Enterprise cooperation relationship mining model training method, friend recommendation method and system

Abstract

The invention provides an enterprise cooperation relationship mining model training method and a friend recommendation method and system, and the method comprises the steps: firstly integrating multi-source data, constructing a structured enterprise label through optical character recognition and a natural language processing technology, and constructing an enterprise relationship graph with an enterprise as a node and a plurality of business relationships as edges on the basis of the structured enterprise label; then, node pairs with business exchange are selected from the atlas as positive samples, node pairs which do not exchange but meet specific conditions are selected as negative samples, and a training set is formed; a model based on an inductive graph neural network encoder, a multi-relation graph attention layer, a cross-relation fusion layer and a link prediction layer is adopted for training, and parameters are optimized through a marginal contrast loss function. During application, a trained model is utilized to calculate the cooperation probability between a target enterprise and an unknown enterprise, and potential cooperation partners are recommended to a user through instant messaging according to the cooperation probability. According to the invention, the efficiency and the intelligent level of business expansion of enterprises are obviously improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, and in particular to an enterprise cooperation relationship mining model training method, a friend recommendation method and system. BACKGROUND

[0002] In the current enterprise operation and expansion process, instant messaging (IM) software has become an indispensable collaboration tool. However, the built-in friend recommendation function of existing instant messaging software (such as WeChat for Enterprise, DingTalk, etc.) is mostly limited to the personal social dimension, such as relying on mobile phone address book synchronization, colleague relationship within the same organizational structure, geographical proximity or fuzzy search matching. This kind of recommendation mechanism essentially serves acquaintanceship or internal collaboration, and is seriously out of touch with the core business needs of enterprise users seeking external potential business partners and building business upstream and downstream relationships. On the other hand, traditional ways of business expansion, such as attending offline exhibitions, relying on industry association referrals, making telephone sales or advertising, generally have problems such as low efficiency, high cost, information asymmetry and difficulty in continuous scaling, especially the inability to effectively discover and utilize the large number of hidden business relationships in the market. This leads to the contradiction of "information island" and "information overload" coexisting for enterprises: valuable connections are deeply buried in massive data and difficult to discover, while a large amount of screening cost is wasted in invalid information. Therefore, the existing technology lacks a solution that can deeply integrate enterprise multi-dimensional data, intelligently analyze potential business associations between enterprises, and accurately and actively recommend through instant messaging, a high-frequency application scenario. SUMMARY

[0003] In view of this, the embodiments of the present application provide an enterprise cooperation relationship mining model training method, a friend recommendation method and system to eliminate or improve one or more defects in the prior art, and solve the problems of low efficiency, high cost and weak information processing capability in the prior art in the mining of potential business relationships in the business expansion scenario.

[0004] In one aspect, the present application provides an enterprise cooperation relationship mining model training method, which comprises the following steps: obtaining raw data containing enterprise registration information, transaction history data, instant messaging history data and enterprise upstream and downstream relationship data based on a multi-source database and cleaning and storing, the raw data including picture or text format; text recognition and keyword extraction are performed on the raw data based on optical character recognition and natural language processing, structured enterprise labels for various characteristic attributes are constructed, transaction relationships are mined according to the transaction data, communication relationships are mined according to the instant messaging history data, and business association relationships are mined according to the enterprise upstream and downstream relationship data; an enterprise relationship graph is constructed, taking enterprises as nodes, the enterprise labels as node attributes, and the transaction relationships, the communication relationships and the business association relationships as edges; In the enterprise relationship graph, node pairs having business transactions are constructed as positive sample pairs, and node pairs not having business transactions but satisfying preset sampling constraint conditions are constructed as negative sample pairs; the positive sample pairs and the negative sample pairs are constructed as a training sample set; An initial enterprise relationship prediction network based on an inductive graph neural network encoder, a multi-relation graph attention layer, a cross-relation fusion layer and a link prediction layer is obtained; the inductive graph neural network encoder performs feature encoding on the enterprise relationship graph to obtain embedding representations of the nodes, and the embedding representations of the nodes in the positive sample pairs or the negative sample pairs are connected and output the prediction of the cooperation probability score through the multi-relation graph attention layer, the cross-relation fusion layer and the link prediction layer; a marginal contrast loss is constructed to update the parameters of the initial enterprise relationship prediction network, and a target enterprise cooperation relationship mining model is obtained.

[0005] In some embodiments, the enterprise labels include basic labels for marking the industry category and regional distribution of the enterprise, operating feature labels for marking the core business content, dynamic behavior labels for marking the transaction mode and cooperation preference, network relationship labels for marking the enterprise community attributes, and predictive analysis labels for marking the transaction risk.

[0006] In some embodiments, the business transactions include enterprise pairs having business transactions, enterprise user pairs connected in the instant messaging history, and enterprise relationship pairs adding friends and having business interactions, and the business transactions are required to be within a preset time period; the preset sampling constraint conditions include industry constraint, regional constraint, enterprise label approximation constraint and topological proximity constraint of the enterprise relationship graph.

[0007] In some embodiments, the inductive graph neural network encoder adopts a GraphSAGE model; the multi-relation graph attention layer extracts embedding representations for business cooperation relationships, friend relationships, supply chain relationships and industry relationships respectively; the cross-relation fusion layer includes a fusion layer and a fully connected layer; and the link prediction layer adopts a multi-layer perceptron dot product model.

