CN117786194A - Recommendation method and device based on labels and real-time behavior analysis - Google Patents
Recommendation method and device based on labels and real-time behavior analysis Download PDFInfo
- Publication number
- CN117786194A CN117786194A CN202211142142.2A CN202211142142A CN117786194A CN 117786194 A CN117786194 A CN 117786194A CN 202211142142 A CN202211142142 A CN 202211142142A CN 117786194 A CN117786194 A CN 117786194A
- Authority
- CN
- China
- Prior art keywords
- combined
- tag
- end system
- client
- customer
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000004458 analytical method Methods 0.000 title claims abstract description 37
- 238000000034 method Methods 0.000 title claims abstract description 36
- 230000006399 behavior Effects 0.000 claims description 96
- 238000004590 computer program Methods 0.000 claims description 26
- 230000003542 behavioural effect Effects 0.000 claims description 8
- 238000003032 molecular docking Methods 0.000 claims description 6
- 238000010586 diagram Methods 0.000 description 10
- 238000012545 processing Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 230000008569 process Effects 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000008878 coupling Effects 0.000 description 1
- 238000010168 coupling process Methods 0.000 description 1
- 238000005859 coupling reaction Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 210000001503 joint Anatomy 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Landscapes
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a recommendation method and device based on labels and real-time behavior analysis, wherein the method comprises the following steps: the embedded point module collects behavior events accessed by clients in the current service scene of the front-end system; the automatic flow engine module matches a customer for which the collected behavior event accords with a preset combined behavior event, forms a combined tag of the customer according to a customer attribute tag configured for the customer based on service requirements, a basic tracking attribute tag and a combined behavior event tracking tag, and pushes the combined tag of the customer to the front-end system; the front-end system is used for recommending a next business scene interface for the clients conforming to the combined labels. The method and the system can form any combination label by combining the client attribute label according to the behavior event of the client based on the service requirement, select the client group for real-time tracking, further realize real-time pushing of the service scene meeting the client requirement and improve the user experience.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a recommendation method and device based on labels and real-time behavior analysis.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
Currently, in internet operation, there are different operation requirements for different user groups, and it is desirable to recommend specific products or active pages after certain specific pages are accessed for users conforming to specific labels.
Under the requirement, large companies often build professional big data teams to excavate, analyze and process based on big data technology. Processing is carried out from mass data based on business logic, and hundreds of millions of clients are marked with client labels of all dimensions.
Meanwhile, in a user front-end system (generally, a website and a mobile phone APP are commonly used, hereinafter referred to as a front-end system), user behavior information is recorded in the embedded point system by building an own embedded point system or utilizing a third-party embedded point system and utilizing a JS call or API interface mode.
The mass data labels and the mass embedded point information are split and separated from the front-end system, and in the process of accessing the front-end system by a client, the real-time behavior and the labels of the client cannot be combined randomly in real time or in near real time (within 5 seconds according to service requirements) so as to present the next service scene to the client meeting the conditions.
Disclosure of Invention
The embodiment of the invention provides a recommendation method based on labels and real-time behavior analysis, which is used for forming any combination label by combining customer attribute labels according to behavior events of customers, selecting customer groups for real-time tracking, and further realizing real-time pushing of business scenes meeting customer requirements, and comprises the following steps:
the embedded point module collects behavior events accessed by clients in the current service scene of the front-end system;
the automatic flow engine module matches a customer for which the collected behavior event accords with a preset combined behavior event, forms a combined tag of the customer according to a customer attribute tag configured for the customer based on service requirements, a basic tracking attribute tag and a combined behavior event tracking tag, and pushes the combined tag of the customer to the front-end system; the front-end system is used for recommending a next business scene interface for the clients conforming to the combined labels.
The embodiment of the invention also provides a recommendation device based on the labels and the real-time behavior analysis, which is used for forming any combination label by combining the customer attribute labels according to the behavior events of customers, selecting a customer group for real-time tracking, and further realizing real-time pushing of business scenes meeting the requirements of the customers, and comprises the following steps:
the embedded point module is used for collecting behavior events accessed by a client in a current service scene of the front-end system;
the automatic flow engine module is used for matching and determining clients of which the collected behavior events accord with preset combined behavior events, forming combined tags of the clients according to client attribute tags configured for the clients based on service requirements, basic tracking attribute tags and combined behavior event tracking tags, and pushing the combined tags of the clients to the front-end system; the front-end system is used for recommending a next business scene interface for the clients conforming to the combined labels.
