CN114547470A

CN114547470A - Data processing method and device, electronic equipment and storage medium

Info

Publication number: CN114547470A
Application number: CN202210216400.0A
Authority: CN
Inventors: 李志强
Original assignee: Beijing Tendcloud Tianxia Technology Co ltd
Current assignee: Beijing Tendcloud Tianxia Technology Co ltd
Priority date: 2022-03-07
Filing date: 2022-03-07
Publication date: 2022-05-27

Abstract

The disclosure provides a data processing method and device, electronic equipment and a storage medium, and relates to the technical field of internet. The data processing method comprises the following steps: receiving a content identification of target content and a conversion value of the target content, the target content being configured to recommend a target application, the conversion value being configured to indicate user behavior information associated with the target application; determining the target application corresponding to the target content based on the corresponding relation between the preset content identification and the application identification; analyzing the user behavior information from the conversion value based on the conversion value configuration information of the target application; attributing the user behavior information to the target content; and determining the recommendation effect of the target content based on the user behavior information.

Description

Data processing method and device, electronic equipment and storage medium

Technical Field

The present disclosure relates to the field of internet technologies, and in particular, to a data processing method and apparatus, an electronic device, a storage medium, and a computer program product.

Background

With the development of the internet, various applications emerge endlessly. In order for an application to reach more potential users, the developer of the application typically pushes content (e.g., articles, advertising pictures, etc.) for recommending the application through a variety of channels. In addition, the recommendation effect of each channel needs to be monitored so as to optimize the selection of the channel and the content pushed in the channel.

The approaches described in this section are not necessarily approaches that have been previously conceived or pursued. Unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. Similarly, unless otherwise indicated, the problems mentioned in this section should not be considered as having been acknowledged in any prior art.

Disclosure of Invention

The present disclosure provides a data processing method and apparatus, an electronic device, a storage medium, and a computer program product.

According to an aspect of the present disclosure, there is provided a data processing method including: receiving a content identification of target content and a conversion value of the target content, the target content being configured to recommend a target application, the conversion value being configured to indicate user behavior information associated with the target application; determining the target application corresponding to the target content based on the corresponding relation between the preset content identification and the application identification; analyzing the user behavior information from the conversion value based on the conversion value configuration information of the target application; attributing the user behavior information to the target content; and determining the recommendation effect of the target content based on the user behavior information.

According to an aspect of the present disclosure, there is provided a data processing apparatus including: a receiving module configured to receive a content identification of a target content and a conversion value of the target content, the target content configured to recommend a target application, the conversion value configured to indicate user behavior information associated with the target application; the first determination module is configured to determine the target application corresponding to the target content based on the corresponding relation between preset content identification and application identification; the analysis module is configured to analyze the user behavior information from the conversion value based on the conversion value configuration information of the target application; an attribution module configured to attribute the user behavior information to the target content; and a second determination module configured to determine a recommendation effect of the target content based on the user behavior information.

According to an aspect of the present disclosure, there is also provided an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor. The memory stores a computer program that, when executed by the at least one processor, implements the above-described method.

According to an aspect of the present disclosure, there is also provided a non-transitory computer readable storage medium storing a computer program which, when executed by a processor, implements the above-described method.

According to an aspect of the disclosure, there is also provided a computer program product comprising a computer program which, when executed by a processor, implements the above-described method.

According to one or more embodiments of the present disclosure, user behavior information indicated by a conversion value can be flexibly configured according to different scene requirements. By analyzing the conversion value of the target content pushed by the channel, the user behavior information in the target application generated under the guidance of the target content can be determined. Furthermore, the recommendation effect of the target content is determined based on the user behavior information in the target application, and the channel recommendation effect is flexibly, accurately and efficiently monitored.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the embodiments and, together with the description, serve to explain the exemplary implementations of the embodiments. The illustrated embodiments are for purposes of illustration only and do not limit the scope of the claims. Throughout the drawings, identical reference numbers designate similar, but not necessarily identical, elements.

FIG. 1 illustrates a schematic diagram of an exemplary system in which various methods described herein may be implemented, according to an embodiment of the present disclosure;

FIG. 2 shows a schematic diagram of a system configuration and monitoring process according to an embodiment of the present disclosure;

FIG. 3 shows a flow diagram of a data processing method according to an embodiment of the present disclosure;

FIG. 4 shows a schematic diagram of a data processing process according to an embodiment of the present disclosure;

FIG. 5 shows a block diagram of a data processing apparatus according to an embodiment of the present disclosure; and

FIG. 6 shows a block diagram of an exemplary electronic device, according to an embodiment of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, the timing relationship, or the importance relationship of the elements, and such terms are used only to distinguish one element from another. In some examples, a first element and a second element may refer to the same instance of the element, and in some cases, based on the context, they may also refer to different instances.