[0008] In some embodiments, the calculation formula of the marginal contrast loss is: ; wherein, represents the margin contrast loss, and is the positive sample pair, and is the negative sample pair, represents the predicted value of the cooperation probability between the negative sample pairs, represents the predicted value of the cooperation probability between the positive sample pairs; margin represents a margin parameter, and N represents the number of samples.

[0009] In another aspect, the present application also provides a business friend recommendation method, the method comprising the following steps: Based on the multi-source database, the original data containing enterprise registration information, transaction history data, instant messaging history data and enterprise upstream and downstream relationship data are obtained and cleaned and stored, and the original data includes picture or text format; Based on optical character recognition and natural language processing, the original data is subjected to text recognition and keyword extraction, and structured enterprise labels for various feature attributes are constructed; transaction relationships are mined according to the transaction data, communication relationships are mined according to the instant messaging history data, and business association relationships are mined according to the enterprise upstream and downstream relationship data; an enterprise relationship graph is constructed, taking enterprises as nodes, taking the enterprise labels as node attributes, and taking the transaction relationships, the communication relationships and the business association relationships as edges; For a target enterprise node, the enterprise relationship graph is input into the target enterprise cooperation relationship mining model in the enterprise cooperation relationship mining model training method according to any one of claims 1 to 5, to output the cooperation probability score between the target enterprise node and the enterprise node without business transactions; The first set number of candidate enterprise nodes with the highest cooperation probability score are screened out, and the instant messaging system sends the recommended business card about the candidate enterprise nodes to the target enterprise node according to the set frequency.

[0010] In some embodiments, the method further comprises: Monitoring the feedback results of the target enterprise node to the recommended business card, the feedback results including: ignoring the business card, adding the business card, whether to accept information after adding, whether to interact after adding, and interaction frequency; Updating the enterprise relationship graph using the feedback results, and optimizing and updating the target enterprise cooperation relationship mining model based on the updated enterprise relationship graph.

[0011] In another aspect, the present application also provides a business cooperation relationship mining system, comprising a processor, a memory and a computer program / instruction stored on the memory, the processor being configured to execute the computer program / instruction, and the system implementing the steps of the above method when the computer program / instruction is executed.

[0012] In another aspect, the present application also provides a computer readable storage medium, having a computer program / instruction stored thereon, the computer program / instruction being executed by a processor to implement the steps of the above method.

[0013] In another aspect, the present application also provides a computer program product, comprising a computer program / instruction, the computer program / instruction being executed by a processor to implement the steps of the above method.

[0014] The business cooperation relationship mining model training method, friend recommendation method and system of the present application integrate enterprise registration information, transaction history, instant messaging records and known upstream and downstream relationships and other multi-source heterogeneous data to construct an enterprise relationship graph rich in semantic information. The graph takes enterprises as nodes, the attributes of which are composed of structured labels extracted from original data by optical character recognition and natural language processing technology, and the edges represent various actual relationships such as transactions, communications and business associations. On this basis, positive and negative training sample pairs are constructed from the graph, and an inductive graph neural network model is trained, the differentiated importance of different types of business relationships is accurately captured by using a multi-relation graph attention layer, and these information is aggregated by a cross-relation fusion layer, and finally a cooperation probability is output by a link prediction layer. The training process uses a margin contrast loss function, aiming to widen the score gap between positive and negative sample pairs, thereby improving the discrimination ability of the model. The present application converts scattered enterprise data into a dynamic knowledge system that can be deeply learned by machines, and can actively and accurately identify partners with high cooperation potential from a large number of enterprises, greatly improving the efficiency and accuracy of business expansion.

[0015] Additional advantages, objects, and features of the application will be set forth in part by the description that follows, and will become apparent to those skilled in the art upon examination of the following figures and detailed description thereof or can be learned by practice of the application. The objects and other advantages of the application can be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.

[0016] It will be understood by those skilled in the art that the objects and advantages of the present application can be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings. BRIEF DESCRIPTION OF DRAWINGS

[0017] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, are not intended to limit the scope of the invention. In the drawings: Figure 1 This is a flowchart illustrating the training method for an enterprise cooperation relationship mining model according to an embodiment of the present invention.

[0018] Figure 2 This is a flowchart illustrating the enterprise friend recommendation method according to another embodiment of the present invention.

[0019] Figure 3 This is a logical diagram of an enterprise friend recommendation scheme according to another embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the embodiments and accompanying drawings. Here, the illustrative embodiments and descriptions of this invention are used to explain the invention, but are not intended to limit the invention.

[0021] It should also be noted that, in order to avoid obscuring the invention with unnecessary details, only the structures and / or processing steps closely related to the solution according to the invention are shown in the accompanying drawings, while other details that are not closely related to the invention are omitted.

[0022] It should be emphasized that the term "including / comprises" as used herein refers to the presence of a feature, element, step, or component, but does not exclude the presence or addition of one or more other features, elements, steps, or components.

