CN113032075A - Information processing method and electronic device - Google Patents
Information processing method and electronic device Download PDFInfo
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- CN113032075A CN113032075A CN202110333619.4A CN202110333619A CN113032075A CN 113032075 A CN113032075 A CN 113032075A CN 202110333619 A CN202110333619 A CN 202110333619A CN 113032075 A CN113032075 A CN 113032075A
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- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/44—Arrangements for executing specific programs
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- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
- G06F3/0481—Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
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Abstract
The embodiment of the application provides an information processing method and an electronic device, wherein the method comprises the following steps: obtaining an input operation; if the input operation meets the trigger condition of the desktop of the display system, acquiring the current time; obtaining a target application program determined by a recommendation engine based at least on the current time; marking the icon identification of the target application program on a recommended desktop; and responding to the input operation to display the recommended desktop. The information processing method can enable the electronic equipment to automatically generate the recommended desktop for enabling the user to quickly find the target application program icon, simplify user operation and improve the use experience of the user on the electronic equipment.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to an information processing method and an electronic device.
Background
With the diversification of functions of electronic devices such as mobile phones and computers, users have different needs for the electronic devices in different scenes. For example, the types of APPs and associated operations that the user opens at various points in time are different. With the opened APP featuring a sequential chain.
About 8:00, when the vehicle is driven out, the Gaode map is opened, and the GPS is used for navigation
About 9:00, will use tiger to smell and read the relevant industry news
At noon of 12:00, QQ music is used, and Bluetooth is turned on to connect the earphone
20:00 take a rest at home and use peace and elite, play games and turn on the bluetooth connection headset.
However, in the current electronic devices such as mobile phones or computers, a plurality of APPs are arranged in a default ordering manner, that is, randomly arranged, or arranged according to conditions such as color, function, initial sequence of names, and use frequency, however, no matter how the APPs are arranged, a user needs to input multiple operations to quickly find a target APP, and can find the target APP only by sliding the screen for multiple times.
Disclosure of Invention
The embodiment of the application provides an information processing method, based on which an electronic device can automatically generate a recommended desktop for enabling a user to quickly find a target application icon, and also provides an electronic device applying the information processing method.
In order to solve the above technical problem, an embodiment of the present application provides an information processing method, where the method includes:
obtaining an input operation;
if the input operation meets the trigger condition of the desktop of the display system, acquiring the current time;
obtaining a target application program determined by a recommendation engine based at least on the current time;
marking the icon identification of the target application program on a recommended desktop;
and responding to the input operation to display the recommended desktop.
Optionally, the target application determined by the get recommendation engine based on at least the current time further includes:
and obtaining a target system function based on the target application program, wherein the identification of the target system function is used for calibrating the recommended desktop.
Optionally, the recommended desktops displayed in response to the input operation are different corresponding to different times.
Optionally, after the input operation meets the trigger condition of the display system desktop, the method further includes:
obtaining the name of the application program in the use state at the current time and the last time;
the names of the application programs in the use state at the current time and the last time are sent to the recommendation engine, and the recommendation engine determines the target application program or/and the target system function
Optionally, the recommendation engine has an algorithmic model for determining the target application, the algorithmic model being trained based on historical data of the electronic device;
the method further comprises the following steps:
activating the recommendation engine.
Optionally, the activating the recommendation engine comprises:
if the algorithm model of the recommendation engine determines that the sliding expected value is smaller than the system default expected value based on the historical data, activating the recommendation engine to enable the recommendation engine to determine the target application program or/and the target system function based on the current time and the name of the application program in the use state at the last moment;
and if the algorithm model of the recommendation engine determines that the sliding expected value is greater than or equal to the system default expected value based on the historical data, continuing training the algorithm model of the recommendation engine based on the newly generated historical data until the sliding expected value is less than the system default expected value.
