CN111651226B - Virtual button ordering method and device, electronic equipment and storage medium - Google Patents
Virtual button ordering method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
A virtual button ordering method, comprising: acquiring environmental characteristic data and acquiring multiple groups of sorting weights of virtual buttons of a target application program; determining a current environmental state according to the environmental characteristic data; acquiring priorities corresponding to each group of sorting weights in the current environment state; determining a first sorting weight from the plurality of groups of sorting weights according to the priority; acquiring first operation data corresponding to the first sequencing weight; determining a target rewarding value corresponding to the first ordering weight according to the first operation data; adjusting the priority of a first sequencing weight in a pre-trained sequencing model according to a target reward value and a preset reward value, and determining the target sequencing weight from a plurality of groups of sequencing weights through the adjusted sequencing model; and sorting the virtual buttons according to the target sorting weight. The application also comprises a virtual button ordering device, electronic equipment and a medium. The application can improve the ordering effect of the virtual buttons. In addition, the application also relates to artificial intelligence technology.
Description
Technical Field
The present invention relates to the field of intelligent terminals, and in particular, to a virtual button sorting method, a virtual button sorting device, an electronic device, and a storage medium.
Background
Currently, virtual buttons of Applications (APP) may be ordered by an ordering algorithm. However, in practice, it has been found that the sorting algorithm does not take into account the consistency and time-series characteristics of the operation behavior of these virtual buttons, resulting in poor sorting of the virtual buttons.
Therefore, how to improve the sorting effect of the virtual buttons is a technical problem to be solved.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a virtual button sorting method, apparatus, electronic device, and storage medium, which can improve the sorting effect of virtual buttons.
A first aspect of the present invention provides a virtual button ordering method, the method comprising:
acquiring environmental characteristic data of a target application program, and acquiring multiple groups of sorting weights of virtual buttons of the target application program, wherein each group of sorting weights comprises one sorting weight of each virtual button;
determining a current environment state according to the environment characteristic data;
Acquiring priorities corresponding to each group of sorting weights in the current environment state;
determining a first sorting weight from the plurality of groups of sorting weights according to the priority;
acquiring first operation data corresponding to the first ordering weight, wherein the first operation data refers to the number of times that the virtual button is clicked and the service time after being ordered according to the first ordering weight;
determining a target rewarding value corresponding to the first sorting weight according to the first operation data, wherein the higher the target rewarding value is, the better the sorting effect of the virtual buttons according to the first sorting weight is indicated;
according to the target rewarding value and a preset rewarding value, adjusting the priority of the first sequencing weight in the pre-trained sequencing model;
determining target sorting weights from the plurality of groups of sorting weights through the adjusted sorting model;
and sorting the virtual buttons according to the target sorting weight.
In one possible implementation manner, before the acquiring the environmental feature data of the target application program and acquiring the multiple sets of sorting weights of the virtual buttons of the target application program, the method further includes:
Acquiring historical operation data corresponding to the virtual button;
determining the active weight of the virtual button according to the historical operation data;
and sequencing the virtual buttons according to the active weights to obtain a first sequencing order.
In one possible implementation manner, after the sorting all the virtual buttons according to the active weights to obtain the first sorting order, the method further includes:
acquiring a plurality of groups of random weights of the virtual buttons;
determining a plurality of second arrangement orders of the virtual buttons according to the plurality of groups of random weights and the active weights;
acquiring second operation data corresponding to the second arrangement sequence;
determining a first prize value for each of the second arrangements according to the second operational data;
judging whether the first rewarding value is larger than a preset rewarding value threshold value or not;
and if the first rewarding value is larger than a preset rewarding value threshold value, initializing parameters of the sorting model to be trained according to the second arrangement sequence to obtain an initial sorting model, wherein the initial sorting model comprises an action cost function.
In one possible implementation manner, the initializing parameters of the ordering model to be trained according to the second order, and after obtaining the initial ordering model, the method further includes:
Acquiring third operation data, wherein the third operation data is the clicked times and the service time of the virtual button after being sequenced according to the sequencing weight output by the initial sequencing model;
and training the initial sequencing model according to a second reward value corresponding to the third operation data to obtain the trained sequencing model.
