of a music friend circle input by a user, and adding the user into the music friend circle based on the system operation instruction of the music friend circle; the system operation instruction comprises a creation instruction or an addition instruction; and after the user joins the music friend circle, receiving a function operation instruction input by the user, so that the user uses a corresponding function to carry out social contact with the user in the music friend circle. According to the social contact method based on the intelligent sound listening equipment and the intelligent sound listening equipment, a social contact function is added on the basis of a traditional sound listening product, the friend making and cooperation requirements of children are met, the cooperation ability and creativity of the children are promoted, the music experience of the children is enriched, and the social contact effect of the children is improved. And meanwhile, parents are helped to better manage music listening and social circles of children.

This invention discloses a satellite trunking terminal user information publishing and comment / like interaction system. After a satellite terminal forms a cluster social circle through online cluster grouping and adding friends, in addition to supporting one-call / group calling and real-time SMS services, it will also support value-added services such as terminal users publishing information to their group user circle and friend user circle, and receiving comments and likes. The terminal APP adds two communication sections, group activity and friend activity. Clicking on these sections displays information published by associated users, which can be in the form of SMS messages, data, or status emoticons. Users can comment or like these messages. Users can also actively publish information and receive comments and likes from other users. Associated information is exchanged bidirectionally between the satellite terminal and the ground station core network. The satellite trunking air interface data transmission path ensures the integrity and reliability of the interactive data, allowing users to publish information and comment / like simply by clicking to send to the satellite, adding new application functions to trunking communication.

This application discloses a training method and apparatus for a model for identifying the permanent location of users on a different network, aiming to address the problem of inaccurate identification of the permanent locations of users on a different network. The solution provided in this application includes: obtaining communication behavior information of multiple users on the same network; generating a complete graph of the social circles corresponding to each user on the same network based on the communication behavior information of the multiple users on the same network; generating multiple training samples and corresponding training labels based on the complete graph of the social circles corresponding to the multiple users on the same network and the corresponding communication behavior information; and training a pre-built time-series-based recurrent neural network model based on the multiple training samples and corresponding training labels.

The invention provides a transaction risk detection method and device, a storage medium and electronic equipment, and the method comprises the steps: obtaining user data of a target user, the user data comprising an address book, a call record and a historical transfer record; calculating a social relation score between the target user and the transaction counterparty based on the user data; according to the social relation score, performing social circle layer division on the target user and the transaction counterparty to obtain a division result; obtaining transaction intention information corresponding to the transaction instruction, wherein the transaction intention information comprises a transaction type selected from predefined options and a transaction purpose description text input by a user; performing semantic contradiction detection and risk word matching on the transaction intention information by using a semantic rule base to identify the intention risk of the target user, and obtaining an intention risk identification result; and based on the division result and the intention risk identification result, determining a transaction risk level corresponding to the transaction instruction through a risk decision matrix. And the transaction fraud risk can be accurately identified.

The present invention relates to the technical fields of artificial intelligence and social networks, and discloses a multi-channel social network circle recognition device and method based on enhanced network contrast constraints. The device includes an input module, a solution module, and an output module. The work of the input module is data preprocessing, which abstracts the multi-channel social network existing in the real world into a multi-layer network structure. The solution module includes a network generation model and a node representation model. Based on the node representation model, the low-dimensional representations of the learnable enhanced network and the original input network are calculated, and based on the two obtained low-dimensional representations, the loss function is solved, and the parameters of the network generation model and the node representation model are optimized in reverse, so that the device has higher accuracy and robustness. Finally, the obtained multi-layer network low-dimensional representation is imported into the output module, and the community division is obtained by using Kmeans. In essence, the obtained community division corresponds to a social circle formed by a group of network user accounts in the real world.

