CN116957626A

CN116957626A - User preference evaluation method and device

Info

Publication number: CN116957626A
Application number: CN202310877264.4A
Authority: CN
Inventors: 肖宇涵
Original assignee: Wuxian Shenghuo Beijing Information Technology Co ltd
Current assignee: Wuxian Shenghuo Beijing Information Technology Co ltd
Priority date: 2023-07-17
Filing date: 2023-07-17
Publication date: 2023-10-27

Abstract

The application relates to a user preference evaluation method and a device. The method comprises the following steps: acquiring data of each scene of a target user in multi-scene application in a preset period, wherein the data at least comprises the following two items: consumption behavior data, page access data, and portrait data; determining a scene score of each scene of the target user according to at least two items of data of each scene; the preferences of the target user for the scenes in the multi-scene application are determined by comparing the scene scores of each scene of the target user. The method and the system can comprehensively evaluate the liveness and participation degree of the target user in different scenes and accurately acquire the preference information of the target user on the scenes.

Description

User preference evaluation method and device

Technical Field

The application relates to the technical field of computers, in particular to a user preference evaluation method and device.

Background

With the popularization of mobile internet, more and more Applications (APP) have multiple functions, for example, an e-commerce shopping Application increases a community interaction function, and an e-commerce shopping Application increases a function of e-commerce shopping, or some applications have a photo taking and picture repairing function, a community interaction function, a shopping function and the like. These multi-functional applications may be referred to as multi-scenario APPs, which may include, for example, at least an e-commerce scenario and a community scenario.

However, multi-scenario APP is often difficult to comprehensively and effectively evaluate the liveness and participation degree of users in different scenarios, which makes it difficult for APP operators to comprehensively understand the requirements and behavior characteristics of users, resulting in poor user experience and operation effect.

Disclosure of Invention

To overcome the problems in the related art, embodiments of the present application provide a user preference evaluation method and apparatus. The technical proposal is as follows:

according to a first aspect of an embodiment of the present application, there is provided a user preference evaluation method including:

acquiring data of each scene of a target user in multi-scene application in a preset period, wherein the data at least comprises the following two items: consumption behavior data, page access data, and portrait data;

determining a scene score of each scene of the target user according to at least two items of data of each scene;

the preferences of the target user for the scenes in the multi-scene application are determined by comparing the scene scores of each scene of the target user.

In an embodiment of the present application, obtaining consumption behavior data of a target user includes:

for each scene in the multi-scene application, acquiring the consumption behavior times of a target user for a plurality of preset items;

and calculating consumption behavior data of the target user in each scene according to the consumption behavior times of the plurality of preset items and the weights of the plurality of preset items.

In an embodiment of the present application, obtaining page access data of a target user includes:

for each scene in the multi-scene application, acquiring the page stay time of the target user;

and calculating page access data of the target user in each scene according to the page stay time of the target user and a preset threshold value.

In one embodiment of the present application, obtaining portrait data of a target user includes:

for each scene in the multi-scene application, acquiring tag preference of a target user;

and calculating the portrait data of the target user in each scene according to the number of the label preference of the target user.

In an embodiment of the present application, determining a scene score of each scene of the target user according to the acquired at least two items of data includes:

and for each scene in the multi-scene application, calculating the scene score of each scene of the target user according to the acquired at least two items of data and the preset coefficient.

According to a second aspect of an embodiment of the present application, there is provided a user preference evaluation apparatus including:

the acquisition module is used for acquiring data of each scene of the target user in the multi-scene application in a preset period, wherein the data at least comprises the following two items: consumption behavior data, page access data, and portrait data;

the first determining module is used for determining scene scores of each scene of the target user according to at least two items of data of each scene;

and the second determining module is used for determining the preference of the target user to the scenes in the multi-scene application by comparing the scene scores of each scene of the target user.

In an embodiment of the present application, the acquisition module includes at least two of the following acquisition units:

the first acquisition unit is used for acquiring consumption behavior data of the target user: for each scene in the multi-scene application, acquiring the consumption behavior times of a target user for a plurality of preset items; calculating consumption behavior data of a target user in each scene according to the consumption behavior times of a plurality of preset items and the weights of the plurality of preset items;

a second acquiring unit, configured to acquire page access data of a target user: for each scene in the multi-scene application, acquiring the page stay time of the target user; according to the page stay time of the target user and a preset threshold value, calculating page access data of the target user in each scene;

a third acquisition unit configured to acquire portrait data of a target user: for each scene in the multi-scene application, acquiring tag preference of a target user; and calculating the portrait data of the target user in each scene according to the number of the label preference of the target user.

