Task matching method based on user information and task characteristics

Abstract

The invention relates to the technical field of task matching, and provides a task matching method based on user information and task characteristics, which comprises the following steps: step 1, collecting and analyzing user information which comprises but is not limited to employment information, skilled direction, experience and price; 2, analyzing task information, wherein the task information comprises but is not limited to basic information and additional information; the basic information collection includes but is not limited to task titles, task description, task categories, budget ranges, time requirements, task importance and geographic position requirements; 3, establishing a multi-dimensional matching model, and mapping the user information and the task features; step 4, optimizing and sequencing matching results by utilizing a machine learning algorithm; and 5, dynamically adjusting the matching weight, and continuously improving the matching precision according to historical data. According to the task matching method based on the user information and the task features, the task allocation efficiency and stability are improved, and personalized task recommendation is provided.

Description

Technical Field

[0001] The present invention relates to the technical field of task matching, and in particular to a task matching method based on user information and task features. Background Art

[0002] The task matching platform is an online platform that provides task publishing and task undertaking services, aiming to help task publishers find suitable task executors and ensure that tasks can be completed efficiently. The platform usually has functions such as task publishing, task classification, user matching, online communication and commission settlement, providing users with a convenient way to manage tasks.

[0003] Current task matching platforms generally perform simple matching based on keywords in task descriptions and user skill tags, conduct preliminary screening based on task categories and user expertise, and match tasks based on task budgets and user quotations. However, current task matching platforms have the following technical problems:

[0004] 1. Low matching efficiency: Existing systems often use simple keyword matching or single-dimensional sorting, which cannot quickly find the most suitable task performers from a large number of users. Manual screening is time-consuming and labor-intensive, and it is difficult to cope with large-scale task matching needs.

[0005] 2. Insufficient matching accuracy: Traditional methods are difficult to fully consider the multi-dimensional characteristics of users (such as skills, experience, price expectations, etc.) and the multi-dimensional requirements of tasks. Single-dimensional matching (such as only considering price or skill tags) often leads to inappropriate matching results.

[0006] 3. Insufficient use of user information: Existing systems often ignore implicit features in user information, such as industry experience reflected in work history. It is difficult to effectively integrate and utilize users' dynamic information, such as performance in recent tasks.

[0007] 4. Lack of personalized recommendations: It is impossible to provide customized task recommendations based on users’ unique needs and preferences. It is difficult to balance the needs and expectations of both task publishers and task recipients. Summary of the invention

[0008] The present invention mainly solves the technical problems of low matching efficiency, insufficient matching accuracy, insufficient utilization of user information and lack of personalized recommendation in the task matching platform of the prior art, and proposes a task matching method based on user information and task characteristics to improve the efficiency and stability of task allocation and provide personalized task recommendations.

[0009] The present invention provides a task matching method based on user information and task features, comprising:

[0010] Step 1: Collect and analyze user information, including but not limited to employment information, areas of expertise, experience, and price;

[0011] Step 2, parsing the task information, the task information includes but is not limited to basic information and additional information; the basic information includes but is not limited to task title, task description, task category, budget range, time requirement, task importance, and geographical location requirement; the additional information includes but is not limited to a list of required skills, experience level requirements, estimated workload, project size and complexity, attachments, and reference materials;

[0012] Step 3: Establish a multi-dimensional matching model to map user information and task features;

[0013] Step 4: Use machine learning algorithms to optimize and sort the matching results;

[0014] Step 5: Dynamically adjust the matching weight to continuously improve the matching accuracy based on historical data.

[0015] Furthermore, the methods of collecting and analyzing user information include, but are not limited to, user registration, personal data completion, platform usage process, questionnaires, and third-party data integration;

[0016] The user registers to obtain the user's basic information, identity verification information, and educational background;

[0017] The personal data is completed to obtain the user's skill tags, work experience, professional certificates, and portfolio;

[0018] The platform usage process includes but is not limited to task history, evaluation feedback, behavior data, and social interaction;

[0019] The questionnaire survey mentioned above is to conduct user surveys regularly to understand preferences, needs and satisfaction, and to collect user feedback and suggestions on platform functions;

[0020] The third-party data integration includes but is not limited to social media account association, evaluation of other professional platforms or integration of credit records.

[0021] Further, the step 2 includes the following steps 201 to 204:

[0022] Step 201, performing text preprocessing on task information;

[0023] Step 202, performing natural language processing on the task information;

[0024] Step 203, extracting key information using a rule-based method and / or a machine learning algorithm, and constructing a task feature vector based on the key information;

[0025] Step 204, analyze the task information and perform one or more of task subject identification, semantic analysis, complexity assessment, similar task matching, priority and urgency analysis, quality control requirements, collaboration needs analysis, risk assessment, standardization and structuring, dynamic update, and feedback loop.