[0023] Existing instant messaging software's friend recommendation function in enterprise scenarios is primarily based on personal social networks (such as address books and colleague relationships), and its recommendation logic is severely out of sync with the business expansion needs of enterprises. Enterprises still heavily rely on traditional methods such as offline channels and manual searches to find potential partners, which suffers from inefficiency, high costs, lack of sustainability, and inability to uncover implicit business relationships. This application aims to solve the aforementioned pain points by providing a technical solution that integrates multi-dimensional enterprise data, intelligently analyzes potential business connections between enterprises, and utilizes the high-frequency application of instant messaging software for precise recommendations and proactive outreach.

[0024] Specifically, this invention provides a method for training a corporate partnership mining model, such as... Figure 1 As shown, the method includes the following steps S101~S104: Step S101: Based on the multi-source database, obtain raw data containing enterprise registration information, transaction history data, instant messaging history data, and enterprise upstream and downstream relationship data, clean and store the raw data, which includes image or text format.

[0025] Step S102: Text recognition and keyword extraction are performed on the raw data based on optical character recognition and natural language processing, structured enterprise labels for various characteristic attributes are constructed, transaction relationships are mined from transaction data, communication relationships are mined from instant messaging history data, and business association relationships are mined from enterprise upstream and downstream relationship data; an enterprise relationship graph is constructed, with enterprises as nodes, enterprise labels as node attributes, and transaction relationships, communication relationships, and business association relationships as edges.

[0026] Step S103: In the enterprise relationship graph, node pairs with business transactions are constructed as positive sample pairs, and node pairs that do not have business transactions but meet the preset sampling constraints are constructed as negative sample pairs; the positive sample pairs and the negative sample pairs are constructed as a training sample set.

[0027] Step S104: An initial enterprise relationship prediction network based on an inductive graph neural network encoder, a multi-relation graph attention layer, a cross-relation fusion layer, and a link prediction layer is obtained; the inductive graph neural network encoder encodes the features of the enterprise relationship graph to obtain the embedding representation of each node, and the embedding representation of the nodes in the positive sample pair or the negative sample pair is connected and then outputted through the multi-relation graph attention layer, the cross-relation fusion layer, and the link prediction layer to predict the cooperation probability score; a marginal contrast loss is constructed to update the parameters of the initial enterprise relationship prediction network, and a target enterprise cooperation relationship mining model is obtained.

[0028] In step S101, in order to accurately predict the cooperation relationship between enterprises, multi-dimensional data is integrated, and information from different channels reflecting different aspects of enterprises is combined. Enterprise registration information is usually obtained from a business database and includes basic static attributes of the enterprise, such as enterprise name, registered address, legal representative, registered capital, scope of business, etc. This is the core of building an enterprise basic portrait. Transaction history data is mainly obtained from the ERP (Enterprise Resource Planning) and CRM (Customer Relationship Management) systems of the enterprise. This is a proof of the existence of substantial business transactions between enterprises, including purchase / sale orders, contracts, invoices, and payment records. Instant messaging history data is obtained from the internal IM (Instant Messaging) platform and records the communication behavior between enterprise employees, such as added friend relationships, chat frequency, and group common members. This reflects the degree of communication between enterprises. Enterprise upstream and downstream relationship data may be obtained from enterprise self-labeling, supply chain platforms, or public reports, and clearly indicates the supplier-customer relationship between enterprises.

[0029] After obtaining the raw data, since the data sources are diverse and the formats are different, such as business licenses being pictures, contracts being text, and transaction records being structured data, the raw data needs to be cleaned, standardized, and aligned, and stored for subsequent analysis.

[0030] Step S102 is a key step of converting raw data into structured knowledge, using a graph model to depict entities and their relationships. It can intuitively record the complex relationship network between enterprises.

[0031] The picture data such as business license is analyzed by using optical character recognition technology, and the key text information (such as business scope) is converted into processable text data; then, through natural language processing technology, these texts are deeply analyzed, and operations including word segmentation, entity recognition, keyword extraction and industry classification are performed, so as to construct multi-dimensional structured feature labels for each enterprise, such as "industry: intelligent manufacturing", "main product: industrial robot", "scale: medium" and the like, which together constitute the attribute vector of the node in the graph. On this basis, the system mines the association between enterprises from different dimensions to construct the edges of the graph: based on the transaction records (such as orders, contracts) in the ERP and CRM systems, the transaction relationship is mined; based on the friend relationship and interaction frequency of instant messaging platforms, the communication relationship is mined; according to the known supply chain directory or enterprise self-description data, the business association relationship is mined, such as explicit upstream and downstream, investment relationship. Finally, all these information is imported into a graph database such as Neo4j database, forming a large heterogeneous information network with enterprises as nodes and various business relationships as edges. This network not only statically depicts the attributes and direct associations of enterprises, but also its graph structure itself contains rich implicit business logic and community features that can be mined by machine learning models, laying a solid data foundation for subsequent intelligent prediction.

[0032] In some embodiments, the enterprise labels include basic labels for marking the industry category and regional distribution of the enterprise, operating feature labels for marking the core business content, dynamic behavior labels for marking the transaction mode and cooperation preference, network relationship labels for marking the enterprise community attributes, and predictive analysis labels for marking the transaction risk.

