阅读说明：本技术 用户数据匹配方法、装置、电子设备和计算机可读介质 (User data matching method and device, electronic equipment and computer readable medium ) 是由 丁兴华 叶远峰 仝守玉 李赞 于 2018-07-13 设计创作，主要内容包括：本公开实施例提供一种用户数据匹配方法、装置、电子设备和计算机可读介质。所述方法包括：获取用户画像模板库基础数据信息集；获取目标用户特征信息；基于用户画像模板库基础数据信息集和目标用户特征信息将用户画像模板库基础数据信息集拆解为用户画像模板库基础数据信息子集、并分发给与其相匹配的目标用户；基于目标用户对所述用户画像模板库基础数据信息子集的反馈信息构建所述目标用户画像、匹配目标用户数据。能够提升用户数据匹配的有效性,改善用户数据的使用体验度。(The embodiment of the disclosure provides a user data matching method and device, electronic equipment and a computer readable medium. The method comprises the following steps: acquiring a basic data information set of a user portrait template base; acquiring characteristic information of a target user; the method comprises the steps that a user portrait template library basic data information set is disassembled into user portrait template library basic data information subsets based on a user portrait template library basic data information set and target user characteristic information, and the user portrait template library basic data information subsets are distributed to target users matched with the user portrait template library basic data information subsets; and constructing the target user picture and matching target user data based on feedback information of the target user to the user picture template base basic data information subset. The effectiveness of user data matching can be improved, and the use experience of the user data is improved.)

1. A method for matching user data, comprising:

acquiring a basic data information set of a user portrait template base;

acquiring characteristic information of a target user;

the method comprises the steps that a user portrait template base basic data information set is disassembled into user portrait template base basic data information subsets based on a user portrait template base basic data information set and target user characteristic information and distributed to matched target users;

and constructing the target user picture and matching target user data based on feedback information of the target user to the user picture template base basic data information subset.

2. The method of claim 1, further comprising: and receiving feedback information of the target user and forming structured data.

3. The method of claim 2, further comprising: outputting the structured data in a visual and/or machine readable form.

4. The method of claim 2, further comprising: and optimizing a data disassembling and matching distribution mode according to the structural data fed back by the target user to the user portrait template base basic data information subset.

5. The method of claim 4, wherein optimizing data parsing and matching distribution based on structured data of the target user's feedback of the user representation template library base data information subset comprises: and comparing the statistical result with the statistical result obtained when the basic data information set of the user portrait template base is integrally distributed, and adjusting and optimizing data disassembly and matching distribution modes according to the comparison result.

6. The method of claim 5, wherein optimizing data disassembly based on structured data of the target user's feedback of the user representation template library base data information subset comprises: and when the basic data of the user portrait template library is distributed, distributing the basic data to different target users in an integral data set distribution mode and a disassembled data subset mode, receiving and comparing feedback structured data of the target users in different disassembling modes, and optimizing the disassembling mode of the data set according to a comparison result.

7. The method of claim 1, further comprising: and acquiring the basic data information set of the user portrait template library in a mode of manually creating or collecting user historical data.

8. The method of claim 1, wherein the step of obtaining target user characteristic information comprises: and analyzing the acquired characteristic information of the target user according to the historical data and/or acquiring the characteristic information of the target user in a manual setting mode.

9. The method of claim 1, wherein the matching of the user portrait template library basic data information subset and the target user is achieved by means of calculation of characteristic vector Euclidean distance and/or cosine similarity.

10. The method of claim 1, wherein the step of matching target user data comprises: pushing service information to the target user according to the constructed target user image, wherein the service information comprises at least one of the following items: advertisements, news, shopping guides, map navigation, application options.

11. A user data matching apparatus, comprising:

the user portrait template library basic data information set acquisition module is used for acquiring a user portrait template library basic data information set;

the target user characteristic information acquisition module is used for acquiring target user characteristic information;

the data disassembly matching distribution module is used for disassembling the basic data information set of the user portrait template base into a basic data information subset of the user portrait template base based on the basic data information set of the user portrait template base and the target user characteristic information and distributing the basic data information subset to matched target users;

and the user portrait and data matching module is used for constructing a target user portrait and matching target user data based on feedback information of a target user to the user portrait template base basic data information subset.