In an embodiment of the present application, the first determining module is configured to:

According to a third aspect of an embodiment of the present application, there is provided a user preference evaluation apparatus including:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to:

According to a fourth aspect of embodiments of the present application there is provided a computer readable storage medium having stored thereon computer instructions which when executed by a processor implement the steps of the method of any of the first aspects of embodiments of the present application.

According to the technical scheme provided by the embodiment of the application, the user preference assessment method for the multi-scene APP is provided, and the liveness and participation degree of the target user in different scenes are comprehensively assessed through comprehensive behaviors and data such as consumption behaviors, page access and user portraits, so that the preference information of the target user for the scenes is accurately obtained, the potential requirements of the user in the specific scenes are explored, or crowd screening in the different scenes is facilitated, and therefore the operation strategy is optimized.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.

FIG. 1 is a flowchart illustrating a user preference evaluation method according to an exemplary embodiment.

FIG. 2 is a flowchart illustrating a user preference evaluation method according to an exemplary embodiment.

Fig. 3 is a block diagram illustrating a user preference evaluation apparatus according to an exemplary embodiment.

Fig. 4 is a block diagram illustrating a user preference evaluation apparatus according to an exemplary embodiment.

Fig. 5 is a block diagram illustrating a user preference evaluation apparatus according to an exemplary embodiment.

Detailed Description

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of apparatus and methods consistent with aspects of the application as detailed in the accompanying claims.

At present, multi-scenario APP is often difficult to comprehensively and effectively evaluate the liveness and participation degree of users in different scenarios, which makes it difficult for APP operators to comprehensively understand the demands and behavior characteristics of users. The embodiment of the application provides a user preference evaluation method which can be applied to terminals such as a server and a computer and can determine the preference of a user to a scene in a multi-scene APP. As shown in fig. 1, the method includes the following steps S101 to S103:

in step S101, data of each scene of the target user in the multi-scene application in a preset period is acquired, where the data at least includes two following items: consumption behavior data, page access data, and portrait data.

The preset period is, for example, 1 day, 7 days, 14 days, 1 month, 2 months, etc., and the calculation logic under different calculation periods is similar and will not be described again.

The multi-scenario applications include, for example, e-commerce scenarios and community scenarios. The method comprises the steps of acquiring data of a target user in an e-commerce scene and data of the target user in a community scene. It should be noted that the same type of data under different scenarios is acquired. For example, consumption behavior data and page access data of an e-commerce scene are obtained, and consumption behavior data and page access data of a community scene are obtained; or, acquiring consumption behavior data, page access data and portrait data of the e-commerce scene, and acquiring consumption behavior data, page access data and portrait data of the community scene.

For the e-commerce scene, the consumption behavior data may be data based on active behaviors such as browsing, searching, purchasing, commenting, and evaluating; for community scenarios, the consumption behavior data may be data based on active behavior such as browsing, commenting, point-in, sharing, collecting, focusing, etc. The page access data may be, for example, data based on page stay time of each scene. The portrait data may be data based on, for example, a tag, a preference, or the like of the user in each scene.

In step S102, a scene score of each scene of the target user is determined from at least two items of data of each scene.

Since at least two pieces of data of the same type are acquired for each scene in step S101, the respective scene scores of each scene can be calculated from at least two pieces of data of each scene. The scene score of a scene represents the liveness and participation of a target user in the scene.

In step S103, the preference of the target user for the scenes in the multi-scene application is determined by comparing the scene scores of each scene of the target user.

In the step, scores of different scenes are compared to judge which service scene the user belongs to.

According to the technical scheme, the user preference assessment method for the multi-scene APP is provided, the activity and participation degree of a target user in different scenes are comprehensively assessed through comprehensive behaviors and data such as consumption behaviors, page access and user portraits, so that preference information of the target user on the scenes is accurately obtained, potential requirements of the user in specific scenes or crowd screening in different scenes are facilitated, and therefore an operation strategy is optimized.

In one embodiment of the present application, when the data in step S101 includes consumption behavior data, acquiring the consumption behavior data of the target user includes the following steps A1 to A2:

in step A1, for each scene in the multi-scene application, the number of consumption behaviors of the target user for a plurality of preset items is obtained.