[0033] The basic label is usually directly mapped from the industry code and registered address in the enterprise registration information, such as "industry: manufacturing" and "region: Yangtze River Delta". The operating feature label is mainly obtained by natural language processing technology on the "business scope" text in the business license, such as "core business: precision bearing manufacturing". The dynamic behavior label is the behavior mode obtained by analyzing historical transaction data and contract records, such as "transaction mode: high frequency small amount" and "cooperation preference: state-owned enterprise". The network relationship label is based on the constructed enterprise relationship graph, and the centrality and clustering coefficient of the node are analyzed by using graph calculation algorithm, or it is identified by community discovery algorithm, such as "network centrality: high" and "belonging community: automobile industry chain cluster". The predictive analysis label is a derivative index generated by inputting all the above labels and time series data into a special machine learning prediction model to infer the future state of the enterprise, such as "transaction risk: medium" and "growth potential: high". The label system collectively constitutes a three-dimensional and quantitative digital portrait of the enterprise.

[0034] In step S103, positive and negative sample pairs are constructed based on the enterprise relationship graph. The positive sample pair is the enterprise pair that has actually cooperated in the historical data. When constructing the negative sample pair, based on the preset sampling constraint condition, the negative sample that is difficult to distinguish is constructed, that is, the enterprise that looks like it should cooperate but actually does not cooperate.

[0035] Specific business cooperation includes enterprise pairs that have business transactions, enterprise user pairs that have established connections in instant messaging history, and enterprise relationship pairs that have added friends and have business interactions, and the business cooperation is required within a preset time period. The preset sampling constraint conditions include industry constraint, regional constraint, enterprise label approximation constraint and topological proximity constraint of the enterprise relationship graph. Among them, the enterprise label approximation constraint requires the similarity of the enterprise labels between the negative sample pairs to be higher than a set value, and the topological proximity constraint requires the node distance between the negative samples in the enterprise relationship graph to be less than a set hop number.

[0036] In step S104, the role of the inductive graph neural network encoder is to read the structure and node attributes of the entire graph, and generate an embedded representation in the form of a vector for each enterprise node. This vector combines all the information of the enterprise and its network location features. Its inductive property means that it can generate a vector for new enterprises that have not been seen during training, ensuring the scalability of the model.

[0037] The multi-relation graph attention layer is the core of the model and can identify the importance of different relations. The multi-relation graph attention layer is a graph neural network component that simultaneously considers the relation type and node content: it first splits each edge into several subgraphs according to the relation name, then independently runs a multi-head attention mechanism within each relation to calculate the weight coefficient of who should pay more attention to whom, thereby obtaining a relation-specific node embedding. In this way, information in different semantic dimensions such as supply chain, transaction, friends, and the same industry can be modeled separately, and finally these vectors in the perspective of relations are weighted and summarized through a fusion layer, so that the model can not only distinguish the difference between cooperation and chatting, but also comprehensively judge the possibility of business transactions between two companies. In some embodiments, the inductive graph neural network encoder adopts a GraphSAGE model; the multi-relation graph attention layer extracts embedding representations for business cooperation relations, friend relations, supply chain relations, and the same industry relations, respectively.

[0038] The cross-relation fusion layer fuses the multiple embedding representations output by the multi-relation graph attention layer for an enterprise node to form a more comprehensive and powerful node representation. The cross-relation fusion layer includes a fusion layer and a fully connected layer.

[0039] The link prediction layer receives the final vector representations of two enterprises, performs calculations, and outputs a cooperation probability score between 0 and 1. The link prediction layer adopts a multi-layer perceptron dot product model.

[0040] During the model training process, the margin contrast loss is the learning goal of the model. It forces the score of the positive sample pair to be significantly higher than that of the corresponding negative sample pair. The process of adjusting the model parameters is to constantly widen this score difference. For example, during training, the model will process both positive sample pairs and negative sample pairs. The loss function will calculate that if the model scores the positive sample as 0.9 and the negative sample as 0.7, although the order is correct, the score difference is not large enough, for example, the target score difference is 0.5, the model will be punished and adjust the internal parameters accordingly to strive to push the positive sample score to 0.95 and the negative sample score to 0.4 in the next round.

[0041] In some embodiments, the calculation formula of the margin contrast loss is: ; Wherein, represents the margin contrast loss, and is a positive sample pair, and is a negative sample pair, represents the predicted value of the cooperation probability between the negative sample pairs, represents the predicted value of the cooperation probability between the positive sample pairs; margin represents the margin parameter, and N represents the number of samples.

[0042] In another aspect, the present application also provides a business friend recommendation method, as shown in the following steps S201-S204: Figure 2 Step S201: Obtain raw data containing business registration information, transaction history data, instant messaging history data and business upstream and downstream relationship data based on a multi-source database and clean and store, the raw data including picture or text format.

[0043] Step S202: Perform text recognition and keyword extraction on the raw data based on optical character recognition and natural language processing, and construct structured business labels for various feature attributes; mine transaction relationships according to transaction data, mine communication relationships according to instant messaging history data, and mine business association relationships according to business upstream and downstream relationship data; construct a business relationship graph, taking enterprises as nodes, enterprise labels as node attributes, and transaction relationships, communication relationships and business association relationships as edges.

[0044] Step S203: For a target enterprise node, input the enterprise relationship graph into the target enterprise cooperation relationship mining model in the above enterprise cooperation relationship mining model training method to output the cooperation probability score between the target enterprise node and the enterprise node without business transactions.

[0045] Step S204: Screen out the first set number of candidate enterprise nodes with the highest cooperation probability score, and send the recommended business card of the candidate enterprise node to the target enterprise node through the instant messaging system according to the set frequency.

[0046] Part of steps S201 and S202 can refer to the description of steps S101 and S102, and the purpose is to establish a business relationship graph based on data mining for subsequent recognition processing.

[0047] ​Step S203 is the core reasoning and application stage of the present application. In this step, the trained target enterprise cooperation relationship mining model is deployed to the production environment for performing actual prediction tasks. Specifically, the system inputs the entire, real-time updated enterprise relationship graph together with the specified target enterprise node (i.e., the enterprise for which partners are to be found) into the model. The model, by virtue of its complex pattern recognition ability learned in the training stage, performs forward propagation calculation on the graph: first, it generates embedding representations for all nodes in the graph (including the target enterprise) that contain rich structural and attribute information through its internal graph neural network encoder; then, the model iterates through the node pairs formed by the target enterprise and all enterprise nodes in the graph that have no business transactions (i.e., enterprises that have not established direct transactions or friend relationships), and calculates their cooperation probability scores one by one via the multi-relation attention layer, the fusion layer, and the link prediction layer. This score is a continuous value between 0 and 1, quantifying the likelihood of the two enterprises establishing a cooperative relationship in the future from the model's perspective. This process realizes the intelligent and efficient screening of the most potential cooperation targets from the massive and complex enterprise network data.

[0048] Step S204 is a key step for converting intelligent prediction results into actual business value, focusing on recommendation strategies and user experience. This step first sorts all probability scores output by step S203 and selects the top first set number of enterprises with the highest scores, such as the top ten as candidate enterprise nodes. Subsequently, the system encapsulates the information of these candidate enterprises into a recommendation card and pushes it to the relevant employees of the target enterprise through the interface of the instant messaging system. The form can include a possible friend list or a specific recommendation message card. To ensure the effectiveness of the recommendation and avoid disturbing the user, this step introduces a set frequency to control the push frequency. This means that the recommendation is not a one-time bombardment, but follows a strategic rhythm, such as pushing on a weekly or monthly basis, or intelligently linking with user activity and business scenarios, such as the user searching for related products. This design not only ensures the proactive reach of business opportunities but also fully respects user experience, allowing the recommendation system to naturally integrate into the user's daily workflow, thereby efficiently and cost-effectively facilitating new business connections.

[0049] In some embodiments, the method further includes steps S205 and S206: Step S205: Monitor the feedback results of the target enterprise node on the recommendation card, including ignoring the card, adding the card, whether accepting information after adding, whether interacting after adding, and interaction frequency.

[0050] Step S206: Update the enterprise relationship graph using the feedback results and optimize the target enterprise cooperation relationship mining model based on the updated enterprise relationship graph.

[0051] Step S205 is a key data collection link for building the self-evolution capability of the system. The core of this step is to finely monitor and quantify the whole-link behavior of the user after receiving the recommendation. The feedback result recorded by the system is a signal sequence from shallow to deep, with increasing value: ignoring the business card is a direct negative feedback, indicating that this recommendation may not match the user's needs; adding the business card is a strong positive interest signal; and whether to accept the information after adding and the subsequent interaction frequency further measure the activity and quality after relationship establishment, and high-frequency deep interaction is the highest verification of the value of cooperative relationship. By systematically capturing these behaviors, the system converts the originally subjective and fuzzy user intention into structured and quantifiable data, providing important real-time feedback samples for subsequent model optimization.

[0052] Step S206 realizes the core closed-loop optimization logic of the system. In this step, the feedback results collected from S205 are used to dynamically update the enterprise relationship graph. For example, when two enterprise users successfully add as friends through recommendation, an instant messaging friend edge will be added between the two corresponding nodes in the graph; if they have business interactions afterwards, the weight or attribute of the edge will be enhanced. This real-time updated and more rich graph constitutes the new data basis for model optimization. Subsequently, the system can use these new positive samples (such as enterprise pairs that successfully establish connection and interact) and negative samples (such as recommendations that are continuously ignored) to perform incremental training or fine-tuning on the deployed target enterprise cooperation relationship mining model. This enables the model to continuously learn the latest user behavior patterns and constantly correct its prediction bias, so that the entire system forms an enhanced cycle of recommendation-feedback-learning-more accurate recommendation, and becomes more and more intelligent over time.

[0053] On the other hand, the present application also provides an enterprise cooperation relationship mining system, comprising a processor, a memory and a computer program / instructions stored on the memory, wherein the processor is configured to execute the computer program / instructions, and when the computer program / instructions are executed, the system implements the steps of the above method.

[0054] On the other hand, the present application also provides a computer readable storage medium having a computer program / instructions stored thereon, wherein the computer program / instructions are executed by a processor to implement the steps of the above method.

[0055] On the other hand, the present application also provides a computer program product comprising a computer program / instructions, wherein the computer program / instructions are executed by a processor to implement the steps of the above method.

[0056] The present application will be described below in conjunction with a specific embodiment: The present embodiment provides an intelligent and automated enterprise friend recommendation scheme, such asFigure 3 As shown, it aims to accurately predict and recommend the most likely business partners by analyzing internal and platform data, and to improve business connection efficiency.

[0057] The embodiment includes the following steps: 1) Data collection and integration: Collect and integrate data from multiple sources, including user-added enterprise friends, a large enterprise registration database (including detailed business scope), business data between enterprises (such as orders, contracts, invoices, etc.), and known upstream and downstream relationships of enterprises. Data comes from the ERP (Enterprise Resource Planning) and CRM (Customer Relationship Management) systems of enterprises, or is extracted, cleaned, and stored from related enterprise collaboration platforms; at the same time, relevant business scope is obtained from business license pictures.

[0058] 2) Enterprise portrait construction: Based on the above data, a multi-dimensional feature portrait is constructed for each enterprise, including industry labels, product / service keywords, business size, relationship network features, etc. Process text data, and extract keywords and industry classification and related features through natural language processing (NLP) technology.

[0059] Specifically, based on NLP natural language processing technology, keyword extraction and industry classification are performed on the business scope text, multi-dimensional feature quantization is performed to form a structured enterprise label system, and enterprise state features in the enterprise label system are updated in real time based on business data.

[0060] The enterprise portrait construction method uses a multi-level label system to build, including: structured basic labels: industry classification, regional distribution, and other static attributes based on rule mapping; NLP extracted business feature labels: core business features are mined from business scope text through multi-algorithm fusion keyword extraction technology; dynamic behavior labels: transaction patterns, cooperation preferences, and other behavior features based on business data analysis; graph structure network labels: centrality, community attributes, and other network features calculated from enterprise relationship graphs; predictive analysis labels: growth, risk level, and other predictive indicators derived through machine learning models.

[0061] 3) Relationship graph construction: Use graph database technology to construct an enterprise knowledge graph with enterprises as nodes and various relationships as edges. Edge relationships can include upstream and downstream, cooperation, same industry, and added friends. For example, static relationships in the relationship graph include upstream and downstream associations, same industry relationships, and same region relationships. Dynamic relationships in the relationship graph include business frequency and instant messaging interaction intensity. Displayed relationships in the relationship graph include added friend relationships, etc.

[0062] 4) Machine learning model training: using historical business data as positive samples, training a prediction model. The model learns the feature patterns of business relationships between enterprises. The model includes an inductive graph neural network encoder, a multi-relation graph attention layer, a cross-relation fusion layer, and a link prediction layer.

[0063] In the relationship graph, construct positive and negative sample pairs. Positive samples are pairs of enterprise nodes that have actually cooperated in historical data, including pairs of enterprises that have business data, i.e., actual business records such as orders, contracts, and invoices; pairs of enterprise users that have successfully established connections in instant messaging systems; pairs of enterprises that have high-value interaction relationships after adding friends; and pairs of enterprises that have effective business interactions. At the same time, set a time window constraint to limit effective business relationships within the training time window. Negative sample pairs refer to pairs of enterprise nodes that have not cooperated in historical data but have potential correlation possibilities. Select pairs of enterprises in the same industry that have no business cooperation as same-industry negative samples, pairs of enterprises in the same region that have no cooperation as same-region negative samples, pairs of enterprises with high feature vector similarity but no actual relationship as feature similarity negative samples, and pairs of enterprises with a distance less than a set number of hops in the graph but no direct business relationship as topological proximity negative samples.

[0064] During model training, construct a margin contrast loss to make the cooperation probability score of positive samples significantly higher than that of negative samples.

[0065] The calculation formula of the margin contrast loss is: ; Wherein, represents the margin contrast loss, and is a positive sample pair, and is a negative sample pair, represents the predicted value of the cooperation probability between negative sample pairs, represents the predicted value of the cooperation probability between positive sample pairs; margin represents the margin parameter, and N represents the number of samples.

[0066] 5) Potential partner prediction and recommendation: for a target enterprise, use the trained model to calculate the "cooperation probability" score among its unconnected enterprises, and select the top several enterprises with the highest scores.

[0067] 6) Presentation of recommendation results: in the interface of instant messaging software, such as the "Discovery" page and the "Friend Addition" page, present the recommendation results to the employees of the target enterprise in the form of "Recommended Business Cards".

[0068] The recommended information includes the user ID, name, and avatar of the recommended user, the enterprise ID, name, and business scope of the enterprise information; and the push location can be set in the possible enterprise friend push box.

[0069] 7) Recommendation frequency: set the recommendation period and frequency through a timing task or chat room subscription, to avoid disturbing some unnecessary enterprise users. Specifically, the triggering opportunity can be based on user activity and intelligent triggering of business scenarios, to control the recommendation frequency to avoid disturbing the user, and the recommendation content and form can be adjusted according to different user preferences.

[0070] 8) Closed-loop scenario collection: monitor the subsequent operation behavior of the user who receives the "recommended business card": collect the operation feedback of the user on the recommended enterprise business card: such as ignoring the card, adding the card, whether the opposite enterprise user receives after adding, whether there is interaction after forming a friend relationship, and the relevant interaction frequency will be used as sample data to enrich the enterprise portrait and relationship graph, to realize the training data of the intelligent recommendation big model, form a relationship closed loop, and more and more accurate and complete recommendation results, and improve the success rate.