12. An electronic device, comprising: a processor and a memory, the memory having a medium with program code stored thereon, the electronic device being capable of performing the method of any of claims 1-10 when the processor reads the program code stored on the medium.

13. A computer readable medium having computer readable instructions stored thereon which are executable by a processor to implement the method of any one of claims 1 to 10.

Technical Field

The present disclosure relates to the field of data application technologies, and in particular, to a user data matching method, apparatus, electronic device, and computer readable medium.

Background

Currently, in data applications, particularly information applications, which involve interaction with users, there is often involved the distribution of data information to users, the distributed data information may be delivered or pushed service information, such as advertisements, news, shopping guides, map navigation and application options, and so on.

Disclosure of Invention

In view of solving the technical problems in the prior art, an object of the present disclosure is to provide a user matching method, apparatus, electronic device and computer readable medium. The method, the device, the electronic equipment and the computer readable medium can reduce data distribution cost and overcome the defect that user experience is not easy to maintain.

To achieve the purpose, the following technical scheme is adopted in the disclosure:

in a first aspect, the present disclosure provides a user data matching method, including:

acquiring a basic data information set of a user portrait template base;

acquiring characteristic information of a target user;

the method comprises the steps that a user portrait template base basic data information set is disassembled into user portrait template base basic data information subsets based on a user portrait template base basic data information set and target user characteristic information and distributed to matched target users;

and constructing the target user picture and matching target user data based on feedback information of the target user to the user picture template base basic data information subset.

In a second aspect, the present disclosure further provides a user data matching apparatus, including:

the user portrait template library basic data information set acquisition module is used for acquiring a user portrait template library basic data information set;

the target user characteristic information acquisition module is used for acquiring target user characteristic information;

the data disassembly matching distribution module is used for disassembling the basic data information set of the user portrait template base into a basic data information subset of the user portrait template base based on the basic data information set of the user portrait template base and the target user characteristic information and distributing the basic data information subset to matched target users;

and the user portrait and data matching module is used for constructing a target user portrait and matching target user data based on feedback information of a target user to the user portrait template base basic data information subset.

In a third aspect, the present disclosure also provides an electronic device, including: a processor and a memory, the memory having a medium with program code stored therein, the electronic device being capable of performing any of the user data matching methods described in this disclosure when the processor reads the program code stored in the medium.

In a fourth aspect, the present disclosure also provides a computer readable medium storing computer readable instructions executable by a processor to implement any of the methods of user data matching described in the present disclosure.

Compared with the prior art, the technical scheme of the embodiment of the disclosure has at least the following beneficial effects:

(1) by constructing the data which is distributed to the target user by matching the target user portrait, the distribution of invalid data is avoided, so that the cost of distributing data is reduced, and the user experience of the user on the distributed data can be kept and improved;

(2) the target user characteristic information and the feedback of the target user are acquired by collecting the user historical data, the target user portrait is dynamically analyzed and constructed, the characteristics of the user are mastered, intelligent matching and data distribution are achieved, the pertinence, flexibility and/or accuracy of data distribution are improved, and the continuous improvement of the user experience of application is facilitated.

Drawings

FIG. 1 is a schematic illustration of a user representation provided by an embodiment of the present disclosure;

fig. 2 is a schematic flow chart diagram of a user data matching method provided by the embodiment of the disclosure;

FIG. 3 is a flowchart illustrating a user data matching method according to a preferred embodiment of the present disclosure;

fig. 4 is a schematic structural diagram of a user data matching apparatus according to a preferred embodiment of the present disclosure;

fig. 5 is a schematic structural diagram of an electronic device provided in an embodiment of the present disclosure;

fig. 6 is a schematic diagram of a computer-readable storage medium provided by an embodiment of the disclosure.