The terminology used in the description of the various described examples in this disclosure is for the purpose of describing particular examples only and is not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the elements may be one or more. Furthermore, the term "and/or" as used in this disclosure is intended to encompass any and all possible combinations of the listed items.

To facilitate an understanding of the embodiments of the present disclosure, a number of terms referred to by the embodiments of the present disclosure are explained first.

1. Advertiser (Advertiser): a legal person, other economic organization or individual who designs, makes, or distributes advertisements to sell products or provide services to others on their own or on behalf of others. Advertisers are publishers of advertising campaigns, and are merchants selling or advertising their products and services over the internet. Any merchant that promotes, sells, or sells its products or services may act as an advertiser. In an application development scenario, the promoted product is an application and the advertiser is the developer (organization or individual) of the application.

2. Channel (content platform): the medium for advertisement putting is a material tool which can realize information transmission between an advertiser and a target user. The channels may be, for example, search engines, content information applications, social applications, and the like.

3. Attributing channels: and matching the behaviors of the user for installing and using the application with the channel to determine the channel for pushing the user to install the application.

In the related art, channel attribution is generally performed in a precise matching manner. Advertisers push their advertisements through multiple channels. When a user browses and clicks an advertisement, the channel acquires click data of the user, records identifiers of the user terminal, such as an International Mobile Equipment Identity (IMEI) of an Android platform, an Android _ ID (unique Identifier of Android Equipment), an Identifier of an advertisement (IDFA) of an iOS platform and the like, and sends the Identifier of the user terminal to a third-party monitoring platform. After the user installs the application based on the guidance of the advertisement, the registration, login, collection and other behaviors are all performed in the application, and an application developer can acquire the user behaviors, record the identification of the user terminal and report the identification to the third-party monitoring platform. Based on the identification of the user terminal, the third-party monitoring platform can correlate the behavior of the user clicking the advertisement with the in-application behavior after the application is installed, so that channel attribution is realized.

However, after the iOS platform releases the att (application Tracking privacy) privacy policy terms, the ability of the developer to acquire the IDFA is limited, channel attribution is difficult to be performed by the above-mentioned precise matching method, and attribution can only be achieved by using an attribution interface SKAdNetwork (hereinafter, abbreviated as "SKAN") provided by the iOS platform.

When using the SKAN interface, a developer needs to define a conversion value, which may represent some behavior of the user in the developer application (which may be referred to as "conversion behavior"), such as registration, login, collection, purchase, and so on. The conversion value may represent a degree of conversion of the user in the developer application. The developer can configure the meaning represented by the different conversion values by himself. In the SKAN interface, the conversion value is a binary value of 6 bits, and the value range is 0-63.

For a third-party monitoring platform, attribution results of SKAN interfaces need to be integrated and analyzed, and monitoring and evaluation of recommendation effects of various channels are achieved, so that developers can optimize channels and advertisement contents pushed in the channels conveniently. Therefore, the data processing method is used for flexibly and accurately monitoring the recommendation effect of each channel.

Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

Fig. 1 illustrates a schematic diagram of an exemplary system 100 in which various methods described herein may be implemented, according to an embodiment of the present disclosure. As shown in fig. 1, the system 100 includes a user terminal 150, an attribution server 160, a content server 170, a monitoring server 180, and a development terminal 190.

The user terminal 150 is a terminal device used by a user, including but not limited to a Personal Computer (PC), a mobile phone, a tablet computer, a smart wearable device, and the like. The user terminal 150 is usually installed with a plurality of applications, some of which may have advertisement slots, so that the applications may be used as a channel for promoting other applications or products.

For example, as shown in FIG. 1, a user may launch an application in user terminal 150 and enter application interface 110. Three

information flow ads

112, 114, 116 are included in the interface 110, one for each recommendation application A, B, C. The user clicks on the advertisement 114 and may enter the download interface 120 for the application B that he recommends. Download interface 120 typically presents information such as application B's icon 122, detailed descriptions, user comments, etc. After the user clicks the install button 122 in the download interface 120, application B begins to be downloaded and installed. After installation is complete, the icon 122 for application B will be presented on the desktop 130 of the user terminal 150. The user may launch application B by clicking on icon 122 on desktop 130 and enter into login interface 140 of application B. The user may click a register button 142 in the interface 140 to complete account registration or enter an account, password and click a login button 144 to complete login. Further, the user can browse, collect, like, buy, etc. in the application B. The relevant interfaces for browsing, favorites, etc. operations are not shown in FIG. 1 due to space limitations.

