Dynamic user portrait construction and updating system based on machine learning and big data analysis

Abstract

The invention relates to the field of data processing, in particular to a dynamic user portrait construction and updating system based on machine learning and big data analysis. The system comprises a data collection module, a data preprocessing module, a feature extraction module, a user portrait construction module and a real-time updating module. The data collection module is used for comprehensively collecting user data from multiple channels; the data preprocessing module is used for cleaning, integrating and standardizing the collected original data; the feature extraction module is used for extracting valuable features from the preprocessed data; the user portrait construction module constructs a multi-dimensional and fine-grained portrait for the user based on the extracted features; and the real-time updating module continuously monitors the user behavior data and dynamically updates the user portrait according to a preset updating strategy. According to the method, mass data can be efficiently processed, user features can be deeply mined, user portraits can be updated in real time, and user experience can be improved, so that more personalized and accurate services and marketing strategies can be provided.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a dynamic user portrait construction and update system based on machine learning and big data analysis. Background Art

[0002] In the existing technology, the construction and update of user portraits mainly rely on traditional data analysis methods and limited data sources. These methods are incapable of dealing with the massive, multi-dimensional and dynamic user data in the current Internet environment. The following is an overview of several common user portrait construction and update technologies currently on the market: statistical-based user portraits, rule-based user portraits, user portraits from a single data source, non-real-time updated user portraits, and limited machine learning applications. The above technologies have the following shortcomings:

[0003] 1. Limited data processing capabilities:

[0004] Inadequate processing of massive data: Existing technologies often have difficulty efficiently processing the massive data generated in the Internet environment, resulting in low data utilization and inability to fully tap the value of user information;

[0005] Data redundancy and noise: During the data collection process, a large amount of redundant and noisy data is easily mixed in, and the existing technology has limited ability to clean and integrate such data, which affects the accuracy and efficiency of subsequent analysis;

[0006] 2. Feature extraction is not comprehensive:

[0007] Shallow feature mining: Many systems only mine shallow features of user data, such as basic attributes and consumption frequency, but fail to deeply mine users’ potential needs and complex behavioral characteristics.

[0008] Algorithm limitations: Although some systems have introduced machine learning algorithms, they are often limited to simple classification or clustering algorithms;

[0009] 3. Lack of real-time performance:

[0010] Non-real-time updates: Most existing systems update user profiles on a regular basis, such as daily, weekly, or monthly. This fails to capture changes in user interests and behaviors in a timely manner, resulting in a lag in user profiles.

[0011] Poor dynamic adaptability: In the face of rapid changes in user behavior, existing technologies often have difficulty in quickly adjusting user profiles and are unable to provide timely and accurate personalized services and marketing strategies;

[0012] 4. Single data source:

[0013] Limited dimensions: Many systems rely on a single data source to build user portraits, which results in limited dimensions of user portraits and cannot fully reflect the true appearance of users;

[0014] Data bias: A single data source may have data bias or insufficiency, affecting the accuracy and completeness of user portraits;

[0015] 5. Poor user experience:

[0016] Insufficient personalized services: Due to the inaccuracy and lag in the construction and updating of user portraits, the personalized services and precision marketing provided often fail to meet the actual needs of users;

[0017] Privacy protection issues: How to protect user privacy during data collection and processing has become a major problem. Existing technologies often have deficiencies in privacy protection, which can easily lead to a crisis of user trust.

[0018] In summary, the existing background technology solutions have defects to varying degrees in terms of data processing capabilities, comprehensiveness of feature extraction, real-time performance, diversity of data sources, and user experience. Summary of the invention

[0019] The purpose of the present invention is to address the problems existing in the background technology and propose a dynamic user portrait construction and update system based on machine learning and big data analysis, which can efficiently process massive data, deeply mine user characteristics, update user portraits in real time and improve user experience, so as to provide more personalized and accurate services and marketing strategies.

[0020] The technical solution of the present invention is a dynamic user portrait construction and update system based on machine learning and big data analysis, including a data collection module, a data preprocessing module, a feature extraction module, a user portrait construction module and a real-time update module; the data collection module is used to comprehensively collect user data from multiple channels, and the user data includes static information and dynamic information, the static information includes user basic attribute data, and the dynamic information includes user behavior data; the data preprocessing module is communicated with the data collection module, and is used to clean, integrate and standardize the collected raw data to ensure data quality and lay a foundation for subsequent analysis; the feature extraction module is communicated with the data preprocessing module, and is used to extract valuable features from the preprocessed data to describe the user's personality and behavior; the user portrait construction module is communicated with the feature extraction module, and a multi-dimensional and fine-grained portrait is constructed for the user based on the extracted features; the real-time update module is communicated with the data collection module and the user portrait construction module respectively, and by continuously monitoring the user behavior data, it captures the changes in user interests in real time, and dynamically updates the user portrait according to a preset update strategy.

[0021] Preferably, it also includes a portrait application interface that is communicatively connected to the user portrait construction module. The portrait application interface is used to provide standardized user portrait query and application interface, and supports third-party applications to provide personalized services and precision marketing based on user portraits.