For example, for an e-commerce scenario, the number of times of browsing, searching, purchasing, commenting, chasing, etc. a plurality of preset items is obtained. And for the community scene, the times of browsing, commenting, clicking, sharing, collecting, paying attention to a plurality of preset items are obtained.

It should be noted that the data may be filtered after the number of times a plurality of preset items are acquired. When the data of the existing items is too small, the subsequent score calculation may not be included. The principle of screening is to compare the data in different scenes as much as possible.

In step A2, according to the number of consumption behaviors of the plurality of preset items and the weights of the plurality of preset items, consumption behavior data of the target user in each scene is calculated.

The weights of the preset items can be set according to the service weights. Each weight value may be adjusted. In this embodiment, a specific manner of acquiring consumption behavior data of the target user is given.

In an embodiment of the present application, when the data in step S101 includes page access data, acquiring the page access data of the target user includes the following steps B1 to B3:

in step B1, for each scene in the multi-scene application, a page stay time of the target user is acquired.

In step B2, according to the page stay time of the target user and a preset threshold value, calculating page access data of the target user in each scene.

In an embodiment of the present application, when the data in step S101 includes page access data, image data of a target user is acquired, including the following steps C1 to C2:

in step C1, for each scene in the multi-scene application, the tag preference of the target user is acquired.

In step C2, the portrait data of the target user in each scene is calculated according to the number of tag preferences of the target user.

The application provides a method for comparing the number of the tag preferences, which is characterized in that the tag preferences of users in different scenes are different, and the different scenes can not be directly compared because the different scenes have respective tag preference calculation modes. The greater number of tag preferences may indicate to the user that the activity is higher.

The number of tags in different scenes is different, such as one community content, there may be many tags (cotton dolls/stars/IP/comedy) and so on, but the tags of the e-commerce commodity are generally not as many. At this time, the number of tags is normalized.

In an embodiment of the present application, step S102 determines a scene score of each scene of the target user according to the acquired at least two items of data, including:

In the same scenario, each different item of data may have a different preset coefficient. The same item of data in different scenes may also have different preset coefficients. The preset system can be customized according to the service, and the following two aspects need to be considered: importance of different items of data in each scene; normalization, scaling the score of each number to between 0-1 as much as possible.

The implementation is described in detail below by way of examples.

FIG. 2 is a schematic flow chart diagram illustrating a user preference evaluation method according to an exemplary embodiment. The method may be performed by a server. As shown in fig. 2, the method comprises the following steps:

step S201, for each scene in the multi-scene application, obtaining the number of consumption behaviors of the target user for a plurality of preset items.

In this step, the number of consumption behaviors of the target user in a certain period (for example, 7 days) in each scene is counted together, and the number is arranged from more to less according to the counted number. Examples are shown in table one below. As shown in table one, the number of browsing, searching, purchasing, commenting, evaluating and other items of the target user a under the electronic market scene is respectively obtained, the number of browsing is 30 ten thousand, the number of purchasing is 3 ten thousand and so on. And respectively acquiring the times of browsing, commenting, clicking, sharing, collecting, paying attention and other multiple items of the target user A in the community scene.

List one

After the data is acquired, the data may also be screened. When the data of the existing items is too small, the subsequent score calculation may not be included. For example, the number of the items such as comments and chasing of the e-commerce scene in table one is only 2 thousands of times and 1 thousands of times respectively, for example, the number of the items such as collection and attention of the community scene is only 3 thousands of times and 1 thousands of times respectively. At this time, the items such as comments, comments and the like of the e-commerce scene in the data and the items such as collection, attention and the like of the community scene can be removed. The principle of screening is to make the data in different scenes as comparable as possible.

Step S202, calculating consumption behavior data of a target user in each scene according to the consumption behavior times of a plurality of preset items and the weights of the plurality of preset items.

In this step, for each scene, weighted calculation scores are performed using the screened data as consumption behavior data in each scene. For example:

target user a e-commerce consumption score = a1 x number of target user a browses + b1 x number of target user a purchases + c1 x number of target user a purchases + d1 x number of target user a searches;

target user a community consumption score = a2 x number of target user a browses + b2 x number of target user a comments + c2 x number of target user a praise + d2 x number of target user a shares;

wherein a1, b1, c1, d1, a2, b2, c2, d2, etc. are weights of preset items in each scene, and the weights can be selected according to the service weights. In one embodiment, there may also be a case where a1=a2.