[0026] Furthermore, the step 204 includes the following steps 2041 to 2051:

[0027] Step 2041, analyzing the task description in the task information to identify the task subject;

[0028] Step 2042, performing semantic analysis on the task description;

[0029] Step 2043, performing complexity evaluation on the task information;

[0030] Step 2044, comparing the task information with the historical task database to identify similar historical tasks;

[0031] Step 2045, assessing the priority according to the time requirement, budget range and task importance; identifying the key words and phrases in the task description that imply urgency, and determining the urgency of the task;

[0032] Step 2046, analyzing the quality-related terms and standards in the task description, identifying specific audit, test or acceptance criteria, and determining the task quality control requirements;

[0033] Step 2047, evaluating whether the task requires teamwork and identifying possible subtasks or modules in the task;

[0034] Step 2048, identifying potential risk factors in the task description and evaluating the feasibility and potential challenges of the task;

[0035] Step 2049, converting the information extracted from step 2041 to step 2042 into a standardized format;

[0036] Step 2050, obtaining in real time whether the user has posted supplementary information and questions, and dynamically updating the analysis task information;

[0037] Step 2051, collect feedback after the task is completed and evaluate the accuracy of the analysis.

[0038] Furthermore, the step 3 includes the following steps 301 to 314:

[0039] Step 301, construct a feature vector of a matching model; the feature vector includes a user feature vector and a task feature vector; the user feature vector U and the task feature vector T are defined as follows:

[0040] U=[u1,u2,...,u n ],T=[t1,t2,...,t n ]

[0041] Where n is the number of feature vector dimensions;

[0042] Step 302, using cosine similarity to calculate the similarity between the user feature vector and the task feature vector:

[0043] similarity(U,T)=(U·T) / (||U||*||T||);

[0044] Step 303, assign a weight to each feature vector and calculate a weighted matching score:

[0045] score(U,T)=Σ(w i *similarity(u i ,t i ))

[0046] Among them, w i is the weight of the i-th dimension, Σw i =1;

[0047] Step 304, perform nonlinear transformation on the feature vector:

[0048] U'=f(U)=[f(u1),f(u2),...,f(u n )]T'=f(T)=[f(t1),f(t2),...,f(t n )]

[0049] Among them, f can use nonlinear activation functions such as ReLU and sigmoid;

[0050] Step 305, use a multi-layer perceptron (MLP) to further process the converted feature vector:

[0051] H=MLP([U',T'])

[0052] Among them, MLP contains multiple fully connected layers and nonlinear activation functions;

[0053] Step 306, introduce the attention mechanism to dynamically adjust the importance of different features:

[0054] A=Attention(H)H'=A⊙H

[0055] Among them, ⊙ represents element-by-element multiplication;

[0056] Step 307, adding residual connections to alleviate the difficulty of deep network training:

[0057] R=H'+[U',T']

[0058] Step 308, use an output layer to map the processed features to the final matching score:

[0059] score_final=σ(W_out·R+b_out)

[0060] Where σ is the sigmoid activation function;

[0061] Step 309: Use historical matching data to train the matching model and minimize the loss function:

[0062] L = Σ(y_true-score_final) 2 +λ||θ|| 2

[0063] Among them, y_true is the actual matching result (0 or 1), λ||θ|| 2 is the L2 regularization term;

[0064] Step 310, iteratively updating the parameters of the matching model using an optimization algorithm;

[0065] Step 311, integrate multiple models with different structures and adjust the matching model:

[0066] score_ensemble=Σ(w i *score_model i )

[0067] Among them, w i is the weight of each model;

[0068] Step 312, dynamically adjusting the matching model according to real-time feedback and platform status;

[0069] Step 313, analyze the model decision basis: SHAP_values ​​= SHAP (model, [U, T])

[0070] Step 314, consider multiple objective functions and use a multi-objective optimization algorithm (such as NSGA-II) to find a balance between the objectives:

[0071] score_multi=[score_accuracy,score_satisfaction,score_efficiency]

[0072] Through the processing from step 301 to step 314 above, a complex and powerful multi-dimensional matching model is established, which can map user information and task features.

[0073] Furthermore, in step 4, the machine learning algorithm uses a gradient boosting decision tree algorithm to optimize the matching results.

[0074] Further, the step 5 includes the following steps 501 to 515:

[0075] Step 501, setting initial weights based on expert knowledge and historical data, and automatically generating initial weight distribution using Bayesian optimization method;

[0076] Step 502, collecting user feedback data;

[0077] Step 503, using a time decay model to perform dynamic adjustment;

[0078] Step 504, online learning algorithm, real-time update of feature weights:

[0079]

[0080] Among them, η is the learning rate, L_t is the loss function at time t;

[0081] Step 505, using the multi-armed bandit strategy to perform dynamic adjustment;

[0082] Step 506, performing context-aware adjustment;

[0083] Step 507, performing differentiated weight adjustment;

[0084] Step 508, perform abnormality detection and processing;

[0085] Step 509, performing A / B testing;

[0086] Step 510, performing long short-term memory optimization;

[0087] Step 511, performing transfer learning;

[0088] Step 512, performing federated learning;

[0089] Step 513, reinforcement learning optimization;

[0090] Step 514, performing causal inference;

[0091] Step 515, perform automated hyperparameter tuning.

