CN103365853A - Appropriate use behavior record and feedback method of mobile device - Google Patents

Abstract

An adaptive use behavior recording and feedback method of a mobile device continuously records the use habit of a user for an internal application program in the normal use state of the mobile device and stores the use habit into a database of the mobile device or a cloud end so as to establish a historical record of the use of the application program. When the user uses the mobile device next time, the mobile device can automatically detect the current using environment and compare the current using environment with the historical records in the database to obtain at least one application program meeting the recommended conditions. And finally, displaying the recommendable application program list on a recommendation field of the mobile device picture so as to recommend a user to start using.

Description

Appropriate use behavior record and feedback method of mobile device

technical field

The present invention relates to an adaptive mobile device, in particular to a method that can record the user's usual habit of using the mobile device, so as to actively recommend application programs to the user when the user is in the same environment again.

Background technique

Context Aware Computing (Context Aware Computing) is a technology that responds to the location of the user, the current date, time, and even environmental parameters such as gravity, temperature, and humidity to provide the user with the most suitable service for the current situation. . For example, the current popular Location-Based Service (LBS) is a typical situation-aware computing.

The LBS service can provide the most appropriate service according to the location of the user. For example, the user can install an application program (Application, referred to as App) for recommending restaurants on the smart mobile device. When the user arrives For an unfamiliar location, as long as the application is opened, the application will obtain the location of the user through the GPS of the mobile device, so as to find out the restaurant information near the location in the database, and recommend it to the user for reference. In this way, the user can effectively exclude restaurants that are far away, thereby saving traffic time.

The above-mentioned application program complies with the LBS standard and belongs to an extended application of situation-aware computing. However, most of the existing technologies allow users to execute a single application program, and the application program provides context-aware computing services to users. However, when there are many applications in the mobile device, even if the user must use the application at the same time and place every day, it still takes time to search among the numerous applications, which is quite inconvenient.

Therefore, a novel method should be provided in the market, which can record the user's habit of using the application program on the mobile device, and when experiencing the same usage environment again, according to the usage habit (ie, date, time, location, etc.) , to actively recommend applications that users may want to use, so as to make mobile devices more humanized and avoid the trouble of manually searching for users.

Contents of the invention

The main purpose of the present invention is to provide an adaptive use behavior recording and feedback method for mobile devices, which can continuously record the user's daily usage habits when using the mobile device, so that when the user is in the same use environment again, The mobile device actively recommends the application program to the user, so that the use of the mobile device is more humanized.

In order to achieve the above object, the present invention provides a mobile device adaptive usage behavior recording and feedback method, which is applied to a mobile device. The mobile device adaptive usage behavior recording and feedback method includes:

a) the mobile device is activated;

b) Judging the current use environment;

c) comparing the current usage environment with the historical records in a database;

d) Obtain a recommendation list that meets the recommendation conditions according to the comparison result; and

e) displaying at least one application on the recommendation list in a recommendation field on the display screen of the mobile device.

As mentioned above, wherein the step e is to determine the priority order of the at least one application program displayed on the recommendation field according to the number of times the at least one application program has been used in the past, wherein the number of times each application program has been used is recorded in this history.

As mentioned above, wherein the step a also includes the following steps before:

a01) the mobile device is activated;

a02) detecting that an application program on the mobile device is used;

a03) The mobile device records the usage habits of the user for the application;

a04) proceed to step a03, store in the database, to update the historical record;

a05) judging whether the use is over; and

a06) If the mobile device has not been used, repeat steps a02 to a05.

As mentioned above, wherein the database is built in the internal memory of the mobile device.

As mentioned above, the database is located in the cloud, and the database is connected to the mobile device through a network system.

As mentioned above, the usage habit in step a03 is the time when the application is used; the usage environment in step b is the current time; At least one application that has been used to form the recommendation list.

As mentioned above, wherein the mobile device divides the time of each day into eight time periods, and each time period covers three hours respectively, the step c is to compare the time period with the historical record, and the step e is to compare the time period with the time period At least one application program on the recommendation list is displayed for a unit.

As mentioned above, the usage habit in step a03 is the date and time when the application program is used; the usage environment in step b is the current date and time; At least one application that has been used on the date and the time is retrieved to form the recommendation list.

As mentioned above, wherein the mobile device divides the date into working days and holidays, and divides the time of each day into eight time periods, each of which covers three hours, and the step c is the unit of time period and the The historical records are compared, and the step e is to display at least one application program on the recommendation list in units of time periods.