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the recommendation method based on the label and the real-time behavior analysis is realized when the processor executes the computer program.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the recommendation method based on the label and the real-time behavior analysis when being executed by a processor.
The embodiment of the invention also provides a computer program product, which comprises a computer program, wherein the computer program realizes the recommendation method based on the label and the real-time behavior analysis when being executed by a processor.
In the embodiment of the invention, the recommendation scheme based on the tag and the real-time behavior analysis is implemented by: the embedded point module collects behavior events accessed by clients in the current service scene of the front-end system; the automatic flow engine module matches a customer for which the collected behavior event accords with a preset combined behavior event, forms a combined tag of the customer according to a customer attribute tag configured for the customer based on service requirements, a basic tracking attribute tag and a combined behavior event tracking tag, and pushes the combined tag of the customer to the front-end system; the front-end system is used for recommending a next service scene interface for the client conforming to the combined label, can form any combined label by combining the client attribute label according to the service requirement and aiming at the behavior event of the client, and selects the client group for real-time tracking, so that the service scene conforming to the client requirement is pushed in real time, and the user experience is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
FIG. 1 is a flow chart of a recommendation method based on labels and real-time behavior analysis in an embodiment of the invention;
FIG. 2 is a diagram of a recommended system architecture based on tags and real-time behavioral analysis in an embodiment of the invention;
FIG. 3 is a flow chart of an embodiment of the present invention applied to an automatic flow engine module;
FIG. 4 is a schematic structural diagram of a recommendation device based on tag and real-time behavior analysis according to an embodiment of the present invention;
fig. 5 is a schematic diagram of a computer device according to an embodiment of the invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings. The exemplary embodiments of the present invention and their descriptions herein are for the purpose of explaining the present invention, but are not to be construed as limiting the invention.
The data acquisition, storage, use, processing and the like in the technical scheme meet the relevant regulations of national laws and regulations.
The term "and/or" is used herein to describe only one relationship, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist together, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.
In the description of the present specification, the terms "comprising," "including," "having," "containing," and the like are open-ended terms, meaning including, but not limited to. Reference to the terms "one embodiment," "a particular embodiment," "some embodiments," "for example," etc., means that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. The sequence of steps involved in the embodiments is used to schematically illustrate the practice of the present application, and is not limited thereto and may be appropriately adjusted as desired.
The embodiment of the invention provides a recommendation scheme based on label and real-time behavior analysis, which is characterized in that an automatic flow engine system (a system for marking business labels on customers conforming to logic judgment) carries out real-time tracking by configurationally importing customers of labels of any combination and different dimensions (generally, determining target guest groups by considering factors). The recommendation scheme based on the tag and the real-time behavior analysis is described in detail below.
Fig. 1 is a flow chart of a recommendation method based on tag and real-time behavior analysis in an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step 101: the embedded point module collects behavior events accessed by clients in the current service scene of the front-end system;
step 102: the automatic flow engine module matches a customer for which the collected behavior event accords with a preset combined behavior event, forms a combined tag of the customer according to a customer attribute tag configured for the customer based on service requirements, a basic tracking attribute tag and a combined behavior event tracking tag, and pushes the combined tag of the customer to the front-end system; the front-end system is used for recommending a next business scene interface for the clients conforming to the combined labels.
The recommendation method based on the label and the real-time behavior analysis provided by the embodiment of the invention works: the embedded point module collects behavior events accessed by clients in the current service scene of the front-end system; the automatic flow engine module matches a customer for which the collected behavior event accords with a preset combined behavior event, forms a combined tag of the customer according to a customer attribute tag configured for the customer based on service requirements, a basic tracking attribute tag and a combined behavior event tracking tag, and pushes the combined tag of the customer to the front-end system; the front-end system is used for recommending a next service scene interface for the client conforming to the combined label, can form any combined label by combining the client attribute label according to the service requirement and aiming at the behavior event of the client, and selects the client group for real-time tracking, so that the service scene conforming to the client requirement is pushed in real time, and the user experience is improved. The recommendation method based on the tag and the real-time behavior analysis is described in detail below.
In one embodiment, in the step 101, the embedded point module collects behavior events accessed by the client in the current service scenario of the front-end system, which may include: the embedded point module collects behavior events accessed by a client in the current service scene of the front-end system through a JS interface or API interface standard docking mode.
In the implementation, the embedded point module provided for the front-end system JS and the API in two standard docking modes can improve the flexibility and accuracy of recommendation.