The application discloses a family relationship recognition method and system based on an AHP algorithm, relates to the technical field of communication service management, and comprises the following steps: obtaining service relationship data and social circle characteristic data; constructing a candidate number pair set; extracting at least two types of indexes in communication interaction, service association, spatial co-occurrence and time co-occurrence for the number pair and quantifying to obtain index values; determining index weights and calculating relationship scores based on an AHP (analytic hierarchy process); determining a family relationship number pair set according to a preset screening rule, training a decision tree model for the remaining number pairs and predicting and supplementing; constructing a relationship structure accordingly; determining a collection priority in the relationship structure according to the scores and recursively merging to generate and output a family circle. Through the technical scheme, multi-source data fusion and multi-dimensional weighted scoring driven family circle structured recognition are realized, coverage and result consistency are improved, misjudgment and repeated collection risk are reduced, and a data basis is provided for all-family perspective operation.

The application belongs to the technical field of computer systems related to old-age care, and in particular to a community mutual aid old-age care system and a mutual aid old-age care method, the community mutual aid old-age care system comprising a community selection module, a quick help module, a mutual aid market module, a my group module, a community contribution list module, an emergency help module, a co-creation space module, a social circle module, an activity calendar module, an auction module, a tea room module, a news information module, a forget age mall module, a message module, and a personal center module, different modules and methods are integrated in the system to realize mutual aid old-age care among the elderly, and timely and efficient help can be provided, and rich function modules suitable for the elderly, such as circles and tea rooms, are provided, which greatly improves the activity level, thereby realizing high-quality, high-efficiency and high-activity community mutual aid old-age care.

The application relates to the technical field of communication, and discloses a left-behind family identification method, device and equipment and a storage medium, the method comprising the following steps: identifying an old-young family based on a local user's social circle, identifying off-site workers through an off-site worker group identification model, determining the home of the off-site workers, and identifying a left-behind family according to the old-young family and the home of the off-site workers; since the off-site workers are identified through the off-site worker group identification model, and the left-behind family is identified according to the family member definition of the left-behind family, the old-young family is identified, the off-site workers are identified, and finally the off-site workers are associated with the old-young family, so that the left-behind family is identified, the accuracy of the off-site worker identification is improved, and the left-behind family identification is realized.

The present disclosure relates to a method and device for positioning out-of-network users and an electronic device, and relates to the technical field of communication networks. The method comprises: counting communication signaling, and communication behavior data, location data and working time characteristics of in-network users in a specific time interval; filtering in-network users using the communication signaling, the communication behavior data, the location data and the working time characteristics to obtain target in-network users with different logistics occupations in the specific time interval; filtering candidate base stations where the target in-network users and out-of-network users come into contact; and inputting cumulative communication behavior data of the out-of-network users and the target in-network users at the candidate base stations into a trained out-of-network user residence model to obtain the residence of the out-of-network users in different time periods. In this way, the problem that the social circle cannot be effectively extracted and the residence of the out-of-network users cannot be accurately calculated can be avoided, and the residence of the out-of-network users can be accurately positioned.

The present application belongs to the technical field of insurance product pricing, and more specifically, relates to a method for optimizing dynamic risk pricing of insurance products by integrating multiple factors; collecting multi-source information data and performing data preprocessing; constructing a spatiotemporal cumulative risk index based on travel trajectory analysis of on-board OBD data, combining high-risk road sections, driving behavior, and environmental factors; using a graph convolutional network to model the risk transfer effect in the social circle of car owners, capturing the mutual influence between car owners in the social circle; using a neural network based on multi-task learning to comprehensively process dynamic risks, social risk characteristics, and other relevant data; optimizing the prediction of the probability of accidents and the amount of losses through a shared network layer; and generating insurance pricing in combination with external market data to ensure the comprehensiveness and accuracy of insurance pricing; solving the blind spots in traditional pricing methods, making insurance pricing more intelligent and accurate, and able to reflect the actual risk status of car owners in real time.

The invention discloses an applet system for displaying business cards and products of enterprises, through the design of a business card holder function module, a ranking module and a shopping mall management module, own business card information can be shared to a friend circle and a WeChat group through one key, fission type propagation is realized, a social circle is expanded, the page view of the business cards is high, and the business cards are ranked in the front; the business card can be seen by more people, promotion of enterprise products is facilitated, each product has an exclusive two-dimensional code, the selling point of the product can be directly watched through a video by scanning the code, the cognition depth of a user on the product is improved, transaction is facilitated, the exclusive two-dimensional code of the product can be quickly shared to a friend circle or a WeChat circle, one-to-many propagation is realized, and the user experience is improved. The product sales probability is improved; the sales channel is expanded; through the design of the enterprise information management unit, customer information and customer follow-up records can be added, and customers under the names of demission employees can be transferred, so that the mastering right of enterprises to customer data is ensured, and customer loss is reduced.