As mentioned above, the usage habit in step a03 refers to the date, time and location when the application is used; the usage environment in step b is the current date, time and location; in step d, it is determined by At least one application that has been used at the date, the time and the location is extracted from the history record to form the recommendation list.

As mentioned above, wherein the mobile device divides the date into working days and holidays, and divides the time of each day into eight time periods, each of which covers three hours, and the step c is the unit of time period and the The historical records are compared, and the step e is to display at least one application program on the recommendation list in units of time periods.

As mentioned above, a step a031 is further included after the step a03: the mobile device receives the current location input by the user.

As mentioned above, the mobile device has a location detection unit, and the location detection unit obtains the current location through the network or the global positioning system.

As mentioned above, wherein the step a also includes the following steps before:

a11) The mobile device opens an internal management interface;

a12) Obtain the historical record from the database and display it on the management interface;

a13) The management interface accepts external manipulation to adjust the historical record; and

a14) The management interface accepts external manipulation to set the recommendation condition.

As mentioned above, in the step a14, the parameter to be compared in the history record is set to be at least one of the date, time and location that have been used.

Compared with the prior art, the present invention achieves the effect that the mobile device can continuously record the usage habits of the user, for example, the user is used to opening a specific application program on the mobile device on a specific date, a specific time and a specific location. After the mobile device has recorded the usage habit, when the user turns on the mobile device again on the specific date, time and place, the mobile device can actively recommend the specific application program to the user.

As mentioned above, the present invention can implement application program recommendation service according to the user's usage habits, so that the user does not need to go through complicated steps to find the desired application program. Therefore, it is a rare innovative invention that can improve the user's convenience and make the mobile device more humanized.

The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments, but not as a limitation of the present invention.

Description of drawings

FIG. 1 is a schematic diagram of a mobile device according to a preferred embodiment of the present invention;

Fig. 2 is a system block diagram of a preferred embodiment of the present invention;

Fig. 3 is the recording flowchart of a preferred embodiment of the present invention;

Fig. 4 is the recording flowchart of another preferred embodiment of the present invention;

Fig. 5 is a system block diagram of another preferred embodiment of the present invention;

Fig. 6 is a setting flow chart of a preferred embodiment of the present invention;

Fig. 7 is a recommended flowchart of a preferred embodiment of the present invention;

Fig. 8 is a schematic diagram of time slot allocation in a preferred embodiment of the present invention;

Fig. 9 is a schematic diagram of time slot allocation in another preferred embodiment of the present invention;

FIG. 10 is a schematic diagram of a mobile device according to another preferred embodiment of the present invention;

Among them, reference signs:

1…mobile device;

11...display screen;

12…recommended field;

2… application program;

3...database;

31...Application field;

32 ... using the date field;

33 ... using the time field;

34 ... using the location field;

4...Management interface;

5... position detection unit;

U1…usage habit;

R1...recommended list;

S10～S16...steps;

S120～S124...steps;

S20～S28...steps;

S30-S42...steps.

Detailed ways

A preferred embodiment of the present invention will be described in detail below with reference to the accompanying drawings.

First please refer to FIG. 1 , which is a schematic diagram of a mobile device according to a preferred embodiment of the present invention. The present invention discloses mainly a usage behavior record and feedback method used on a mobile device 1 (such as a notebook computer, a smart phone, a tablet computer, etc., the present embodiment takes a tablet computer as an example). This method can record the user's usage habits of the mobile device 1 , more specifically, record the user's usage habits of the applications (Applications, Apps for short) 2 on the mobile device 1 . For example, if the user is used to reading newspapers on the MRT at eight o'clock on Monday morning on the way to work, then the mobile device 1 will record this usage through the method of the present invention. Habit.

As shown in FIG. 1 , the mobile device 1 has a display screen 11 , and a plurality of the application programs 2 are displayed on the display screen 11 for users to click and use. Moreover, there is a recommendation field 12 on the display screen 11. When the user uses the mobile device 1 at a specific point in time or at a specific location, the mobile device 1 will execute the method of the present invention, combining the current use environment with the use Compare the user's past usage habits to determine the application program 2 that the user may want to use, and display it on the recommendation field 12 .