The present disclosure is described in further detail below. The following examples are merely illustrative of the present disclosure and do not represent or limit the scope of the claims that follow.

Detailed Description

The technical scheme of the disclosure is further explained by the specific implementation mode in combination with the attached drawings.

To better illustrate the present disclosure, and to facilitate an understanding of the technical solutions of the present disclosure, typical but non-limiting examples of the present disclosure are as follows: it should be specifically noted that the embodiments listed in the description of the present disclosure are only exemplary embodiments given for convenience of description, and should not be construed as the only correct embodiments of the present disclosure, nor as a restrictive description of the scope of the present disclosure.

With the continuous development of data applications, the experience requirements of users (i.e., audience users) on the received data are higher and higher, and it is necessary to distribute the data matching the users based on improving the experience of the users. For example, whether the use of delivered advertising data information, pushed news data information, map navigation data information meets the preferences of the audience users, has a good user experience, etc., requires that the data provider can analyze the characteristics of the users in advance. In addition, the characteristics of the user are not constant, and therefore, the characteristics of the user need to be tracked intelligently and adjusted accordingly.

For ease of understanding, the present disclosure is described with respect to a newsfeed as an example, but it is not intended that the scope of the present disclosure be limited to newsfeeds.

News push is currently the most popular data distribution method in information applications. News preferences vary from user to user. To describe the characteristics of different users, the present disclosure describes the user characteristics in user portrayal.

The user portrait is a target user model established on a series of real data, users are learned through user research, the users are distinguished into different types according to differences of targets, behaviors and viewpoints of the users, and then typical features are extracted from each type to form a character prototype. Referring to fig. 1, "female, living: 12 o 'clock evening-7 o' clock morning, liking yoga/jogging, having children at home, infancy, watching movies frequently, liking lancome, love making up, frying stock, using bank: industry, liking the sea to be elusive, ordinary mobile phone payment, liking to do dishes, paying attention to wearable equipment, paying attention to fashion, in school bus, love watching drama, own housing/repayment, China mobile, 4G high-traffic user, hotel: middle-high grade, often go to Shanghai, often go to Starbucks, 80-th white collar, and residential area: the typical characteristics such as ' work and rest rule, attention quality, life health, love to try fresh things and small fund ' are obtained as the user portrait of the user through analysis of the characteristics such as Beijing '. By utilizing a big data analysis means, the characteristics of a large number of collected user samples are analyzed, and typical user images in the user samples can form a basic data information set in a user image template library.

An embodiment of the user data matching method according to the present disclosure is described below with reference to fig. 2 and 3.

Referring to fig. 2, the user data matching method includes:

s1: acquiring a basic data information set of a user portrait template base; s2: acquiring characteristic information of a target user; s3: the method comprises the steps that a user portrait template library basic data information set is disassembled into user portrait template library basic data information subsets based on a user portrait template library basic data information set and target user characteristic information, and the user portrait template library basic data information subsets are distributed to target users matched with the user portrait template library basic data information subsets; s4: and constructing the target user picture and matching target user data based on feedback information of the target user to the user picture template base basic data information subset.

The embodiment determines the preference of the user, such as favorite news types and the like, by constructing the target user portrait, and particularly, by means of dynamic feedback of the user, the determined preference of the user is more accurate, so that the distributed data better conforms to the preference of the user, the effectiveness of the distributed data is improved, the distribution cost of the data is reduced, and meanwhile, the experience of the user can be continuously improved.

As a more preferred embodiment, referring to P1 in FIG. 3, the user representation template library base data information set is obtained by manually creating or collecting user history data prior to obtaining the user representation template library base data information set. The manual creation is mainly related characteristic information filled in by users during registration, but some users are not willing to fill in more information except user names and passwords, so that the related characteristic information of the users cannot be directly acquired, and the related characteristics of the users can be analyzed by using big data analysis means such as machine learning and the like through historical data of the users, such as browsed news contents, plates and contents for making comments. After a large number of user feature samples of users are obtained, user portraits are established by using technical means such as clustering and classification in machine learning, namely typical user portraits are extracted from the user portraits to form a basic data information set of a user portraits template library.

In some more preferred embodiments, the feature information of the user can be acquired by combining manual filling of the user and prediction through machine learning, so that the experience of the user during filling is improved. For example, when the user fills in the related information, the information of the next feature having an association relation with the user related information is predicted simultaneously by a machine learning means, such as when the user fills in the reading time of 6: 00-8: 00, predicting the reading content in the time period as body building according to the information of the existing mobile phone, automatically prompting the user, and automatically filling by the machine after the user approves so as to reduce the complexity of text input by the user and improve the experience of the user during filling, and under the condition that the information of automatic feature analysis of the machine is not ideal, the user can further correct in a manual filling mode.

To facilitate understanding of the technical solution, the following table shows a basic data information set of a simplified user portrait template library, where information indicated by each basic data number is a basic data information, for example, the basic data information indicated by a1 is: reading time is 6: 00-8: 00, B1 indicates that the reading is fitness.

The implementation method effectively reduces the intensity of information filling of the user even by means of artificial intelligence, and ensures that the user experience is not greatly influenced.

As a more preferred embodiment, the basic data information set of the user portrait template library in the foregoing embodiments of the present disclosure may be stored in a specific database to facilitate the invocation of other modules.

Referring to P2 in fig. 3, when the target user feature information is obtained, it may also be analyzed according to historical data and/or manually set, and together with the basic data information set of the user portrait template library, it may be used as a basis for disassembling the basic data information set of the user portrait template library.

Referring to P3 in fig. 3, the user representation template library base data information set is decomposed into user representation template library base data information subsets based on the user representation template library base data information set and the target user characteristic information, and distributed to the matching target users.

And the matched target user (see the matched user in the figure 3) feeds back the received user image template library basic data information subset to form feedback information.

Referring to P4 in fig. 3, after receiving the feedback information of the target user, the structured data is formed. The structured data is output in a visual and/or machine readable form. The visual output means that feedback information of a target user is output and represents related data in a human-readable data representation form such as a list, characters, graphics, images, and a data map. The machine-readable output refers to that the feedback information of the target user outputs related data in the forms of computer information interaction, transmission, identification, statistics and analysis.

The embodiment utilizes the structured data to not only facilitate the storage of the data, but also facilitate the processing and display of the data, thereby reducing the implementation cost of the technical scheme.

As a more preferred embodiment, the feedback structured data optimizes the data disassembly and matching distribution mode. Further, the method for disassembling, matching and distributing the optimized structured data according to the feedback of the target user to the user portrait template base basic data information subset comprises the following steps: and comparing the statistical result with the statistical result obtained when the basic data information set of the user portrait template base is integrally distributed, and adjusting and optimizing data disassembly and matching distribution modes according to the comparison result.

The method for disassembling and matching and distributing the structural data optimization data according to the feedback of the target user to the user portrait template base basic data information subset comprises the following steps: and when the basic data of the user portrait template library is distributed, distributing the basic data to different target users in an integral data set distribution mode and a disassembled data subset mode, receiving and comparing feedback structured data of the target users in different disassembling modes, and optimizing the disassembling mode of the data set according to a comparison result.

According to the embodiment, the data set disassembling mode is continuously optimized by using user feedback, and the data set disassembling efficiency is favorably improved.

As a more preferred embodiment, the step of breaking down the underlying data set of the user representation template library into subsets:

assuming that the basic data set comprises N basic data, and matching is performed on M target users, the disassembling mode is as follows:

user portrait template subset 1: { T11, T12, T13, … T1i }, number i,

user portrait template subset 2: { T21, T22, T23, … T2j }, number j,

user portrait template subset 3: { T31, T32, T33, … T3k }, in the number k,

…

user portrait template subset X: { Tx1, Tx2, Tx3, … Txq }, number q

It is necessary to satisfy i + j + k + … + q ≦ M × N, that is, it is necessary to ensure that the total number of distributed subsets does not exceed M × N, and the number of distributed base data is at most M. Wherein i, j, k, …, q are all less than N.