The invention provides a method for recognizing a person based on a user photo and electronic equipment, relates to the technical field of terminals, and can improve the accuracy of person recognition. The method comprises the steps that the electronic equipment obtains N first character IDs from a picture library application, the N first character IDs correspond to N first characters, and each first character in the N first characters has various social relations with others in the picture library application; the electronic equipment determines a target person ID from the N first person IDs, and determines a person corresponding to the target person ID as a target person; wherein the target person meets one or more of the following conditions; the gender of the target person is the same as the user gender pre-stored in the electronic equipment, the age of the target person is matched with the user age pre-stored in the electronic equipment, and the target person establishes a social relationship with the plurality of social circles.

The invention provides a student behavior management method and system based on AI identification. The method comprises the following steps: acquiring a monitoring image of a target student, and performing AI identification on the monitoring image to obtain personal state data, character type data and social circle data corresponding to the target student; obtaining an in-school state trend of the target student according to the personal state data and the character type data; and when the school state trend is a descending trend, bullying source analysis is performed on the social circle data to generate a bullying traceability result, and the bullying traceability result is used for feeding back the students who have the social bullying behavior with the target student to the school. According to the method provided by the invention, the bullying person can be identified more accurately by analyzing the monitoring image.

The present application belongs to the technical field of e-commerce user behavior prediction, and particularly relates to a purchase behavior linkage prediction method and system based on e-commerce user social relationship mining. The method acquires user social data, user purchase data and commodity metadata information, constructs a user social network into a weighted directed graph structure, and calculates the influence parameter of each user node. Then, the purchase behavior trajectory vector is constructed according to the purchase data, the user is clustered and divided, and the linkage factor is determined according to the purchase time difference, the commodity price interval and the purchase frequency. Further, the linkage prediction model is constructed by combining the social influence, the linkage factor and the user consumption characteristics, the probability of the target user occurring the same purchase behavior after being influenced by the social circle is output, the fitting deviation of the prediction result is evaluated, and the model parameters are updated based on the feedback data. The scheme can improve the recognition and prediction accuracy of the social driving purchase behavior.

The invention relates to the technical field of ticket service pushing, and discloses a ticket service system information pushing method based on artificial intelligence. The method comprises the following steps: S1, acquiring ticket business purchase big data of multiple types of users, extracting user portrait features, ticket business attribute features and purchase time sequence features in the ticket business purchase big data, clustering adjacent ticket business venues into venue areas by adopting a mean shift clustering method, performing dimensionality reduction on text features through an automatic encoder, extracting visual features through a pre-trained ResNet-50 model, and obtaining a ticket business buying result; and constructing a multi-dimensional first training data set, and training a first ticket business recommendation model based on the graph neural network by using the first training data set. The ticket business system information pushing method based on artificial intelligence has the advantages that the limitation of only depending on ticket buying records is broken through by incorporating potential influence factors such as user ticket business browsing tracks and associated user ticket buying preferences in a social relation chain, user short-term interest fluctuation and social circle layer influence can be more comprehensively captured, and the like.

The invention discloses a vehicle-mounted social circle building method and device, equipment and a medium. The method comprises the following steps: acquiring a current position of a vehicle or a navigation destination of vehicle-mounted navigation; when the current position or the navigation destination is in a pre-defined circle area, adding a user social account corresponding to the vehicle to a social group corresponding to the circle area; wherein the social group is used for the joining users to publish and receive messages, so that the social accounts of the users of which the vehicle positions are in the same circle area and the users of which the navigation driving is about to reach the circle area are concentrated in the same social group; therefore, the users in the same social group can share and receive the related information in the corresponding circle region in real time, and real-time and accurate reference information is provided for the users who drive to reach the circle region.