Optionally, the algorithm model of the recommendation engine comprises:
gathering user data of the electronic device, the user data being relevant to a user operating the electronic device;
and if the user data meets a quantity condition, inputting the user data meeting the quantity condition into the algorithm model for training the algorithm model.
Optionally, the system desktop includes a plurality of pages, the method further comprising:
if the display position of the icon identifier of the target application program is determined not to be located on the currently displayed page, controlling the recommended desktop to be displayed in priority to the currently displayed page; or
Determining whether the target system function is currently in an enabled state;
and if the desktop is not in the starting state, the recommended desktop is controlled to be displayed in priority to the currently displayed page.
Optionally, the trigger condition is any condition for displaying the system desktop;
alternatively, the first and second electrodes may be,
the trigger condition is a sliding switch between multiple pages of a system desktop.
Another embodiment of the present application further provides an electronic device, including:
a first obtaining module for obtaining an input operation;
the second obtaining module is used for obtaining the current time under the condition that the input operation meets the triggering condition of the desktop of the display system;
a third obtaining module for obtaining a target application determined by a recommendation engine based at least on the current time;
the processing module is used for marking the icon identification of the target application program on a recommended desktop;
and the display module is used for responding to the input operation and displaying the recommended desktop.
Based on the disclosure of the above embodiments, it can be known that, when it is determined that the user wants to display the system desktop according to the input operation of the user, the recommendation engine may automatically obtain the current time, and determine the target application program that the user may want to use according to at least the current time, then calibrate the target application program on the recommended desktop, and display the recommended desktop for the user in response to the input operation. Through the recommendation desktop, a user does not need to slide and browse the original system desktop to find the target application program, the icon of the target application program can be directly presented to the user by the recommendation desktop, and the user can use the target application program only by clicking the icon, so that the operation of the user is greatly simplified, and convenience is provided for the user to use.
Drawings
Fig. 1 is a flowchart of an information processing method in the embodiment of the present application.
Fig. 2 is a structural diagram of a recommended desktop in the embodiment of the present application.
Fig. 3 is a flowchart of an information processing method in another embodiment of the present application.
Fig. 4 is a flowchart of an information processing method in another embodiment of the present application.
Fig. 5 is a flowchart of an actual application of the information processing method according to the present application.
Fig. 6 is a block diagram of an electronic device in an embodiment of the present application.
Detailed Description
Specific embodiments of the present application will be described in detail below with reference to the accompanying drawings, but the present application is not limited thereto.
It will be understood that various modifications may be made to the embodiments disclosed herein. The following description is, therefore, not to be taken in a limiting sense, but is made merely as an exemplification of embodiments. Other modifications will occur to those skilled in the art within the scope and spirit of the disclosure.
The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the disclosure and, together with a general description of the disclosure given above, and the detailed description of the embodiments given below, serve to explain the principles of the disclosure.
These and other characteristics of the present application will become apparent from the following description of preferred forms of embodiment, given as non-limiting examples, with reference to the attached drawings.
It should also be understood that, although the present application has been described with reference to some specific examples, a person of skill in the art shall certainly be able to achieve many other equivalent forms of application, having the characteristics as set forth in the claims and hence all coming within the field of protection defined thereby.
The above and other aspects, features and advantages of the present disclosure will become more apparent in view of the following detailed description when taken in conjunction with the accompanying drawings.
Specific embodiments of the present disclosure are described hereinafter with reference to the accompanying drawings; however, it is to be understood that the disclosed embodiments are merely examples of the disclosure that may be embodied in various forms. Well-known and/or repeated functions and structures have not been described in detail so as not to obscure the present disclosure with unnecessary or unnecessary detail. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a basis for the claims and as a representative basis for teaching one skilled in the art to variously employ the present disclosure in virtually any appropriately detailed structure.
The specification may use the phrases "in one embodiment," "in another embodiment," "in yet another embodiment," or "in other embodiments," which may each refer to one or more of the same or different embodiments in accordance with the disclosure.
