Next prediction method based on user behavior sequence data analysis

Abstract

The invention relates to the technical field of data analysis and prediction, in particular to a next prediction method based on user behavior sequence data analysis. According to the technical scheme, the next prediction method based on user behavior sequence data analysis comprises a next prediction process framework based on user behavior sequence data analysis; according to the method, the data acquisition range is broadened systematically, the user behavior is regarded as a complex event composed of multiple signals instead of a single action identifier, and context information is crossed and fused, so that each behavior node is converted into a feature vector rich in context information from an isolated symbol, and the user behavior is analyzed. The input information of the model is greatly enriched, for new users or users with sparse behaviors, even if the behavior sequence is very short, rich context features can provide additional inference basis for the model, and the cold start problem is effectively relieved.

Description

Technical Field

[0001] This invention relates to the field of data analysis and prediction technology, and in particular to a prediction method based on user behavior sequence data analysis. Background Technology

[0002] In the current field of internet applications and intelligent services, accurately predicting users' next behavior is of great significance for improving user experience and business value. Traditional user behavior prediction methods are mainly based on statistical analysis and simple machine learning models, which have obvious limitations when dealing with complex user behavior sequences.

[0003] Existing prediction methods based on user behavior sequences generally suffer from significant drawbacks such as limited data representation and a lack of contextual information. They typically rely heavily on identifier sequences generated by direct user interactions. This simplification at the data level makes it difficult for models to construct deep user intent profiles, because an isolated behavior ID cannot reflect the specific environment, user state, or external triggers at the time the behavior occurred. This lack of information dimension directly leads to data sparsity and cold start problems. For new or sparse users, the model cannot make reliable inferences due to the short sequence of behavior IDs available for learning. At the same time, for any user, the model struggles to distinguish subtle differences in intent for the same behavior in different contexts.

[0004] To address the aforementioned issues, this solution systematically broadens the scope of data collection, treating user behavior as a complex event composed of multiple signals rather than a single action identifier. The solution not only collects core behavior sequences but also integrates user static profiles, device information, precise timestamps, geographic location data, and even auxiliary information from text content or sensor signals. In the feature extraction stage, embedding technology maps behavior IDs to dense vectors containing semantic relationships; various temporal features such as behavior intervals, session durations, and periodic patterns are derived from timestamps; and contextual information such as user demographics and device type is cross-fused, transforming each behavior node from an isolated symbol into a feature vector rich in contextual information. This significantly enriches the model's input information, providing a solid data foundation for the model to understand the motivations behind user behavior. For new users or users with sparse behavior, even if their behavior sequences are short, the rich contextual features can provide the model with additional inference evidence, effectively alleviating the cold start problem. Summary of the Invention

[0005] To overcome the significant shortcomings of existing prediction methods based on user behavior sequences, such as limited data representation and lack of contextual information, these methods typically rely heavily on identifier sequences generated by direct user interactions. This simplification at the data level makes it difficult for models to construct deep user intent profiles, because an isolated behavior ID cannot reflect the specific environment, user state, or external triggers at the time the behavior occurred. This lack of information dimension directly leads to data sparsity and cold start problems. For new or sparse users, the model cannot make reliable inferences due to the short sequence of behavior IDs available for learning. At the same time, for any user, the model struggles to distinguish subtle differences in intent for the same behavior in different contexts.

[0006] The technical solution of this invention is: a next-order prediction method based on user behavior sequence data analysis, comprising the following steps: S11: Systematically collect raw user behavior data from multiple heterogeneous data sources and transmit it to the central storage system; S12: Perform deduplication, missing value imputation, format correction, noise filtering, and standardization on the original data; S13: Convert the cleaned behavioral sequence into a numerical vector and extract several key features; S14: Construct a Transformer-based model that integrates attention mechanisms, meta-learning, and interpretability modules; S15: Employs strategies including data augmentation, distributed training, and adversarial training to efficiently optimize model parameters using preprocessed data; S16: The model performance was comprehensively evaluated on the test set using multiple metrics and ablation experiments, and its generalization ability and robustness were verified. S17: Systematically search for and adjust key hyperparameter combinations in the model and training process; S18: Encapsulate the optimized model as an API service and integrate it into the production environment; S19: Continuously monitor the model's performance in the production environment and establish an iterative update mechanism for the model based on feedback data and performance changes.

[0007] Preferably, the data collection and acquisition process includes the following steps: S21: Identify and list all user behavior data sources, including server logs, mobile application event streams, database records, and third-party API interfaces. By reviewing system architecture documents and user interaction flowcharts, determine the access protocol for each data source. Configure real-time data streams using data acquisition tools such as Apache Flume or Kafka. Set up data pipelines to transfer raw data to distributed storage systems such as HDFS or Amazon S3, and record metadata about the data sources, including format, frequency, and size. S22: Configure the data acquisition agent to be deployed on both the user and server sides, integrate the SDK into the mobile application, capture user click, browsing, and transaction events, set event triggers, automatically record timestamps, user IDs, and behavior types based on user actions, serialize data using JSON or Protocol Buffers format, transmit it to the central server over the network, and implement a data buffering mechanism to handle network latency issues; S23: Establish data access permission and compliance check processes, define data anonymization rules according to GDPR or CCPA regulations, use encryption tools TLS or SSL to protect data transmission, configure access control lists to restrict unauthorized access, and audit data sources regularly; S24: Deploy monitoring tools such as Prometheus or Datadog to track data collection performance, set metrics including data throughput, latency, and error rate, configure alarm rules to trigger notifications when the data stream is interrupted, use the ELK stack log analysis tool to record collection events, and generate reports to optimize data source configuration; S25: Achieve multimodal data integration, extract user behavior sequences from social media APIs, sensor data, and historical databases, use the ETL tool Apache NiFi to transform data formats, create a unified data catalog mapping for each source schema, and check consistency through data validation scripts; S26: Set up data backup and recovery mechanisms, use incremental backup strategies to periodically copy data to off-site storage, configure the snapshot tool AWS EBS Snapshots to protect data integrity, test the recovery process to simulate data loss scenarios, and document the backup plan for disaster recovery; S27: Optimize the data acquisition network configuration, distribute data traffic through a load balancer to avoid single points of failure, use CDN to accelerate data uploads from edge devices, adjust TCP / IP parameters to reduce transmission latency, and implement the GZIP data compression algorithm to reduce bandwidth usage.

[0008] Preferably, data cleaning and preprocessing includes the following steps: S31: Perform data deduplication, calculate the fingerprint of each record using the hash algorithm MD5 or SHA-256, compare duplicate entries and retain unique instances, apply the distributed computing framework Apache Spark to process large-scale datasets, write scripts to automatically delete duplicate records, and record deduplication statistics for quality assessment; S32: Handle missing value issues. Identify null or NaN fields through data analysis, use time series linear imputation or context-based mean replacement, apply the pandas library in Python using a custom function to repair missing values, and verify whether the data distribution is consistent after imputation. S33: Correct data format inconsistencies, parse the date and time fields in the original data to conform to the ISO 8601 standard, convert the text encoding to UTF-8 to avoid garbled characters, use regular expressions to match and repair abnormal characters, and run a format verification tool to check that all fields conform to the predefined schema; S34: Filter outliers and noisy data, calculate the Z-score or IQR range for numerical fields, define thresholds to remove data points outside the range, apply sliding window smoothing techniques to handle abrupt changes in the time series, and use visualization tools such as Matplotlib to check the filtering effect; S35: Standardize and normalize data scaling. Apply Min-Max scaling or Z-score standardization to numerical features, using the StandardScaler function from the Scikit-learn library. Perform one-hot encoding or label encoding on categorical features and save the transformation parameters. S36: Segment user behavior sequences into training and test sets, dividing them chronologically to avoid data leakage. Use stratified sampling to ensure the representativeness of user distribution, set the split ratio to 20-80, and generate a sequence ID mapping file to track the source of each subset; S37: Implement a data quality assessment process, run automated test scripts to calculate these metrics, generate quality reports for problem areas, and adjust cleaning rules based on the results.

[0009] Preferably, the sequence data encoding and feature extraction process includes the following steps: S41: Convert the original action sequence into a numerical vector, use the embedding layer Word2Vec or GloVe to simulate the relationship between action items, train the embedding model to learn action representations on the historical sequence, adjust the vector dimension to balance expressive power and computational cost, and save the embedding matrix for feature mapping; S42: Extract time features from sequence timestamps, calculate behavioral intervals, sequence duration, and periodic patterns, use Python's datetime library to parse time information, generate features such as hours, days of the week, and seasons, and normalize time values ​​to a uniform range; S43: Construct sequence length and position encoding, add a position index vector to each sequence, generate Transformer-style position encoding using sine and cosine functions, handle variable-length sequences by padding or truncating to a fixed length, and record the actual sequence length for model input; S44: Derived contextual features are derived from user metadata, including user demographics, device type, and geographic location. These features are fused into the sequence representation, interaction terms are created using feature cross-validation, and PCA is applied to reduce feature dimensionality. S45: Achieve multimodal feature fusion by extracting auxiliary features from text, image, or sensor data, using the pre-trained BERT model to process text descriptions, CNN to process image data, and concatenating these features into the main sequence vector; S46: Apply sequence segmentation techniques to process long sequences. Use the sliding window method to divide the long sequence into overlapping subsequences. Set the window size and step size based on business requirements. Calculate the statistical features of each subsequence, such as mean and variance, and generate subsequence labels for training. S47: Optimize the feature selection process by using recursive feature elimination or tree-based importance scoring to filter the most relevant features to reduce noise, implement automated feature selection scripts, and validate the performance of the feature set on the validation set.

[0010] As a preferred approach, the design of an innovative model framework includes the following steps: S51: Design the core structure of a Transformer-based sequence model, use a multi-head self-attention mechanism to calculate the weights between actions within the sequence, use a position-feedforward network to process the attention output, stack multiple encoder layers to capture deep dependencies, and configure residual connections and layer normalization to stabilize training; S52: Integrates a time-aware attention mechanism, adds a time decay factor to self-attention, adjusts attention weights based on behavior time intervals, uses learnable parameters to simulate the effects of time, and tests the effects of different decay functions such as exponential decay; S53: Add a meta-learning module to enhance generalization ability. The MAML algorithm is used in the model initialization stage to train the model to quickly adapt to new user sequences. The meta-training set is used to simulate low-sample scenarios, and the meta-update step size is optimized to balance the convergence speed. S54: Construct a dynamic sequence processing component, using LSTM or GRU units as basic recurrent units, combining an attention mechanism to focus on key action steps, performing sequence padding mask processing on variable-length inputs, and outputting hidden states for the next prediction; S55: Embed an interpretability module, integrate it into the model output layer using SHAP or LIME tools, generate feature importance scores to explain prediction results, visualize attention weight mapping of key behaviors, and document the explanation logic for auditing; S56: Implement a multi-task learning framework, add auxiliary tasks such as sequence classification or anomaly detection, share encoding layers to reduce overfitting, define a loss function to weight the main task and auxiliary tasks, and adjust the weights based on validation performance; S57: Optimize the model input / output interface, define the input tensor shape including sequence length and feature dimension, output the next probability distribution over the behavior vocabulary, normalize the output using a Softmax layer, and configure the sequence generation mode for inference.

[0011] Preferably, the model training strategy includes the following steps: S61: Prepare the training data loader, use the DataLoader class of PyTorch or TensorFlow to batch load sequence data, set the batch size based on GPU memory, implement sequence padding and packing to process variable-length sequences, and apply data augmentation such as random masking or sequence rearrangement; S62: Define a loss function that combines cross-entropy loss and a regularization term. Use label smoothing to handle class imbalance, add L2 regularization to prevent overfitting, ignore padding positions when calculating the loss, and optimize the efficiency of loss calculation. S63: Select the optimization algorithm AdamW or SGD with momentum, set the learning rate scheduler such as cosine annealing or step descent, configure gradient clipping to limit gradient explosion, initialize model parameters using Xavier or He initialization, and monitor training dynamics; S64: Implement distributed training acceleration by using multi-GPU or TPU clusters via Horovod or PyTorch DDP, splitting data across devices, synchronizing gradient updates, and debugging communication bottlenecks to optimize training speed; S65: Integrates early stopping and model checkpointing mechanisms, monitors the validation set loss after each epoch, sets a patience threshold to stop training when there is no improvement, saves the best model weights to disk, and records the training history for analysis; S66: Apply a course-based learning strategy, starting with simple sequences and gradually increasing complexity. Sort the training data based on sequence length or difficulty, adjust the learning rate according to the course progress, and verify the impact of the strategy on convergence; S67: Enhance robustness by performing adversarial training, adding adversarial examples to the training data, generating perturbations using FGSM or PGD methods, calculating the adversarial loss and merging it into the total loss, and testing the model's resistance to noise.

[0012] Preferably, the model evaluation and validation process includes the following steps: S71: Divide the evaluation dataset into a reserved test set and a validation set, use time series splitting to ensure time order, define evaluation metrics including accuracy, recall, F1-score, and MRR, use automated evaluation scripts to calculate these metrics, and generate a confusion matrix to analyze error patterns; S72: Conduct ablation experiments to test innovative components, sequentially removing the attention mechanism, meta-learning module, or time-aware layer, comparing the performance of the baseline model and the complete model, using the statistical test t-test to verify significance, and documenting the contribution of each component; S73: Evaluate the model's generalization ability by sampling from the test set at different time periods, calculating metrics across users and across time, using K-fold cross-validation to reduce variance, and analyzing signs of overfitting or underfitting; S74: Test real-time prediction performance by deploying the model in a simulated environment, inputting streaming sequence data, measuring inference latency and throughput, using the performance analysis tool cProfile to identify bottlenecks, and optimizing code to reduce response time; S75: Verify interpretability output, manually check the importance of features generated by SHAP or LIME, compare prediction results with real user behavior, run case studies on specific sequences, and adjust interpretation module parameters to improve clarity; S76: Compare the performance with existing benchmark models GRU or Transformer baselines, train the comparison model on the same dataset, calculate the relative percentage improvement using standard evaluation protocols, and analyze the advantageous scenarios; S77: Perform robustness testing by injecting noise or missing values ​​into the test data, observing changes in model performance, testing stability using adversarial examples, and recording failure cases.

[0013] Preferably, the hyperparameter optimization and tuning process includes the following steps: S81: Define the hyperparameter search space, including learning rate, batch size, number of layers, number of attention heads, and hidden layer dimension. Record the parameters using the configuration management tool Hydra or a JSON file, and set the range based on prior knowledge. S82: Implement grid search or random search strategies, evaluate different combinations of hyperparameters on the validation set, accelerate the search process using parallel computing, record the performance metrics of each combination, and select the optimal parameter set; S83: Apply Bayesian optimization methods, use tools such as Optuna or Hyperopt to model the relationship between hyperparameters and performance, define an objective function to minimize the validation loss, run multiple iterations to converge to the optimum, and visualize the optimization process; S84: Optimize regularization hyperparameters dropout rate and L2 weights, adjust parameters through cross-validation, monitor training and validation loss curves to prevent overfitting, and document the final settings; S85: Optimize sequence-related parameters such as maximum sequence length and window size, test the impact of different values ​​on model performance, use business logic to guide the selection, and verify stability on the test set; S86: Calibrate the model's output probabilities by adjusting the output distribution on the validation set using Platt scaling or isotonic regression to improve probability calibration and test the predictive consistency of the calibrated model; S87: An automated hyperparameter tuning pipeline that integrates search, training, and evaluation steps with scripts, uses version control to track parameter changes, and generates tuning reports summarizing the best configuration.

[0014] Preferably, the system integration and deployment process includes the following steps: S91: Package the trained model into a deployable format, export the model using ONNX or TensorFlow SavedModel, optimize the graph structure for inference, test format compatibility and target platforms, and document the interface specifications; S92: Build a real-time inference service, create a REST API using the web framework Flask or FastAPI, define endpoints to receive sequence data and return prediction results, perform input validation and error handling, and configure load balancing to handle high concurrency; S93: Deploy the model to the production environment, choose the cloud platform AWS SageMaker or Kubernetes cluster, use Docker for containerized applications, set up an auto-scaling policy based on traffic, and monitor resource usage for CPU and memory; S94: Integrates data pipelines and inference services, connects real-time data streams from Kafka topics to model input, optimizes throughput through sequence buffering and batch processing, uses message queues to ensure data order, and tests end-to-end latency; S95: Implement an A / B testing framework, deploy multiple model versions, route some traffic to different versions, collect user feedback data, compare prediction accuracy and business metrics, and decide on full deployment based on the results; S96: Adds model version management and rollback mechanisms, uses MLflow tools to track model versions, sets up automated deployment pipelines, quickly rolls back to previous versions when performance degrades, and documents deployment history; S97: Optimizes inference performance, applies model quantization to reduce memory usage, uses GPU acceleration libraries to optimize computation, tests inference speed under real load, and continuously monitors service level agreements.

[0015] Preferably, when performing performance monitoring and iterative updates, the following steps are included: S101: Set up a real-time monitoring dashboard to visualize model performance metrics, including prediction accuracy, latency, and error rate, using Grafana or Kibana. Configure alerts to notify the team when metrics are abnormal, and review dashboard data regularly. S102: Collect user feedback and production data, log prediction results and actual user behavior, store feedback in the database, analyze the difference identification model, and generate weekly reports to summarize trends; S103: Implement the model retraining process, regularly update the model with new data, set retraining triggers based on performance thresholds or time intervals, automate data preprocessing and training steps, and verify the performance of the new model on the test set; S104: Monitor changes in data distribution, use statistical tools to compare and contrast the distributions of training data and real-time data, detect concept drift, adjust feature engineering or model parameters to adapt to changes, and document drift handling strategies; S105: Optimize system resource usage, analyze monitoring data to identify bottlenecks, adjust deployment configurations such as container resource limits, implement cost control measures, and perform performance tuning regularly; S106: Conduct regular security audits, check for vulnerabilities in models and services, update dependency libraries to patch security risks, and test protection against attacks; S107: Iterative improvement models are based on monitoring insights, analyzing error cases to identify improvement points, updating model architecture or feature sets, running small-scale experiments to verify improvements, and successfully integrating changes into the production process.

[0016] The beneficial effects of this invention are: 1. Existing prediction methods based on user behavior sequences generally suffer from significant drawbacks, such as limited data representation and a lack of contextual information. They typically rely heavily on identifier sequences generated by direct user interactions. This simplification at the data level makes it difficult for models to construct deep user intent profiles, as an isolated behavior ID cannot reflect the specific environment, user state, or external triggers at the time of the behavior. This lack of information dimension directly leads to data sparsity and cold start problems. For new or sparse users, the short sequence of behavior IDs available for learning prevents the model from making reliable inferences. Furthermore, for any user, the model struggles to distinguish subtle differences in intent for the same behavior across different contexts. This solution systematically broadens the scope of data collection, treating user behavior as a complex event composed of multiple signals rather than a single action identifier. The solution not only collects core behavior data but also... In addition to sequences, it integrates user static profiles, device information, precise timestamps, geographic location data, and even auxiliary information from text content or sensor signals. In the feature extraction stage, embedding technology maps behavior IDs into dense vectors containing semantic relationships; various time features such as behavior intervals, session durations, and periodic patterns are derived from timestamps; and contextual information such as user demographics and device types are cross-fused, transforming each behavior node from an isolated symbol into a feature vector rich in contextual information. This greatly enriches the model's input information and provides a solid data foundation for the model to understand the motivations behind user behavior. For new users or users with sparse behavior, even if their behavior sequences are short, the rich contextual features can provide the model with additional inference basis, effectively alleviating the cold start problem. 2. Existing prediction methods based on user behavior sequences suffer from insufficient ability to handle complex temporal dynamics and long-range dependencies. Many traditional models, such as variants based on recurrent neural networks, face the challenge of vanishing or exploding gradients when capturing correlations between distant behaviors in long sequences. This limits the effective memory range of the model and makes it difficult to correlate users' early interests with current decisions. Furthermore, while standard attention mechanisms or Transformer models alleviate the long-term dependency problem, their self-attention computation is essentially position-independent, injecting sequential information only through simple positional encoding. They fail to explicitly model the crucial factor of time intervals between behaviors. In real-world scenarios, user behavior sequences have highly variable temporal elasticity. Even if the content of an action is the same, the impact of an action that occurred yesterday on the current prediction should be significantly different from that of an action that occurred a year ago. Explicit modeling of this time decay effect is generally lacking, making it impossible to accurately measure the true importance of historical behavior to the current prediction. At the same time, existing model parameters are usually optimized once on a fixed data distribution. When faced with new user groups or data with drastically different behavioral patterns, they become less effective. When the distribution shifts significantly, the model's performance deteriorates sharply. This solution addresses this by designing an integrated, innovative model architecture with time-awareness and meta-learning capabilities. The time-aware attention mechanism, building upon standard content relevance calculations, introduces a learnable time decay function. This function dynamically adjusts attention weights based on the time intervals between actions, ensuring that recent actions naturally receive higher attention, while the influence of distant actions decays reasonably according to their actual time distance. This allows the model to more precisely characterize the evolution and shifts in user interests. The meta-learning module employs a model-agnostic meta-learning algorithm, simulating few-shot learning tasks during the training phase to specifically train the model, enabling it to quickly adapt to new users or tasks. Furthermore, through dynamic sequence segmentation and processing techniques, the model can flexibly handle behavioral sequences of varying lengths, ensuring that the model effectively extracts key information regardless of sequence length. The model not only more accurately captures truly important historical behaviors in long-term dependencies but also significantly reduces model maintenance costs and extends its effective lifespan when user behavior patterns change or when facing entirely new users. Attached Figure Description

[0017] Figure 1 The diagram shown is a flowchart illustrating the next prediction method framework based on user behavior sequence data analysis of the present invention. Figure 2 The diagram illustrates the data collection and acquisition process of the next prediction method based on user behavior sequence data analysis according to the present invention. Figure 3 The diagram shown illustrates the data cleaning and preprocessing flow of the next prediction method based on user behavior sequence data analysis according to the present invention. Figure 4 The diagram illustrates the sequence data encoding and feature extraction process of the next prediction method based on user behavior sequence data analysis according to the present invention. Detailed Implementation

[0018] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0019] Please see Figure 1-4 This invention provides an embodiment of a next-item prediction method based on user behavior sequence data analysis, comprising the following steps: S11: Systematically collect raw user behavior data from multiple heterogeneous data sources and transmit it to the central storage system; S12: Perform deduplication, missing value imputation, format correction, noise filtering, and standardization on the original data; S13: Convert the cleaned behavioral sequence into a numerical vector and extract several key features; S14: Construct a Transformer-based model that integrates attention mechanisms, meta-learning, and interpretability modules; S15: Employs strategies including data augmentation, distributed training, and adversarial training to efficiently optimize model parameters using preprocessed data; S16: The model performance was comprehensively evaluated on the test set using multiple metrics and ablation experiments, and its generalization ability and robustness were verified. S17: Systematically search for and adjust key hyperparameter combinations in the model and training process; S18: Encapsulate the optimized model as an API service and integrate it into the production environment; S19: Continuously monitor the model's performance in the production environment and establish an iterative update mechanism for the model based on feedback data and performance changes.

[0020] Preferably, the data collection and acquisition process includes the following steps: S21: Identify and list all user behavior data sources, including server logs, mobile application event streams, database records, and third-party API interfaces. By reviewing system architecture documents and user interaction flowcharts, determine the access protocol for each data source. Configure real-time data streams using data acquisition tools such as Apache Flume or Kafka. Set up data pipelines to transfer raw data to distributed storage systems such as HDFS or Amazon S3, and record metadata about the data sources, including format, frequency, and size. S22: Configure the data acquisition agent to be deployed on both the user and server sides, integrate the SDK into the mobile application, capture user click, browsing, and transaction events, set event triggers, automatically record timestamps, user IDs, and behavior types based on user actions, serialize data using JSON or Protocol Buffers format, transmit it to the central server over the network, and implement a data buffering mechanism to handle network latency issues; S23: Establish data access permission and compliance check processes, define data anonymization rules according to GDPR or CCPA regulations, use encryption tools TLS or SSL to protect data transmission, configure access control lists to restrict unauthorized access, and audit data sources regularly; S24: Deploy monitoring tools such as Prometheus or Datadog to track data collection performance, set metrics including data throughput, latency, and error rate, configure alarm rules to trigger notifications when the data stream is interrupted, use the ELK stack log analysis tool to record collection events, and generate reports to optimize data source configuration; S25: Achieve multimodal data integration, extract user behavior sequences from social media APIs, sensor data, and historical databases, use the ETL tool Apache NiFi to transform data formats, create a unified data catalog mapping for each source schema, and check consistency through data validation scripts; S26: Set up data backup and recovery mechanisms, use incremental backup strategies to periodically copy data to off-site storage, configure the snapshot tool AWS EBS Snapshots to protect data integrity, test the recovery process to simulate data loss scenarios, and document the backup plan for disaster recovery; S27: Optimize the data acquisition network configuration, distribute data traffic through a load balancer to avoid single points of failure, use CDN to accelerate data uploads from edge devices, adjust TCP / IP parameters to reduce transmission latency, and implement the GZIP data compression algorithm to reduce bandwidth usage.

[0021] Preferably, data cleaning and preprocessing includes the following steps: S31: Perform data deduplication, calculate the fingerprint of each record using the hash algorithm MD5 or SHA-256, compare duplicate entries and retain unique instances, apply the distributed computing framework Apache Spark to process large-scale datasets, write scripts to automatically delete duplicate records, and record deduplication statistics for quality assessment; S32: Handle missing value issues. Identify null or NaN fields through data analysis, use time series linear imputation or context-based mean replacement, apply the pandas library in Python using a custom function to repair missing values, and verify whether the data distribution is consistent after imputation. S33: Correct data format inconsistencies, parse the date and time fields in the original data to conform to the ISO 8601 standard, convert the text encoding to UTF-8 to avoid garbled characters, use regular expressions to match and repair abnormal characters, and run a format verification tool to check that all fields conform to the predefined schema; S34: Filter outliers and noisy data, calculate the Z-score or IQR range for numerical fields, define thresholds to remove data points outside the range, apply sliding window smoothing techniques to handle abrupt changes in the time series, and use visualization tools such as Matplotlib to check the filtering effect; S35: Standardize and normalize data scaling. Apply Min-Max scaling or Z-score standardization to numerical features, using the StandardScaler function from the Scikit-learn library. Perform one-hot encoding or label encoding on categorical features and save the transformation parameters. S36: Segment user behavior sequences into training and test sets, dividing them chronologically to avoid data leakage. Use stratified sampling to ensure the representativeness of user distribution, set the split ratio to 20-80, and generate a sequence ID mapping file to track the source of each subset; S37: Implement a data quality assessment process, run automated test scripts to calculate these metrics, generate quality reports for problem areas, and adjust cleaning rules based on the results.

[0022] Preferably, the sequence data encoding and feature extraction process includes the following steps: S41: Convert the original action sequence into a numerical vector, use the embedding layer Word2Vec or GloVe to simulate the relationship between action items, train the embedding model to learn action representations on the historical sequence, adjust the vector dimension to balance expressive power and computational cost, and save the embedding matrix for feature mapping; S42: Extract time features from sequence timestamps, calculate behavioral intervals, sequence duration, and periodic patterns, use Python's datetime library to parse time information, generate features such as hours, days of the week, and seasons, and normalize time values ​​to a uniform range; S43: Construct sequence length and position encoding, add a position index vector to each sequence, generate Transformer-style position encoding using sine and cosine functions, handle variable-length sequences by padding or truncating to a fixed length, and record the actual sequence length for model input; S44: Derived contextual features are derived from user metadata, including user demographics, device type, and geographic location. These features are fused into the sequence representation, interaction terms are created using feature cross-validation, and PCA is applied to reduce feature dimensionality. S45: Achieve multimodal feature fusion by extracting auxiliary features from text, image, or sensor data, using the pre-trained BERT model to process text descriptions, CNN to process image data, and concatenating these features into the main sequence vector; S46: Apply sequence segmentation techniques to process long sequences. Use the sliding window method to divide the long sequence into overlapping subsequences. Set the window size and step size based on business requirements. Calculate the statistical features of each subsequence, such as mean and variance, and generate subsequence labels for training. S47: Optimize the feature selection process by using recursive feature elimination or tree-based importance scoring to filter the most relevant features to reduce noise, implement automated feature selection scripts, and validate the performance of the feature set on the validation set.

[0023] As a preferred approach, the design of an innovative model framework includes the following steps: S51: Design the core structure of a Transformer-based sequence model, use a multi-head self-attention mechanism to calculate the weights between actions within the sequence, use a position-feedforward network to process the attention output, stack multiple encoder layers to capture deep dependencies, and configure residual connections and layer normalization to stabilize training; S52: Integrates a time-aware attention mechanism, adds a time decay factor to self-attention, adjusts attention weights based on behavior time intervals, uses learnable parameters to simulate the effects of time, and tests the effects of different decay functions such as exponential decay; S53: Add a meta-learning module to enhance generalization ability. The MAML algorithm is used in the model initialization stage to train the model to quickly adapt to new user sequences. The meta-training set is used to simulate low-sample scenarios, and the meta-update step size is optimized to balance the convergence speed. S54: Construct a dynamic sequence processing component, using LSTM or GRU units as basic recurrent units, combining an attention mechanism to focus on key action steps, performing sequence padding mask processing on variable-length inputs, and outputting hidden states for the next prediction; S55: Embed an interpretability module, integrate it into the model output layer using SHAP or LIME tools, generate feature importance scores to explain prediction results, visualize attention weight mapping of key behaviors, and document the explanation logic for auditing; S56: Implement a multi-task learning framework, add auxiliary tasks such as sequence classification or anomaly detection, share encoding layers to reduce overfitting, define a loss function to weight the main task and auxiliary tasks, and adjust the weights based on validation performance; S57: Optimize the model input / output interface, define the input tensor shape including sequence length and feature dimension, output the next probability distribution over the behavior vocabulary, normalize the output using a Softmax layer, and configure the sequence generation mode for inference.

[0024] Preferably, the model training strategy includes the following steps: S61: Prepare the training data loader, use the DataLoader class of PyTorch or TensorFlow to batch load sequence data, set the batch size based on GPU memory, implement sequence padding and packing to process variable-length sequences, and apply data augmentation such as random masking or sequence rearrangement; S62: Define a loss function that combines cross-entropy loss and a regularization term. Use label smoothing to handle class imbalance, add L2 regularization to prevent overfitting, ignore padding positions when calculating the loss, and optimize the efficiency of loss calculation. S63: Select the optimization algorithm AdamW or SGD with momentum, set the learning rate scheduler such as cosine annealing or step descent, configure gradient clipping to limit gradient explosion, initialize model parameters using Xavier or He initialization, and monitor training dynamics; S64: Implement distributed training acceleration by using multi-GPU or TPU clusters via Horovod or PyTorch DDP, splitting data across devices, synchronizing gradient updates, and debugging communication bottlenecks to optimize training speed; S65: Integrates early stopping and model checkpointing mechanisms, monitors the validation set loss after each epoch, sets a patience threshold to stop training when there is no improvement, saves the best model weights to disk, and records the training history for analysis; S66: Apply a course-based learning strategy, starting with simple sequences and gradually increasing complexity. Sort the training data based on sequence length or difficulty, adjust the learning rate according to the course progress, and verify the impact of the strategy on convergence; S67: Enhance robustness by performing adversarial training, adding adversarial examples to the training data, generating perturbations using FGSM or PGD methods, calculating the adversarial loss and merging it into the total loss, and testing the model's resistance to noise.

[0025] Preferably, the model evaluation and validation process includes the following steps: S71: Divide the evaluation dataset into a reserved test set and a validation set, use time series splitting to ensure time order, define evaluation metrics including accuracy, recall, F1-score, and MRR, use automated evaluation scripts to calculate these metrics, and generate a confusion matrix to analyze error patterns; S72: Conduct ablation experiments to test innovative components, sequentially removing the attention mechanism, meta-learning module, or time-aware layer, comparing the performance of the baseline model and the complete model, using the statistical test t-test to verify significance, and documenting the contribution of each component; S73: Evaluate the model's generalization ability by sampling from the test set at different time periods, calculating metrics across users and across time, using K-fold cross-validation to reduce variance, and analyzing signs of overfitting or underfitting; S74: Test real-time prediction performance by deploying the model in a simulated environment, inputting streaming sequence data, measuring inference latency and throughput, using the performance analysis tool cProfile to identify bottlenecks, and optimizing code to reduce response time; S75: Verify interpretability output, manually check the importance of features generated by SHAP or LIME, compare prediction results with real user behavior, run case studies on specific sequences, and adjust interpretation module parameters to improve clarity; S76: Compare the performance with existing benchmark models GRU or Transformer baselines, train the comparison model on the same dataset, calculate the relative percentage improvement using standard evaluation protocols, and analyze the advantageous scenarios; S77: Perform robustness testing by injecting noise or missing values ​​into the test data, observing changes in model performance, testing stability using adversarial examples, and recording failure cases.

[0026] Preferably, the hyperparameter optimization and tuning process includes the following steps: S81: Define the hyperparameter search space, including learning rate, batch size, number of layers, number of attention heads, and hidden layer dimension. Record the parameters using the configuration management tool Hydra or a JSON file, and set the range based on prior knowledge. S82: Implement grid search or random search strategies, evaluate different combinations of hyperparameters on the validation set, accelerate the search process using parallel computing, record the performance metrics of each combination, and select the optimal parameter set; S83: Apply Bayesian optimization methods, use tools such as Optuna or Hyperopt to model the relationship between hyperparameters and performance, define an objective function to minimize the validation loss, run multiple iterations to converge to the optimum, and visualize the optimization process; S84: Optimize regularization hyperparameters dropout rate and L2 weights, adjust parameters through cross-validation, monitor training and validation loss curves to prevent overfitting, and document the final settings; S85: Optimize sequence-related parameters such as maximum sequence length and window size, test the impact of different values ​​on model performance, use business logic to guide the selection, and verify stability on the test set; S86: Calibrate the model's output probabilities, adjust the output distribution on the validation set using Platt scaling or isotonic regression to improve probability calibration, and test the predictive consistency of the calibrated model; S87: An automated hyperparameter tuning pipeline that integrates search, training, and evaluation steps with scripts, uses version control to track parameter changes, and generates tuning reports summarizing the best configuration.

[0027] Preferably, the system integration and deployment process includes the following steps: S91: Package the trained model into a deployable format, export the model using ONNX or TensorFlow SavedModel, optimize the graph structure for inference, test format compatibility and target platforms, and document the interface specifications; S92: Build a real-time inference service, create a REST API using the web framework Flask or FastAPI, define endpoints to receive sequence data and return prediction results, perform input validation and error handling, and configure load balancing to handle high concurrency; S93: Deploy the model to the production environment, choose the cloud platform AWS SageMaker or Kubernetes cluster, use Docker for containerized applications, set up an auto-scaling policy based on traffic, and monitor resource usage for CPU and memory; S94: Integrates data pipelines and inference services, connects real-time data streams from Kafka topics to model input, optimizes throughput through sequence buffering and batch processing, uses message queues to ensure data order, and tests end-to-end latency; S95: Implement an A / B testing framework, deploy multiple model versions, route some traffic to different versions, collect user feedback data, compare prediction accuracy and business metrics, and decide on full deployment based on the results; S96: Adds model version management and rollback mechanisms, uses MLflow tools to track model versions, sets up automated deployment pipelines, quickly rolls back to previous versions when performance degrades, and documents deployment history; S97: Optimizes inference performance, applies model quantization to reduce memory usage, uses GPU acceleration libraries to optimize computation, tests inference speed under real load, and continuously monitors service level agreements.

[0028] Preferably, when performing performance monitoring and iterative updates, the following steps are included: S101: Set up a real-time monitoring dashboard to visualize model performance metrics, including prediction accuracy, latency, and error rate, using Grafana or Kibana. Configure alerts to notify the team when metrics are abnormal, and review dashboard data regularly. S102: Collect user feedback and production data, log prediction results and actual user behavior, store feedback in the database, analyze the difference identification model, and generate weekly reports to summarize trends; S103: Implement the model retraining process, regularly update the model with new data, set retraining triggers based on performance thresholds or time intervals, automate data preprocessing and training steps, and verify the performance of the new model on the test set; S104: Monitor changes in data distribution, use statistical tools to compare and contrast the distributions of training data and real-time data, detect concept drift, adjust feature engineering or model parameters to adapt to changes, and document drift handling strategies; S105: Optimize system resource usage, analyze monitoring data to identify bottlenecks, adjust deployment configurations such as container resource limits, implement cost control measures, and perform performance tuning regularly; S106: Conduct regular security audits, check for vulnerabilities in models and services, update dependency libraries to patch security risks, and test protection against attacks; S107: Iterative improvement models are based on monitoring insights, analyzing error cases to identify improvement points, updating model architecture or feature sets, running small-scale experiments to verify improvements, and successfully integrating changes into the production process.

[0029] Example 1 This embodiment uses a large-scale e-commerce platform as an application scenario to build an intelligent system that can accurately predict the next time a user may click or purchase a product.

[0030] The platform first systematically identifies and connects to all relevant heterogeneous data sources, including: user clickstreams, browsing logs, and search queries recorded by the front-end web server and mobile application SDK; transaction records stored in the back-end order database; and click feedback data from third-party advertising networks. It utilizes Apache Kafka to build a high-throughput real-time data pipeline, continuously feeding this data into Amazon S3 distributed storage. During data collection, the mobile-integrated SDK automatically captures every user interaction, such as product exposure, dwell time, and adding items to the shopping cart, and serializes it using Protocol Buffers format, including a timestamp accurate to milliseconds, an encrypted user ID, device model, and current network environment. Simultaneously, a strict data governance process is established, anonymizing all personally identifiable information before transmission to comply with data privacy regulations, and monitoring the health of the data stream through Prometheus to ensure data integrity and timeliness.

[0031] The raw data stored in the data lake undergoes a series of cleaning and standardization processes. Data engineers use the Apache Spark distributed computing framework to run cleaning scripts. First, the SHA-256 hash value of each record is calculated for deduplication, removing duplicate logs caused by network retries, etc. For missing fields, such as missing page dwell time due to user session interruption, the average of actions before and after within the same session is used for filling. All timestamps are uniformly parsed and converted to the ISO 8601 standard format, and text information is uniformly encoded in UTF-8 to ensure global consistency. The Z-score method is used to identify and filter abnormal values ​​caused by system errors. Finally, numerical features such as product prices are normalized using Min-Max, and categorical features such as product categories are one-hot encoded. The processed data is then divided into training and test sets in chronological order to prevent future information leakage.

[0032] In this stage, the cleaned user behavior sequences are transformed into rich features that the model can understand. Each user's behavior is mapped into a dense numerical vector through the Word2Vec model. This vector can capture the semantic relationships between behaviors. At the same time, key time features are extracted from the timestamps, such as the interval between two behaviors, the day of the week in which the behavior occurred, and the time of day. The user's own metadata, such as historical purchasing power and the city of residence, are also used as contextual features along with device information. For long-sequence users, the sliding window technique is used to divide them into multiple fixed-length subsequences for processing. Finally, the state of each user at a certain moment is represented as a comprehensive feature vector that integrates behavioral semantics, temporal context, and user profile.

[0033] The core prediction model employs an enhanced Transformer architecture, with a time-aware multi-head self-attention mechanism at its heart. When calculating attention weights, it considers not only the relevance of behavioral content but also introduces a learnable time decay function, clearly distinguishing the impact of clicks from a week ago on the current prediction from those from a minute ago. To address the issue of sparse data for new or low-frequency users, the model integrates a meta-learning module. By simulating the task of "predicting new user interests with limited behavioral data" during the training phase, the model gains the ability to quickly adapt to new users. Furthermore, an interpretability module is embedded within the model to generate feature importance scores for the final prediction results. The model is built using the PyTorch framework and employs a distributed training strategy across multiple GPUs, combining advanced strategies such as curriculum learning and adversarial training to ensure efficient and stable convergence.

[0034] The trained and evaluated models are packaged into the TensorFlow SavedModel format and deployed in a Kubernetes-based containerized cloud environment. A high-concurrency RESTful API service is built using FastAPI. This service receives real-time requests from the platform's frontend or recommendation engine, carrying pre-processed context of the latest user behavior sequences. Upon receiving a request, the service calls the model to perform millisecond-level inference, calculating the probability distribution of the user's next most likely behavior. The service uses a load balancer to handle traffic spikes and is tightly integrated with existing Kafka data streams to ensure that real-time behavioral data can be used for prediction immediately. At the same time, the platform deploys an A / B testing framework to redirect some user traffic to the new model version to compare its performance with the old version on key business metrics.

[0035] After the system goes live, the performance of the prediction service is monitored around the clock through the Grafana dashboard, tracking the accuracy, response latency, and error rate of online predictions in real time. All model prediction results and subsequent user behavior are fully logged and compared with the prediction results regularly. Once the monitoring system detects that the model performance begins to decline due to user interest drift, or statistical tests reveal a significant difference between the online data distribution and the training data distribution, the automated pipeline will trigger the model retraining process. Retraining uses the latest user behavior data, automating the entire process from data cleaning to model validation, and seamlessly deploying the new model with better performance. This forms a self-optimizing and continuously evolving intelligent prediction closed loop, ensuring that the recommendation system can maintain high accuracy and user satisfaction in the long term.

[0036] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A method for predicting the next item based on user behavior sequence data analysis; characterized in that: It includes the following steps: S11: Systematically collect raw user behavior data from multiple heterogeneous data sources and transmit it to the central storage system; S12: Perform deduplication, missing value imputation, format correction, noise filtering, and standardization on the original data; S13: Convert the cleaned behavioral sequence into a numerical vector and extract several key features; S14: Construct a Transformer-based model that integrates attention mechanisms, meta-learning, and interpretability modules; S15: Employs strategies including data augmentation, distributed training, and adversarial training to efficiently optimize model parameters using preprocessed data; S16: The model performance was comprehensively evaluated on the test set using multiple metrics and ablation experiments, and its generalization ability and robustness were verified. S17: Systematically search for and adjust key hyperparameter combinations in the model and training process; S18: Encapsulate the optimized model as an API service and integrate it into the production environment; S19: Continuously monitor the model's performance in the production environment and establish an iterative update mechanism for the model based on feedback data and performance changes.

2. The next prediction method based on user behavior sequence data analysis according to claim 1, characterized in that: The data collection and acquisition process includes the following steps: S21: Identify and list all user behavior data sources, including server logs, mobile application event streams, database records, and third-party API interfaces. By reviewing system architecture documents and user interaction flowcharts, determine the access protocol for each data source. Configure real-time data streams using data acquisition tools such as Apache Flume or Kafka. Set up data pipelines to transfer raw data to distributed storage systems such as HDFS or Amazon S3, and record metadata about the data sources, including format, frequency, and size. S22: Configure the data acquisition agent to be deployed on both the user and server sides, integrate the SDK into the mobile application, capture user click, browsing, and transaction events, set event triggers, automatically record timestamps, user IDs, and behavior types based on user actions, serialize data using JSON or Protocol Buffers format, transmit it to the central server over the network, and implement a data buffering mechanism to handle network latency issues; S23: Establish data access permission and compliance check processes, define data anonymization rules according to GDPR or CCPA regulations, use encryption tools TLS or SSL to protect data transmission, configure access control lists to restrict unauthorized access, and audit data sources regularly; S24: Deploy monitoring tools such as Prometheus or Datadog to track data collection performance, set metrics including data throughput, latency, and error rate, configure alarm rules to trigger notifications when the data stream is interrupted, use the ELK stack log analysis tool to record collection events, and generate reports to optimize data source configuration; S25: Achieve multimodal data integration, extract user behavior sequences from social media APIs, sensor data, and historical databases, use the ETL tool Apache NiFi to transform data formats, create a unified data catalog mapping for each source schema, and check consistency through data validation scripts; S26: Set up data backup and recovery mechanisms, use incremental backup strategies to periodically copy data to off-site storage, configure the snapshot tool AWS EBS Snapshots to protect data integrity, test the recovery process to simulate data loss scenarios, and document the backup plan for disaster recovery; S27: Optimize the data acquisition network configuration, distribute data traffic through a load balancer to avoid single points of failure, use CDN to accelerate data uploads from edge devices, adjust TCP / IP parameters to reduce transmission latency, and implement the GZIP data compression algorithm to reduce bandwidth usage.

3. The next prediction method based on user behavior sequence data analysis according to claim 1, characterized in that: Data cleaning and preprocessing includes the following steps: S31: Perform data deduplication, calculate the fingerprint of each record using the hash algorithm MD5 or SHA-256, compare duplicate entries and retain unique instances, apply the distributed computing framework Apache Spark to process large-scale datasets, write scripts to automatically delete duplicate records, and record deduplication statistics for quality assessment; S32: Handle missing value issues. Identify null or NaN fields through data analysis, use time series linear imputation or context-based mean replacement, apply the pandas library in Python using a custom function to repair missing values, and verify whether the data distribution is consistent after imputation. S33: Correct data format inconsistencies, parse the date and time fields in the original data to conform to the ISO 8601 standard, convert the text encoding to UTF-8 to avoid garbled characters, use regular expressions to match and repair abnormal characters, and run a format verification tool to check that all fields conform to the predefined schema; S34: Filter outliers and noisy data, calculate the Z-score or IQR range for numerical fields, define thresholds to remove data points outside the range, apply sliding window smoothing techniques to handle abrupt changes in the time series, and use visualization tools such as Matplotlib to check the filtering effect; S35: Standardize and normalize data scaling. Apply Min-Max scaling or Z-score standardization to numerical features, using the StandardScaler function from the Scikit-learn library. Perform one-hot encoding or label encoding on categorical features and save the transformation parameters. S36: Segment user behavior sequences into training and test sets, dividing them chronologically to avoid data leakage. Use stratified sampling to ensure the representativeness of user distribution, set the split ratio to 20-80, and generate a sequence ID mapping file to track the source of each subset; S37: Implement a data quality assessment process, run automated test scripts to calculate these metrics, generate quality reports for problem areas, and adjust cleaning rules based on the results.

4. The next prediction method based on user behavior sequence data analysis according to claim 1, characterized in that: The following steps are included in sequence data encoding and feature extraction: S41: Convert the original action sequence into a numerical vector, use the embedding layer Word2Vec or GloVe to simulate the relationship between action items, train the embedding model to learn action representations on the historical sequence, adjust the vector dimension to balance expressive power and computational cost, and save the embedding matrix for feature mapping; S42: Extract time features from sequence timestamps, calculate behavioral intervals, sequence duration, and periodic patterns, use Python's datetime library to parse time information, generate features such as hours, days of the week, and seasons, and normalize time values ​​to a uniform range; S43: Construct sequence length and position encoding, add a position index vector to each sequence, generate Transformer-style position encoding using sine and cosine functions, handle variable-length sequences by padding or truncating to a fixed length, and record the actual sequence length for model input; S44: Derived contextual features are derived from user metadata, including user demographics, device type, and geographic location. These features are fused into the sequence representation, interaction terms are created using feature cross-validation, and PCA is applied to reduce feature dimensionality. S45: Achieve multimodal feature fusion by extracting auxiliary features from text, image, or sensor data, using the pre-trained BERT model to process text descriptions, CNN to process image data, and concatenating these features into the main sequence vector; S46: Apply sequence segmentation techniques to process long sequences. Use the sliding window method to divide the long sequence into overlapping subsequences. Set the window size and step size based on business requirements. Calculate the statistical features of each subsequence, such as mean and variance, and generate subsequence labels for training. S47: Optimize the feature selection process by using recursive feature elimination or tree-based importance scoring to filter the most relevant features to reduce noise, implement automated feature selection scripts, and validate the performance of the feature set on the validation set.

5. The next prediction method based on user behavior sequence data analysis according to claim 1, characterized in that: The following steps are included in designing an innovative model framework: S51: Design the core structure of a Transformer-based sequence model, use a multi-head self-attention mechanism to calculate the weights between actions within the sequence, use a position-feedforward network to process the attention output, stack multiple encoder layers to capture deep dependencies, and configure residual connections and layer normalization to stabilize training; S52: Integrates a time-aware attention mechanism, adds a time decay factor to self-attention, adjusts attention weights based on behavior time intervals, uses learnable parameters to simulate the effects of time, and tests the effects of different decay functions such as exponential decay; S53: Add a meta-learning module to enhance generalization ability. The MAML algorithm is used in the model initialization stage to train the model to quickly adapt to new user sequences. The meta-training set is used to simulate low-sample scenarios, and the meta-update step size is optimized to balance the convergence speed. S54: Construct a dynamic sequence processing component, using LSTM or GRU units as basic recurrent units, combining an attention mechanism to focus on key action steps, performing sequence padding mask processing on variable-length inputs, and outputting hidden states for the next prediction; S55: Embed an interpretability module, integrate it into the model output layer using SHAP or LIME tools, generate feature importance scores to explain prediction results, visualize attention weight mapping of key behaviors, and document the explanation logic for auditing; S56: Implement a multi-task learning framework, add auxiliary tasks such as sequence classification or anomaly detection, share encoding layers to reduce overfitting, define a loss function to weight the main task and auxiliary tasks, and adjust the weights based on validation performance; S57: Optimize the model input / output interface, define the input tensor shape including sequence length and feature dimension, output the next probability distribution over the behavior vocabulary, normalize the output using a Softmax layer, and configure the sequence generation mode for inference.

6. The next prediction method based on user behavior sequence data analysis according to claim 1, characterized in that: The model training strategy includes the following steps: S61: Prepare the training data loader, use the DataLoader class of PyTorch or TensorFlow to batch load sequence data, set the batch size based on GPU memory, implement sequence padding and packing to process variable-length sequences, and apply data augmentation such as random masking or sequence rearrangement; S62: Define a loss function that combines cross-entropy loss and a regularization term. Use label smoothing to handle class imbalance, add L2 regularization to prevent overfitting, ignore padding positions when calculating the loss, and optimize the efficiency of loss calculation. S63: Select the optimization algorithm AdamW or SGD with momentum, set the learning rate scheduler such as cosine annealing or step descent, configure gradient clipping to limit gradient explosion, initialize model parameters using Xavier or He initialization, and monitor training dynamics; S64: Implement distributed training acceleration by using multi-GPU or TPU clusters via Horovod or PyTorch DDP, splitting data across devices, synchronizing gradient updates, and debugging communication bottlenecks to optimize training speed; S65: Integrates early stopping and model checkpointing mechanisms, monitors the validation set loss after each epoch, sets a patience threshold to stop training when there is no improvement, saves the best model weights to disk, and records the training history for analysis; S66: Apply a course-based learning strategy, starting with simple sequences and gradually increasing complexity. Sort the training data based on sequence length or difficulty, adjust the learning rate according to the course progress, and verify the impact of the strategy on convergence; S67: Enhance robustness by performing adversarial training, adding adversarial examples to the training data, generating perturbations using FGSM or PGD methods, calculating the adversarial loss and merging it into the total loss, and testing the model's resistance to noise.

7. The next prediction method based on user behavior sequence data analysis according to claim 1, characterized in that: The model evaluation and validation process includes the following steps: S71: Divide the evaluation dataset into a reserved test set and a validation set, use time series splitting to ensure time order, define evaluation metrics including accuracy, recall, F1-score, and MRR, use automated evaluation scripts to calculate these metrics, and generate a confusion matrix to analyze error patterns; S72: Conduct ablation experiments to test innovative components, sequentially removing the attention mechanism, meta-learning module, or time-aware layer, comparing the performance of the baseline model and the complete model, using the statistical test t-test to verify significance, and documenting the contribution of each component; S73: Evaluate the model's generalization ability by sampling from the test set at different time periods, calculating metrics across users and across time, using K-fold cross-validation to reduce variance, and analyzing signs of overfitting or underfitting; S74: Test real-time prediction performance by deploying the model in a simulated environment, inputting streaming sequence data, measuring inference latency and throughput, using the performance analysis tool cProfile to identify bottlenecks, and optimizing code to reduce response time; S75: Verify interpretability output, manually check the importance of features generated by SHAP or LIME, compare prediction results with real user behavior, run case studies on specific sequences, and adjust interpretation module parameters to improve clarity; S76: Compare the performance with existing benchmark models GRU or Transformer baselines, train the comparison model on the same dataset, calculate the relative percentage improvement using standard evaluation protocols, and analyze the advantageous scenarios; S77: Perform robustness testing by injecting noise or missing values ​​into the test data, observing changes in model performance, testing stability using adversarial examples, and recording failure cases.

8. The next prediction method based on user behavior sequence data analysis according to claim 1, characterized in that: The following steps are included when performing hyperparameter optimization and tuning: S81: Define the hyperparameter search space, including learning rate, batch size, number of layers, number of attention heads, and hidden layer dimension. Record the parameters using the configuration management tool Hydra or a JSON file, and set the range based on prior knowledge. S82: Implement grid search or random search strategies, evaluate different combinations of hyperparameters on the validation set, accelerate the search process using parallel computing, record the performance metrics of each combination, and select the optimal parameter set; S83: Apply Bayesian optimization methods, use tools such as Optuna or Hyperopt to model the relationship between hyperparameters and performance, define an objective function to minimize the validation loss, run multiple iterations to converge to the optimum, and visualize the optimization process; S84: Optimize regularization hyperparameters dropout rate and L2 weights, adjust parameters through cross-validation, monitor training and validation loss curves to prevent overfitting, and document the final settings; S85: Optimize sequence-related parameters such as maximum sequence length and window size, test the impact of different values ​​on model performance, use business logic to guide the selection, and verify stability on the test set; S86: Calibrate the model's output probabilities, adjust the output distribution on the validation set using Platt scaling or isotonic regression to improve probability calibration, and test the predictive consistency of the calibrated model; S87: An automated hyperparameter tuning pipeline that integrates search, training, and evaluation steps with scripts, uses version control to track parameter changes, and generates tuning reports summarizing the best configuration.

9. The next prediction method based on user behavior sequence data analysis according to claim 1, characterized in that: The following steps are included when performing system integration and deployment: S91: Package the trained model into a deployable format, export the model using ONNX or TensorFlow SavedModel, optimize the graph structure for inference, test format compatibility and target platforms, and document the interface specifications; S92: Build a real-time inference service, create a REST API using the web framework Flask or FastAPI, define endpoints to receive sequence data and return prediction results, perform input validation and error handling, and configure load balancing to handle high concurrency; S93: Deploy the model to the production environment, choose the cloud platform AWS SageMaker or Kubernetes cluster, use Docker for containerized applications, set up an auto-scaling policy based on traffic, and monitor resource usage for CPU and memory; S94: Integrates data pipelines and inference services, connects real-time data streams from Kafka topics to model input, optimizes throughput through sequence buffering and batch processing, uses message queues to ensure data order, and tests end-to-end latency; S95: Implement an A / B testing framework, deploy multiple model versions, route some traffic to different versions, collect user feedback data, compare prediction accuracy and business metrics, and decide on full deployment based on the results; S96: Adds model version management and rollback mechanisms, uses MLflow tools to track model versions, sets up automated deployment pipelines, quickly rolls back to previous versions when performance degrades, and documents deployment history; S97: Optimize inference performance, apply model quantization to reduce memory usage, use GPU acceleration libraries to optimize computation, test inference speed under real load, and continuously monitor service level agreements.

10. The next prediction method based on user behavior sequence data analysis according to claim 1, characterized in that: When performing performance monitoring and iterative updates, the following steps are included: S101: Set up a real-time monitoring dashboard to visualize model performance metrics, including prediction accuracy, latency, and error rate, using Grafana or Kibana. Configure alerts to notify the team when metrics are abnormal, and review dashboard data regularly. S102: Collect user feedback and production data, log prediction results and actual user behavior, store feedback in the database, analyze the difference identification model, and generate weekly reports to summarize trends; S103: Implement the model retraining process, regularly update the model with new data, set retraining triggers based on performance thresholds or time intervals, automate data preprocessing and training steps, and verify the performance of the new model on the test set; S104: Monitor changes in data distribution, use statistical tools to compare and contrast the distributions of training data and real-time data, detect concept drift, adjust feature engineering or model parameters to adapt to changes, and document drift handling strategies; S105: Optimize system resource usage, analyze monitoring data to identify bottlenecks, adjust deployment configurations such as container resource limits, implement cost control measures, and perform performance tuning regularly; S106: Conduct regular security audits, check for vulnerabilities in models and services, update dependency libraries to patch security risks, and test protection against attacks; S107: Iterative improvement models are based on monitoring insights, analyzing error cases to identify improvement points, updating model architecture or feature sets, running small-scale experiments to verify improvements, and successfully integrating changes into the production process.

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