The invention provides a method and device for establishing a relation between objects, electronic equipment, a computer readable storage medium and a computer program product, and the method comprises the steps: in a virtual social circle of a first object, displaying an object identifier of at least one second object belonging to the virtual social circle, and displaying at least one expression element; in response to an emission operation for a target expression element in the at least one expression element, emitting the target expression element to the at least one object identifier; and in response to the target expression element hitting the target object identifier, establishing a relation between the first object and a target second object corresponding to the target object identifier. Through the method and the device, the establishment efficiency of the relation between the objects and the diversity of establishment modes can be improved.

The technical problem to be solved by the present invention is to use the historical behavior information of new e-commerce users on the content platform to extract user interest preferences and complete personalized user cold start recommendations in response to the defects of the existing technology. To achieve the above purpose, the present invention is based on a cold start recommendation method, device and storage device for user interest migration, and uses the behavioral footprints of new users on other platforms to construct user group attributes. Then, based on the theory that people are divided into groups, clustering is performed according to user interest characteristics, and users are divided into multiple subsets. Then, based on the historical behavior of users in each subset, product preferences are calculated as candidate sets for this type of users. By adopting the technical solution, it is possible to more accurately predict products that new users may like, so that new users can find products that may be of interest to them more quickly, and find suitable social circles for themselves more quickly, so that new users can get a more friendly user experience on the new platform, thereby improving the retention rate of new users.

The invention discloses an integrated blessing listing display and social interaction system which breaks through the limitation that traditional blessing lists are single in content and deficient in interaction, each listing can display a plurality of blessing contents in turn through a multi-user content intelligent management module, users can publish personal wishes and browse blessing information of others, and the user experience is improved. The sense of participation is obviously improved; information health is guaranteed by a content auditing and safety mechanism of the system, and the user does not need to worry about contact with illegal content; historical content is permanently reserved in the cloud end and can be looked up at any time, and the problems that a traditional paper / wood hang tag is prone to being lost and difficult to trace are solved. According to the social mode taking the blessing content as the link, strangers are contacted due to common willingness, the isolated state of permissions and willingness of a traditional blessing place is broken, the user is helped to establish an interest-oriented social circle, and the blessing behavior is converted into an emotional resonance bridge from one-way expression.

The application relates to the technical field of social data processing, in particular to a large language model social data label attribute generation method for personalized recommendation, which comprises the following steps: constructing a multi-modal social feature correlation matrix; generating an initial label set adapted to a social scene; realizing multi-dimensional quantification of label attributes; constructing a label hierarchical relationship through an improved semantic dependency tree, calculating a time decay coefficient based on a behavior occurrence time interval, and taking the user proportion of the same label in a core social circle as a group correlation value. Through the construction of the multi-modal social feature correlation matrix, the complexity of multi-modal data in the social scene can be adapted, and the generated label system is more suitable for the multi-element characteristics of social data. Through the improved semantic dependency tree, the label hierarchical relationship and the time decay coefficient are automatically constructed, and the depth analysis ability of social semantics is improved.

The invention provides a real-time analysis method for sales data of coded products, and the method comprises the steps: tracking an original ticket buying store code and a prize claiming store code of a customer, and forming a customer ticket buying social network store code list in combination with identity lists of other customers who buy tickets in the same bank; analyzing a ticket buying frequency change value of the customer in the original store based on the ticket buying store code segment before redeeming and the ticket buying store code segment after redeeming, and extracting the ticket buying frequency change of the customer who buys the ticket in the same bank in the social circle layer of the customer in the store comparison file; on the basis of the relevance between the decreasing amplitude of the original store ticket buying frequency after the prize claim and the original store ticket buying frequency before the prize claim and the increasing amplitude of the occurrence frequency of the prize claim store code in the ticket buying code segment after the prize claim, determining that the customer has a loss sign of transferring from the original ticket buying store to the prize claim store; and after the loss sign is confirmed, the forward accumulation trend of the contribution deviation value of the expected contribution value and the actual contribution value of the store prize claiming in the continuous statistical period is identified.