For example, the calculation formula of the weight a1 is a1=1000/browsing times, i.e., the reciprocal of the total browsing times multiplied by a coefficient. This coefficient may be adjusted according to the traffic weight, such as the weight b2=2000/number of purchases, since purchases are more important than browsing.

Step S203, for each scene in the multi-scene application, acquiring the page stay time of the target user.

In this step, the page stay time of the target user in a certain period (for example, 7 days) under each scene is counted together. It should be noted that outliers may be culled in connection with business scenarios. For example, when the residence time of a user on a page is too short, the data of the residence time need to be removed, because the user only wants to enter the e-commerce page of the APP with high subjective probability.

And 204, calculating page access data of the target user in each scene according to the page stay time of the target user and a preset threshold value.

For each scene, the stay time score of the target user a is calculated as page access data of the target user in each scene. For example:

target user a e-commerce dwell time score = maximum (target user a e-commerce page dwell time/X1, Y1)

Target user a community stay length score = maximum (target user a community page stay length/X2, Y2)

Wherein X1, X2 are preset thresholds, dividing the target user's page stay time by the preset thresholds is mainly for score normalization, because stay time score needs to be compared with consumption score. Y1 and Y2 are preset maximum values, and the maximum values are set to handle the excessive abnormal values.

Step S205, for each scene in the multi-scene application, acquires the tag preference of the target user.

By way of example, community tag preference data for target users within a certain period (e.g., 7 days) under each scenario is aggregated. The community tag preferences of the target user a are: cotton doll: 0.6, star chasing: 0.3, two-dimensional: 0.1, the trademark preference of the target user a is: shoes: 0.4, clothing: 0.6.

step S206, calculating the portrait data of the target user in each scene according to the number of the label preference of the target user.

Because different businesses have own label preference score calculation modes, the label preference scores are not convenient to directly compare, but can be compared from the angle of the label preference number. Because the number of the tag preference in different scenes is different, a certain degree of tag preference can indicate that the activity of the user is higher. Such as a community of content, may have many tag preferences (e.g., cotton doll/star/IP/comedy) etc., but the tags for e-commerce goods are typically not as many (e.g., shoes). In this step, the portrait score normalized by the number of tag preferences is used for each scene as portrait data of the target user in each scene. For example:

target user a e-commerce portrayal score = target user a tag preference number/P1

Target user a community image score = target user a label preference number/P2

Wherein the values of P1 and P2 can be given in connection with the traffic, such as p1=p2=average number of tag preferences per scene content.

Step S207, determining scene scores of each scene of the target user according to at least two items of data of each scene and preset coefficients.

Target user a e-commerce score = M1 x consumption score of target user a e-commerce + M2 x target user a e-commerce residence time score + M3 x target user a e-commerce portrait score;

target user a community score = N1 x consumption score of target user a community + N2 x target user a community stay length score + N3 x target user a community image score;

wherein each preset coefficient M1, M2, M3, N1, N2, N3 can be customized according to the service, and when the service is customized, the following two aspects need to be considered: importance of each score in each scene; playing a normalized lease, scaling each score as much as possible to between 0-1.

Step 208, determining the preference of the target user for the scenes in the multi-scene application by comparing the scene scores of each scene of the target user.

In this step, the target user a e-commerce score and the target user a community score may be directly compared, so as to determine whether the target prefers an e-commerce scene or a community scene. In an embodiment of the present application, the user with the front activity level may be selected for operation or marketing by sorting the scores of multiple users in a single scene. In one embodiment of the application, intersections may be taken for a plurality of scene scoring top users. In an embodiment of the present application, the fluctuation of scores of a plurality of periods may be observed, and the fluctuation of the activity tendency of the target user for which field is comprehensively evaluated.

The technical scheme adopted by the application can comprehensively evaluate the behavior of the user in the multi-scene APP by utilizing the consumption behavior, the stay time, the user portrait and other data of each scene in the multi-scene APP, thereby being beneficial to comprehensively knowing the user; and the scores of different scenes such as the E-commerce and the community are normalized, and the scores are compared, so that the potential requirements of the user in a specific scene can be found, and the operation strategy is optimized.

The following are examples of the apparatus of the present application that may be used to perform the method embodiments of the present application.

Fig. 3 is a block diagram illustrating a user preference evaluation apparatus according to an exemplary embodiment, which may be a server or a part of a server, or may be a terminal or a part of a terminal, and which may be implemented as part or all of an electronic device by software, hardware, or a combination of both. As shown in fig. 3, the user preference evaluation apparatus includes:

the acquiring module 301 is configured to acquire data of each scene of the target user in the multi-scene application in a preset period, where the data at least includes two following items: consumption behavior data, page access data, and portrait data;

a first determining module 302, configured to determine a scene score of each scene of the target user according to at least two items of data of each scene;

a second determining module 303 is configured to determine a preference of the target user for the scenes in the multi-scene application by comparing scene scores of each scene of the target user.

In one embodiment, the acquisition module 301 includes at least two acquisition units:

In an embodiment, the first determining module 302 is configured to:

Fig. 4 is a block diagram illustrating a user preference evaluation apparatus 40, which may be a server or a part of a server or a terminal or a part of a terminal, according to an exemplary embodiment, including:

a processor 4001;

a memory 4002 for storing instructions executable by the processor 4001;

wherein the processor 4001 is configured to:

Fig. 5 is a block diagram of a user preference evaluation apparatus 800, which may be a computer, server, etc., according to an example embodiment.

The apparatus may include one or more of the following components: a processing component 802, a memory 804, a power component 806, a multimedia component 808, an audio component 810, an input/output (I/O) interface 812, a sensor component 814, and a communication component 816.

The processing component 802 generally controls overall operation of the apparatus 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. Processing element 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interactions between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

The memory 804 is configured to not store various types of data to support operations at the device 800. Examples of such data include instructions for any application or method operating on the device 800, contact data, phonebook data, messages, pictures, videos, and the like. The memory 804 may be implemented by any type or combination of volatile or nonvolatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disk.

The power supply component 806 provides power to the various components of the device 800. The power components 806 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the device 800.

The multimedia component 808 includes a screen between the device 800 and the user that provides an output interface. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and/or a rear camera. The front camera and/or the rear camera may receive external multimedia data when the apparatus 800 is in an operational mode, such as a photographing mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

The audio component 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the device 800 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 further includes a speaker for outputting audio signals.

The I/O interface 812 provides an interface between the processing component 802 and peripheral interface modules, which may be a keyboard, click wheel, buttons, etc. These buttons may include, but are not limited to: homepage button, volume button, start button, and lock button.

The sensor assembly 814 includes one or more sensors for providing status assessment of various aspects of the apparatus 800. For example, the sensor assembly 814 may detect an on/off state of the device 800, a relative positioning of the components, such as a display and keypad of the device 800, the sensor assembly 814 may also detect a change in position of the device 800 or a component of the device 800, the presence or absence of user contact with the device 800, an orientation or acceleration/deceleration of the device 800, and a change in temperature of the device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 514 may also include an acceleration sensor, a gyroscopic sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 816 is configured to facilitate communication between the apparatus 800 and other devices, either in a wired or wireless manner. The device 800 may access a wireless network based on a communication standard, such as a private intercom network, wiFi,2G, 3G, 4G, or 5G, or a combination thereof. In one exemplary embodiment, the communication component 816 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, the communication component 816 further includes a Near Field Communication (NFC) module to facilitate short range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, ultra Wideband (UWB) technology, bluetooth (BT) technology, and other technologies.

In an exemplary embodiment, the apparatus 800 may be implemented by one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic elements for executing the methods described above.

In an exemplary embodiment, a non-transitory computer readable storage medium is also provided, such as memory 804 including instructions executable by processor 820 of apparatus 800 to perform the above-described method. For example, the non-transitory computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.

A non-transitory computer readable storage medium, which when executed by a processor of apparatus 800, enables apparatus 800 to perform the above-described user preference evaluation method, the method comprising:

Other embodiments of the application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.

It is to be understood that the application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims

1. A user preference evaluation method, comprising:

2. The method of claim 1, wherein obtaining consumption behavior data of the target user comprises:

3. The method of claim 1, wherein obtaining page access data for the target user comprises:

4. The method of claim 1, wherein obtaining representation data of the target user comprises:

5. The method of claim 1, wherein determining a scene score for each scene of the target user based on the acquired at least two items of data comprises:

6. A user preference evaluation apparatus, comprising:

7. The apparatus of claim 6, wherein the acquisition module comprises at least two acquisition units:

8. The apparatus of claim 6, wherein the first determining module is configured to:

9. A user preference evaluation apparatus, comprising:

a processor;

a memory for storing processor-executable instructions;

wherein the processor is configured to:

10. A computer readable storage medium having stored thereon computer instructions, which when executed by a processor, implement the steps of the method of any of claims 1-5.