In the step 102, as shown in fig. 3, the step 102 may include: step 1021: acquiring behavior events accessed by the acquired current service scene; step 1022: the clients which are matched and determined that the collected behavior events accord with the preset combined behavior events; step 1023: forming a combined label of the client according to a client attribute label configured for the client based on service requirements, a basic tracking attribute label and a combined behavior event tracking label; step 1024: pushing the customer's combined tag to the front-end system; the front-end system is used for recommending a next business scene interface for the clients conforming to the combined labels.
In the step 102, as shown in fig. 2, the automatic flow engine module may match and determine the client whose collected behavior event meets the preset combined behavior event from the client data platform (may be a database set in the client data platform, where the database stores massive client information and a tag configured for the client data platform).
In the step 102, the automatic flow engine module may form a combined label of the customer according to the customer attribute label, the basic tracking attribute label, and the combined behavior event tracking label configured for the customer by the marketing configuration center as shown in fig. 2, i.e., the step of configuring the customer (configuring the customer attribute label, the basic tracking attribute label, and the combined behavior event tracking label for the customer) may be performed by the marketing configuration center in fig. 2.
In one embodiment, in the step 102, pushing the combined label of the customer to the front-end system may include: and pushing the combined label of the client to the front-end system through a message pushing module.
In implementation, a message pushing module (as shown in fig. 2) that interacts with other service systems in high performance pushes the combined label of the client to the front-end system, so that the recommendation efficiency can be improved.
In one embodiment, in the step 102, pushing the combined label of the customer to the front-end system may include: and pushing the combined label of the client to the front-end system in the form of an API interface.
In the implementation, in the form of an API interface, the combined label of the client is pushed to the front-end system, so that the recommending efficiency can be improved.
In the above step 102, the next service scenario is the next service scenario of the current service scenario in step 101, for example, a scenario of participating in a lottery.
In order to facilitate an understanding of how the present invention may be practiced, a detailed description will be given below with reference to fig. 2 as an example.
The recommendation method based on tag and real-time behavior analysis provided by the embodiment of the invention is mainly divided into five parts, namely a client data platform based on hadoop technology and responsible for collecting client information and client tag information, a buried point module provided for a front-end system JS and an API in two standard docking modes, an automatic flow engine module based on real-time calculation of a computer memory, a message pushing module for high-performance interaction with other service systems, and a guest group tag real-time matching module. The overall architecture is shown in fig. 2, and the detailed roles of the various parts in fig. 2 are as follows:
1. the client data platform defines the data classification and classification management (mainly based on label management, so as to be convenient for screening client groups, such as basic attribute class, underwriting class, claim class, credit class, behavior class and the like of common first-class classification, and the next-class classification is refined according to the present level) standard, including the merging principle of clients (the clients relate to a plurality of business lines or three-way channel sources, in the embodiment of the invention, the clients of multiple sources are merged according to client elements (such as mobile phone numbers, names, certificate types, certificate numbers and the like), and unique client identification numbers are established), and the management of client labels. The customer data platform may contain basic attribute information of the customer, customer tag information (basic attribute class, underwriting class, claim class, product class, service class, behavior class, credit class, etc.), customer business base information (policy information, claim information, dialing information, etc.), and behavioral events (internet platform behavior, underwriting corrective action, claim action, etc.).
2. The marketing configuration center is a set of visual configuration management system, and is configured by operators according to service requirements, and customer groups can be defined according to rules (the rules can be preset by users according to the service requirements), for example, customer attributes are selected to be Beijing area+female+30 years old (customer attribute tags belong to basic attribute type tags), and the tags are selected to be in possession of a vehicle insurance policy+continuous underwriting for 3 years (basic tracking attribute tags, such as underwriting type, claim type, product type, service type, credit type tags and the like). And then carrying out marketing configuration selection, such as issuing a short message, sending push information and the like. Meanwhile, combined event tracking is supported, such as logging in APP in a certain time period (such as 9 months, 1 day and 9 months, 30 days), inquiring about own insurance policy, and performing modifying operation on the insurance policy in the period (combined behavior event tracking labels, such as Internet platform behaviors, insurance modifying behaviors, claim settling behaviors and the like, belong to behavior class labels). The tag information combination (combination tag) may include: basic attribute class, underwriting class, claims class, product class, service class, behavior class, credit class, etc.
3. The embedded point module is a set of standardized interface system provided for the front-end system, and behavior events of clients accessed by the front-end system such as APP are collected to the client data platform in real time through the embedded point interface, such as whether to query a policy, purchase the policy, whether to carry out policy correction, whether to succeed in correction, whether to carry out APP login and the like.