[0092] The present invention provides a task matching method based on user information and task features, which is applied to a task matching platform and can improve the efficiency and accuracy of task allocation. Compared with the prior art, it has the following advantages:

[0093] 1. Efficiency

[0094] 1.1. Fast matching: Through multi-dimensional feature vectors and efficient similarity calculation, the system can quickly complete matching in large-scale user and task data.

[0095] 1.2. Parallel processing: The algorithm design supports parallel computing, which can make full use of modern computing hardware to further improve processing speed.

[0096] 1.3. Incremental update: Supports incremental data update, without the need to recalculate all data every time, which improves the system's response speed.

[0097] 1.4. Real-time responsiveness: Using Incremental Decision Trees, the model structure is updated every N samples, and the incremental learning algorithm is adopted to update the model in real time; it can quickly respond to changes in user behavior and preferences.

[0098] 2. Accuracy

[0099] 2.1. Multi-dimensional consideration: Comprehensively consider multiple dimensions such as skills, experience, price, time, etc. to provide more comprehensive matching results. Adopt multi-objective reinforcement learning method, use Multi-Objective Deep Q-Networks (MODQN) to optimize multiple objective functions, and can optimize multiple potentially conflicting objectives at the same time.

[0100] 2.2. Semantic understanding: Through advanced natural language processing technology, task descriptions and user skills can be understood more accurately.

[0101] 2.3. Context-awareness: Considering the contextual information of the task, such as time and location, to provide a match that is more in line with the actual situation.

[0102] 2.4. Robustness and stability: Use the Stacking integration method to integrate multiple base models, reduce the instability of a single model, and have strong resistance to noisy data and abnormal inputs.

[0103] 2.5. Interpretability: Using a hybrid model of decision tree and neural network, decision = Rule_Engine (NN_output), integrating rule-based system and machine learning model, the matching results have high interpretability, which is easy for users to understand and trust.

[0104] 2.6. Solution to cold start problem: Use meta-learning technology to quickly adapt to new scenarios, use MAML (Model-Agnostic Meta-Learning) algorithm to adapt to new users / tasks, and effectively handle the matching problem of new users and new tasks.

[0105] 3. Adaptability

[0106] 3.1. Dynamic weight adjustment: Through the reinforcement learning algorithm, the system can automatically adjust the weight of each dimension according to the actual matching effect.

[0107] 3.2. Market adaptation: Ability to quickly adapt to emerging task types and changing user skill structures.

[0108] 3.3 Personalized recommendation: Provide personalized task recommendations based on the user's historical behavior and preferences. Provide highly personalized task matching for each user, introduce personalized attention mechanism, and dynamically adjust features for each user.

[0109] 3.4. Privacy protection: Differential privacy technology is used to add noise to sensitive data, which effectively protects user privacy while improving matching accuracy.

[0110] 3.5. Scalability: It adopts a distributed computing framework and uses Apache Spark for large-scale data processing and model training. The system architecture has good scalability and can handle large-scale data and concurrent requests.

[0111] 3.6. Continuous learning capability: Using progressive networks, it retains previous knowledge while adapting to new tasks. It can continuously learn from new data, continuously improve performance, and achieve lifelong learning of the model. BRIEF DESCRIPTION OF THE DRAWINGS

[0112] Figure 1 is a flow chart of the implementation of the task matching method based on user information and task features provided by the present invention;

[0113] Figure 2 is a flowchart for implementing step 1 provided by the present invention;

[0114] Figure 3 It is a flow chart for implementing step 2 provided by the present invention;

[0115] Figure 4 It is a flow chart for implementing step 3 provided by the present invention;

[0116] Figure 5 It is a flow chart for implementing step 5 provided by the present invention. DETAILED DESCRIPTION

[0117] In order to make the technical problems solved by the present invention, the technical solutions adopted and the technical effects achieved clearer, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are only used to explain the present invention, rather than to limit the present invention. It should also be noted that, for the convenience of description, only the parts related to the present invention are shown in the accompanying drawings, rather than all the contents.

[0118] like Figure 1 As shown, an embodiment of the present invention provides a task matching method based on user information and task features, including the following process:

[0119] Step 1: Collect and analyze user information, including but not limited to employment information, areas of expertise, experience, and price.

[0120] The methods of collecting and analyzing user information include but are not limited to user registration, personal data completion, platform usage process, questionnaires, and third-party data integration.