Taking the above-mentioned embodiment as an example, when the user uses the mobile device 1 again on the MRT at eight o'clock in the morning on Monday of the next week, the mobile device 1 will automatically detect the current usage environment, and based on the user's past Using habits, the newspaper reading application 2 is automatically recommended to the user (that is, the newspaper reading application 2 is displayed on the recommendation field 12 ).

As mentioned above, through the method of the present invention, the user does not need to waste time and effort to find the habitually used application program 2 among a large number of application programs 2 . In this embodiment, four application programs 2 displayed on the recommendation field 12 are taken as an example, but it is not limited thereto.

It is worth mentioning that the usage behavior recording and feedback method of the present invention can be written as a resident program (routine daemon), which is resident and executed in the mobile device 1; The program is written into the firmware of the mobile device 1, but it is not limited thereto.

Continue referring to FIG. 2 , which is a system block diagram of a preferred embodiment of the present invention. As shown in FIG. 2, the mobile device 1 can continuously record the user's usage habits U1 when the user uses the application program 2 on it, and continuously store the usage habits U1 in a database 3, so as to It constitutes a history record H1 of the application program being used. In this way, with the accumulation of time, the history record H1 is constantly updated, which can be completely close to the user's usage habits of the application programs 2 , thus providing quite accurate recommendation services.

The database 3 can be mainly established in the internal memory of the mobile device 1, or built in the cloud, and connected to the mobile device 1 through a network system, but not limited thereto. If it is the former, then the mobile device 1 can use the method of the present invention without being connected to the Internet, and if it is the latter, it can have a larger storage space, and the mobile device 1 must have a network connection function so that the mobile device 1 can be updated. This history record H1 in the database 3 is retrieved and used.

However, although the mobile device 1 can automatically record the usage habits of the user to form the history record H1, the history record H1 may differ from the user's actual needs. For example, although the user usually takes the MRT to work at 8:00 on Monday morning and thus does not use navigation software, the user occasionally needs to drive to the customer's office for negotiation, so the user needs to use the navigation software. In this case, the user may wish that the mobile device 1 can automatically recommend the navigation software at 8 o'clock every morning. If the mobile device 1 can only perform comparative analysis based on the historical record H1, then the above-mentioned The request will not be fulfilled.

If the above problems are solved, the mobile device 1 can also provide a management interface 4 , and the user can operate the management interface 4 on the mobile device 1 to obtain the history record H1 in the database 3 . In this way, the user can adjust the historical record H1 through the management interface 4, modify it to the appearance he actually needs, and store it back into the database 3 after the adjustment is completed. In addition, the user can also set the priority order of the update action of the history record H1, which should be mainly recorded by the mobile device 1 itself or manually adjusted by the user, and cannot be limited. Therefore, when the user uses the mobile device 1 in a certain usage environment, the mobile device 1 will compare the current usage environment with the historical record H1, and take out at least one application program 2 that meets the recommended conditions to form a The recommended list R1, and then display one or more application programs 2 included in the recommended list R1 on the recommended field 12 for reference by the user.

The following Table 1 is a schematic form of the historical record H1 to make the description of this embodiment clearer, but the record format of the historical record H1 is not limited to the following table:

Table I

As shown in Table 1 above, through the method of the present invention, the mobile device 1 can record various historical information of the application program 2 on it, such as the date, time, location, etc. used, and can record each application program 2 The number of times it has been used, so that the user's usage habits can be obtained from the historical record H1. It is worth mentioning that the application program 2 displayed on the recommendation field 12 in FIG. four), then the mobile device 1 can determine the priority order displayed on the recommendation field 12 according to how many times these application programs 2 have been used in the past (refer to the usage times field in Table 1). And, when the user does not adopt the order of the most preferred application program 2 recommended by the mobile device 1, it can be deleted from the recommendation field 12. At this time, the application program recommended in the first order will be replaced by the most frequently used application program. Other applications 2 make up.

As mentioned above, the method of the present invention can mainly be recorded in the following two ways: 1. Calculate the number of times the icon (icon) of the application program 2 is clicked on the desktop of the mobile device 1 or in the recommendation field 12 and, 2. The number of times the application program 2 is opened (for example, the user clicks the icon of the Facebook application program to enter the Facebook page, then opens the browser to enter other web pages, and then switches back to the Facebook page. In this In this case, although the icon of the Facebook application is only clicked once, its use will be recorded as twice). However, the above descriptions are only preferred specific examples of the present invention and should not be limited thereto.

Referring to FIG. 3 , it is a recording flowchart of a preferred embodiment of the present invention. First, the user activates the mobile device 1 equipped with the method, and uses at least one application program 2 on the mobile device 1 (step S10). While the application program 2 is being used, the mobile device 1 will automatically record the user's usage habits U1 for the application program 2 (step S12), and store them in the database 3 (step S14), to form the History H1 (first time) and updated (after the first time). Finally, the mobile device 1 judges whether the user has finished using (such as making the mobile device 1 sleep or shutting down, etc.) (step S16), if the user continues to use the mobile device 1, then returns to the step S10, the mobile device 1 Wait for the user to use other application programs 2, and record other usage habits U1, so as to update the history record H1.

Referring to Fig. 4, it is a recording flowchart of another preferred embodiment of the present invention. In the above-mentioned step S12, the mobile device 1 can see the settings of the user. When the application program 2 on it is used, record the date when the application program 2 is used (step S120), and record the application program 2 is used. The time of use (step S122), and record the location where the application program 2 is used (step S124). It is worth mentioning that the user can set the usage habits to be recorded by the mobile device 1 through the above-mentioned management interface 4. One of them is used as an example without limitation.

For example, if the user sets that the mobile device 1 only needs to record the time when the application program 2 is used, then the history record H1 will not have the fields of the date of use and the location of use, and the user is used to using these applications The date and location of Procedure 2 will not be recorded. In this way, when the mobile device 1 recommends the application program 2 to the user, it will only refer to the current "time" as a parameter, so there will be more application programs 2 meeting the recommendation conditions.

For another example, if the user is used to using the application program 1 at 15:00 on Monday and the application program 2 at 15:00 on Friday, when the user uses the mobile device 1 at 15:00 on the next Monday, if the mobile device 1 Only record the "time" used in advance, then after comparison and analysis, it will recommend the application 1 and 2 to the user at the same time; and if the mobile device 1 is set in advance to record the used time at the same time

"time" and "date", then the mobile device 1 will only recommend one application program to the user after the comparison. Therefore, the above-mentioned steps S120, S122 and S124 can exist independently or at the same time, depending on the level of detail the user wants to record, and should not be limited.

Continue referring to FIG. 5 , which is a system block diagram of another preferred embodiment of the present invention. When the above steps S120 and S122 record the date and time when the application is used, the mobile device 1 automatically retrieves the date and time when the application is used, and stores it in the database 3 . In addition, the mobile device 1 can have a position detection unit 5, and the mobile device 1 can obtain the position when being used by the network or the Global Positioning System (GPS) through the position detection unit 5, and store it in The database 3. Furthermore, if the mobile device 1 does not have the location detection unit 5, or cannot connect to the network, then the mobile device 1 can display an information screen when recording the usage habit U1, to require the user to manually input the location , so as to update the usage location field in the history record H1. However, the above descriptions are only preferred examples of the present invention and should not be limited thereto.

If the mobile device 1 simultaneously executes the above-mentioned steps S120 to S124 when recording the usage habit U1, then in the history record H1 of the database 3, there will be an application program field 31, a usage date field 32, and a usage time. The field 33 and the location field 34 are used to simultaneously record the name of the application 2 being used, as well as the date, time and location of being used. Moreover, the user can set a recommendation condition, and the mobile device 1 can decide which of the fields 31-34 to compare with according to the recommendation condition, and when the recommendation condition is met, the corresponding Application 2 is recommended to the user.

Referring to FIG. 6 , it is a setting flow chart of a preferred embodiment of the present invention. The user can open the management interface 4 on the mobile device 1 (step S20), to obtain the history record H1 in the database 3 through the management interface 4 (step S22), whereby the user can see the actual If necessary, fine-tune the history record H1 (step S24). Moreover, the user can further set the above-mentioned recommendation conditions through the management interface 4 (step S26 ), and finally store these settings (step S28 ).

Specifically, when the applications 2 on the mobile device 1 are used, it can routinely record all usage data, including the date, time and location of usage. When recommending, it depends on whether the current use environment meets the recommendation condition, and decides whether to recommend it to the user. For example, if the recommendation condition is set to match the "time" parameter, all application programs 2 in the history record H1 that match the current time will be included in the recommendation list R1; and if the recommendation condition is set to The "date", "time" and "location" parameters must all match, then only the application 2 in the history record H1 that matches the current date, time and location will be included in the recommendation list R1, so that Recommended for users. That is to say, the more recommendation conditions are set, the fewer the number of listed applications 2 in the recommendation list R1 will be.

Continue referring to FIG. 7 , which is a recommended flowchart of a preferred embodiment of the present invention. It is worth mentioning that when the mobile device 1 intends to implement the recommendation service, the database 3 needs to store the history record H1 with a certain reference value before it can be executed.

First, the user turns on the mobile device 1 (step S30), and the mobile device 1 automatically detects the current usage environment (step S32), wherein the usage environment mainly includes the current date, current time, and current location, etc., But not limited. Next, the mobile device 1 compares the current usage environment with the historical record H1 in the database 3 (step S34) to obtain a recommendation list R1 (step S36), wherein a recommendation list R1 records a or more applications2 that meet the criteria for this referral.

Finally, the application programs 2 are sorted according to the number of times they are used, and the application programs 2 are sequentially displayed in the recommendation field 12 on the display screen 11 (step S38 ). After the step S38, the user can click to use one of the multiple application programs 2 displayed on the recommendation field 12 (step S40), and, after the application program 2 is used, record the user's usage habits U1 to the database 3 to update the history record H1 (step S42).

The following Table 2 is a preferred example for illustration:

Table II

As in the example in Table 2 above, it is assumed that the user uses the mobile device 1 in the office at 8:00 am on Wednesday, and the mobile device 1 detects the user's behavior, and therefore recommends applications to the user. It is worth mentioning that, in this embodiment, the mobile device 1 can classify dates into weekdays and holidays for comparative analysis. In the above embodiment, the current date is Wednesday (working day), the time is 8:00 in the morning, and the location is the office.

In the first embodiment, if the recommendation condition setting only needs to meet the "time" parameter to make recommendations, then as shown in Table 2, applications 3, 4, 5, and 6 all meet the time "eight o'clock in the morning" Therefore, the mobile device 1 will recommend application programs 3, 4, 5 and 6 to the user.

In the second embodiment, if the recommendation condition setting needs to meet both the "date" and "time" parameters before it can be recommended, as shown in Table 2, applications 3, 4, and 5 all meet the date of "work" "day" and time are recommended conditions of "eight o'clock in the morning", so the mobile device 1 will recommend the application programs three, four and five to the user.

In the third embodiment, if the recommendation condition setting needs to meet the "date", "time" and "location" parameters at the same time, the recommendation can be made, as shown in Table 2, only applications 3 and 5 meet the date at the same time The recommended conditions are "working day", time "8:00 am" and location "office". Therefore, among many applications, the mobile device 1 will only recommend application programs 3 and 5 to the user. Applications four and six will not be seen on the recommendation field 12 .

In addition to the above-mentioned division of dates into working days and holidays for comparison, the mobile device 1 can also divide a day's time into multiple time periods according to requirements, to facilitate comparison. Please refer to FIG. 8 and FIG. 9 at the same time, which are schematic diagrams of time slot allocation of a preferred embodiment and another preferred embodiment of the present invention, respectively. The mobile device 1 can mainly divide the time of each day into eight periods, each of which covers three hours, as shown in the embodiment in FIG. 9 , namely 0:00 to 3:00; 3:00 6:00 to 9:00; 9:00 to 12:00; 12:00 to 15:00; 15:00 to 18:00; 18:00 to 21:00; 21:00 to 24 :00 and so on for eight periods. For example, 6:00, 6:30, 7:00 to 9:00 all belong to the category covered by time period three. However, the above description is only a preferred example of the present invention, and the mobile device 1 can also divide the time into two time periods, four time periods, twelve time periods or twenty-four time periods according to actual needs, and should not Be limited.

In the above embodiment, the mobile device 1 can compare the usage environment with the history record H1 in units of time periods. As shown in the above example, the current usage time is 8:00 in the morning, which belongs to time period 3. Therefore, if the "time" parameter is set as one of the recommended conditions, only the applications that meet the time of "time period 3" Will be included in the recommendation list R1.

Finally, please refer to FIG. 10 , which is a schematic diagram of a mobile device according to another preferred embodiment of the present invention. The recommendation field 12 on the mobile device 1 mainly displays at least one application program 2 on the recommendation list R1 in units of the above time periods. As shown in FIG. 10 , four application programs recommended to the user during period three (6:00 to 9:00) are displayed (that is, after comparison of the mobile device 1 , it is considered that the user used to use 6 applications in the past. :00 to 9:00 in the application). Moreover, the user can slide left or right on the recommendation field 12 to view recommended application programs in other time periods, and use, delete or adjust them. And, after the user uses it, the usage record of the used application program will be stored in the database 3 to update the history record H1.

Certainly, the present invention also can have other multiple embodiments, without departing from the spirit and essence of the present invention, those skilled in the art can make various corresponding changes and deformations according to the present invention, but these corresponding Changes and deformations should belong to the scope of protection of the appended claims of the present invention.

Claims (15)

1. An adaptive use behavior record and feedback method of a mobile device, applied to a mobile device, characterized in that the adaptive use behavior record and feedback method of the mobile device include: a) the mobile device is activated; b) Judging the current use environment; c) comparing the current usage environment with the historical records in a database; d) Obtain a recommendation list that meets the recommendation conditions according to the comparison result; and e) displaying at least one application on the recommendation list in a recommendation field on the display screen of the mobile device. 2. The method according to claim 1, characterized in that the step e is to determine the at least one application program according to the number of times the at least one application program has been used in the past. The priority order displayed on the recommendation field, wherein the number of times each application program has been used is recorded in the history record. 3. The method according to claim 1, wherein the method for recording and giving back the adaptive usage behavior of the mobile device, further comprising the following steps before the step a: a01) the mobile device is activated; a02) detecting that an application program on the mobile device is used; a03) The mobile device records the usage habits of the user for the application; a04) proceed to step a03, store in the database, to update the historical record; a05) judging whether the use is over; and a06) If the mobile device has not been used, repeat steps a02 to a05. 4. The method for recording and giving back adaptive usage behavior of a mobile device according to claim 3, wherein the database is established in the internal memory of the mobile device. 5. The method for recording and giving back adaptive usage behaviors of mobile devices according to claim 3, wherein the database is located in the cloud, and the database is connected to the mobile device through a network system. 6. The method for recording and giving back adaptive usage behavior of mobile devices according to claim 3, characterized in that, the usage habit in the step a03 is the time when the application program is used; the usage environment in the step b is the current time; in the step d, extract at least one application program that has been used at that time from the history record to form the recommendation list. 7. The method according to claim 6, characterized in that the mobile device divides the time of each day into eight periods, and each period covers three hours respectively. Step c is to compare with the historical records in units of time periods, and step e is to display at least one application program on the recommendation list in units of time periods. 8. The method for recording and giving back adaptive usage behavior of mobile devices according to claim 3, characterized in that, the usage habit in step a03 is the date and time when the application program is used; The usage environment is the current date and time; in the step d, at least one application that has been used on the date and the time is extracted from the history record to form the recommendation list. 9. The method according to claim 8, characterized in that the mobile device divides the date into working days and holidays, and divides the time of each day into eight periods, Each time period covers three hours respectively, and the step c is to compare with the historical records in units of time periods, and the step e is to display at least one application program on the recommendation list in units of time periods. 10. The method for recording and giving back adaptive usage behavior of a mobile device according to claim 3, wherein the usage habit in the step a03 refers to the date, time and location when the application program is used; the step b The usage environment in is the current date, time and location; in the step d, at least one application that has been used on the date, the time and the location is extracted from the history record to form the recommendation list. 11. The method according to claim 10, characterized in that the mobile device divides the date into working days and holidays, and divides the time of each day into eight periods, Each time period covers three hours respectively, and the step c is to compare with the historical records in units of time periods, and the step e is to display at least one application program on the recommendation list in units of time periods. 12 . The method for recording and feedback of adaptive usage behavior of a mobile device according to claim 10 , further comprising a step a031 after the step a03 : the mobile device receives the current location input by the user. 13 . 13. The method according to claim 10, characterized in that the mobile device has a location detection unit, and the location detection unit obtains the current location through the network or the global positioning system . 14. The method for recording and giving back adaptive usage behavior of mobile devices according to claim 3, characterized in that, before the step a, the following steps are also included: a11) The mobile device opens an internal management interface; a12) Obtain the historical record from the database and display it on the management interface; a13) The management interface accepts external manipulation to adjust the historical record; and a14) The management interface accepts external manipulation to set the recommendation condition. 15. The method according to claim 14, characterized in that, in the step a14, the parameter to be compared in the history record is set as the date when it was used At least one of , time and location.

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