4. The automatic flow engine module is used for tracking selected customer groups in real time according to the setting of a marketing configuration center, combining a buried point system, aiming at customers conforming to a combined event, marking the customers for the second time to form new labels [ Beijing area+female+30 years old+owned car insurance policy+continuous underwriting for 3 years+9 months+modified ], and providing all the labels of the customers to the front-end system information in the form of an API interface.
5. The front-end system can query the client labels in the operation configuration page, and only clients meeting the specific events can display and participate in the next service scene, such as participating in a lottery.
In summary, the recommendation method based on the tag and the real-time behavior analysis provided by the embodiment of the invention has the advantages that:
1. the front-end system and the present system (the system applying the recommendation method based on the tag and the real-time behavior analysis) do not have a strong coupling relation. The system and the front-end system are developed through plug-in units, standardized in a butt joint mode, and high in expansibility.
2. The system is configured, uses the business requirement as a guide, and combines the behavior events of the clients with the client labels to be combined at will.
3. The big data technology and the buried point technology related in the system have strong reliability.
The embodiment of the invention also provides a recommending device based on the labels and the real-time behavior analysis, as described in the following embodiment. Since the principle of the device for solving the problem is similar to that of the recommended method based on the tag and the real-time behavior analysis, the implementation of the device can be referred to the implementation of the recommended method based on the tag and the real-time behavior analysis, and the repetition is omitted.
Fig. 4 is a schematic structural diagram of a recommendation device based on tag and real-time behavior analysis in an embodiment of the present invention, and as shown in fig. 4, the device includes:
the embedded point module 01 is used for collecting behavior events accessed by a client in a current service scene of a front-end system;
the automatic flow engine module 02 is used for matching and determining clients with collected behavior events conforming to preset combined behavior events, forming combined tags of the clients according to client attribute tags configured for the clients based on service requirements, basic tracking attribute tags and combined behavior event tracking tags, and pushing the combined tags of the clients to the front-end system; the front-end system is used for recommending a next business scene interface for the clients conforming to the combined labels.
In one embodiment, the buried point module is specifically configured to: and collecting behavior events accessed by the client in the current service scene of the front-end system through a JS interface or API interface standard docking mode.
In one embodiment, the automatic flow engine module is specifically configured to: and pushing the combined label of the client to the front-end system through a message pushing module.
In one embodiment, the automatic flow engine module is specifically configured to: and pushing the combined label of the client to the front-end system in the form of an API interface.
Based on the foregoing inventive concept, as shown in fig. 5, the present invention further proposes a computer device 500, including a memory 510, a processor 520, and a computer program 530 stored on the memory 510 and executable on the processor 520, wherein the processor 520 implements the recommended method based on the tag and the real-time behavior analysis when executing the computer program 530.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the recommendation method based on the label and the real-time behavior analysis when being executed by a processor.
The embodiment of the invention also provides a computer program product, which comprises a computer program, wherein the computer program realizes the recommendation method based on the label and the real-time behavior analysis when being executed by a processor.
In the embodiment of the invention, the recommendation scheme based on the tag and the real-time behavior analysis is implemented by: the embedded point module collects behavior events accessed by clients in the current service scene of the front-end system; the automatic flow engine module matches a customer for which the collected behavior event accords with a preset combined behavior event, forms a combined tag of the customer according to a customer attribute tag configured for the customer based on service requirements, a basic tracking attribute tag and a combined behavior event tracking tag, and pushes the combined tag of the customer to the front-end system; the front-end system is used for recommending a next service scene interface for the client conforming to the combined label, can form any combined label by combining the client attribute label according to the service requirement and aiming at the behavior event of the client, and selects the client group for real-time tracking, so that the service scene conforming to the client requirement is pushed in real time, and the user experience is improved.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (11)
1. A recommendation method based on labels and real-time behavioral analysis, comprising:
the embedded point module collects behavior events accessed by clients in the current service scene of the front-end system;
the automatic flow engine module matches a customer for which the collected behavior event accords with a preset combined behavior event, forms a combined tag of the customer according to a customer attribute tag configured for the customer based on service requirements, a basic tracking attribute tag and a combined behavior event tracking tag, and pushes the combined tag of the customer to the front-end system; the front-end system is used for recommending a next business scene interface for the clients conforming to the combined labels.
2. The recommendation method based on labels and real-time behavior analysis according to claim 1, wherein the embedded point module collects behavior events accessed by clients in a current business scenario of a front-end system, and the recommendation method comprises the following steps: the embedded point module collects behavior events accessed by a client in the current service scene of the front-end system through a JS interface or API interface standard docking mode.