[0121] The user registration can obtain the user's basic information, identity verification information and educational background. The basic information includes but is not limited to user name, email address, phone number, age, gender, etc. The identity verification information includes but is not limited to real-name authentication, ID card number, bank account, etc. The educational background includes but is not limited to the highest degree, graduation school, major, etc.

[0122] The personal data is complete and can obtain the user's skill tags, work experience, professional certificates, and portfolios. The skill tags are the skills that the user selects or fills in. The work experience includes but is not limited to the user's past work experience, position, time period, etc. The professional certificates are relevant qualification certificates, professional certifications, etc. The portfolio is the representative works or project cases uploaded by the user.

[0123] The platform usage process includes but is not limited to task history, evaluation feedback, behavior data, and social interaction. The task history is a record of tasks accepted and completed. The evaluation feedback comes from the ratings and comments of the task publisher. The behavior data includes but is not limited to login frequency, browsing preferences, search history, etc. The social interaction is the communication record and collaboration status with other users.

[0124] The questionnaire survey is used to conduct regular user surveys to understand preferences, needs and satisfaction, and to collect user feedback and suggestions on platform functions.

[0125] The third-party data integration includes but is not limited to social media account association (such as LinkedIn), evaluations from other professional platforms, or integration of credit records.

[0126] Step 2, parse the task information, which includes but is not limited to basic information and additional information; the basic information collected includes but is not limited to task title, task description, task category (such as design, programming, writing, etc.), budget range, time requirement, task importance, geographical location requirement (if any); the additional information includes but is not limited to the required skills list, experience level requirement, estimated workload (hours or days), project scale and complexity, attachments, and reference materials.

[0127] like Figure 2 As shown, the step 2 includes the following steps 201 to 204:

[0128] Step 201: perform text preprocessing on task information.

[0129] Text preprocessing can remove special characters and extra whitespace, correct obvious spelling errors, expand common abbreviations, and identify and standardize technical terminology.

[0130] Step 202: Perform natural language processing (NLP) on the task information.

[0131] Break task information into individual words or phrases.

[0132] Mark the split words or phrases, distinguishing nouns, verbs, adjectives, etc.

[0133] Extract the proper nouns in a specific field from the split words or phrases. Proper nouns include technology names, tools, platforms, etc.

[0134] Perform syntactic analysis on task information, understand sentence structure, and identify the relationship between key information.

[0135] Step 203: extract key information using a rule-based method and / or a machine learning algorithm, and construct a task feature vector based on the key information.

[0136] The key information is not limited to core skill requirements, work content focus, deliverable types, time and progress requirements, and quality standards.

[0137] The task feature vector includes explicit and implicit requirements.

[0138] Step 204: Analyze the task information and perform one or more of the following: task theme identification, semantic analysis, complexity assessment, similar task matching, priority and urgency analysis, quality control requirements, collaboration needs analysis, risk assessment, standardization and structuring, dynamic update, and feedback loop. Figure 3 As shown, the step 204 includes the following steps 2041 to 2051:

[0139] Step 2041, analyzing the task description in the task information to identify the task subject.

[0140] In the present invention, algorithms such as LDA (latent Dirichlet allocation) are used to identify the task topics of the task description in the task information. The task topics include main topics and sub-topics.

[0141] Step 2042, perform semantic analysis on the task description.

[0142] In the present invention, word embedding technology (such as Word2Vec, BERT) is used to understand the semantics of the task description; the semantic similarity between the task description and the skill label and industry terminology is calculated; and the implicit requirements and contextual information in the task description are identified.

[0143] Step 2043, perform complexity evaluation on the task information.

[0144] Assess task complexity based on factors such as task description, budget range, time requirements, etc., use machine learning models to predict the required skill level and experience requirements, and estimate the actual workload required to complete the task.

[0145] Step 2044, compare the task information with the historical task database to identify similar historical tasks.

[0146] Identify similar historical tasks for reference pricing and skill requirements

[0147] Step 2045, evaluate the priority according to the time requirement, budget range and task importance; identify the keywords and phrases in the task description that imply the urgency, and determine the urgency of the task.

[0148] Step 2046, analyze the quality-related terms and standards in the task description, identify specific review, testing or acceptance criteria, and determine the task quality control requirements.

[0149] Step 2047, evaluate whether the task requires teamwork and identify possible subtasks or modules in the task.

[0150] Step 2048, identify potential risk factors in the task description and evaluate the feasibility and potential challenges of the task.

[0151] Step 2049, converting the information extracted from step 2041 to step 2042 into a standardized format.

[0152] In this step, structured task metadata is generated to facilitate subsequent matching and retrieval.

[0153] Step 2050, obtaining in real time whether the user has posted additional information and questions, and dynamically updating the analysis task information.

[0154] The task analysis results are updated based on the additional information and questions and answers from the task publisher, and the analysis algorithm is continuously optimized using the machine learning model.

[0155] Step 2051, collect feedback after the task is completed and evaluate the accuracy of the analysis.