In one possible implementation manner, the adjusting the priority of the first ranking weight in the pre-trained ranking model according to the target prize value and the preset prize value includes:
judging whether the target rewarding value is larger than a preset rewarding value or not;
if the target rewarding value is larger than a preset rewarding value, upgrading and adjusting the priority of the first sequencing weight in the sequencing model; or (b)
And if the target rewarding value is smaller than or equal to a preset rewarding value, carrying out degradation adjustment on the priority of the first sequencing weight in the sequencing model.
In one possible implementation, the method further includes:
resetting the first parameters of the trained sequencing model every other preset time period to obtain a first pre-training model;
Retraining the first pre-trained model to obtain a first ranking model to reorder the virtual buttons through the first ranking model.
In one possible implementation, the method further includes:
receiving feedback values for the trained ranking model;
judging whether the feedback value is smaller than a preset feedback value threshold value or not;
if the feedback value is smaller than a preset feedback value threshold, resetting the second parameter of the trained sequencing model to obtain a second pre-training model;
retraining the second pre-trained model to obtain a second ranking model.
A second aspect of the present invention provides a virtual button ordering apparatus, the apparatus comprising:
the system comprises an acquisition module, a storage module and a control module, wherein the acquisition module is used for acquiring environmental characteristic data of a target application program and acquiring multiple groups of sequencing weights of virtual buttons of the target application program, wherein each group of sequencing weights comprises one sequencing weight of each virtual button;
the determining module is used for determining the current environment state according to the environment characteristic data;
the acquisition module is further used for acquiring the priority corresponding to each group of the sorting weights in the current environment state;
The determining module is further configured to determine a first ranking weight from the plurality of sets of ranking weights according to the priority;
the acquisition module is further configured to acquire first operation data corresponding to the first ordering weight, where the first operation data refers to the number of times that the virtual button is clicked and the service time after being ordered according to the first ordering weight;
the determining module is further configured to determine, according to the first operation data, a target prize value corresponding to the first ranking weight, where the higher the target prize value is, the better ranking effect of the virtual buttons according to the first ranking weight is indicated;
the adjustment module is used for adjusting the priority of the first sequencing weight in the pre-trained sequencing model according to the target rewarding value and the preset rewarding value;
the determining module is further configured to determine a target ranking weight from the multiple sets of ranking weights through the adjusted ranking model;
and the sorting module is used for sorting the virtual buttons according to the target sorting weight.
A third aspect of the invention provides an electronic device comprising a processor and a memory, the processor being arranged to implement the virtual button ordering method when executing a computer program stored in the memory.
A fourth aspect of the present invention provides a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the virtual button ordering method.
According to the technical scheme, in the invention, different environment states of the target application program can be determined according to different environment characteristic data of the target application program, the priorities of a plurality of groups of sorting weights corresponding to different environment states are different, namely, the sorting of the virtual buttons is different under different use environments, and the priority of the sorting weights can be adjusted according to the current operation data so as to continuously update the sorting model, so that the sorting model can always output the optimal sorting, and the sorting effect of the virtual buttons is improved.
Drawings
FIG. 1 is a flow chart of a preferred embodiment of a virtual button ordering method of the present disclosure.
FIG. 2 is a functional block diagram of a preferred embodiment of a virtual button ordering apparatus according to the present disclosure.
Fig. 3 is a schematic structural diagram of an electronic device implementing a virtual button ordering method according to a preferred embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.
The virtual button ordering method of the embodiment of the invention is applied to the electronic equipment, and can also be applied to a hardware environment formed by the electronic equipment and a server connected with the electronic equipment through a network, and the virtual button ordering method is executed by the server and the electronic equipment together. Networks include, but are not limited to: a wide area network, a metropolitan area network, or a local area network.
A server may refer to a computer system that provides services to other devices (e.g., electronic devices) in a network. If a personal computer can provide file transfer protocol (File Transfer Protocol, FTP) service to the outside, the server can also be called. In a narrow sense, a server is dedicated to some high-performance computers, and can provide services to the outside through a network, and compared with a common personal computer, the server has higher requirements on stability, security, performance and the like, so that the server is different from the common personal computer in terms of hardware such as a CPU, a chipset, a memory, a disk system, a network and the like.