3. The recommendation method based on tag and real time behavioral analysis of claim 1, wherein pushing the customer's combined tag to the front-end system comprises: and pushing the combined label of the client to the front-end system through a message pushing module.
4. The recommendation method based on tag and real time behavioral analysis of claim 1, wherein pushing the customer's combined tag to the front-end system comprises: and pushing the combined label of the client to the front-end system in the form of an API interface.
5. A recommendation device based on labels and real-time behavioral analysis, comprising:
the embedded point module is used for collecting behavior events accessed by a client in a current service scene of the front-end system;
the automatic flow engine module is used for matching and determining clients of which the collected behavior events accord with preset combined behavior events, forming combined tags of the clients according to client attribute tags configured for the clients based on service requirements, basic tracking attribute tags and combined behavior event tracking tags, and pushing the combined tags of the clients to the front-end system; the front-end system is used for recommending a next business scene interface for the clients conforming to the combined labels.
6. The recommendation device based on tag and real-time behavior analysis according to claim 5, wherein the embedded point module is specifically configured to: and collecting behavior events accessed by the client in the current service scene of the front-end system through a JS interface or API interface standard docking mode.
7. The recommendation device based on tag and real-time behavioral analysis of claim 5, wherein said automatic flow engine module is specifically configured to: and pushing the combined label of the client to the front-end system through a message pushing module.
8. The recommendation device based on tag and real-time behavioral analysis of claim 5, wherein said automatic flow engine module is specifically configured to: and pushing the combined label of the client to the front-end system in the form of an API interface.
9. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 4 when executing the computer program.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, implements the method of any of claims 1 to 4.
11. A computer program product, characterized in that it comprises a computer program which, when executed by a processor, implements the method of any of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211142142.2A CN117786194A (en) | 2022-09-20 | 2022-09-20 | Recommendation method and device based on labels and real-time behavior analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211142142.2A CN117786194A (en) | 2022-09-20 | 2022-09-20 | Recommendation method and device based on labels and real-time behavior analysis |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117786194A true CN117786194A (en) | 2024-03-29 |
Family
ID=90389661
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211142142.2A Pending CN117786194A (en) | 2022-09-20 | 2022-09-20 | Recommendation method and device based on labels and real-time behavior analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117786194A (en) |
-
2022
- 2022-09-20 CN CN202211142142.2A patent/CN117786194A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| RU2544755C2 (en) | Method, system and server for managing friends' feed in network | |
| CN105023165A (en) | Method, device and system for controlling release tasks in social networking platform | |
| CN107330718B (en) | Media anti-cheating method and device, storage medium and terminal | |
| CN110910282B (en) | System, method and storage medium for earning commission of multi-role customer service for real estate electronic commerce | |
| CN111522735B (en) | Shunting method and device for test experiment | |
| CN107528761A (en) | A kind of method and system of wechat public number automatic information reply | |
| CN107766470B (en) | Intelligent statistical method, intelligent statistical display method and device for data sharing | |
| CN112506925A (en) | Data retrieval system and method based on block chain | |
| CN111371672A (en) | Message pushing method and device | |
| KR20190143791A (en) | Method, apparatus and program for matching procurement bidding information | |
| CN111932200A (en) | Remote bidding evaluation system | |
| CN110019542A (en) | The method, apparatus of the generation method of business connection, generation organizational member database and identification member of the same name in enterprise's map | |
| Maqsood et al. | Five case studies applying soft systems methodology to knowledge management | |
| CN113327146A (en) | Information tracking method and device | |
| CN102930387A (en) | Electronic information enterprise evaluation service system and method based on cloud computing | |
| CN117786194A (en) | Recommendation method and device based on labels and real-time behavior analysis | |
| CN116090888A (en) | Decision engine configuration management method, device, electronic equipment and program product | |
| CN109062986A (en) | A kind of classification processing method and device of label | |
| CN113806555A (en) | Operation abnormity identification method, system, device and storage medium for APP | |
| CN112800340A (en) | Task pushing method and device, electronic equipment and storage medium | |
| CN112862598A (en) | Channel information management method and device, electronic equipment and medium | |
| CN109033343B (en) | Method and apparatus for generating information | |
| CN113744066B (en) | Information pushing method and device | |
| CN104484465A (en) | Method and system of fast assigning and replying messages in network community | |
| CN102930388A (en) | Cloud-computing-based pharmaceutical enterprise evaluation service system and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |