METHOD FOR CLASSIFYING ADVERTISEMENT NETWORKS FOR MOBILE APPLICATIONS AND SERVER THEREOF

Abstract

A method for classifying ad networks to be used by an app, comprising the following steps performed by a server (10): for each app in a database (12), computing a quality index of the app based on evaluating parameters related to the app using an ad network and related to other apps using the same ad network; for each app using the ad network, computing an aggressivity level of the ad network based on aggressivity parameters which measure intrusivity, impact and effectivity of the ad network in the app; ranking all the apps of the database (12) according to the computed quality index and aggressivity level; for each ad network, computing a single classification metric based on structural parameters related to the ad network and to the apps using it; ranking all the ad networks according to the computed classification metric; delivering the rankings of the mobile applications and ad networks to both private users (14) and professional users (15).

Claims

1. A computer-implemented method for classifying ad networks to be used by a mobile application, characterized by comprising: for each mobile application stored in a database (12), computing, by a server (10) a quality index of the mobile application based on evaluating parameters related to the mobile application using an ad network and evaluating parameters related to other mobile applications using the same ad network; for each mobile application using the ad network, computing, by the server (10), an aggressivity level of the ad network based on aggressivity parameters which measure intrusivity, impact and effectivity of the ad network in the mobile application; ranking, by the server (10), all the mobile application stored in the database (12) according to the quality index and aggressivity level computed for each mobile application; for each ad network, computing, by the server (10), a single classification metric of the ad network based on structural parameters related to the ad network; ranking, by the server (10), all the ad networks according to the computed classification metric; delivering, by the server (10), the ranking of the mobile applications and the ranking of ad networks to both private users (14) and professional users (15), the private users (14) being end-users of the mobile applications, and professional users (15) being developers of the mobile applications or administrators of the ad networks.

2. The method according to claim 1, wherein the quality index Q is computed for the mobile application as:
Q=ω.sub.1η.sub.1α.sub.1+ω.sub.2η.sub.2α.sub.2+. . . +ω.sub.iη.sub.iα.sub.i where α.sub.i are the evaluating parameters and i denotes the number of evaluating parameters, η.sub.i is a normalization factor such that η.sub.iMAX α.sub.i=1, and ω.sub.i is a weight factor, positive or negative, such that Σ.sub.iω.sub.i=MAX Q.

3. The method according to claim 1, wherein the evaluating parameters are selected from: number of downloads for the mobile application from the application provider (11), number of days spent by the mobile application in the application provider (11), average user evaluation of the mobile application, number of permissions to mobile device resources required by the mobile application, and kind of said permissions.

4. The method according to claim 1, wherein the aggressivity level L of the ad network for the mobile application is computed as:
L=Σ.sub.iω.sub.iη.sub.iβ.sub.i where β.sub.i are the aggressivity parameters and i denotes the number of aggressivity parameters, η.sub.i is a normalization factor such that η.sub.iMAX β.sub.i=1, and ω.sub.i is a weight factor, positive or negative, such that Σ.sub.iω.sub.i=MAX L.

5. The method according to claim 1, wherein the aggressivity parameters are selected from: number of detections by antivirus for the mobile application, presence of banners in the mobile application, presence of advertisement videos in the mobile application, presence of full screen advertisements in the mobile application, number of detected advertisements in the mobile application.

6. The method according to claim 1, wherein the aggressivity parameters used to compute the aggressivity level L of the ad network include aggressivity parameters of all the versions of the mobile application using the ad network and aggressivity parameters of dead mobile applications which used the ad network.

7. The method according to claim 1, wherein the single metric M of the ad network is computed as:
M=Σ.sub.iω.sub.iη.sub.iφ.sub.i where φ.sub.i are the structural parameters of the ad network and i denotes the number of structural parameters, η.sub.i is a normalization factor such that η.sub.i MAX φ.sub.i=1, and ω.sub.i is a weight factor, positive or negative, such that Σ.sub.iω.sub.i=MAX M.

8. The method according to claim 1, wherein the structural parameters of the ad network are selected from: number of mobile applications using the ad network, rate of dead applications among the mobile applications using the ad network, average lifetime of the mobile applications using the ad network, average user evaluation among the mobile applications using the ad network, and average aggressivity level among the mobile applications using the ad network.

9. The method according to claim 1, wherein the ranking is delivered to the private user (14) before installing the mobile application by the private user (14).

10. The method according to claim 1, wherein the ranking is delivered to the professional user (15) which is an app developer before selecting the ad network to be used in a specific mobile application by the app developer developing the specific mobile application.

11. The method according to claim 1, wherein the ranking is delivered to the professional user (15), which is an ad network administrator, periodically updated.

12. The method according to claim 1, further comprising periodically checking, by the server (10), the mobile application provider (11) to update the mobile applications and versions of mobile application stored in the database (12).

13. The method according to claim 1, wherein the ranking is periodically updated and delivered to the private users (14) and the professional users (15).

14. A server (10) for classifying ad networks to be used by a mobile application, characterized by comprising a processor configured to: for each mobile application stored in a database (12) to which the processor has access, compute a quality index of the mobile application based on evaluating parameters related to the mobile application using an ad network and evaluating parameters related to other mobile applications using the same ad network; for each mobile application using the ad network, compute an aggressivity level of the ad network based on aggressivity parameters which measure intrusivity, impact and effectivity of the ad network in the mobile application; define a first ranking of all the mobile application stored in the database (12) according to the quality index and aggressivity level computed for each mobile application; for each ad network, compute a single classification metric of the ad network based on structural parameters related to the ad network; define a second ranking of all the ad networks according to the computed classification metric; delivering the first ranking of the mobile applications and the second ranking of the ad networks to both private users (14) and professional users (15), the private users (14) being end-users of the mobile applications, and professional users (15) being developers of the mobile applications or administrators of the ad networks.

Description

DESCRIPTION OF THE DRAWINGS

[0033] For the purpose of aiding the understanding of the characteristics of the invention, according to a preferred practical embodiment thereof and in order to complement this description, the following Figures are attached as an integral part thereof, having an illustrative and non-limiting character:

[0034] FIG. 1 shows a schematic diagram of the system architecture and parties involved in implementing the method for classifying ad networks and apps, according to a preferred embodiment of the invention.

PREFERRED EMBODIMENT OF THE INVENTION

[0035] The embodiments of the invention can be implemented in a variety of architectural platforms, operating and server systems, devices, systems, or applications. Any particular architectural layout or implementation presented herein is provided for purposes of illustration and comprehension only and is not intended to limit aspects of the invention.

[0036] A preferred embodiment of the invention relates to a method of assessment and classification of ad networks for apps. [0037] For each application including a specific ad network, the app is analyzed to assess its quality and potential risks, evaluating parameters such as number of detections by antivirus, number of requested permissions, kind of requested permissions, average evaluation in the app market by end-users, lifetime in the app market, etc. This analysis includes an analysis of the app code (specific system calls, required permissions, etc.). This analysis allows evaluating the typical usage of each ad network and the health of the apps using it.

[0038] An example of quality indexing for an app may be:

Q.sub.a=ω.sub.1η.sub.1α.sub.1+ω.sub.2η.sub.2α.sub.2+. . . +ω.sub.iη.sub.iα.sub.i

where Q.sub.a is the quality index of app a,
η.sub.i is a normalization factor, such that

η.sub.iMAXα.sub.i=1

α.sub.i are the different evaluating considered parameters, and
ω.sub.i is a weight factor, such that

Σ.sub.iω.sub.i=MAX Q

so that the resulting quality index Q may be expressed in the scale of choice, for instance from 0 to 10.

[0039] The weight factor ω.sub.i may be negative. For instance, if the weight factor of the average evaluation is positive, the weight factor applied to the number of detections by antivirus is negative.

[0040] An example of quality index for an app may consider the following parameters (listed with the sign of the weight factor ⋅ and with the corresponding normalization factor η): [0041] number of downloads (positive weightω, η: maximum number of downloads for an app in the marketplace), [0042] number of days spent in the marketplace (positive weight ω, η: maximum number of days sect in the marketplace by an app), [0043] number of detections by antivirus (negative weight ω, η: maximum number of antivirus detections for an app), [0044] average user evaluation (positive weight ω, η: maximum user evaluation, usually η=5), [0045] number of required permissions to the device resources (negative weight ω, η: maximum number of required permissions by an app), [0046] presence of banners (negative weight ω, normalization factor η=1), [0047] presence of ad videos (negative weight ω, normalization factor η=1), [0048] full screen ad (negative weight ω, normalization factor η=1), [0049] number of detected ads (negative weight ω, η: maximum number of detected ads in an app), [0050] number of apps with the same ad network that have been withdrawn from the market (negative weight ω, η: total number of apps using the same ad network).

[0051] The weight factors ω.sub.i are properly tuned to highlight the aspects that the quality index Q of the app considers as more significant. For instance, higher weights for the number of detections by antivirus, better highlights the app potential risk; while higher weight for the app lifetime and average evaluation, better highlights the app quality. [0052] For each app including a specific ad network, the level of aggressivity of the ad network is evaluated (e.g., as a function of three factors: intrusivity, impact and effectivity) for both the present and the past (taking into account previous versions of the apps using the ad network as well as dead apps which used the ad network but not exist any longer in the marketplace). This evaluation of the ad network aggresivity level L is performed similarly to the previous point, L=Σ.sub.iω.sub.iη.sub.iβ.sub.i, but only considering aggressivity parameters β.sub.i (i.e., presence of banners, presence of videos, presence of full-screen ads, number of detected ads, number of detections by antivirus, etc.). [0053] For each ad network, a set of (structural) parameters is evaluated such as number of apps including said ad network, rate of dead apps among the applications including said ad network, average lifetime of the apps including the ad network, average user evaluation among the apps including the ad network, average aggressivity among the apps including said ad network, etc. These parameters, related to the ad network and to the apps using it, may be combined similarly as explained above to result in a single metric M, in order to ease the ad network evaluation and comparison, and to establish a ranking among different ad networks.

[0054] A possible architecture implementing the described method (Analysis Service) is depicted in FIG. 1. The main solution is implemented in a server (10), which periodically checks the app marketplace or mobile application provider (11) for new apps and new versions of known apps. Apps are stored in a database (12) and periodically analyzed and ranked as described above by the server (10). The server (10) analyses and ranks also the different ad networks as described above. The obtained results providing information about the apps and their ads and the ranking of ad networks and apps may be delivered (13) to the users (14, 15), e.g. to a display to be seen by both private users (14) and professional users (15), directly consulting the server (10) and being directly integrated by the app provider (11), i.e., the marketplace bonded to the apps which they are referring to.

[0055] Here a summary of the info and advantages which the different kind of users (14, 15) may get from the described solution is presented: [0056] End Users or private users (14), which are the users of mobile devices using apps and who select apps to be used. The private users (14) consult the server (10) to get information (in advance) about eventual security risks connected to a specific app as well as the level of aggressivity and intrusivity of the ad network(s) which the app integrates. This information is available to the private users (14) before installing an app, and also the information is periodically updated and delivered to the private users (14) after having installed the app. [0057] Professional Users (15) may be app developers or ad network administrators/providers: The app developers select an ad network from the app provider (11) to be integrated in their apps and they can get from the server (10) information about eventual security risks connected to a specific ad network, as well as their ranking, the status about apps integrating them and the average ranking of apps integrating them. All this information is available for app developers both before selecting an ad network and after having selected the ad network as the available information is periodically updated by the server (10). Ad network administrators are able to know the ranking of their ad network and how it stands compared to the others by consulting the server (10), as well as they can get the status about the apps integrating the adds from the ad network and the corresponding average ranking. This information is available for the ad network administrators periodically.

[0058] Note that in this text, the term “comprises” and its derivations (such as “comprising”, etc.) should not be understood in an excluding sense, that is, these terms should not be interpreted as excluding the possibility that what is described and defined may include further elements, steps, etc.