When the user generates a conversion behavior such as registration, login, collection, approval, etc. in the application B, the application B generates a conversion value corresponding to the conversion behavior and sends the conversion value to the attribution server 160 (i.e., SKAN server).

The attribution server 160 performs channel attribution, determines channel content (corresponding to the campaignID in SKAN) to which the conversion value corresponds, and sends the attribution result to the content server 170. The content server 170 corresponds to an advertisement network in SKAN, i.e., Ad networks.

The content server 170 further sends the conversion value and the attribution result (content identification) to the monitoring server 180. Monitoring server 180 may be, for example, a server of any third party monitoring platform.

After receiving the conversion value and the content identifier, the monitoring server 180 may analyze the conversion value and the content identifier, determine an application (i.e., application B) promoted by the content identifier, and determine user behavior information (e.g., related information of user-triggered behaviors such as login, collection, approval, and purchase).

The development terminal 190 is a terminal device used by a developer of the application B, including but not limited to a Personal Computer (PC), a mobile phone, a tablet, a smart wearable device, and the like. The developer may access the third party monitoring platform through the development terminal 190. The third-party monitoring platform may be, for example, a website, or an application installed in the development terminal 190. Accordingly, the monitoring server 180 may push the user behavior information corresponding to each popularization channel (i.e., each content identifier) of the application B to the development terminal 190 to be displayed to the developer, so that the developer can know the recommendation effect of the application in each channel.

It will be appreciated that in order to implement the data processing link from the attribution server 160 to the development terminal 190 in FIG. 1, the attribution server 160(SKAN), the content server 170 (advertising network), the monitoring server 180 and the development terminal 190 need to be configured accordingly. Fig. 2 shows a schematic diagram of a system configuration and monitoring process 200 according to an embodiment of the disclosure.

As shown in fig. 2, in step S201, the monitoring server 180 performs media interfacing with the advertising network 170, and synchronizes a plurality of conversion value configuration methods and corresponding conversion value parsing methods provided by the monitoring server 180 to the advertising network 170.

In step S202, the monitoring server 180, in response to a channel recommendation effect monitoring request initiated by a developer in the monitoring platform for its application, assigns an application identifier (APPKey) to the application for uniquely identifying the application in the monitoring platform. The monitoring server 180 further generates an SDK (Software Development Kit) corresponding to the APPKey for configuring the conversion value. And then, responding to the conversion value configuration operation of the developer in the monitoring platform, and generating conversion value configuration information corresponding to the APPKey.

In step S203, the monitoring server 180 transmits the SDK containing the conversion value configuration information to the development terminal 190.

In step S204, the developer embeds the SDK in its application through the development terminal 190, thereby completing the configuration of the conversion value.

In step S205, the SKAN160 generates an advertisement plan, which is uniquely identified by the campaignID, in response to a request by the advertising network.

In step S206, the monitoring server 180 acquires the campaignID.

In step S207, the monitoring server 180 associates the campaignID with the application identification APPKey, and generates a short link accompanied by the campaignID.

In step S208, the monitoring server 180 sends the short link to the advertising network 170.

In step S209, the advertisement network 170 embeds a short link in the advertisement content for recommending the application, so that when the user installs the application under the guidance of the advertisement content and generates a developer-defined conversion behavior, the application generates a corresponding conversion value and sends it to the SKAN. After the SKAN completes attribution, the advertising network 170 receives attribution result data returned by the SKAN, and further returns the data to the monitoring server 180.

In step S210, the monitoring server 180 receives data returned from the advertising network, where the data includes the conversion value and the campaignID.

In step S211, the monitoring server 180 determines a corresponding APPKey based on the campaigned. Analyzing the conversion value based on the conversion value configuration information under the APPKey to obtain user behavior information corresponding to the conversion value, and evaluating the advertisement putting effect based on the user behavior information.

In step S212, the monitoring server 180 transmits the evaluation result to the development terminal 190.

In step S213, the evaluation result is presented to the developer through the monitoring platform.

FIG. 3 shows a flow diagram of a data processing method 300 according to an embodiment of the present disclosure. The method 300 is executed at a monitoring server (e.g., the monitoring server 180 shown in fig. 1 and 2) for flexibly, accurately and efficiently monitoring the recommendation effects of different channels. As shown in FIG. 3, the method 300 includes steps S310-S350.

In step S310, a content identification of the target content and a conversion value of the target content are received. The target content is configured to recommend the target application, and the conversion value is configured to indicate user behavior information associated with the target application.

In step S320, a target application corresponding to the target content is determined based on a preset correspondence between the content identifier and the application identifier.

In step S330, user behavior information is parsed from the conversion value based on the conversion value configuration information of the target application.

In step S340, the user behavior information is attributed to the target content.

In step S350, a recommendation effect of the target content is determined based on the user behavior information.

According to the embodiment of the disclosure, the user behavior information indicated by the conversion value can be flexibly configured according to different scene requirements. By analyzing the conversion value of the target content pushed by the channel, the user behavior information in the target application generated under the guidance of the target content can be determined. Furthermore, the recommendation effect of the target content is determined based on the user behavior information in the target application, and the channel recommendation effect is flexibly, accurately and efficiently monitored.

According to some embodiments, the targeted content may be, for example, an advertisement for promoting a targeted application, and accordingly, the content identification of the targeted content may be, for example, a campaignID.

According to some embodiments, the method 300 further comprises: receiving conversion value configuration information of a target application before the step S310 is performed; and sending the application identification and the conversion value configuration information of the target application to a content platform for pushing the target content.

The conversion value configuration information is set by a developer of the target application and is used for defining the corresponding relation between the conversion value and the user behavior information in the target application. Based on the embodiment, after the developer sets the conversion value configuration information, the conversion value configuration information is sent to the monitoring platform. After receiving the conversion value configuration information, the monitoring platform further synchronizes the application identifier of the target application and the conversion value configuration information to a content platform (such as an advertisement network), so that the content platform can understand the meaning of the conversion value conveniently, and the content platform can complete the value evaluation and the push optimization of the content (advertisement).

According to some embodiments, in step S320, the target application corresponding to the target content may be determined based on the preset correspondence between the campaigned and the APPKey.

According to some embodiments, the method 300 further comprises: before the converted value is analyzed through the step S330, it is determined whether the converted value is an integer within a preset value range; and in response to determining that the converted value is an integer within a preset value range, determining the converted value as an effective value for analysis.

In SKAN, the translation value is a 6-bit (bit) binary value, and therefore, the translation value should be an integer from 0 to 63. Based on the above embodiment, after receiving the converted value, it is first judged whether it falls within the range of 0 to 63. If the converted value is an integer from 0 to 63, the converted value is a valid value, and the process further proceeds to step S330 to analyze it. If the converted value is not an integer in the range of 0 to 63, the converted value is an invalid value, and the step S330 is not required to be performed to analyze the invalid value. Therefore, unnecessary calculation can be avoided, and the efficiency and the accuracy of data processing can be improved.

In an embodiment of the present disclosure, the translation value is configured to indicate user behavior information associated with the target application. The user behavior information may be used to evaluate the recommendation effect of the target content.

There are many ways to configure the user behavior information and the corresponding transformation values. For example, the user behavior information may include event trigger information, behavior value information, user remaining days, and the like.

According to some embodiments, the user behavior information includes event trigger information of the user, the event trigger information including whether an operation event (e.g., login, favorite, like, comment, etc.) for the target application is triggered or the number of times the operation event for the target application is triggered.

According to a first configuration, the translation value is a binary code, the translation value configuration information may be used to configure the binary code to include at least one valid bit, each valid bit of the at least one valid bit corresponds to an operation event, and the value of each valid bit is used to record whether a corresponding operation event is triggered.

As described above, in SKAN, the translation value is a 6-bit binary code, and accordingly, 6 valid bits can be set at the maximum, that is, 6 operation events can be monitored at the maximum. The significance is typically set starting from the lowest bit of the binary code (i.e., the rightmost bit of the binary code). For example, a developer may configure three operational events that he wants to monitor, such as registration, login, payment, and accordingly, the binary includes three significant bits, such as the lower three bits of the binary (i.e., the rightmost three bits). If the value of a certain valid bit is 1, the corresponding operation event is triggered; if the value of the valid bit is 0, it indicates that the corresponding operation event is not triggered.

Based on the conversion value configuration information, after receiving the conversion value, it is first determined whether the conversion value is an integer in the range of 0 to 63, and if so, it is determined as an effective value. Further, the conversion value is converted into a binary code with 6 bits, and whether the corresponding operation event is triggered or not is judged by reading the value of each valid bit. For example, after receiving the decimal conversion value 6, the decimal conversion value is judged to be a valid value through validity check. It is then converted into binary code 000110 with three lower significant digits corresponding to registration, login, and payment events, respectively, so that registration, login events are triggered. Accordingly, the number of times of registration and login event triggering under the content identification (campaignID) may be increased by 1.

According to a second configuration manner, the conversion value configuration information may be used to configure a conversion value corresponding to each of the plurality of operation events, where the conversion value is positively correlated with the trigger sequence of the corresponding operation event, that is, the conversion value is larger as the trigger sequence of the operation event is later. Based on the above-mentioned conversion value configuration information, accordingly, in step S330, the conversion value may be parsed according to the following steps: determining a target operation event corresponding to the conversion value; and marking the target operation event and the operation event of which the triggering sequence in the plurality of operation events precedes the target operation event as triggered.