Because the target user receives only a subset of the basic data set, the quantity of the subset is less than the total quantity of the data of the basic data set, so that the user is not tired by the huge quantity of data to be fed back, and the method is beneficial to maintaining good user experience.

To ensure the effectiveness of the subset being fed back, it is then necessary to match the subset with the audience users. As a preferred embodiment, the step of distributing the subset of user representation templates to matching audience users comprises:

the distribution process is a process of matching the characteristics of the user portrait template subset and the target user, and different matching algorithms can be adopted, as a more preferable embodiment, the matching algorithm can be implemented by calculating the similarity between two objects, for example: there are two objects X, Y, both containing N-dimensional features, X ═ X (X1, X2, X3, … …., xn), Y ═ Y1, Y2, Y3, … …., yn), the similarity between X and Y is calculated, and the specific algorithm can be: euclidean distance method, manhattan distance method, minkowski distance method, cosine similarity method, or pearson correlation coefficient method.

Different user portrait template subsets can be distributed to target users with close characteristics through the matching calculation of the user portrait template subsets and the target users; in some more preferred embodiments, it should be ensured that the same audience user is not assigned two user representation template subsets in the same survey.

The target user, upon receiving the user portrait template subset, performs feedback and forms the feedback information into structured data that is output in a visual and/or machine-readable form. The visual output means that feedback information of a target user is output and represents related data in a human-readable data representation form such as a list, characters, graphics, images, and a data map. The machine-readable output refers to that the feedback information of the target user outputs related data in the forms of computer information interaction, transmission, identification, statistics and analysis.

Referring to P4 in fig. 3, for feedback data output in a machine readable manner can be further fed back to P3 responsible for disassembling and distributing, P3 further optimizes the disassembling and matching distribution mode according to the above feedback structured data, and the specific process may be: and when the basic data of the user portrait template library is distributed, distributing the basic data to different target users in an integral data set distribution mode and a disassembled data subset mode, receiving and comparing feedback structured data of the target users in different disassembling modes, and optimizing the disassembling mode of the data set according to a comparison result. A preferred specific implementation may be: two groups are set when the basic data set of the user portrait template base is disassembled: the group A is distributed in the form of an integral data set, and the group B is distributed in the form of a disassembled subset. The specific gravity of the two feedbacks is counted from the collected results, vector comparison is performed, and the two effects are approximated by overwriting the disassembled subsets of the B group (e.g., controlling the amount of basic data of each subset, etc.).

The feedback rate of the user to the basic data information subset of the user portrait template library is improved through different disassembling modes, and one preferable specific implementation mode can be as follows: similarly, basic data is disassembled, for example, when full data is disassembled (in fact, the disassembly is not performed), the obtained user feedback rate V (the number of the recovery problems in unit time) is used as a reference value, and after the parameters of the disassembly scale are rewritten, the change of V is compared, so that the disassembly mode is optimized.

The feedback rate of the target user is improved through different disassembling modes, and the specific implementation mode of finding out the optimal solution when the feedback rate is highest and the result is closest to the result when the feedback result is issued with the whole data set can be as follows: on the premise of ensuring the effect, the feedback rate is improved as much as possible. Statistical regression analysis or the like can be performed on the results in the previous two steps in order to find the optimal solution.

Referring to P5 in fig. 3, a user profile of the target user is further constructed based on the feedback of the user, so as to obtain the favorite news characteristics of the target user, and based on the characteristics, matching data (see P6 in fig. 3), that is, matching news data, is obtained and pushed to the user.

According to the embodiment, by means of user image template subset, intelligent matching of feature matching and the like, accuracy of data matching is guaranteed, and information amount fed back by a required user is reduced, so that pertinence, flexibility and accuracy of data distribution are greatly improved, and user experience is also greatly improved.

