Intelligent literature pushing system based on multi-dimensional data mining
By using multi -dimensional data mining technology in the active push system, combining user behavior characteristics and historical reading data, the establishment of user feature models, realizing intelligent push, solving the problem that the existing system cannot accurately identify user interest, improved user satisfaction, Essence
Patent Information
- Application Number
- CN202411478516.7
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-22
- Publication Date
- 2025-01-24
AI Technical Summary
The existing active push system is not intelligent enough in content selection and cannot accurately identify and predict user interests, resulting in users losing interest in notifications and decreasing system satisfaction.
Adopt intelligent literature push systems based on multi -dimensional data mining, establish user feature models and predict through the intelligent analysis unit combined with the first data and the second data; at the same time, the third data is analyzed and screened through the intelligent screening unit to achieve users Monitoring and management of database data.
It realizes multi-dimensional data mining and intelligent push, accurately identifies and predicts user interests, can push required information to users in a timely and targeted manner, and improves customer satisfaction.
Smart Images
Figure CN119357472A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of data processing technology, and more specifically, to an intelligent document push system based on multi-dimensional data mining. Background Art
[0002] With the rapid development of artificial intelligence technology, the active push system was born. The system will display relevant content or products according to user requests, which improves the user's sense of participation. However, the system also has some problems, that is, the content selection is not smart enough, and not all users are interested in active push notifications. More and more users will choose to ignore or even turn off notifications. Over time, their satisfaction with the system and products has also declined.
[0003] Therefore, it is very necessary to design an intelligent document push system based on multi-dimensional data mining. Summary of the invention
[0004] In view of the shortcomings of the prior art, the purpose of the present invention is to provide an intelligent document push system based on multi-dimensional data mining, which combines the first data with the second data features through an intelligent analysis unit to establish a user feature model and make predictions. At the same time, an intelligent screening unit is used for third data analysis and screening to monitor and manage the data in the user database, and the intelligent push unit is used to screen out documents in the document library that meet the prediction based on the first data and the second data, and the active push unit is used to screen out documents in the document library that meet the screening based on the third data, thereby realizing multi-dimensional data mining and intelligent push, and improving customer satisfaction.
[0005] To achieve the above object, the present invention provides the following technical solutions: An intelligent document push system based on multi-dimensional data mining comprises a user database, a processing module, an AI analysis module and a push module; the user database is used to store user-related data; the processing module is used to sort and integrate the relevant data stored in the user database; the AI analysis module is used to analyze and filter the relevant data sorted and integrated by the processing module, construct the user reading feature model, and track and predict the reading behavior of the target user through the feature model; the push module is used to filter out documents that meet the prediction in the document library and push them to the user.
[0006] The present invention is further configured as follows: the user database includes first data, second data and third data; the first data is the channel content subscribed by the user terminal, which belongs to basic data; the second data is the user behavior characteristics found from the target user behavior event sequence; the third data is the channel content after the system actively pushes the document and is integrated and screened by the processing module, which belongs to additional data.
[0007] The present invention is further configured as follows: the processing module includes a data processing unit and a data integration unit; the data processing unit is used to receive and organize the first data and the second data; the data integration unit is used to receive and integrate the third data.
[0008] The present invention is further configured as follows: the AI intelligent analysis unit includes an intelligent analysis unit and an intelligent screening unit; the intelligent analysis unit is used to combine the first data with the second data features, establish a user feature model and make predictions; the intelligent screening unit is used for the third data analysis and screening, and monitor and manage the data in the user database.
[0009] The present invention is further configured as follows: the push module includes an intelligent push unit and an active push unit; the intelligent push unit screens out documents in the document library that meet the prediction based on the first data and the second data; the active push unit screens out documents in the document library that meet the screening based on the third data.
[0010] The present invention is further configured as follows: the user database is connected to the processing module through communication; the push module is connected to the document library through communication; the input end of the intelligent analysis unit is electrically connected to the data sorting unit; the intelligent screening unit is electrically connected to the data integration unit; the input end of the intelligent push unit is electrically connected to the intelligent analysis unit; and the input end of the active push unit is electrically connected to the intelligent screening unit.
[0011] The present invention is further configured as follows: the AI analysis module converts the collected historical behaviors of the target user into a descriptive text form, uses a transformer to extract the historical behavior features of the target user from the user historical behavior description text, matches it with the first data, constructs the user reading feature model, and predicts the reading behavior of the target user through the feature model.
[0012] The advantages of the present invention are: The present invention uses an intelligent analysis unit to combine the first data with the second data features, establishes a user feature model and performs predictions, and at the same time, uses an intelligent screening unit for third data analysis and screening to monitor and manage the data in the user database, and uses an intelligent push unit to screen out documents that meet the predictions based on the first data and the second data in the document library, and uses an active push unit to screen out documents that meet the screening based on the third data in the document library, thereby realizing multi-dimensional data mining, intelligent push, and accurately identifying and predicting the interests or preferences of various users, thereby actively pushing the required information to users in a targeted and timely manner to improve customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS
[0013] Figure 1 The present invention is a flowchart of an intelligent document push system based on multi-dimensional data mining. DETAILED DESCRIPTION
[0014] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application may be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.
[0015] It should be noted that, unless otherwise specified, all technical and scientific terms used in this application have the same meanings as commonly understood by ordinary technicians in the technical field to which this application belongs.
[0016] In the present invention, unless otherwise specified, the directions used, such as "up" and "down", usually refer to the directions shown in the drawings, or to the vertical, perpendicular or gravity directions; similarly, for ease of understanding and description, "left" and "right" usually refer to the left and right shown in the drawings; "inside" and "outside" refer to the inside and outside relative to the outline of each component itself, but the above-mentioned directions are not used to limit the present invention. Example
[0017] See also Figure 1 , the present invention provides the following technical solutions: An intelligent document push system based on multi-dimensional data mining, specifically, a user database, a processing module, an AI analysis module and a push module; the user database is used to store user-related data; the processing module is used to organize and integrate the relevant data stored in the user database; the AI analysis module is used to analyze and filter the relevant data organized and integrated by the processing module, construct a user reading feature model, and track and predict the reading behavior of the target user through the feature model; the push module is used to filter out documents that meet the prediction in the document library and push them to the user.
[0018] The working principle of the first embodiment of the present invention is as follows: an intelligent analysis unit is used to combine the first data with the second data features to establish a user feature model and perform predictions. At the same time, an intelligent screening unit is used for third data analysis and screening to monitor and manage the data in the user database. An intelligent push unit is used to screen out documents in the document library that meet the predictions based on the first data and the second data. An active push unit is used to screen out documents in the document library that meet the screening based on the third data, thereby realizing multi-dimensional data mining and intelligent push, and improving customer satisfaction. Example
[0019] See also Figure 1, this second embodiment makes the following improvements on the basis of the first embodiment. Specifically, the user database includes first data, second data and third data; the first data is the channel content subscribed by the user terminal, which belongs to the basic data; the second data is the user behavior characteristics found from the target user behavior event sequence; the third data is the channel content after the system actively pushes the document and is integrated and screened by the processing module, which belongs to the additional data.
[0020] The processing module includes a data processing unit and a data integration unit; the data processing unit is used for receiving and sorting the first data and the second data; the data integration unit is used for receiving and integrating the third data.
[0021] The AI intelligent analysis unit includes an intelligent analysis unit and an intelligent screening unit; the intelligent analysis unit is used to combine the first data with the second data features, establish a user feature model and make predictions; the intelligent screening unit is used for the third data analysis and screening, and monitor and manage the data in the user database.
[0022] The push module includes an intelligent push unit and an active push unit; the intelligent push unit selects documents that meet the prediction according to the first data and the second data in the document library; the active push unit selects documents that meet the screening according to the third data in the document library.