It will be appreciated that the above embodiment is capable of monitoring up to 63 operational events, since in SKAN the maximum value of the conversion value is 63.

For example, a developer wants to monitor five operation events, which are respectively registered, logged in, collected, added to a shopping cart and paid according to the sequence triggered by the user, and the corresponding conversion values of the five events are respectively 1, 2, 3, 4 and 5. If the received conversion value is 4 (the corresponding binary code is 000100), it indicates that the target operation event (i.e. adding shopping cart) corresponding to the conversion value 4 and the operation events (i.e. registering, logging in, collecting) before the target operation event are triggered. Accordingly, the trigger times of registration, login, collection, and shopping cart entry events under the content identification (campaignID) may all be increased by 1.

According to a third configuration, the conversion value configuration information may be used to configure the conversion value to record the number of triggers of the target operation event for the target application. For example, the target operation event may be a login event, and if the received conversion value is 5 (the corresponding binary code is 000101), it indicates that the login event is triggered 5 times. Accordingly, the PV (Page View, access amount, PV value accumulation of a content that a single user accesses the same content multiple times) amount under the content identification (campaignID) may be added by 5, and the UV (Unique viewer, access user, a single user accesses the same content multiple times in one day, UV value of the content is added by only 1) amount may be added by 1.

According to a fourth configuration mode, the user behavior information includes behavior value information, and the conversion value configuration information may be used to configure the conversion value as the number of triggers of the target operation event for the target application, and to configure the unit value generated by triggering the target operation event each time. Based on the above-mentioned conversion value configuration information, accordingly, in step S330, the conversion value may be parsed according to the following steps: and determining behavior value information based on the triggering times and unit value of the target operation event.

For example, the target operational event may be a payment event and the unit value may be an average payment amount for the user, e.g., 6 dollars. Accordingly, the behavioral value information may be the product of the number of triggers of the payment event (i.e., the number of payments) and the unit value (average payment amount), i.e., the total payment amount generated by the user. If the received conversion value is 5 (corresponding to 000101), it means that the payment event is triggered 5 times. And multiplying the triggering times 5 by the unit value 6 yuan to obtain the corresponding behavior value information of 5 × 6-30 yuan. Accordingly, the payment amount under the content identification (campaignID) may be increased by 30 dollars.

According to a fifth configuration, the user behavior information includes the number of days of user survival, and accordingly, the conversion value configuration information may be used to configure the conversion value to record the number of days of user survival of the target application. The number of days saved refers to the number of days the user continuously launches the target application. For example, if the received conversion value is 5 (corresponding to 000101), it indicates that the user has 5 days to live, i.e. the user starts the target application every day for 5 consecutive days. Accordingly, 1 may be added to the 1-day, 2-day, 3-day, 4-day, and 5-day remaining user amounts under the content identification (campaignID).

According to the sixth configuration, the conversion value can also be configured by using multiple free combinations of the above-described configurations. For example, the conversion value is a binary code, the binary code may be divided into a plurality of bit groups, each bit group in the plurality of bit groups includes at least one bit and is used for indicating a type of user behavior information, and as shown above, the user behavior information includes event trigger information, behavior value information, and user remaining days.

For example, in SKAN, a binary code with a value of 6 bits is converted, and the binary code is divided into three bit groups on average, each bit group including two bits. The two bit positions of the high-order bit group respectively correspond to two events of registration and login and are used for recording whether the two events are triggered or not; two bits of the middle bit group are used for recording the triggering times of the payment event, and the unit value of the payment event is configured to be 10 yuan; two bits of the lower bit group are used for recording the number of days of user retention. If the received conversion value is 45 (the corresponding binary code is 101101), the registration event can be triggered by analyzing the two high-order bits; by analyzing the middle two bits, it can be derived that the payment event is triggered 3 times, and the corresponding generated value (i.e. behavior value information) is 3 × 10 ═ 30 elements; by analyzing the two lower bits, it can be obtained that the number of days the user remains is 1. Accordingly, the amount of registered users under the content identification (campaignID) may be increased by 1, the payment amount may be increased by 30 dollars, and the amount of remaining users may be increased by 1 day.

In step S340, the user behavior information is attributed to the target content. That is, the corresponding user behavior information is generated due to the guidance of the target content (e.g., advertisement).

According to some embodiments, a plurality of indexes for evaluating the recommendation effect of the target content, such as a registered user amount, a logged-in user amount, a collected user amount, a generated payment amount, a PV amount, a UV amount, a 1-7 day remaining user amount, and the like, may be preset. Accordingly, in step S350, values of the respective indexes may be calculated based on the user behavior information, and the recommendation effect of the target content may be evaluated by the index values. Generally, the larger the value of each index of the target content is, the better the recommendation effect thereof is.