The electronic device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware of the electronic device comprises, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA), a digital processor (DSP), an embedded device and the like. The electronic device may also include a network device and/or a user device. Wherein the network device includes, but is not limited to, a single network device, a server group composed of a plurality of network devices, or a Cloud based Cloud Computing (Cloud Computing) composed of a large number of hosts or network devices, wherein Cloud Computing is one of distributed Computing, and is a super virtual computer composed of a group of loosely coupled computer sets. The user equipment includes, but is not limited to, any electronic product that can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad, a voice control device or the like, for example, a personal computer, a tablet computer, a smart phone, a personal digital assistant PDA and the like.
Referring to fig. 1, fig. 1 is a flowchart of a virtual button ordering method according to a preferred embodiment of the present invention. The sequence of steps in the flowchart may be changed and some steps may be omitted according to different needs. The execution subject of the virtual button ordering method may be an electronic device.
S11, acquiring environmental characteristic data of a target application program, and acquiring multiple groups of sorting weights of virtual buttons of the target application program, wherein each group of sorting weights comprises one sorting weight of each virtual button.
The environment characteristic data may include user basic information, virtual button scheduling click data, a use time of the corresponding function of the virtual button, and the like (i.e., historical behavior information).
Wherein the plurality of sets of ranking weights may be used to represent various ranking orders. The virtual buttons can be ranked according to the corresponding weight, and the higher the ranking weight is, the earlier the ranking position of the virtual buttons is. Wherein, a set of ordering weights may be represented by a set of vectors, such as U (U1, U2, &. Cndot.).
In the embodiment of the invention, the virtual buttons have a plurality of groups of arrangement sequences, and the arrangement sequences are stored in the form of ordering weights.
As an optional implementation manner, the method may further include, before the acquiring the environmental feature data of the target application program and acquiring the plurality of sets of sorting weights of the virtual buttons of the target application program to acquire the environmental feature data, and acquiring the plurality of sets of sorting weights:
Acquiring historical operation data corresponding to the virtual button;
determining the active weight of the virtual button according to the historical operation data;
and sequencing the virtual buttons according to the active weights to obtain a first sequencing order.
In this alternative embodiment, an active weight may be set for each virtual button according to the historical operation data corresponding to the virtual button, where the more virtual buttons that are used, the greater the corresponding active weights. Then, virtual buttons with high active weights are arranged in front, so that the sorting effect can be improved.
As an alternative embodiment, the method further comprises:
judging whether repeated arrangement sequences exist in the first arrangement sequence or not;
and deleting the repeated arrangement sequence if the repeated arrangement sequence exists in the first arrangement sequence.
In this alternative embodiment, because different users have different habits and correspond to different historical operation data, there are a plurality of obtained first arrangements, and the repeated first arrangements can be deleted, so that the time of the reinforcement learning later can be reduced.
As an optional implementation manner, after sorting all the virtual buttons according to the active weights to obtain a first sorting order, the method further includes:
Acquiring a plurality of groups of random weights of the virtual buttons;
determining a plurality of second arrangement orders of the virtual buttons according to the plurality of groups of random weights and the active weights;
acquiring second operation data corresponding to the second arrangement sequence;
determining a first prize value for each of the second arrangements according to the second operational data;
judging whether the first rewarding value is larger than a preset rewarding value threshold value or not;
and if the first rewarding value is larger than a preset rewarding value threshold value, initializing parameters of the sorting model to be trained according to the second arrangement sequence to obtain an initial sorting model, wherein the initial sorting model comprises an action cost function.
The second operation data may be data such as the number of times the virtual button is clicked, the time when the function corresponding to the virtual button is used, and the like, in the second arrangement order of the virtual buttons.
The ranking model may determine an action with highest benefit in a certain state by using an action cost function; i.e. determining the optimal ranking of the virtual buttons in the current environmental state of the target application, such that the prize value obtained is highest in this optimal ranking.
The action cost function is also called as a Q function, and mainly comprises the steps of constructing an environment state and ordering weights into a table (Q-table), and storing the priority of each group of ordering weights in each environment state. A set of ranking weights for the maximum prize value benefit may be determined based on the priority, and the virtual buttons may then be ranked based on the set of ranking weights.