Hereinafter, embodiments of the present application will be described in detail with reference to the accompanying drawings.
As shown in fig. 1 and fig. 2, an embodiment of the present application provides an information processing method, including:
obtaining an input operation;
if the input operation meets the triggering condition of the desktop of the display system, acquiring the current time;
obtaining a target application program determined by a recommendation engine based at least on a current time;
marking the icon identification of the target application program on the recommended desktop;
and displaying the recommended desktop in response to the input operation.
For example, taking an electronic device as a smart phone as an example, the smart phone is installed with a plurality of different APPs (application programs), and the plurality of different APPs are disposed on a multi-page system desktop. When a user inputs an operation to the electronic device, the operation may be any operation, such as clicking, double-clicking a display screen, sliding the display screen, closing a currently running application, looking at the screen, or inputting a specific gesture, or inputting a voice command. After the electronic device receives an input operation, the input operation is recognized, if the input operation is determined to meet a trigger condition for displaying a system desktop, that is, the input operation is an operation for indicating the electronic device to display the system desktop, the electronic device obtains current time and inputs the current time into a recommendation engine, and the recommendation engine can determine a target application program which the user may want to use currently based on the current time, for example, the current time is 8 am, the user may need to drive to work, and at this time, the electronic device can determine that the user may need to use a map and navigation software, so that the map and the navigation software are determined as the target application program, and corresponding icons of the map and the navigation software are calibrated on the recommendation desktop. For another example, if the time is 8 pm, the user may be at rest at home, so the electronic device may determine that the user may want to play a game at this time, watch a video, and determine that the game and the video software are the target application, and mark the icon corresponding to the target application on the recommended desktop. After the recommended desktop is formed, the electronic equipment responds to the input operation, the recommended desktop is displayed for the user, the user can quickly find the application program which the user wants to use based on the recommended desktop, and the user does not need to manually slide the desktop for multiple times to find the target application program.
Therefore, as can be seen from the above, the present embodiment has the advantages that when it is determined that the user wants to display the system desktop according to the input operation of the user, the recommendation engine automatically obtains the current time, determines the target application program that the user may want to use according to at least the current time, then marks the target application program on the recommended desktop, and displays the recommended desktop for the user in response to the input operation. Through the recommendation desktop, a user does not need to slide and browse the original system desktop to find the target application program, the icon of the target application program can be directly presented to the user by the recommendation desktop, and the user can use the target application program only by clicking the icon, so that the operation of the user is greatly simplified, and convenience is provided for the user to use.
Further, in this embodiment, the obtaining of the target application determined by the recommendation engine based on at least the current time further includes:
and obtaining target system functions based on the target application program, wherein the identification of the target system functions is used for being marked on the recommended desktop.
For example, as shown in fig. 2, many applications need a certain usage environment when used, such as a bluetooth environment, an environment with a positioning function turned on, or an environment with a network, and therefore, in order to enable a specific target application to be quickly used by a user, the recommendation engine analyzes and determines the usage environment and usage conditions of the specific target application based on the specific target application. For example, when the target application program is a map program, the recommendation engine may analyze and determine that the target application program needs to be used on the premise that the electronic device starts a positioning function such as a GPS, and the recommendation engine may determine that the positioning function is a target system function, mark an identifier of the target system function on a recommendation desktop, and set adjacent to the identifier of the map program, so that a user can operate more easily. Or, the target application is a music APP, so that the target application usually uses a bluetooth function, and at this time, icons of the bluetooth function can be marked on the recommended desktop together. Of course, the recommendation engine may also determine whether the target system function is currently in an on state, specifically, the recommendation engine may send a request to a processor of the electronic device to enable the processor to determine the target system function, or may call a set interface by itself to check the system setting, and the like, and the specific method is not specific. If the target system function is found to be started after the determination, the icon of the target system function is not calibrated on the recommended desktop, and if the target system function is found not to be started, the icon of the target system function is calibrated on the recommended desktop.
Further, the icons on the recommended desktop are not fixed, that is, the recommended desktop displayed by the electronic device each time is not fixed, but the recommended desktops displayed in response to the input operation are different corresponding to different times. Because the target applications that the user needs to use are not completely the same in function, the recommendation engine may determine different target applications corresponding to different times, such as 8 am, the target applications may be a map and a navigation program, the noon may be an alarm clock program for waking up a user in noon break, and the evening may be a program for watching a video, a game program, etc., and when the target applications are different, the recommendation desktops calibrated based on the icons of the different target applications are different.
Further, the trigger condition in this embodiment is a condition of any display system desktop; or, the triggering condition is the sliding switch among a plurality of pages of the desktop of the system,
as shown in fig. 3, in this embodiment, if the input operation satisfies the trigger condition of the display system desktop, the method further includes:
obtaining the name of the application program in the use state at the current time and the last time;
the names of the application programs in use at the current time and the last time are sent to a recommendation engine, and the recommendation engine determines the target application program or/and the target system function
Specifically, after it is determined that the input operation satisfies the trigger condition, the name of the application program that is currently in use at the current time and at the previous time is obtained, for example, the user closes the application program that is currently in use in order to display the system desktop, or changes the application program that is currently in use to the background running, and the closing or changing operation to the background running is regarded as the input operation input by the user, and the input operation is regarded as the user's intention to display the system desktop. Of course, the present invention is not limited to this, and the user may perform the operation of locking the screen and then unlocking the screen again when using a certain APP, or the user may be interrupted by an incoming call when using a certain APP and then hang up the phone. When the system desktop includes multiple pages, the triggering condition may also be a switch between the multiple-page system desktops, for example, a sliding operation applied by a user on any one page of the system desktop to flip the next page of the system desktop may determine that the triggering condition is satisfied. That is, the user operation or the operation of automatically jumping to the system desktop after the system executes a certain task can be regarded as meeting the trigger condition. When the trigger condition is determined to be met, the electronic device or the recommendation engine may determine the current time and the name of the application program in the use state at the previous time, where the specific time value at the previous time is not determined, which is supposed to be the name of the application program in the use state before the user inputs the input operation. Then, the recommendation engine comprehensively determines the target application program and the target system function which are required to be used by the user at the next time based on the current time and the name of the application program in the use state at the last time, and finally generates a recommendation desktop for presenting to the user.
Further, the recommendation engine in this embodiment has an algorithmic model for determining the target application, which is trained based on historical data of the electronic device.
As shown in fig. 4 and 5, the algorithm model of the recommendation engine in this embodiment includes:
collecting user data of the electronic equipment, wherein the user data is related to the operation of the electronic equipment by a user;
and if the user data meets the quantity condition, inputting the user data meeting the quantity condition into the algorithm model for training the algorithm model.
For example, the algorithmic model may be a hidden markov model, or other algorithmic model with predictive functionality; the historical data includes the applications used by the user at different times of the day during the past period of time, as well as the length of use, frequency of use, and under what usage environment. For example, when each APP is used, the current APP is started only after another fixed APP or fixed APP is turned off or turned to run in the background, or the current APP is started only when some system function is started or turned off. That is, whether each APP is used after a certain fixed, fixed type APP or system function is stopped. That is, what was in use at the last time may be not only APP but also system functions. In addition, the input time information may be not only clock information but also date, for example, a user may take an airplane to go to a certain place every 25 th month, and when the user takes the airplane, the user may adjust the mobile phone to a flight mode. After the historical data meeting the data size required by the training data is collected, the recommendation engine can be trained based on the historical data, the APP which can be used by the user at different times can be determined after the recommendation engine is trained, when the APPs which can be used at different times are multiple, the recommendation engine can sort the APPs according to the use frequency and the use duration, and then the APP located in the front row is determined as the APP which is most likely to be used by the user at the corresponding current time, namely the target application program. After the training is completed, the recommendation engine may determine, based on the time information and the name of the application and/or system function that was used at the previous time, the target application and the corresponding target system function that most match the user's usage habit at the current time, that is, the target application and the target system function that are most likely to be used by the user at the current time. Moreover, when the input time information includes a date, the recommendation engine may also determine that the system function of the flight mode is the target system function and mark the icon on the recommendation desktop when the recommendation engine determines that the current time is 25 days and 14 days, as described above, when the user frequently takes an airplane at 25 days and 14 days every month, based on the living habits or working habits of the user found when the historical data is learned, so that the user can quickly adjust the electronic device such as a mobile phone to the flight mode.
Specifically, in practical applications, the training of the recommendation engine may refer to the following calculation formula:
the model function is λ ═ (a, B, pi),
Q＝{q1，q2，…，qNnumber of N states
V＝{v1，v2，...，vMM: number of observations
Wherein, Q: hidden state, here mainly APP per open, such as grand map, hundredths, WeChat, etc.; v: and opening corresponding time (period) of each APP, such as morning, noon, evening, weekend morning, weekend afternoon and the like. Wherein, pi represents an initial probability vector and represents a probability matrix of an implicit state at an initial time t ═ 1; a is a hidden state transition probability matrix used for describing transition probability among various states in the model;
A＝[aij]N*N
aij＝P(it+1＝qi|it＝qi) the state at the time t is qiUnder the condition, the time t +1 is shifted to qjProbability of (2)
i＝j∈[1，N]
B is an observation matrix:
B＝[bj(k)]N*Mwherein b isj(k)＝P(ot＝vk|it＝qj)，k＝1，2，..M，j＝1，2，...N
Inputting when training the algorithm model:
I＝(i1，i2，...iT)
O＝(o1，o2，...，oT)
i observed time sequence chain
O: hidden state
And (3) outputting:
P(it+1＝qj|λ，it＝qi，O)
based on the formula, the probability that the first APP can be opened at the known previous moment is qiIn the case of (2), the probability q of opening the second APP at the next moment is calculatedjL, wherein the first APP and the second APP are indefinite, may be any one APP in the electronic device, and are finally based on a plurality of q's corresponding to different second APPsjThe maximum value of | results in the APP that is most likely to open, i.e., the target application.
Further, the information processing method in this embodiment further includes:
the recommendation engine is activated.
The recommendation engine in this embodiment is not started as long as the user requires the display system desktop to generate the recommended desktop, for example, if the number of desktops of the user is only one, generating the recommended desktop is not meaningful, and if the recommendation of the recommended desktop always fails, generating the recommendation is not meaningful. Therefore, the recommendation engine in this embodiment generates the recommendation desktop, which needs to be activated by a trigger condition, and the recommendation engine generates the recommendation desktop and presents the recommendation desktop to the user.
Specifically, activating the recommendation engine in this embodiment includes:
if the algorithm model of the recommendation engine determines that the sliding expected value is smaller than the system default expected value based on the historical data, activating the recommendation engine to enable the recommendation engine to determine the target application program or/and the target system function based on the current time and the name of the application program in the use state at the last moment;
and if the algorithm model of the recommendation engine determines that the sliding expected value is greater than or equal to the system default expected value based on the historical data, continuing training the algorithm model of the recommendation engine based on the newly generated historical data until the sliding expected value is less than the system default expected value.