As a second aspect of the present disclosure, there is also provided an apparatus corresponding to the aforementioned method steps.

The device disclosed in the present disclosure may be a physical hardware device, a virtual system formed by software functional modules, or a device formed by combining hardware and software, but those skilled in the art should understand that the function of any virtual device formed by software functional modules is implemented without the support of the physical hardware device.

The user data matching apparatus (100) shown in fig. 4 includes: the user portrait template library basic data information set acquisition module (101) is used for acquiring a user portrait template library basic data information set; the target user characteristic information acquisition module (102) is used for acquiring target user characteristic information; the data disassembly matching distribution module (103) is used for disassembling the basic data information set of the user portrait template base into a basic data information subset of the user portrait template base based on the basic data information set of the user portrait template base and the target user characteristic information and distributing the basic data information subset to target users matched with the basic data information subset of the user portrait template base; and the user portrait and data matching module (104) is used for constructing a target user portrait and matching target user data based on feedback information of a target user to the user portrait template base data information subset.

Optionally, the apparatus further comprises: and the structured data forming module is used for receiving the feedback information of the target user and forming structured data.

Optionally, the apparatus further comprises: a structured data output module for outputting the structured data in a visual and/or machine readable form.

Optionally, the data parsing, matching and distributing module further includes: and the optimization submodule is used for optimizing a data disassembling and matching distribution mode according to the structural data fed back by the target user to the user portrait template base basic data information subset.

Optionally, the optimization submodule further includes: and the adjusting and optimizing unit is used for comparing the statistical result with the statistical result when the basic data information set of the user portrait template library is integrally distributed, and adjusting and optimizing data disassembling and matching distribution modes according to the comparison result.

Optionally, the adjusting and optimizing unit further includes: and the optimized data set disassembling mode subunit is used for simultaneously distributing the basic data of the user portrait template library to different target users in an integral data set distribution mode and a disassembling data subset mode, receiving and comparing structural data fed back by the target users in different disassembling modes, and optimizing the disassembling mode of the data set according to a comparison result.

Optionally, further comprising: and the data acquisition module is used for acquiring the basic data information set of the user portrait template library in a mode of manually creating or collecting user historical data.

Optionally, the obtaining of the target user feature information includes: and analyzing the acquired characteristic information of the target user according to the historical data and/or acquiring the characteristic information of the target user in a manual setting mode.

Optionally, the matching between the basic data information subset of the user portrait template library and the target user is realized by adopting a calculation mode of characteristic vector euclidean distance and/or cosine similarity.

Optionally, the matching the target user data includes: pushing service information to the target user according to the constructed target user image, wherein the service information comprises at least one of the following items: advertisements, news, shopping guides, map navigation, or application options.

As will be understood by those skilled in the art, the data disassembly matching distribution module (103) is capable of disassembling the basic data information set into subsets based on the basic data information set and the target user characteristic information, and distributing the subsets to target users matched with the basic data information set information acquired by the user image template library basic data information set acquisition module (101) and the target user characteristic information acquired by the target user characteristic information acquisition module (102). A user representation and data matching module (104) constructs a user representation based on information fed back by the target user and provides the user representation with matching data. The specific disassembling, matching and distributing process corresponds to the method disclosed in the present disclosure, and is not repeated here.

As a third aspect of the present disclosure, there is also provided an electronic apparatus including: a processor and a memory, the memory having a medium (computer-readable storage medium) with program code stored therein, the electronic device being capable of performing the following method steps when the processor reads the program code stored in the medium: acquiring a basic data information set of a user portrait template base; acquiring characteristic information of a target user; the method comprises the steps that a user portrait template library basic data information set is disassembled into user portrait template library basic data information subsets based on a user portrait template library basic data information set and target user characteristic information, and the user portrait template library basic data information subsets are distributed to target users matched with the user portrait template library basic data information subsets; and constructing the target user picture and matching target user data based on feedback information of the target user to the user picture template base basic data information subset.