Fig. 4 shows a schematic diagram of a data processing procedure 400 according to an embodiment of the present disclosure.

As shown in fig. 4, in step S410, an advertisement conversion value returned by SKAN is received, and the advertisement is identified as campaignID.

In step S420, the APPKey corresponding to the campaignID is determined.

In step S430, the conversion value is analyzed based on the conversion value configuration information under the APPKey to obtain corresponding user behavior information.

The conversion value can have different configuration modes according to different requirements of service scenes. As shown in fig. 4, the conversion value configuration manner includes, for example, an insight event point, an evaluation of advertisement revenue (corresponding to the fourth configuration manner above), a user retention analysis (corresponding to the fifth configuration manner above), a multiple free combination (corresponding to the sixth configuration manner above), and the like. Wherein, the insight event point mode further comprises a general configuration scheme (corresponding to the first configuration mode above), a custom configuration scheme (corresponding to the second configuration mode above), and a key event frequency tracking scheme (corresponding to the third configuration mode above).

According to another aspect of the present disclosure, a data processing apparatus is also provided. Fig. 5 shows a block diagram of a data processing apparatus 500 according to an embodiment of the present disclosure. As shown in fig. 5, the apparatus 500 includes a receiving module 510, a first determining module 520, a parsing module 530, a attribution module 540, and a second determining module 550.

The receiving module 510 is configured to receive a content identification of a target content configured to recommend a target application and a conversion value of the target content configured to indicate user behavior information associated with the target application.

The first determining module 520 is configured to determine the target application corresponding to the target content based on a preset correspondence between the content identifier and the application identifier.

The parsing module 530 is configured to parse the user behavior information from the translation value based on the translation value configuration information of the target application.

Attribution module 540 is configured to attribute the user behavior information to the target content.

The second determining module 550 is configured to determine a recommendation effect of the target content based on the user behavior information.

It should be understood that the various modules of the apparatus 500 shown in fig. 5 may correspond to the various steps in the method 300 described with reference to fig. 3. Thus, the operations, features and advantages described above with respect to the method 300 are equally applicable to the apparatus 500 and the modules/units comprised thereby. Certain operations, features and advantages may not be described in detail herein for the sake of brevity.

Although specific functionality is discussed above with reference to particular modules, it should be noted that the functionality of the various modules discussed herein may be divided into multiple modules and/or at least some of the functionality of multiple modules may be combined into a single module. For example, the attribution module 540 and the second determination module 550 described above may be combined into a single module in some embodiments.

It should also be appreciated that various techniques may be described herein in the general context of software, hardware elements, or program modules. The various modules described above with respect to fig. 5 may be implemented in hardware or in hardware in combination with software and/or firmware. For example, the modules may be implemented as computer program code/instructions configured to be executed in one or more processors and stored in a computer readable storage medium. Alternatively, the modules may be implemented as hardware logic/circuitry. For example, in some embodiments, one or more of the modules 510 and 550 described above may be implemented together in a System on Chip (SoC). The SoC may include an integrated circuit chip (which includes one or more components of a Processor (e.g., a Central Processing Unit (CPU), microcontroller, microprocessor, Digital Signal Processor (DSP), etc.), memory, one or more communication interfaces, and/or other circuitry), and may optionally execute received program code and/or include embedded firmware to perform functions.

According to another aspect of the present disclosure, there is also provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores a computer program which, when executed by the at least one processor, implements a data processing method according to the above.

According to another aspect of the present disclosure, there is also provided a non-transitory computer readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the data processing method according to the above.

According to another aspect of the present disclosure, there is also provided a computer program product comprising a computer program, wherein the computer program realizes the data processing method according to the above when executed by a processor.

Illustrative examples of such computer devices, non-transitory computer-readable storage media, and computer program products are described below in connection with FIG. 6.

Fig. 6 illustrates an example configuration of a computer device 600 that may be used to implement the methods described herein. For example, the monitoring server 180 shown in fig. 1, 2 may include an architecture similar to the computer device 600. The data processing apparatus 500 described above may also be implemented in whole or at least in part by a computer device 600 or similar device or system.

The computer device 600 may be a variety of different types of devices, such as a server of a service provider, a device associated with a client (e.g., a client device), a system on a chip, and/or any other suitable computer device or computing system. Examples of computer device 600 include, but are not limited to: a desktop computer, a server computer, a notebook or netbook computer, a mobile device (e.g., a tablet, a cellular or other wireless telephone (e.g., a smartphone), a notepad computer, a mobile station), a wearable device (e.g., glasses, a watch), an entertainment device (e.g., an entertainment appliance, a set-top box communicatively coupled to a display device, a gaming console), a television or other display device, an automotive computer, and so forth. Thus, the computer device 600 may range from a full resource device with substantial memory and processor resources (e.g., personal computers, game consoles) to a low-resource device with limited memory and/or processing resources (e.g., traditional set-top boxes, hand-held game consoles).