For example, the electronic device has m pages of system desktops, and each page of system desktop is uniformly provided with a plurality of icons of APPs, so that each APP is found, and the expected value of the number of times of sliding the system desktop, which is required to be input by the user, is (m-1)/2. Specifically, the calculation can be carried out according to the following formula:
wherein, p (x)k) The probability corresponding to the required sliding times is 1/m, wherein m is the number of system desktops and xkWhen the app is at k desktops, the number of swipe operations that need to be entered. For example, the icon of the target application program is exactly located on the current system desktop, the number of sliding operations that need to be input at this time is 0, and if the icon of the target application program is located on the next system desktop, the number of sliding operations that need to be input is 1. Assuming that m is 9, i.e. the electronic device has 9 system desktops, the expected number of swipes needed to find the input for each APP is 4. Further, when the electronic device generates the recommended desktop by using the recommendation engine, the expected value of the sliding times required to be input by the user can be calculated by the following formula:
wherein p is the accuracy of the model, the error rate is 1-p, if the model predicts the target application program successfully, the sliding number is 0, and if the prediction fails, the display of the recommended desktop is equivalent to adding a system desktop, so that the user needs to slide one more system desktop when searching the target application program which the user wants to use, i.e. the sliding number is m +1 times. Therefore, taking m as 9 and the accuracy of the model as 90% as an example, if there is no recommended desktop, the user needs to slide the desktop 4 times on average to find the target application program, and if the recommended desktop is used, the user needs to slide 0.5 times on average to find the target application program, which improves the efficiency by 8 times. After the electronic equipment obtains the expected value of the target application program searched based on the system desktop and the expected value of the target application program searched based on the recommended desktop through calculation in the calculating process, if the expected value of the corresponding recommended desktop is larger than the expected value of the corresponding system desktop through comparison, a recommendation engine is activated to generate a recommended desktop to be presented to a user, so that the user can search the target application program at the first time and needs to input sliding operation. And if the expected value of the corresponding recommended desktop is smaller than the expected value of the corresponding system desktop, the error rate of the model can be determined to be larger, and the model needs to be trained continuously to improve the recommendation accuracy. When it is determined that the model needs to be trained continuously, the model can be trained based on newly generated 'historical data' until the accuracy is improved, so that when the expected value of the corresponding recommended desktop is smaller than the expected value of the corresponding system desktop, the continuous training can be temporarily stopped. Continuing to combine with fig. 5, when training the algorithm model, as described above, it is necessary to combine with APPs used by users at different time periods, to determine whether to implement what kind of association operations and the like for using the APPs, then establish a corresponding time sequence chain based on the collected data, when the collected data amount satisfies the training conditions, it may be selected to divide the collected data into a training set and a test set, train the model using the training set, test the model using the test set, calculate a generalization error, when the generalization error satisfies the requirement, calculate a mathematical expectation value of the model, if the expectation value is greater than the expectation value corresponding to the system desktop, continue to collect the operation data of the users, continue to train the model, otherwise, stop training, and put the model into use.
Further, the system desktop in this embodiment includes a plurality of pages, and the method in this embodiment further includes:
if the display position of the icon identifier of the target application program is determined not to be located on the currently displayed page, controlling the recommended desktop to be displayed in priority to the currently displayed page; or
Determining whether the target system function is currently in an enabled state;
and if the desktop is not in the starting state, controlling the recommended desktop to be displayed in priority to the currently displayed page.
Specifically, the number of pages of the system desktop in this embodiment is multiple, for example, 9 pages, 7 pages, and so on, and it is not particularly limited, after the expected value is calculated and it is determined that the recommendation engine is activated, the recommendation engine determines the target application that is most likely to be used by the user currently, then the electronic device or the recommendation engine may determine whether an icon of the target application is located on the system desktop that can be directly displayed in front of the user currently based on the target application, and if not, the recommendation desktop may be generated and displayed preferentially on the system desktop, that is, the user may see the recommendation desktop first instead of the system desktop. Alternatively, the electronic device may determine whether to preferentially display the recommended desktop after the recommendation engine has generated the recommended desktop. If the target application program needs to be used and the associated target system function needs to be started first, the electronic device or the recommendation engine may first determine whether the target system function is in the enabled state, and if not, preferentially display a recommended desktop regardless of whether the icon position of the target application program is on the current system desktop, and the target application program and the target system function are calibrated on the recommended desktop at the same time. If the target system function is already in the enabled state, whether the recommended desktop is preferentially displayed or not can be determined according to the position of the icon of the target application program, and the icon of the target system function can be displayed or not displayed on the recommended desktop at the moment.