[0156] This step uses feedback information to continuously improve the parsing algorithm.

[0157] Step 3: Establish a multi-dimensional matching model to map user information and task features. Figure 4 As shown, step 3 includes the following steps 301 to 314:

[0158] Step 301: construct a feature vector of a matching model, wherein the feature vector includes a user feature vector and a task feature vector.

[0159] The user feature vector U and the task feature vector T are defined as follows:

[0160] U=[u1,u2,...,u n ],T=[t1,t2,...,t n ]

[0161] Where n is the number of feature vector dimensions, and the task feature vector includes but is not limited to skill matching, price expectations, time availability, experience level, etc.

[0162] Step 302, using cosine similarity to calculate the similarity between the user feature vector and the task feature vector:

[0163] similarity(U,T)=(U·T) / (||U||*||T||)

[0164] Step 303, assign a weight to each feature vector and calculate a weighted matching score:

[0165] score(U,T)=Σ(w i *similarity(u i ,t i ))

[0166] Among them, w i is the weight of the i-th dimension, Σw i =1.

[0167] Step 304, perform nonlinear transformation on the feature vector:

[0168] U'=f(U)=[f(u1),f(u2),...,f(u n )]T'=f(T)=[f(t1),f(t2),...,f(tn )]

[0169] Among them, f can use nonlinear activation functions such as ReLU and sigmoid. This step can capture the complex relationship between features and enhance the model's ability to capture complex patterns.

[0170] Step 305, use a multi-layer perceptron (MLP) to further process the converted feature vector:

[0171] H=MLP([U',T'])

[0172] Among them, MLP contains multiple fully connected layers and nonlinear activation functions to learn the deep relationship between user features and task features.

[0173] Step 306, introduce the attention mechanism to dynamically adjust the importance of different features:

[0174] A=Attention(H)H'=A⊙H

[0175] Where ⊙ represents element-wise multiplication. The attention mechanism can adaptively adjust the feature weights according to the specific user-task pair.

[0176] Step 307, adding residual connections to alleviate the difficulty of deep network training:

[0177] R=H'+[U',T']

[0178] This step helps preserve the original feature information and prevents information from being lost in multiple layers of transmission.

[0179] Step 308, use an output layer to map the processed features to the final matching score:

[0180] score_final=σ(W_out·R+b_out)

[0181] Among them, σ is the sigmoid activation function, which compresses the output to between 0 and 1, indicating the degree of matching.

[0182] Step 309: Use historical matching data to train the matching model and minimize the loss function:

[0183] L = Σ(y_true-score_final) 2 +λ||θ|| 2

[0184] Among them, y_true is the actual matching result (0 or 1), λ||θ|| 2 It is the L2 regularization term to prevent overfitting.

[0185] Step 310: Use an optimization algorithm to iteratively update the parameters of the matching model.

[0186] The optimization algorithm is, for example, a gradient descent method, which updates the parameters of the matching model to continuously improve the matching accuracy.

[0187] Step 311, integrate multiple models with different structures (such as variants based on CNN and RNN), adjust the matching model, and further improve the robustness:

[0188] score_ensemble=Σ(w i *score_model i )

[0189] Among them, w i is the weight of each model, which can be determined by the performance of the validation set.

[0190] The above steps mainly use the following models A and B. Model A is a CNN-based matching model: it uses convolutional neural networks to process features, can capture local relationships between features, and is suitable for processing position-related feature patterns. Model B is an Attention-based matching model, a pure attention mechanism architecture, which can better handle long-distance dependencies between features and dynamically adjust feature importance.

[0191] Step 312: dynamically adjust the matching model according to the real-time feedback and platform status.

[0192] Update model parameters using online learning algorithms. Regularly retrain the matching model to adapt to changes in user behavior and task characteristics.

[0193] Step 313, analyze the model decision basis: SHAP_values ​​= SHAP (model, [U, T])

[0194] In this step, you can use techniques such as SHAP values ​​to analyze the basis for model decisions and provide explainable matching reasons. This helps increase user confidence in matching results and provides insights for further optimization.

[0195] Step 314, consider multiple objective functions and use a multi-objective optimization algorithm (such as NSGA-II) to find a balance between the objectives:

[0196] score_multi=[score_accuracy,score_satisfaction,score_efficiency]

[0197] The objectives include matching accuracy, user satisfaction, platform efficiency, etc.

[0198] Through the processing of the above steps 301 to 314, the present invention establishes a complex and powerful multi-dimensional matching model that can effectively map user information and task features. This model not only takes into account the linear and nonlinear relationships between features, but also introduces advanced technologies such as deep learning and attention mechanisms to capture more subtle matching patterns. At the same time, through model integration, dynamic adjustment and interpretable analysis, the robustness, adaptability and transparency of the model are ensured. Ultimately, this mapping process can generate a comprehensive matching score for each user-task pair, providing a reliable basis for subsequent task allocation and recommendation.

