CN112286772A - Attribution analysis method and device and electronic equipment - Google Patents
Attribution analysis method and device and electronic equipment Download PDFInfo
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Abstract
The embodiment of the invention discloses an attribution analysis method, an attribution analysis device and electronic equipment, wherein the attribution analysis method comprises the following steps: receiving attribution analysis instructions; obtaining a behavior sequence of a user according to a target event and a plurality of events to be attributed; determining a target attribution model according to attribution model selection information; and obtaining attribution result information of the target event according to the target attribution model and the behavior sequence. The invention abstracts the input and output of the algorithm and improves the flexibility of the algorithm under the condition of ensuring the efficiency.
Description
Technical Field
The embodiment of the invention relates to the field of data attribution analysis processing, in particular to an attribution analysis method, an attribution analysis device and electronic equipment.
Background
The existing attribution analysis algorithm needs to write an algorithm according to a given scene and a determined attribution model and calculate, and the process needs to be repeated every time the scene changes or other attribution models are selected.
In the current attribution analysis algorithm, for example, if a user needs to perform attribution analysis on the achievement of a final target event according to a final click, all events to be attributed closest to the target event need to be found, and then contribution calculation is performed. At this time, if attribution is needed according to other attribution models, a new algorithm needs to be written again according to a new rule, and then recalculation is carried out. When different attribution models need to be used when the analysis scene difference is large, the existing method is not flexible and needs repeated calculation.
Disclosure of Invention
The embodiment of the invention aims to provide an attribution analysis method, an attribution analysis device and electronic equipment, which are used for solving the problems that the existing attribution analysis algorithm is not flexible, needs repeated calculation and is difficult to ensure real-time performance.
In order to achieve the above object, the embodiments of the present invention mainly provide the following technical solutions:
in a first aspect, an embodiment of the present invention provides an attribution analysis method, including:
receiving attribution analysis instructions, the attribution analysis instructions comprising a target event, a plurality of events to be attributed, and attribution model selection information;
obtaining a behavior sequence of a user according to the target event and the events to be attributed, wherein the behavior sequence of the user comprises sequencing information of the events to be attributed performed by the user;
determining a target attribution model according to the attribution model selection information;
and obtaining attribution result information of the target event according to the target attribution model and the behavior sequence.
According to an embodiment of the present invention, obtaining a behavior sequence of a user according to the target event and the event to be attributed includes:
acquiring the occurrence time of the target event and the coding information of the plurality of events to be attributed;
acquiring a plurality of behavior information of the user obtained by the coded information of the events to be attributed within a preset time before the occurrence time of the target event, wherein each behavior information comprises specific behavior content and behavior occurrence time information;
generating a behavior sequence of the user according to the plurality of behavior information of the user;
before obtaining a behavior sequence of a user according to the target event and the plurality of events to be attributed, the method further comprises the following steps: all types of events are encoded according to a predetermined rule.
According to an embodiment of the present invention, before obtaining the behavior sequence of the user according to the target event and the plurality of events to be attributed, the method further includes:
carrying out data partitioning according to the occurrence time of the target time and the event name of the target event;
and performing barrel division operation on the basis of the data partition according to the identity of the user.
According to an embodiment of the present invention, further comprising:
receiving a custom algorithm return type instruction;
and setting a custom algorithm return type according to the custom algorithm return type instruction.
In a second aspect, an embodiment of the present invention further provides an attribution analysis device, including:
a communication module for receiving attribution analysis instructions, the attribution analysis instructions comprising a target event, a plurality of events to be attributed, and attribution model selection information;
the control processing module is used for obtaining a behavior sequence of a user according to the target event and the events to be attributed, wherein the behavior sequence of the user comprises sequencing information of the events to be attributed performed by the user; the control processing module is also used for determining a target attribution model according to the attribution model selection information; the control processing module is further used for obtaining attribution result information of the target event according to the target attribution model and the behavior sequence;
and the output module is used for outputting attribution result information of the target event.
According to an embodiment of the present invention, the control processing module is specifically configured to obtain an occurrence time of the target event and coding information of the multiple events to be attributed; acquiring a plurality of behavior information of the user obtained by the coded information of the plurality of events to be attributed within the preset time before the occurrence time of the target event; generating a behavior sequence of the user according to the plurality of behavior information of the user;
each behavior information comprises specific behavior content and behavior occurrence time information; and the control processing module is also used for coding all types of events according to a preset rule before obtaining the behavior sequence of the user according to the target event and the plurality of events to be attributed.
According to an embodiment of the present invention, the control processing module is further configured to perform data partitioning according to the occurrence time of the target time and the event name of the target event, and perform a bucket partitioning operation based on the data partitioning according to the identity of the user.
According to an embodiment of the present invention, the communication module is further configured to receive a custom algorithm return type instruction; the control processing module is also used for setting a custom algorithm return type according to the custom algorithm return type instruction.
In a third aspect, an embodiment of the present invention further provides an electronic device, including: at least one processor and at least one memory; the memory is to store one or more program instructions; the processor is configured to execute one or more program instructions to perform the real-time attribution analysis method according to the first aspect.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium containing one or more program instructions for being executed to perform the real-time attribution analysis method according to the first aspect.
The technical scheme provided by the embodiment of the invention at least has the following advantages:
the attribution analysis method, the attribution analysis device and the electronic equipment abstract input and output of the algorithm, and improve algorithm flexibility under the condition of ensuring efficiency. The bottom layer of the invention utilizes big data distributed frameworks such as Hadoop, HDFS and the like and utilizes the latest OLAP tool Presto to perform query. Rewriting Presto plug-in, realize the real-time attribute analysis demand. And sequencing the single-user behaviors by using a Presto aggregation function, and calculating attribution conversion events and contribution degrees based on the sequenced behavior tracks.
Drawings
FIG. 1 is a flow chart of an attribution analysis method according to an embodiment of the present invention.
Fig. 2 is a block diagram of an attribution analysis apparatus according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided for illustrative purposes, and other advantages and effects of the present invention will become apparent to those skilled in the art from the present disclosure.
In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular system structures, interfaces, techniques, etc. in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In the description of the present invention, it is to be understood that the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The method utilizes big data distributed frameworks such as Hadoop, HDFS and the like, and utilizes the latest OLAP tool Presto to perform query. Rewriting Presto plug-in, realize the real-time attribute analysis demand. Because the bottom layer utilizes the distributed framework, the multi-machine query can be realized, and the fault tolerance is better. Even under the condition of large data volume, the query efficiency can be ensured.
FIG. 1 is a flow chart of an attribution analysis method according to an embodiment of the present invention. As shown in fig. 1, the attribution analysis method according to an embodiment of the present invention includes:
s1: an attribution analysis instruction is received. Wherein, attribution analysis instructions are input to the system of the invention by a user and comprise a target event, a plurality of events to be attributed and attribution model selection information. Illustratively, the target event may be a certain order case of a shopping website, such as the purchase of a product B by a user at a shop X. The plurality of events to be attributed may include a home search box search, an advertising link to a shopping website, and a navigation bar on a shopping webpage, among others. Alternative attribution models are pre-defined, including, for example: first-click attribution models, final-click attribution models, linear attribution models, time-decay attribution models, position-based attribution models, and the like.
In order to provide data query efficiency, before step S2, the present invention further includes: data partitioning is carried out according to the occurrence time of the target time and the event name of the target event, so that the query efficiency is improved; and the barrel dividing operation is carried out on the basis of data partition according to the identity of the user, so that the query efficiency is further improved.
In order to provide data acquisition and query efficiency, the present invention further comprises, before step S2: all types of events are coded according to predetermined rules, such as digitally coded. Illustratively, the user click on the ad page is encoded as a first numeric code, the user click on the navigation bar is encoded as a second numeric code, and so on. The invention can quickly acquire the operation behavior of the user by coding different attribution events.
S2: and obtaining a behavior sequence of the user according to the target event and the plurality of events to be attributed. The behavior sequence of the user comprises sequencing information of the user for carrying out a plurality of events to be attributed.
In one embodiment of the present invention, step S2 includes:
s2-1: the occurrence time of the target event and the coding information of a plurality of events to be attributed are obtained. For example, the user is 10, 15 in 2020: 30 purchased a woman's suit at Y shop, the target time would occur at 10 months, 15 days, 2020: events to be attributed include clicking on an advertisement page, clicking on a navigation bar, clicking on a web page link, searching for goods through a search box, and the like 30. Coded information for each event to be attributed is obtained.
S2-2: and acquiring a plurality of behavior information of the user obtained by the coded information of a plurality of events to be attributed within the preset time before the occurrence time of the target event. The preset time can be 30 minutes or other time, and can be set according to system requirements or historical purchase records of users and can be flexibly adjusted. For example, when the user a purchases through the shopping website in the last year, the maximum time between the order placing time and the website logging-in time is 60 minutes, and the preset time may be set to 60 minutes. Each behavior information includes specific behavior content (e.g., click on an advertisement page, etc.) and behavior occurrence time information.
S2-3: and generating a behavior sequence of the user according to a plurality of behavior information of the user, and conveniently and uniformly calculating contribution degrees of events to be attributed on all conversion paths through the selected target attribution model.
In this embodiment, the calculation utilizes slicestate to cache intermediate variables, such as events to be attributed, previous item associated events, target events, window time, and the like, so as to improve the calculation efficiency of the algorithm. Due business requirements are fully considered, and the calculation efficiency is improved as much as possible on the premise of accuracy.
In one embodiment of the invention, the real-time attribution analysis method further comprises: receiving a custom algorithm return type instruction; and setting the custom algorithm return type according to the custom algorithm return type instruction so as to ensure the flexibility of subsequent application.
S3: and determining a target attribution model according to the attribution model selection information.
It should be noted that, in this embodiment, the sequential execution relationship between the step S2 and the step S3 is not limited, that is, the step S2 may be executed first, and then the step S3 may be executed; step S3 may be executed first, and then step S2 may be executed; it is also possible to simultaneously perform step S2 and step S3.
S4: and obtaining attribution result information of the target event according to the target attribution model and the behavior sequence.
Illustratively, looking at attribution by day, output [ xwhat, attr, counts, valid _ counts, value ], respectively represents how many times a certain event to be attributed is clicked together under attr grouping, wherein the contribution degree is value for effective conversion.
Specifically, one of the pieces of output information is: 100001, [2.0, -1.0,5.0,3.0,1.0 ].
Where 100001 denotes a user ID.
2.0 represents a certain event to be attributed (e.g., clicking on an ad page).
1.0 represents grouping information to be attributed to an event, such as a certain class of advertisements.
5.0 represents 2.0 clicks for an event, e.g., 5 clicks on an ad page.
3.0 represents the number of effective conversions, e.g., the user purchased a woman's suit and clicked on 5 ad pages, where 3 ad pages are for a woman's suit and 2 other ad pages are completely unrelated to the woman's suit, then the number of effective conversions is 3.0.
1.0 represents a contribution degree, wherein the value of the contribution degree and how to determine whether the event to be attributed contributes to the contribution degree are determined according to the selected target attribution model.
The attribution analysis method provided by the embodiment of the invention abstracts the input and output of the algorithm, and improves the flexibility of the algorithm under the condition of ensuring the efficiency. The bottom layer of the invention utilizes big data distributed frameworks such as Hadoop, HDFS and the like and utilizes the latest OLAP tool Presto to perform query. Rewriting Presto plug-in, realize the real-time attribute analysis demand. And sequencing the single-user behaviors by using a Presto aggregation function, and calculating attribution conversion events and contribution degrees based on the sequenced behavior tracks.
Fig. 2 is a block diagram of an attribution analysis apparatus according to an embodiment of the present invention. As shown in fig. 2, the attribution analyzing apparatus according to the embodiment of the present invention includes: a communication module 100, a control processing module 200 and an output module 300.
The communication module 100 is configured to receive an attribution analysis instruction, where the attribution analysis instruction includes a target event, a plurality of events to be attributed, and attribution model selection information. The control processing module 200 is configured to obtain a behavior sequence of the user according to the target event and the multiple events to be attributed, where the behavior sequence of the user includes sequencing information of the multiple events to be attributed performed by the user. The control processing module 200 is also used to determine a target attribution model according to the attribution model selection information. The control processing module 200 is further configured to obtain attribution result information of the target event according to the target attribution model and the behavior sequence. The output module 300 is used for outputting attribution result information of the target event.
In an embodiment of the present invention, the control processing module 200 is specifically configured to obtain an occurrence time of a target event and coding information of a plurality of events to be attributed; acquiring a plurality of behavior information of a user obtained by coding information of a plurality of events to be attributed within a preset time before the occurrence time of a target event; and generating a behavior sequence of the user according to the plurality of behavior information of the user. Wherein each behavior information includes specific behavior content and behavior occurrence time information. The control processing module 200 is further configured to encode all types of events according to a predetermined rule before obtaining a behavior sequence of the user according to the target event and the plurality of events to be attributed.
In an embodiment of the present invention, the control processing module 200 is further configured to perform data partitioning according to the occurrence time of the target time and the event name of the target event, and perform a bucket partitioning operation based on the data partitioning according to the identity of the user.
In one embodiment of the present invention, the communication module 100 is further configured to receive a custom algorithm return type instruction. The control processing module 200 is further configured to set a custom algorithm return type according to the custom algorithm return type instruction.
It should be noted that the specific implementation of the attribution analysis device in the embodiment of the present invention is similar to the specific implementation of the attribution analysis method in the embodiment of the present invention, and specific reference is specifically made to the description of the attribution analysis method, and no further description is given for reducing redundancy.
In addition, other configurations and functions of the attribute analysis device according to the embodiment of the present invention are known to those skilled in the art, and are not described in detail for reducing redundancy.
An embodiment of the present invention further provides an electronic device, including: at least one processor and at least one memory; the memory is to store one or more program instructions; the processor is configured to execute one or more program instructions to perform the attribution analysis method according to the first aspect.
The disclosed embodiments of the present invention provide a computer-readable storage medium having stored therein computer program instructions that, when run on a computer, cause the computer to perform the above-described attribution analysis method.
In an embodiment of the invention, the processor may be an integrated circuit chip having signal processing capability. The Processor may be a general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component.
The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The processor reads the information in the storage medium and completes the steps of the method in combination with the hardware.
The storage medium may be a memory, for example, which may be volatile memory or nonvolatile memory, or which may include both volatile and nonvolatile memory.
The nonvolatile Memory may be a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash Memory.
The volatile Memory may be a Random Access Memory (RAM) which serves as an external cache. By way of example and not limitation, many forms of RAM are available, such as Static random access memory (Static RAM, SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (ddr Data Rate SDRAM), Enhanced SDRAM (ESDRAM), synchlink DRAM (SLDRAM), and Direct Rambus RAM (DRRAM).
The storage media described in connection with the embodiments of the invention are intended to comprise, without being limited to, these and any other suitable types of memory.
Those skilled in the art will appreciate that the functionality described in the present invention may be implemented in a combination of hardware and software in one or more of the examples described above. When software is applied, the corresponding functionality may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made on the basis of the technical solutions of the present invention should be included in the scope of the present invention.
Claims (10)
1. An attribution analysis method, comprising:
receiving attribution analysis instructions, the attribution analysis instructions comprising a target event, a plurality of events to be attributed, and attribution model selection information;
obtaining a behavior sequence of a user according to the target event and the events to be attributed, wherein the behavior sequence of the user comprises sequencing information of the events to be attributed performed by the user;
determining a target attribution model according to the attribution model selection information;
and obtaining attribution result information of the target event according to the target attribution model and the behavior sequence.
2. The real-time attribution analysis method according to claim 1, wherein obtaining a behavior sequence of a user according to the target event and the event to be attributed comprises:
acquiring the occurrence time of the target event and the coding information of the plurality of events to be attributed;
acquiring a plurality of behavior information of the user obtained by the coded information of the events to be attributed within a preset time before the occurrence time of the target event, wherein each behavior information comprises specific behavior content and behavior occurrence time information;
generating a behavior sequence of the user according to the plurality of behavior information of the user;
before obtaining a behavior sequence of a user according to the target event and the plurality of events to be attributed, the method further comprises the following steps: all types of events are encoded according to a predetermined rule.
3. The real-time attribution analysis method according to claim 2, further comprising, before obtaining a behavior sequence of a user according to the target event and the plurality of events to be attributed:
carrying out data partitioning according to the occurrence time of the target time and the event name of the target event;
and performing barrel division operation on the basis of the data partition according to the identity of the user.
4. The real-time attribution analysis method according to claim 1, further comprising:
receiving a custom algorithm return type instruction;
and setting a custom algorithm return type according to the custom algorithm return type instruction.
5. An attribution analysis device, comprising:
a communication module for receiving attribution analysis instructions, the attribution analysis instructions comprising a target event, a plurality of events to be attributed, and attribution model selection information;
the control processing module is used for obtaining a behavior sequence of a user according to the target event and the events to be attributed, wherein the behavior sequence of the user comprises sequencing information of the events to be attributed performed by the user; the control processing module is also used for determining a target attribution model according to the attribution model selection information; the control processing module is further used for obtaining attribution result information of the target event according to the target attribution model and the behavior sequence;
and the output module is used for outputting attribution result information of the target event.
6. The real-time attribution analysis device according to claim 5, wherein the control processing module is specifically configured to obtain an occurrence time of the target event and encoded information of the plurality of events to be attributed; acquiring a plurality of behavior information of the user obtained by the coded information of the plurality of events to be attributed within the preset time before the occurrence time of the target event; generating a behavior sequence of the user according to the plurality of behavior information of the user;
each behavior information comprises specific behavior content and behavior occurrence time information; and the control processing module is also used for coding all types of events according to a preset rule before obtaining the behavior sequence of the user according to the target event and the plurality of events to be attributed.
7. The real-time attribution analysis device according to claim 6, wherein the control processing module is further configured to perform data partitioning according to the occurrence time of the target time and the event name of the target event, and perform a bucket partitioning operation on the basis of the data partitioning according to the identity of the user.
8. The real-time attribution analysis device of claim 5, wherein the communication module is further configured to receive a custom algorithm return type instruction; the control processing module is also used for setting a custom algorithm return type according to the custom algorithm return type instruction.
9. An electronic device, characterized in that the electronic device comprises: at least one processor and at least one memory;
the memory is to store one or more program instructions;
the processor, configured to execute one or more program instructions to perform the real-time attribution analysis method of any one of claims 1-4.
10. A computer-readable storage medium having one or more program instructions embodied therein for performing the real-time attribution analysis method of any one of claims 1-4.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113361954A (en) * | 2021-06-29 | 2021-09-07 | 平安普惠企业管理有限公司 | Attribution analysis method, attribution analysis device, attribution analysis equipment and storage medium |
| CN113570409A (en) * | 2021-07-19 | 2021-10-29 | 上海明略人工智能(集团)有限公司 | Method and device for determining weight value of conversion event, storage medium and electronic device |
| CN114331227A (en) * | 2022-03-08 | 2022-04-12 | 腾讯科技（深圳）有限公司 | Data analysis method and device, electronic equipment and readable medium |
| CN115392799A (en) * | 2022-10-27 | 2022-11-25 | 平安科技（深圳）有限公司 | Attribution analysis method and device, computer equipment and storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140278620A1 (en) * | 2013-03-14 | 2014-09-18 | Oracle International Corporation | Method and system for determining marketing attributions |
| US20160267519A1 (en) * | 2015-03-09 | 2016-09-15 | Adobe Systems Incorporated | Allocating online advertising budget based on return on investment (roi) |
| US20170372347A1 (en) * | 2016-06-22 | 2017-12-28 | International Business Machines Corporation | Sequence-based marketing attribution model for customer journeys |
| US20180115472A1 (en) * | 2016-10-26 | 2018-04-26 | Vmware, Inc. | Detecting and remediating root causes of performance issues |
| CN109558492A (en) * | 2018-10-16 | 2019-04-02 | 中山大学 | A kind of listed company's knowledge mapping construction method and device suitable for event attribution |
| CN111078521A (en) * | 2019-12-18 | 2020-04-28 | 北京三快在线科技有限公司 | Abnormal event analysis method, device, equipment, system and storage medium |
| US20200150949A1 (en) * | 2018-11-13 | 2020-05-14 | Split Software, Inc. | Systems and methods for providing event attribution in software application |
| CN111626898A (en) * | 2020-03-20 | 2020-09-04 | 贝壳技术有限公司 | Method, device, medium and electronic equipment for realizing attribution of events |
-
2020
- 2020-10-14 CN CN202011099036.1A patent/CN112286772B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140278620A1 (en) * | 2013-03-14 | 2014-09-18 | Oracle International Corporation | Method and system for determining marketing attributions |
| US20160267519A1 (en) * | 2015-03-09 | 2016-09-15 | Adobe Systems Incorporated | Allocating online advertising budget based on return on investment (roi) |
| US20170372347A1 (en) * | 2016-06-22 | 2017-12-28 | International Business Machines Corporation | Sequence-based marketing attribution model for customer journeys |
| US20180115472A1 (en) * | 2016-10-26 | 2018-04-26 | Vmware, Inc. | Detecting and remediating root causes of performance issues |
| CN109558492A (en) * | 2018-10-16 | 2019-04-02 | 中山大学 | A kind of listed company's knowledge mapping construction method and device suitable for event attribution |
| US20200150949A1 (en) * | 2018-11-13 | 2020-05-14 | Split Software, Inc. | Systems and methods for providing event attribution in software application |
| CN111078521A (en) * | 2019-12-18 | 2020-04-28 | 北京三快在线科技有限公司 | Abnormal event analysis method, device, equipment, system and storage medium |
| CN111626898A (en) * | 2020-03-20 | 2020-09-04 | 贝壳技术有限公司 | Method, device, medium and electronic equipment for realizing attribution of events |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113361954A (en) * | 2021-06-29 | 2021-09-07 | 平安普惠企业管理有限公司 | Attribution analysis method, attribution analysis device, attribution analysis equipment and storage medium |
| CN113570409A (en) * | 2021-07-19 | 2021-10-29 | 上海明略人工智能(集团)有限公司 | Method and device for determining weight value of conversion event, storage medium and electronic device |
| CN113570409B (en) * | 2021-07-19 | 2024-03-26 | 上海明略人工智能(集团)有限公司 | Determination method and device for conversion event weight value, storage medium and electronic device |
| CN114331227A (en) * | 2022-03-08 | 2022-04-12 | 腾讯科技（深圳）有限公司 | Data analysis method and device, electronic equipment and readable medium |
| CN114331227B (en) * | 2022-03-08 | 2022-06-14 | 腾讯科技（深圳）有限公司 | Data analysis method and device, electronic equipment and readable medium |
| CN115392799A (en) * | 2022-10-27 | 2022-11-25 | 平安科技（深圳）有限公司 | Attribution analysis method and device, computer equipment and storage medium |
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| Publication number | Publication date |
|---|---|
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