As shown in fig. 6, another embodiment of the present application provides an electronic device, including:
a first obtaining module for obtaining an input operation;
the second obtaining module is used for obtaining the current time under the condition that the input operation meets the triggering condition of the desktop of the display system;
a third obtaining module for obtaining a target application determined by the recommendation engine based at least on the current time;
the processing module is used for marking the icon identification of the target application program on the recommended desktop;
and the display module is used for responding to the input operation and displaying the recommended desktop.
The method has the advantages that when the fact that the user wants to display the system desktop is determined according to the input operation of the user, the recommendation engine can automatically acquire the current time, and determine the target application program which the user possibly wants to use according to at least the current time, then the target application program is calibrated on the recommended desktop, and the recommended desktop is displayed for the user in response to the input operation. Through the recommendation desktop, a user does not need to slide and browse the original system desktop to find the target application program, the icon of the target application program can be directly presented to the user by the recommendation desktop, and the user can use the target application program only by clicking the icon, so that the operation of the user is greatly simplified, and convenience is provided for the user to use.
Optionally, the second obtaining module is further configured to:
and obtaining a target system function based on the target application program, wherein the identification of the target system function is used for calibrating the recommended desktop.
Optionally, the recommended desktops displayed in response to the input operation are different corresponding to different times.
Optionally, the processing module is further configured to:
obtaining the name of the application program in the use state at the current time and the last time;
the names of the application programs in the use state at the current time and the last time are sent to the recommendation engine, and the recommendation engine determines the target application program or/and the target system function
Optionally, the recommendation engine has an algorithmic model for determining the target application, the algorithmic model being trained based on historical data of the electronic device;
the processing module is further configured to:
activating the recommendation engine.
Optionally, the activating the recommendation engine comprises:
if the algorithm model of the recommendation engine determines that the sliding expected value is smaller than the system default expected value based on the historical data, activating the recommendation engine to enable the recommendation engine to determine the target application program or/and the target system function based on the current time and the name of the application program in the use state at the last moment;
and if the algorithm model of the recommendation engine determines that the sliding expected value is greater than or equal to the system default expected value based on the historical data, continuing training the algorithm model of the recommendation engine based on the newly generated historical data until the sliding expected value is less than the system default expected value.
Optionally, the algorithm model of the recommendation engine comprises:
gathering user data of the electronic device, the user data being relevant to a user operating the electronic device;
and if the user data meets a quantity condition, inputting the user data meeting the quantity condition into the algorithm model for training the algorithm model.
Optionally, the system desktop includes a plurality of pages, the method further comprising:
if the display position of the icon identifier of the target application program is determined not to be located on the currently displayed page, controlling the recommended desktop to be displayed in priority to the currently displayed page; or
Determining whether the target system function is currently in an enabled state;
and if the desktop is not in the starting state, the recommended desktop is controlled to be displayed in priority to the currently displayed page.
Optionally, the trigger condition is any condition for displaying the system desktop; alternatively, the first and second electrodes may be,
the trigger condition is a sliding switch between multiple pages of a system desktop.
Another embodiment of the present application further provides an electronic device, including:
one or more processors;
a memory configured to store one or more programs;
the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the processing methods described above.
An embodiment of the present application also provides a storage medium on which a computer program is stored, which when executed by a processor implements the information processing method as described above. It should be understood that each solution in this embodiment has a corresponding technical effect in the foregoing method embodiments, and details are not described here.
Embodiments of the present application also provide a computer program product, tangibly stored on a computer-readable medium and comprising computer-executable instructions that, when executed, cause at least one processor to perform an information processing method such as the embodiments described above. It should be understood that each solution in this embodiment has a corresponding technical effect in the foregoing method embodiments, and details are not described here.