[0199] Step 4: Use machine learning algorithms to optimize and sort the matching results.

[0200] The machine learning algorithm of the present invention uses the gradient boosted decision tree (GBDT) algorithm to optimize the matching results. The GBDT model can be expressed as:

[0201] F(U,T)=Σf k (U,T)

[0202] Among them, f k is the kth decision tree.

[0203] The model is trained by minimizing the following loss function:

[0204] L=Σl(y i ,F(U i ,T i ))+ΣΩ(f k )

[0205] Where l is the loss function (such as log loss), y i is the real matching result, Ω(f k ) is the regularization term.

[0206] The Gradient Boosted Decision Tree (GBDT) algorithm is a powerful machine learning technique that can significantly improve matching accuracy. GBDT is an ensemble learning method that gradually optimizes prediction results by building multiple decision trees. Each new tree is generated to correct the residual of the previous tree. The final model is the weighted sum of all trees: F(U,T) = Σf k (U,T), where U is the user feature vector, T is the task feature vector, and f k is the kth decision tree.

[0207] Model training process of gradient boosting decision tree algorithm:

[0208] a) Initialization: F0(U,T) = arg min_γΣl(y i ,γ)

[0209] Initialize the model with a constant value (usually the mean of the target variable).

[0210] b) For each iteration m=1,2,...,M:

[0211] Compute the negative gradient (pseudo residual):

[0212] Fit a regression tree h m (U,T) to residual r im superior;

[0213] Calculate the leaf node area R jm ,j=1,2,...,J m ;

[0214] Calculate the optimal value for each leaf node: jm =arg min_γΣl(y i ,F m-1 (U i ,T i )+γ);

[0215] Updated model: F m (U,T)=F m-1 (U,T)+ν·Σγ jm I((U,T)∈R jm ) where ν is the learning rate, which is used to control overfitting.

[0216] In the matching problem, in order to prevent the model from overfitting, the present invention adopts the regularization term Ω(f k )=γT+λ||w|| 2 , where T represents the number of leaf nodes in the tree, w is the weight vector of the leaf nodes, and γ and λ are hyperparameters used to control the complexity of the model.

[0217] This can be called the "Tree-based DualRegularization Loss Function" because it constrains both the tree structure (number of nodes T) and the leaf weights (w).

[0218] Feature Importance: GBDT can naturally provide feature importance metrics: how often a feature is selected as a split point in the tree, and the cumulative contribution of the feature to the improvement of model predictions.

[0219] Processing categorical features: One-hot encoding of categorical features or using special splitting strategies (such as CategoricalEmbedding)

[0220] Hyperparameter optimization: Use grid search or Bayesian optimization to adjust key hyperparameters, including but not limited to the number of trees, maximum tree depth, learning rate, minimum number of leaf node samples, and L1 and L2 regularization parameters.

[0221] Model Ensemble: Multiple GBDT models can be trained and integrated. Bagging, training models on different data subsets. Boosting, using different initialization or hyperparameter settings.

[0222] Online learning and incremental updating: The model is updated incrementally using a forgetting factor, and parts of the tree are periodically retrained to adapt to the new data distribution.

[0223] Matching score calculation: The final matching score can be expressed as:

[0224] score(U,T)=σ(F(U,T))

[0225] Where σ is the sigmoid function, which compresses the output to between 0 and 1.

[0226] Result sorting: Sort the user-task pairs according to the calculated matching scores:

[0227] ranked_matches = sort([(U i ,T i ,score(U i ,T i ))for all i],key=lambda x:x[2],reverse=True)

[0228] Post-processing: Apply business rules (such as considering user load, task urgency, etc.) and introduce diversity (such as using the maximum marginal correlation algorithm).

[0229] Explainability: Use SHAP (SHapley Additive exPlanations) values ​​to explain the decision rationale for each match.

[0230] Performance monitoring: Track offline evaluation indicators (such as AUC, NDCG) and monitor online indicators (such as click-through rate, completion rate, user satisfaction).

[0231] A / B testing: Compare the actual effects of GBDT with other algorithms (such as neural networks) through online experiments.

[0232] Through this detailed GBDT optimization process, the present invention can fully utilize the multi-dimensional characteristics of users and tasks, learn complex nonlinear relationships, and produce high-quality matching results. This method can not only improve the accuracy of matching, but also provide interpretable results, which helps to continuously improve the performance of the matching system.

[0233] Step 5: Dynamically adjust the matching weights to continuously improve the matching accuracy based on historical data. Figure 5 As shown, the step 5 includes the following steps 501 to 515:

[0234] Step 501, initial weights are set based on expert knowledge and historical data, and the initial weight distribution is automatically generated using the Bayesian optimization method.

[0235] Step 502: Collect user feedback data.

[0236] Feedback data includes but is not limited to recording user behavior of accepting / rejecting tasks, collecting task completion and quality scores, and tracking user satisfaction survey results.