[0071] These behavior data are used as new samples and signals, and are returned to the data layer and the model layer. Positive feedback (such as adding and generating interaction) will strengthen the correctness of the existing model judgment. Negative feedback (such as ignoring) will help the model correct the wrong judgment. As a result, the system forms an enhanced closed loop of "recommendation, feedback, learning, and more accurate recommendation", so that the entire system becomes more and more intelligent over time, and the recommendation success rate becomes higher and higher.

[0072] Therefore, the present embodiment correlates, fuses, and collaboratively analyzes the four types of originally isolated data, including the private relationship chain related to the added enterprise friend, the static attributes of the enterprise in the enterprise database, the dynamic behavior of the enterprise recorded in the business data, and the known upstream and downstream relationships. By constructing an enterprise knowledge graph, not only the enterprise information is stored in the database, but also a graph rich in semantics is constructed, with the enterprise as the node and various types of relationships as the edge. Moreover, the types of edges include but are not limited to: added friend relationship, upstream and downstream relationship, supply relationship, procurement relationship, same industry relationship, and same region relationship. This multi-dimensional heterogeneous graph and portrait are the basis for subsequent intelligent recommendation.

[0073] By constructing a positive and negative sample pair to execute a contrastive learning training model, the embedding features of the enterprise nodes in the graph are mined based on an inductive graph neural network model, the differentiated importance of different types of business relationships is accurately captured using a multi-relationship graph attention layer, and these information is aggregated through a cross-relationship fusion layer. Finally, the cooperation probability is output by a link prediction layer.

[0074] A recommended feedback business closed loop is constructed, for example, the operation behaviors (add, ignore, click, reject, pass, and the interaction frequency generated after passing the application, etc.) of the user to the recommended business card are recorded as new feedback data, which is used to update the model and the graph in real time, so that the subsequent recommended results are more accurate and efficient, and a complete closed loop is realized.

[0075] Further, the recommendation function is deeply integrated into the specific entrance of the IM message, and is presented in the form of a native, lightweight and easy-to-operate "recommended business card", which greatly reduces the use and connection cost of the user.

[0076] Corresponding to the above method, the application also provides a device / system, which comprises a computer device including a processor and a memory, the memory stores computer instructions, and the processor is used to execute the computer instructions stored in the memory, and when the computer instructions are executed by the processor, the device / system realizes the steps of the method as described above.

[0077] The embodiment of the application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by the processor to realize the steps of the above-mentioned edge computing server deployment method. The computer readable storage medium can be a tangible storage medium, such as a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a floppy disk, a hard disk, a removable storage disk, a CD-ROM, or any other form of storage medium known in the technical field.

[0078] In summary, the enterprise cooperation relationship mining model training method, the friend recommendation method and the system construct an enterprise relationship graph rich in semantic information by integrating enterprise registration information, transaction history, instant messaging records and known upstream and downstream relationships and the like. The graph takes enterprises as nodes, the attributes of which are composed of structured labels extracted from original data by optical character recognition and natural language processing technology, and the edges represent various actual relationships such as transactions, communications and business associations. On this basis, positive and negative training sample pairs are constructed from the graph, and an inductive graph neural network model is trained, the differentiated importance of different types of business relationships is accurately captured by using a multi-relation graph attention layer, and these information is converged by a cross-relation fusion layer, and finally a cooperation probability is output by a link prediction layer. The training process adopts a margin contrast loss function, aiming to enlarge the score gap between positive and negative sample pairs, thereby improving the discrimination ability of the model. The scattered enterprise data is converted into a dynamic knowledge system that can be deeply learned by machines, which can actively and accurately identify partners with high cooperation potential from a large number of enterprises, greatly improving the efficiency and accuracy of business expansion.

[0079] Those of ordinary skill in the art will appreciate that the various illustrative components, systems and methods described in connection with the embodiments disclosed herein can be implemented as hardware, software, or both. The particular implementation is dependent on the specific application and design constraints imposed on the overall system. Skilled persons can implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application. When implemented in hardware, for example, the hardware can comprise an electronic circuit, an Application Specific Integrated Circuit (ASIC), a suitable firmware, a plug-in, a functional card, etc. When implemented in software, the elements of the application are the program or code segments to perform a specific task. The program or code segments can be stored in a machine-readable medium, or transmitted by a carrier wave as data signals over a transmission medium or communication link.

[0080] It is to be understood that the application is not limited to the particular configurations and processes described herein and shown in the drawings. For simplicity, detailed descriptions of known methods and apparatuses are omitted so as not to obscure the disclosure. In the above-described embodiments, several specific steps are described and illustrated as examples. However, the method processes of the present application are not limited to the specific steps described and illustrated, and the order of the steps can be changed, or other steps can be added, or replaced, or eliminated, depending on the application.

[0081] In the present application, features described and / or illustrated with respect to one embodiment can be used in the same or a similar way in one or more other embodiments, and / or in combination with or instead of features of other embodiments.

[0082] The above description is only preferred embodiments of the present application, and is not intended to limit the present application. The embodiments of the present application can be variously changed and modified by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the present application.