[0022] Preferably, it also includes a data encryption module, which is used to encrypt, store and transmit user data, set strict data access permissions and audit mechanisms, conduct regular security vulnerability scans and risk assessments, and follow the minimum necessary principle and user authorization principle when collecting and using user data to ensure the legality and compliance of user data.

[0023] Preferably, the data collection module utilizes API interfaces, crawler technology, and data collaboration to acquire user data in real time or periodically, and stores the data in a secure and scalable data warehouse.

[0024] Preferably, the data processing of the data preprocessing module includes cleaning, integration and standardization;

[0025] Cleaning: remove duplicate data, invalid data and outliers;

[0026] Integration: Correlate and merge data from different channels to form a unified data view;

[0027] Standardization: Format the data to ensure consistency in data format and dimension.

[0028] Preferably, the feature extraction module performs feature selection and extraction through a machine learning algorithm, designs a feature extraction model according to business requirements and data characteristics, and extracts key features through model training and optimization.

[0029] Preferably, the user portrait construction module adopts cluster analysis and association rule mining techniques to divide users into different groups or label them accordingly, and builds a user portrait model with practical application value in combination with business needs and scenario settings. It labels each user or user group and calculates the weight of the label to reflect the user's interests, preferences and needs.

[0030] Preferably, the real-time update module sets up a data monitoring and triggering mechanism to capture changes in user behavior data in real time, determines whether the user portrait needs to be updated based on a preset update strategy, and uses incremental learning or online learning technology to update and adjust the user portrait in real time.

[0031] Compared with the prior art, the present invention has the following beneficial technical effects:

[0032] The present invention has the advantages of multi-source data integration and processing capabilities, advanced machine learning algorithm application, real-time update mechanism, standardized interface and ease of use, data processing efficiency, data security and privacy protection, etc. It can efficiently process and accurately analyze massive data, deeply explore user characteristics, update user portraits in real time and improve user experience, providing enterprises with more personalized and accurate services and marketing strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 4 is a system connection block diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0034] like Figure 1 As shown, the dynamic user portrait construction and update system based on machine learning and big data analysis proposed in this embodiment includes a data collection module, a data preprocessing module, a feature extraction module, a user portrait construction module, a real-time update module, a portrait application interface and a data encryption module.

[0035] The data collection module is used to comprehensively collect user data from multiple channels. User data includes static information and dynamic information. Static information includes basic user attribute data, and dynamic information includes user behavior data. It efficiently collects multi-source and heterogeneous user data, and is no longer limited to a single data source. Instead, it collects user data from multiple channels such as social media, e-commerce platforms, and offline stores to enrich the dimensions and depth of user portraits, so that they can more comprehensively and truly reflect the actual situation of users. This multi-source data integration capability ensures the comprehensiveness and accuracy of user portraits, because data from different channels can complement each other to form a more complete and three-dimensional understanding of users. Use API interfaces, crawler technology, data cooperation, etc. to obtain user data in real time or regularly, and store it in a secure and scalable data warehouse. Use data warehouses or big data platforms to store and manage these data for subsequent analysis and mining.

[0036] The data preprocessing module is connected to the data collection module to clean, integrate and standardize the collected raw data to ensure data quality and lay the foundation for subsequent analysis. The data processing of the data preprocessing module includes cleaning, integration and standardization. Cleaning: remove duplicate data, invalid data and outliers. Integration: associate and merge data from different channels to form a unified data view. Standardization: format the data to ensure the consistency of data format and dimension.

[0037] The feature extraction module is connected to the data preprocessing module to perform in-depth mining on the preprocessed user data and extract valuable features from the preprocessed data, such as interest preferences, consumption habits, social tendencies and other key features, which are used to describe the user's personality and behavior. The feature extraction module selects and extracts features through machine learning algorithms (such as deep learning, decision trees, random forests, etc.), designs feature extraction models based on business needs and data characteristics, and continuously adjusts algorithm parameters through model training and optimization to improve the accuracy and efficiency of feature extraction and extract key features.

[0038] The user portrait construction module communicates with the feature extraction module and builds a multi-dimensional, fine-grained portrait for the user based on the extracted features. The user portrait construction module uses cluster analysis and association rule mining techniques to divide users into different groups or label them accordingly. Combined with business needs and scenario settings, it builds a user portrait model with practical application value, labels each user or user group, and calculates the weight of the label to reflect the user's interests, preferences, and needs.

[0039] The real-time update module communicates with the data collection module and the user portrait construction module respectively. By continuously monitoring user behavior data, it captures changes in user interests in real time, and dynamically updates user portraits according to preset update strategies to ensure the timeliness and accuracy of user portraits, so that companies can adjust marketing strategies and service plans in a timely manner. The real-time update module sets up data monitoring and triggering mechanisms to capture changes in user behavior data in real time, and determines whether the user portrait needs to be updated based on preset update strategies (such as time windows, behavior thresholds, etc.). When it is detected that the user behavior reaches the preset time window or behavior threshold, the system automatically triggers the update process. Using incremental learning or online learning technology, the user portrait is updated and adjusted in real time to ensure that the user portrait always reflects the user's latest status.