[0237] Step 503: Perform dynamic adjustment using a time decay model.

[0238] Introduce time decay factor: w(t) = w0*exp(-λt)

[0239] Give less weight to older matches and give more weight to recent performance.

[0240] Step 504, online learning algorithm, real-time update of feature weights:

[0241]

[0242] Among them, η is the learning rate and L_t is the loss function at time t.

[0243] The online learning algorithm is, for example, the Follow-the-Regularized-Leader (FTRL) algorithm.

[0244] Step 505, use the multi-armed bandit strategy to perform dynamic adjustments.

[0245] Specifically, each matching strategy is regarded as an arm. The Thompson Sampling algorithm is used to balance exploration and utilization. The usage frequency of different matching strategies is dynamically adjusted.

[0246] Step 506, perform context-aware adjustment.

[0247] Specifically, we consider context information such as time, location, and user status. We use the Conditional Random Field (CRF) model to capture context dependencies.

[0248] Step 507, perform differentiated weight adjustment.

[0249] Specifically, we set independent weight adjustment strategies for different user groups and task types, and use a multi-task learning framework to simultaneously optimize multiple related weight adjustment tasks.

[0250] Step 508, perform abnormality detection and processing.

[0251] Specifically, the isolation forest algorithm is used to detect abnormal matching results. Special processing is performed on abnormal data to prevent erroneous data from affecting the overall model.

[0252] Step 509, perform A / B testing.

[0253] Specifically, we continue to conduct small-scale A / B tests to evaluate different weight adjustment strategies. We use the multi-armed bandit algorithm to automatically allocate traffic to the best performing strategy.

[0254] Step 510, perform long short-term memory optimization.

[0255] Specifically, the LSTM network is combined to capture the user's long-term preferences and short-term interest changes. The feature weights are dynamically adjusted to balance long-term stability and short-term flexibility.

[0256] Step 511, perform transfer learning.

[0257] Specifically, we use the weight knowledge learned in similar fields to quickly adapt to new task types. We use domain adaptation techniques to handle the distribution differences between different task domains.

[0258] Step 512, perform federated learning.

[0259] Specifically, under the premise of protecting user privacy, the model is updated using data on distributed devices, and the model updates from multiple users are integrated using a secure aggregation protocol.

[0260] Step 513, reinforcement learning optimization.

[0261] Specifically, the matching process is modeled as a Markov decision process (MDP), and the deep Q network (DQN) is used to learn the optimal weight adjustment strategy.

[0262] Step 514, perform causal inference.

[0263] Specifically, counterfactual analysis is used to evaluate the causal effect of weight adjustment, and a double machine learning method is applied to reduce bias.

[0264] Step 515, perform automated hyperparameter tuning.

[0265] Specifically, Bayesian optimization is used to automatically adjust hyperparameters such as learning rate and regularization parameters. Hyperparameters are re-evaluated and updated regularly to adapt to changes in data distribution.

[0266] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that modifying the technical solutions described in the aforementioned embodiments, or replacing part or all of the technical features therein by equivalents, does not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A task matching method based on user information and task features, characterized in that: include: Step 1: Collect and analyze user information, including but not limited to employment information, areas of expertise, experience, and price; Step 2, parsing the task information, the task information includes but is not limited to basic information and additional information; the basic information includes but is not limited to task title, task description, task category, budget range, time requirement, task importance, and geographical location requirement; the additional information includes but is not limited to a list of required skills, experience level requirements, estimated workload, project size and complexity, attachments, and reference materials; Step 3: Establish a multi-dimensional matching model to map user information and task features; Step 4: Use machine learning algorithms to optimize and sort the matching results; Step 5: Dynamically adjust the matching weight to continuously improve the matching accuracy based on historical data.

2. The task matching method based on user information and task features according to claim 1, characterized in that: The methods of collecting and analyzing user information include, but are not limited to, user registration, personal data completion, platform usage, questionnaires, and third-party data integration; The user registers to obtain the user's basic information, identity verification information, and educational background; The personal data is completed to obtain the user's skill tags, work experience, professional certificates, and portfolio; The platform usage process includes but is not limited to task history, evaluation feedback, behavior data, and social interaction; The questionnaire survey mentioned above is to conduct user surveys regularly to understand preferences, needs and satisfaction, and to collect user feedback and suggestions on platform functions; The third-party data integration includes but is not limited to social media account association, evaluation of other professional platforms or integration of credit records.

3. The task matching method based on user information and task features according to claim 1, characterized in that: The step 2 includes the following steps 201 to 204: Step 201, performing text preprocessing on task information; Step 202, performing natural language processing on the task information; Step 203, extracting key information using a rule-based method and / or a machine learning algorithm, and constructing a task feature vector based on the key information; Step 204, analyze the task information and perform one or more of task subject identification, semantic analysis, complexity assessment, similar task matching, priority and urgency analysis, quality control requirements, collaboration needs analysis, risk assessment, standardization and structuring, dynamic update, and feedback loop.