The computer device 600 may include at least one processor 602, memory 604, communication interface(s) 606, display device 608, other input/output (I/O) devices 610, and one or more mass storage devices 612, capable of communicating with each other, such as through a system bus 614 or other suitable connection.

Processor 602 may be a single processing unit or multiple processing units, all of which may include single or multiple computing units or multiple cores. The processor 602 may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitry, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor 602 can be configured to retrieve and execute computer readable instructions stored in the memory 604, mass storage device 612, or other computer readable medium, such as program code for an operating system 616, program code for an application program 618, program code for other programs 620, and so forth.

Memory 604 and mass storage device 612 are examples of computer readable storage media for storing instructions that are executed by processor 602 to implement the various functions described above. By way of example, memory 604 may generally include both volatile and nonvolatile memory (e.g., RAM, ROM, and the like). In addition, mass storage device 612 may generally include a hard disk drive, solid state drive, removable media, including external and removable drives, memory cards, flash memory, floppy disks, optical disks (e.g., CDs, DVDs), storage arrays, network attached storage, storage area networks, and the like. Memory 604 and mass storage device 612 may both be referred to herein collectively as memory or computer-readable storage media, and may be non-transitory media capable of storing computer-readable, processor-executable program instructions as computer program code that may be executed by processor 602 as a particular machine configured to implement the operations and functions described in the examples herein.

A number of program modules may be stored on the mass storage device 612. These programs include an operating system 616, one or more application programs 618, other programs 620, and program data 622, which can be loaded into memory 604 for execution. Examples of such applications or program modules may include, for instance, computer program logic (e.g., computer program code or instructions) for implementing the following components/functions: the server application 112 (including the modules 510-550 described above), the method 300 (including any suitable steps of the method 300), and/or further embodiments described herein.

Although illustrated in fig. 6 as being stored in memory 604 of computer device 600,

modules

616, 618, 620, and 622, or portions thereof, may be implemented using any form of computer-readable media that is accessible by computer device 600. As used herein, "computer-readable media" includes at least two types of computer-readable media, namely computer storage media and communication media.

Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium which can be used to store information for access by a computer device.

In contrast, communication media may embody computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism. Computer storage media, as defined herein, does not include communication media.

The computer device 600 may also include one or more communication interfaces 606 for exchanging data with other devices, such as over a network, direct connection, and the like, as previously discussed. Such communication interfaces may be one or more of the following: any type of network interface (e.g., a Network Interface Card (NIC)), wired or wireless (such as IEEE 802.11 Wireless LAN (WLAN)) wireless interface, worldwide interoperability for microwave Access (Wi-MAX) interface, Ethernet interface, Universal Serial Bus (USB) interface, cellular network interface, Bluetooth^TMAn interface, a Near Field Communication (NFC) interface, etc. The communication interface 606 may facilitate communication within a variety of networks and protocol types, including wired networks (e.g., LAN, cable, etc.) and wireless networks (e.g., WLAN, cellular, satellite, etc.), the internet, and so forth. The communication interface 606 may also provide for communication with external storage devices (not shown), such as in storage arrays, network attached storage, storage area networks, and so forth.

In some examples, a display device 608, such as a monitor, may be included for displaying information and images to a user. Other I/O devices 610 may be devices that receive various inputs from a user and provide various outputs to the user, and may include touch input devices, gesture input devices, cameras, keyboards, remote controls, mice, printers, audio input/output devices, and so forth.

Some exemplary aspects of the disclosure are described below.

The aspect 1 is a data processing method, comprising:

receiving a content identification of target content and a conversion value of the target content, wherein the target content is configured to recommend a target application, and the conversion value is configured to indicate user behavior information associated with the target application;

determining the target application corresponding to the target content based on the corresponding relation between the preset content identification and the application identification;

analyzing the user behavior information from the conversion value based on the conversion value configuration information of the target application;

attributing the user behavior information to the target content; and

and determining the recommendation effect of the target content based on the user behavior information.

Aspect 2 the method of aspect 1, further comprising:

receiving the conversion value configuration information of the target application; and

and sending the application identifier of the target application and the conversion value configuration information to a content platform for pushing the target content.

Aspect 3. the method of aspect 1 or 2, further comprising, prior to said parsing out the user behavior information from the translation value:

determining whether the conversion value is an integer within a preset value range; and

and in response to determining that the conversion value is an integer within the preset value range, determining the conversion value as an effective value for the analysis.