Further, the electronic device is also capable of performing any of the other methods described in this disclosure when the processor reads the program code stored in the medium.

Fig. 5 is a diagram illustrating a hardware structure of an electronic device according to an embodiment of the present disclosure. The electronic device may be implemented in various forms, and the electronic device in the present disclosure may include, but is not limited to, mobile electronic devices such as a mobile phone, a smart phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a navigation apparatus, an in-vehicle electronic device, an in-vehicle display terminal, an in-vehicle electronic rear view mirror, and the like, and fixed electronic devices such as a digital TV, a desktop computer, and the like.

As shown in fig. 5, the electronic device 1100 may include a processor 1120, an input unit 1130, a memory 1140, an output unit 1150, and the like. While fig. 5 illustrates an electronic device having various components, it is to be understood that not all illustrated components are required to be implemented, and that more or fewer components can alternatively be implemented.

Among them, the electronic device processor 1120 is used to execute the user data matching method disclosed in the present disclosure, the input unit 1130 may generate key input data according to a command input by a user to control various operations of the electronic device, and the output unit 1150 provides an output signal. The memory 1140 may store software programs or the like for processing and controlling operations performed by the processor 1120, or may temporarily store data that has been output or is to be output. Memory 1140 may include at least one type of storage medium. Also, the electronic apparatus 1100 may cooperate with a network storage device that performs a storage function of the memory 1140 by way of a network connection. The processor 1120 generally controls the overall operation of the electronic device.

As a fourth aspect of the present disclosure, fig. 6 is a schematic diagram of a computer-readable storage medium provided by an embodiment of the present disclosure. As shown in fig. 6, a computer-readable storage medium 300 having non-transitory computer-readable instructions 301 stored thereon. The non-transitory computer readable instructions 301, when executed by a processor, perform all or part of the steps of the user data matching method of the embodiments of the present disclosure as described above.

Various embodiments of the user data matching method presented in the present disclosure may be implemented using a computer-readable medium, such as computer software, hardware, or any combination thereof. For a hardware implementation, various embodiments of the user data matching method proposed by the present disclosure may be implemented by using at least one of an Application Specific Integrated Circuit (ASIC), a Digital Signal Processor (DSP), a Digital Signal Processing Device (DSPD), a Programmable Logic Device (PLD), a Field Programmable Gate Array (FPGA), a processor, a microprocessor, an electronic unit designed to perform the functions described herein, and in some cases, various embodiments of the user data matching method proposed by the present disclosure may be implemented in the processor 1120 shown in fig. 5. For software implementation, various embodiments of the user data matching method presented in the present disclosure may be implemented with a separate software module that allows at least one function or operation to be performed. The software codes may be implemented by software applications (or programs) written in any suitable programming language, which may be stored in memory 1140 and executed by processor 1120.

Although the above embodiment is a news push, it should be understood by those skilled in the art that by making appropriate changes and adjustments to the data in the above embodiment, service information such as advertisement push, shopping guide, map navigation, selection or recommendation of Application (APP) and the like can be pushed.

The applicant declares that the present disclosure illustrates the detailed structural features of the present disclosure through the above-mentioned embodiments, but the present disclosure is not limited to the above-mentioned detailed structural features, i.e. it does not mean that the present disclosure must rely on the above-mentioned detailed structural features for implementation. It will be apparent to those skilled in the art that any modification of the present disclosure, equivalent substitutions of selected elements of the disclosure, additions of auxiliary elements, selection of particular means, etc., are within the scope and disclosure of the present disclosure.

The preferred embodiments of the present disclosure have been described in detail above, however, the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solution of the present disclosure within the technical idea of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

It should be noted that, in the foregoing embodiments, various features described in the above embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, various combinations that are possible in the present disclosure are not described again.

In addition, any combination of various embodiments of the present disclosure may be made, and the same should be considered as the disclosure of the present disclosure, as long as it does not depart from the spirit of the present disclosure.