4. The task matching method based on user information and task features according to claim 3 is characterized in that: The step 204 includes the following steps 2041 to 2051: Step 2041, analyzing the task description in the task information to identify the task subject; Step 2042, performing semantic analysis on the task description; Step 2043, performing complexity evaluation on the task information; Step 2044, comparing the task information with the historical task database to identify similar historical tasks; Step 2045, assessing the priority according to the time requirement, budget range and task importance; identifying the key words and phrases in the task description that imply urgency, and determining the urgency of the task; Step 2046, analyzing the quality-related terms and standards in the task description, identifying specific audit, test or acceptance criteria, and determining the task quality control requirements; Step 2047, evaluating whether the task requires teamwork and identifying possible subtasks or modules in the task; Step 2048, identifying potential risk factors in the task description and evaluating the feasibility and potential challenges of the task; Step 2049, converting the information extracted from step 2041 to step 2042 into a standardized format; Step 2050, obtaining in real time whether the user has posted supplementary information and questions, and dynamically updating the analysis task information; Step 2051, collect feedback after the task is completed and evaluate the accuracy of the analysis.

5. The task matching method based on user information and task features according to claim 4 is characterized in that: The step 3 includes the following steps 301 to 314: Step 301, construct a feature vector of a matching model; the feature vector includes a user feature vector and a task feature vector; the user feature vector U and the task feature vector T are defined as follows: U=[u1,u2,...,u n ],T=[t1,t2,...,t n ] Where n is the number of feature vector dimensions; Step 302, using cosine similarity to calculate the similarity between the user feature vector and the task feature vector: similarity(U,T)=(U·T) / (||U||*||T||); Step 303, assign a weight to each feature vector and calculate a weighted matching score: score(U,T)=Σ(w i *similarity(u i ,t i )) Among them, w i is the weight of the i-th dimension, Σw i =1; Step 304, perform nonlinear transformation on the feature vector: U'=f(U)=[f(u1),f(u2),...,f(u n )]T'=f(T)=[f(t1),f(t2),...,f(t n )] Among them, f can use nonlinear activation functions such as ReLU and sigmoid; Step 305, use a multi-layer perceptron (MLP) to further process the converted feature vector: H=MLP([U',T']) Among them, MLP contains multiple fully connected layers and nonlinear activation functions; Step 306, introduce the attention mechanism to dynamically adjust the importance of different features: A=Attention(H)H'=A⊙H Among them, ⊙ represents element-by-element multiplication; Step 307, adding residual connections to alleviate the difficulty of deep network training: R=H'+[U',T'] Step 308, use an output layer to map the processed features to the final matching score: score_final=σ(W_out·R+b_out) Where σ is the sigmoid activation function; Step 309: Use historical matching data to train the matching model and minimize the loss function: L=Σ(y_true-score_final) 2 +λ||θ|| 2 Among them, y_true is the actual matching result (0 or 1), λ||θ|| 2 is the L2 regularization term; Step 310, iteratively updating the parameters of the matching model using an optimization algorithm; Step 311, integrate multiple models with different structures and adjust the matching model: score_ensemble=Σ(w i *score_model i ) Among them, w i is the weight of each model; Step 312, dynamically adjusting the matching model according to real-time feedback and platform status; Step 313, analyze the model decision basis: SHAP_values ​​= SHAP (model, [U, T]) Step 314, consider multiple objective functions and use a multi-objective optimization algorithm (such as NSGA-II) to find a balance between the objectives: score_multi=[score_accuracy,score_satisfaction,score_efficiency] Through the processing from step 301 to step 314 above, a complex and powerful multi-dimensional matching model is established, which can map user information and task features.

6. The task matching method based on user information and task features according to claim 5, characterized in that: In step 4, the machine learning algorithm uses a gradient boosting decision tree algorithm to optimize the matching results.

7. The task matching method based on user information and task features according to claim 6, characterized in that: The step 5 includes the following steps 501 to 515: Step 501, setting initial weights based on expert knowledge and historical data, and automatically generating initial weight distribution using Bayesian optimization method; Step 502, collecting user feedback data; Step 503, using a time decay model to perform dynamic adjustment; Step 504, online learning algorithm, real-time update of feature weights: Among them, η is the learning rate, L_t is the loss function at time t; Step 505, using the multi-armed bandit strategy to perform dynamic adjustment; Step 506, performing context-aware adjustment; Step 507, performing differentiated weight adjustment; Step 508, perform abnormality detection and processing; Step 509, performing A / B testing; Step 510, performing long short-term memory optimization; Step 511, performing transfer learning; Step 512, performing federated learning; Step 513, reinforcement learning optimization; Step 514, performing causal inference; Step 515, perform automated hyperparameter tuning.

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