Claims

1. A method for training a corporate partnership mining model, characterized in that, The method includes the following steps: The raw data, including enterprise registration information, transaction history data, instant messaging history data, and enterprise upstream and downstream relationship data, is obtained from a multi-source database, cleaned, and stored. The raw data includes image or text formats. Based on optical character recognition and natural language processing, text recognition and keyword extraction are performed on the raw data to construct structured enterprise tags for multiple feature attributes; transaction relationships are mined based on the transaction data, communication relationships are mined based on the instant messaging history data, and business relationships are mined based on the enterprise's upstream and downstream relationship data; an enterprise relationship graph is constructed, with enterprises as nodes, enterprise tags as node attributes, and transaction relationships, communication relationships, and business relationships as edges; In the enterprise relationship graph, node pairs with business interactions are constructed as positive sample pairs, and node pairs without business interactions but meeting preset sampling constraints are constructed as negative sample pairs; the positive sample pairs and the negative sample pairs are constructed as a training sample set; An initial enterprise relationship prediction network is obtained based on an inductive graph neural network encoder, a multi-relationship graph attention layer, a cross-relationship fusion layer, and a link prediction layer. The inductive graph neural network encoder performs feature encoding on the enterprise relationship graph to obtain the embedding representation of each node. The embedding representations of nodes in the positive sample pair or the negative sample pair are connected and then output as predicted cooperation probability scores through the multi-relationship graph attention layer, the cross-relationship fusion layer, and the link prediction layer. A marginal contrast loss is constructed to update the parameters of the initial enterprise relationship prediction network to obtain a target enterprise cooperation relationship mining model.

2. The training method for the enterprise cooperation relationship mining model according to claim 1, characterized in that, The enterprise tags include basic tags for marking the geographical distribution of the enterprise's industry category, operational characteristic tags for marking the core business content, dynamic behavioral tags for marking transaction patterns and cooperation preferences, network relationship tags for marking the enterprise's community attributes, and predictive analysis tags for marking transaction risks.

3. The training method for the enterprise cooperation relationship mining model according to claim 1, characterized in that, The business interactions include enterprise pairs that have already had business transactions, enterprise user pairs that have established connections in the instant messaging history, and enterprise relationship pairs that have added each other as friends and have business interactions. The business interactions are required to occur within a preset time period. The preset sampling constraints include industry-specific constraints, region-specific constraints, enterprise tag similarity constraints, and topological proximity constraints for the enterprise relationship graph.

4. The training method for the enterprise cooperation relationship mining model according to claim 1, characterized in that, The inductive graph neural network encoder uses the GraphSAGE model; the multi-relationship graph attention layer extracts embedding representations for business cooperation relationships, friend relationships, supply chain relationships, and industry peer relationships respectively; The cross-relationship fusion layer includes a fusion layer and a fully connected layer; the link prediction layer adopts a multilayer perceptron dot product model.

5. The training method for the enterprise cooperation relationship mining model according to claim 1, characterized in that, The formula for calculating the marginal comparison loss is: ； in, This represents the marginal contrast loss. and For the positive sample pair, and For the negative sample pair, This represents the predicted probability of cooperation between the negative sample pairs. The value represents the predicted probability of cooperation between the positive sample pairs; margin represents the marginal parameter, and N represents the number of samples.

6. A method for recommending business partners, characterized in that, The method includes the following steps: The raw data, including enterprise registration information, transaction history data, instant messaging history data, and enterprise upstream and downstream relationship data, is obtained from a multi-source database, cleaned, and stored. The raw data includes image or text formats. Based on optical character recognition and natural language processing, text recognition and keyword extraction are performed on the raw data to construct structured enterprise tags for multiple feature attributes; transaction relationships are mined based on the transaction data, communication relationships are mined based on the instant messaging history data, and business relationships are mined based on the enterprise's upstream and downstream relationship data; an enterprise relationship graph is constructed, with enterprises as nodes, enterprise tags as node attributes, and transaction relationships, communication relationships, and business relationships as edges; For the target enterprise node, the enterprise relationship graph is input into the target enterprise cooperation relationship mining model in the training method of enterprise cooperation relationship mining model according to any one of claims 1 to 5, so as to output the cooperation probability score between the target enterprise node and the enterprise node with no business dealings. A first set number of candidate enterprise nodes with the highest cooperation probability scores are selected, and recommended business cards of the candidate enterprise nodes are sent to the target enterprise node at a set frequency through an instant messaging system.

7. The enterprise friend recommendation method according to claim 6, characterized in that, The method further includes: Monitor the feedback results of the target enterprise node to the recommended business card. The feedback results include: ignoring the business card, adding the business card, whether to accept the information after adding the business card, whether to interact after adding the business card, and the frequency of interaction. The feedback results are used to update the enterprise relationship graph, and the target enterprise cooperation relationship mining model is optimized and updated based on the updated enterprise relationship graph.

8. A system for mining enterprise partnerships, comprising a processor, a memory, and a computer program / instructions stored in the memory, characterized in that, The processor is configured to execute the computer program / instructions, and when the computer program / instructions are executed, the system implements the steps of the method as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method as described in any one of claims 1 to 7.

10. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 7.

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