[0023] The user database is connected to the processing module through communication; the push module is connected to the document library through communication; the input end of the intelligent analysis unit is electrically connected to the data sorting unit; the intelligent screening unit is electrically connected to the data integration unit; the input end of the intelligent push unit is electrically connected to the intelligent analysis unit; the input end of the active push unit is electrically connected to the intelligent screening unit.
[0024] The AI analysis module converts the collected historical behaviors of target users into descriptive text, uses transformer to extract the historical behavior features of target users from the historical behavior description text, matches it with the first data, builds a user reading feature model, and predicts the reading behavior of target users through the feature model.
[0025] Working principle of the second embodiment: Transformer can process sequence data by relying solely on the attention mechanism, thereby eliminating RNN or CNN. The training of this model takes a short time and performs well for both large data sets and limited data sets.
[0026] The intelligent analysis unit combines the channel content (first data) subscribed by the user terminal and the user behavior characteristics (second data) found from the target user behavior event sequence to establish a user characteristic model and make predictions. The intelligent push unit selects documents in the document library that meet the predictions based on the first data and the second data.
[0027] The data integration unit receives and integrates the third data. Specifically, the system regularly pushes different types of documents to customers, and the system analyzes the customer's viewing time of the corresponding document to determine the customer's preference, thereby actively screening the documents.
[0028] Realize multi-dimensional data mining, intelligent push, accurately identify and predict the interests or preferences of various users, so as to proactively push the required information to users in a targeted and timely manner to improve customer satisfaction.
[0029] Obviously, the above-described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.
[0030] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.
[0031] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein.
[0032] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
[0033] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.
Claims
1. An intelligent document push system based on multi-dimensional data mining, characterized by: Includes user database, processing module, AI analysis module and push module; The user database is used to store user-related data; The processing module is used to sort out and integrate relevant data stored in the user database; The AI analysis module is used to analyze and filter the relevant data collated and integrated by the processing module, construct the user reading feature model, and track and predict the target user's reading behavior through the feature model; The push module is used to screen out documents that meet the prediction in the document library and push them to the user.
2. According to claim 1, an intelligent document push system based on multi-dimensional data mining is characterized in that: The user database includes first data, second data and third data; The first data is the channel content subscribed by the user terminal, which belongs to basic data; The second data is a user behavior feature found from a target user behavior event sequence; The third data is the channel content after the system actively pushes the document and is integrated and screened by the processing module, and is additional data.
3. The intelligent document push system based on multi-dimensional data mining according to claim 2 is characterized by: The processing module includes a data processing unit and a data integration unit; The data processing unit is used for receiving and sorting the first data and the second data; the data integration unit is used for receiving and integrating the third data.
4. The intelligent document push system based on multi-dimensional data mining according to claim 3 is characterized by: The AI intelligent analysis unit includes an intelligent analysis unit and an intelligent screening unit; The intelligent analysis unit is used to combine the first data with the second data features, establish a user feature model and make predictions; The intelligent screening unit is used for the third data analysis and screening, and monitors and manages the data in the user database.
5. The intelligent document push system based on multi-dimensional data mining according to claim 4 is characterized in that: The push module includes an intelligent push unit and an active push unit; The intelligent push unit selects documents that meet the prediction according to the first data and the second data from the document library; The active push unit selects documents in the document library that meet the selection criteria according to the third data.
6. The intelligent document push system based on multi-dimensional data mining according to claim 5, characterized in that: The user database is connected to the processing module through communication; the push module is connected to the document library through communication; the input end of the intelligent analysis unit is electrically connected to the data sorting unit; the intelligent screening unit is electrically connected to the data integration unit; the input end of the intelligent push unit is electrically connected to the intelligent analysis unit; the input end of the active push unit is electrically connected to the intelligent screening unit.
7. The intelligent document push system based on multi-dimensional data mining according to claim 6, characterized in that: The AI analysis module converts the collected historical behaviors of the target user into a descriptive text form, uses a transformer to extract the historical behavior features of the target user from the historical behavior description text, matches it with the first data, constructs the user reading feature model, and predicts the reading behavior of the target user through the feature model.