It should be noted that the computer storage media of the present application can be computer readable signal media or computer readable storage media or any combination of the two. The computer readable medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access storage media (RAM), a read-only storage media (ROM), an erasable programmable read-only storage media (EPROM or flash memory), an optical fiber, a portable compact disc read-only storage media (CD-ROM), an optical storage media piece, a magnetic storage media piece, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, antenna, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
It should be understood that although the present application has been described in terms of various embodiments, not every embodiment includes only a single embodiment, and such description is for clarity purposes only, and those skilled in the art will recognize that the embodiments described herein may be combined as suitable to form other embodiments, as will be appreciated by those skilled in the art.
The above embodiments are only exemplary embodiments of the present application, and are not intended to limit the present application, and the protection scope of the present application is defined by the claims. Various modifications and equivalents may be made by those skilled in the art within the spirit and scope of the present application and such modifications and equivalents should also be considered to be within the scope of the present application.
Claims (10)
1. An information processing method, the method comprising:
obtaining an input operation;
if the input operation meets the trigger condition of the desktop of the display system, acquiring the current time;
obtaining a target application program determined by a recommendation engine based at least on the current time;
marking the icon identification of the target application program on a recommended desktop;
and responding to the input operation to display the recommended desktop.
2. The method of claim 1, wherein the obtaining a target application determined by the recommendation engine based at least on the current time further comprises:
and obtaining a target system function based on the target application program, wherein the identification of the target system function is used for calibrating the recommended desktop.
3. The method of claim 1 or 2, wherein the recommended desktops displayed in response to the input operation are different corresponding to different times.
4. The method of claim 3, wherein if the input operation meets a trigger condition of a display system desktop, the method further comprises:
obtaining the name of the application program in the use state at the current time and the last time;
and sending the names of the application programs in the use state at the current time and the last time into the recommendation engine, and determining the target application program or/and the target system function by the recommendation engine.
5. The method of claim 3, wherein the recommendation engine has an algorithmic model for determining the target application, the algorithmic model being trained based on historical data of the electronic device;
the method further comprises the following steps:
activating the recommendation engine.
6. The method of claim 5, wherein the activating the recommendation engine comprises:
if the algorithm model of the recommendation engine determines that the sliding expected value is smaller than the system default expected value based on the historical data, activating the recommendation engine to enable the recommendation engine to determine the target application program or/and the target system function based on the current time and the name of the application program in the use state at the last moment;
and if the algorithm model of the recommendation engine determines that the sliding expected value is greater than or equal to the system default expected value based on the historical data, continuing training the algorithm model of the recommendation engine based on the newly generated historical data until the sliding expected value is less than the system default expected value.
7. The method of claim 6, wherein the algorithmic model of the recommendation engine comprises:
gathering user data of the electronic device, the user data being relevant to a user operating the electronic device;
and if the user data meets a quantity condition, inputting the user data meeting the quantity condition into the algorithm model for training the algorithm model.
8. The method of claim 1, wherein the system desktop includes a plurality of pages, the method further comprising:
if the display position of the icon identifier of the target application program is determined not to be located on the currently displayed page, controlling the recommended desktop to be displayed in priority to the currently displayed page; or
Determining whether the target system function is currently in an enabled state;
and if the desktop is not in the starting state, the recommended desktop is controlled to be displayed in priority to the currently displayed page.
9. The method of claim 8, wherein the trigger condition is any condition that displays the system desktop; alternatively, the first and second electrodes may be,
the trigger condition is a sliding switch between multiple pages of a system desktop.
10. An electronic device, comprising:
a first obtaining module for obtaining an input operation;
the second obtaining module is used for obtaining the current time under the condition that the input operation meets the triggering condition of the desktop of the display system;
a third obtaining module for obtaining a target application determined by a recommendation engine based at least on the current time;
the processing module is used for marking the icon identification of the target application program on a recommended desktop;
and the display module is used for responding to the input operation and displaying the recommended desktop.
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