Aspect 4. the method of any of aspects 1-3, the user behavior information including event trigger information of the user, the event trigger information including whether an operational event for the target application is triggered or a number of triggers of the operational event for the target application.

The method of aspect 5. according to aspect 4, wherein the translation value is a binary, the translation value configuration information is used to configure the binary to include at least one valid bit, each valid bit of the at least one valid bit corresponds to an operation event, and the value of each valid bit is used to record whether the corresponding operation event is triggered.

The method according to aspect 6, aspect 4, wherein the conversion value configuration information is used to configure a conversion value corresponding to each of a plurality of operation events, and the conversion value is positively correlated to a trigger sequence of the corresponding operation event, and the parsing out the user behavior information from the conversion value based on the conversion value configuration information of the target application includes:

determining a target operation event corresponding to the conversion value; and

and marking the target operation event and the operation event of which the trigger sequence in the plurality of operation events precedes the target operation event as triggered.

Aspect 7 the method of aspect 4, wherein the translation value configuration information is used to configure the translation value to record a number of triggers of a target operational event for the target application.

The method of aspect 8. according to any of aspects 1-3, wherein the user behavior information includes behavior value information, the conversion value configuration information is used to configure the conversion value as a number of triggers for a target operation event of the target application and to configure a unit value for each trigger of the target operation event, and wherein the parsing the user behavior information from the conversion value based on the conversion value configuration information of the target application includes:

and determining the behavior value information based on the triggering times of the target operation events and the unit value.

Aspect 9. the method of any of aspects 1-3, wherein the user behavior information includes a number of days of user survival.

Aspect 10 the method of any of aspects 1-3, wherein the translation value is a binary code, the binary code divided into a plurality of bit groups, each bit group of the plurality of bit groups comprising at least one bit and being indicative of a type of user behavior information, the user behavior information comprising event trigger information, behavior value information, and user days of survival.

An aspect 11 is a data processing apparatus comprising:

a receiving module configured to receive a content identification of a target content and a conversion value of the target content, wherein the target content is configured to recommend a target application, and the conversion value is configured to indicate user behavior information associated with the target application;

the first determination module is configured to determine the target application corresponding to the target content based on the corresponding relation between preset content identification and application identification;

the analysis module is configured to analyze the user behavior information from the conversion value based on the conversion value configuration information of the target application;

an attribution module configured to attribute the user behavior information to the target content; and

a second determination module configured to determine a recommendation effect of the target content based on the user behavior information.

An electronic device of aspect 12, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores a computer program that, when executed by the at least one processor, implements the method of any of aspects 1-10.

Aspect 13 a non-transitory computer readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method of any of aspects 1-10.

Aspect 14 a computer program product comprising a computer program, wherein the computer program realizes the method according to any of aspects 1-10 when executed by a processor.

While the disclosure has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative and exemplary and not restrictive; the present disclosure is not limited to the disclosed embodiments. Variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing the claimed subject matter, from a study of the drawings, the disclosure, and the appended claims. In the claims, the word "comprising" does not exclude other elements or steps than those listed and the words "a" or "an" do not exclude a plurality. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

Claims

1. A method of data processing, comprising:

attributing the user behavior information to the target content; and

2. The method of claim 1, further comprising:

3. The method of claim 1 or 2, further comprising, prior to said parsing out the user behavior information from the translation value:

4. The method of any of claims 1-3, the user behavior information comprising event trigger information for a user, the event trigger information comprising whether an operational event for the target application is triggered or a number of triggers for the operational event for the target application.

5. The method of claim 4, wherein the translation value is a binary, the translation value configuration information is used to configure the binary to include at least one valid bit, each valid bit of the at least one valid bit corresponds to an operational event, and the value of each valid bit is used to record whether the corresponding operational event is triggered.

6. The method of claim 4, wherein the conversion value configuration information is used for configuring a conversion value corresponding to each of a plurality of operation events, the conversion value being positively correlated to a trigger sequence of the corresponding operation event, and wherein the parsing the user behavior information from the conversion value based on the conversion value configuration information of the target application comprises:

determining a target operation event corresponding to the conversion value; and

7. The method of claim 4, wherein the translation value configuration information is used to configure the translation value to record a number of triggers for a target operational event of the target application.

8. An electronic device, comprising:

at least one processor; and

the memory stores a computer program which, when executed by the at least one processor, implements the method according to any one of claims 1-7.

9. A non-transitory computer readable storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the method of any of claims 1-7.

10. A computer program product comprising a computer program, wherein the computer program realizes the method according to any of claims 1-7 when executed by a processor.