[0040] The portrait application interface communicates with the user portrait building module. The portrait application interface is used to provide standardized user portrait query and application interface, supporting third-party applications to provide personalized services and precision marketing based on user portraits, improving user experience and satisfaction, and also helping companies achieve more efficient marketing and customer management. Design and implement a set of standardized API interface specifications, including interface definition, parameter description, return result format, etc., and provide support materials such as interface documentation and development kits (SDKs) to facilitate the access and use of third-party applications. This standardized interface design reduces the complexity and cost of system integration and improves the ease of use and scalability of the system. In addition, customized development services can be provided according to user needs to meet application requirements in different scenarios.

[0041] In the process of building and updating user portraits, a series of security measures and technical means are adopted to ensure the security and privacy of user data. The data encryption module uses encryption technology to encrypt, store and transmit user data; set up strict data access rights and audit mechanisms; comply with relevant laws, regulations and industry standards; conduct security vulnerability scans and risk assessments regularly, etc. At the same time, the principle of minimum necessity and user authorization is followed when collecting and using user data to ensure the legality and compliance of user data.

[0042] By gaining insight into user needs, consumption habits, behavioral trends and other aspects, companies can achieve comprehensive, in-depth and dynamic analysis of user data, thereby building a more accurate and comprehensive user portrait, providing strong support for companies to formulate marketing strategies, optimize products and services, and better meet user needs and improve user satisfaction and loyalty.

[0043] This embodiment has the advantages of multi-source data integration and processing capabilities, advanced machine learning algorithm applications, real-time update mechanism, standardized interface and ease of use, data processing efficiency, data security and privacy protection, etc. It can efficiently process and accurately analyze massive data, deeply explore user characteristics, update user portraits in real time and improve user experience, providing enterprises with more personalized and accurate services and marketing strategies.

[0044] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited thereto, and various changes can be made within the knowledge scope of technicians in the relevant technical field without departing from the purpose of the present invention.

Claims

1. A dynamic user portrait construction and update system based on machine learning and big data analysis, characterized by: include: A data collection module is used to comprehensively collect user data from multiple channels. The user data includes static information and dynamic information. The static information includes basic attribute data of users, and the dynamic information includes user behavior data. The data preprocessing module is connected to the data collection module and is used to clean, integrate and standardize the collected raw data to ensure data quality and lay the foundation for subsequent analysis; A feature extraction module, which is in communication with the data preprocessing module and is used to extract valuable features from the preprocessed data to describe the user's personality and behavior; The user portrait construction module communicates with the feature extraction module and constructs a multi-dimensional and fine-grained portrait for the user based on the extracted features; The real-time update module is communicated with the data collection module and the user portrait construction module respectively. By continuously monitoring the user behavior data, it captures the changes in user interests in real time and dynamically updates the user portrait according to the preset update strategy.

2. The dynamic user portrait construction and updating system based on machine learning and big data analysis according to claim 1 is characterized in that: It also includes a portrait application interface that communicates with the user portrait construction module. The portrait application interface is used to provide standardized user portrait query and application interface, and supports third-party applications to provide personalized services and precision marketing based on user portraits.

3. The dynamic user portrait construction and updating system based on machine learning and big data analysis according to claim 1 is characterized in that: It also includes a data encryption module, which is used to encrypt the storage and transmission of user data, set strict data access rights and audit mechanisms, conduct regular security vulnerability scans and risk assessments, and follow the minimum necessary principle and user authorization principle when collecting and using user data to ensure the legality and compliance of user data.

4. The dynamic user portrait construction and updating system based on machine learning and big data analysis according to claim 1 is characterized in that: The data collection module uses API interfaces, crawler technology and data cooperation methods to obtain user data in real time or periodically and store it in a secure and scalable data warehouse.

5. The dynamic user portrait construction and updating system based on machine learning and big data analysis according to claim 4 is characterized in that: Data processing in the data preprocessing module includes cleaning, integration, and standardization; Cleaning: remove duplicate data, invalid data and outliers; Integration: Correlate and merge data from different channels to form a unified data view; Standardization: Format the data to ensure consistency in data format and dimension.

6. The dynamic user portrait construction and updating system based on machine learning and big data analysis according to claim 5 is characterized in that: The feature extraction module selects and extracts features through machine learning algorithms. It designs feature extraction models based on business needs and data characteristics, and extracts key features through model training and optimization.

7. The dynamic user portrait construction and updating system based on machine learning and big data analysis according to claim 6 is characterized in that: The user portrait construction module uses cluster analysis and association rule mining techniques to divide users into different groups or label them accordingly. It combines business needs and scenario settings to build a user portrait model with practical application value, label each user or user group, and calculate the weight of the label to reflect the user's interests, preferences, and needs.

8. The dynamic user portrait construction and updating system based on machine learning and big data analysis according to claim 7 is characterized in that: The real-time update module sets up data monitoring and triggering mechanisms to capture changes in user behavior data in real time, determine whether the user portrait needs to be updated based on the preset update strategy, and use incremental learning or online learning technology to update and adjust the user portrait in real time.

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