The method is characterized in that, assuming that the rewarding decay coefficient is gamma, the learning rate is alpha, and an update equation of the action cost function is as follows:
NewQ(s t ,a t )＝Q(s t ,a t )+α[r t+1 +γmaxQ(s t+1 ,a t )-Q(s t ,a t )]。
in the embodiment of the invention, for example: if the virtual button is detected to be clicked once, the prize value is increased by 5, if the function corresponding to the virtual button is completed once, the prize value is increased by 20, the function service time is converted into the prize value according to a preset proportion, for example, the service time is 10 minutes, and the converted prize value is 2 according to a proportion of 20%. If the first reward value is greater than a preset reward value threshold, the second ranking order is a better ranking, and parameters of the ranking model to be trained can be initialized according to the second ranking order, so that an initial ranking model is obtained.
As an optional implementation manner, according to the second ranking order, the method further includes, after initializing parameters of the ranking model to be trained to obtain an initial ranking model:
Acquiring third operation data, wherein the third operation data is the clicked times and the service time of the virtual button after being sequenced according to the sequencing weight output by the initial sequencing model;
and training the initial sequencing model according to a second reward value corresponding to the third operation data to obtain a pre-trained sequencing model.
The third operation data may be operation data of the target application program by the user after the virtual buttons are ranked according to the ranking weight output by the initial ranking model, where the operation data includes, but is not limited to, the number of times that the virtual buttons are clicked, the time that the functions corresponding to the virtual buttons are used, and the like.
In this alternative embodiment, based on machine learning and model training techniques in the artificial intelligence field, the parameters of the initial ranking model may be trained according to the second reward value, the priority parameters of the ranking order in the initial ranking model may be adjusted, if the reward value is high, the parameters of the priority of the second ranking order in the model may be increased, and if the reward value is low, the parameters of the priority of the second ranking order in the model may be reduced.
S12, determining the current environment state according to the environment characteristic data.
Wherein the current environmental state (S) comprises user basic information and historical behavior information.
S13, acquiring the priority corresponding to each group of sorting weights in the current environment state.
Wherein the same set of ordering weights may correspond to different priorities in different environmental states.
S14, determining a first sorting weight from the plurality of groups of sorting weights according to the priority.
In the embodiment of the invention, the ranking weight with the largest priority can be determined as the first ranking weight from the plurality of groups of ranking weights, or a group of ranking weights can be randomly selected from the plurality of groups of ranking weights to be used as the first ranking weight, and the random selection probability corresponding to the ranking weight with the larger priority is larger. This ensures that the button ordering is most often the user's operating habit, but the user's operating habit may change, occasionally outputting a random button ordering, allowing faster acquisition of user-operated change data, and faster adjustment of parameters to accommodate.
S15, acquiring first operation data corresponding to the first ordering weight, wherein the first operation data refers to the clicked times and the service time of the virtual button after being ordered according to the first ordering weight.
The first operation data may be operation data of the target application program by the user after the virtual buttons are ranked according to the first ranking weight, which includes, but is not limited to, the number of times the virtual buttons are clicked, the time when the functions corresponding to the virtual buttons are used, and the like.
S16, determining a target rewarding value corresponding to the first sorting weight according to the first operation data, wherein the higher the target rewarding value is, the better the sorting effect of the virtual buttons according to the first sorting weight is indicated.
In the embodiment of the invention, for example: if the virtual button is detected to be clicked once, the prize value is increased by 5, if the function corresponding to the virtual button is completed once, the prize value is increased by 20, the function service time is converted into the prize value according to a preset proportion, for example, the service time is 10 minutes, and the converted prize value is 2 according to a proportion of 20%.
And S17, adjusting the priority of the first sequencing weight in the pre-trained sequencing model according to the target reward value and the preset reward value.
As an optional implementation manner, the adjusting the priority of the first ranking weight in the pre-trained ranking model according to the target prize value and the preset prize value includes:
Judging whether the target rewarding value is larger than a preset rewarding value or not;
if the target rewarding value is larger than a preset rewarding value, upgrading and adjusting the priority of the first sequencing weight in the sequencing model; or (b)
And if the target rewarding value is smaller than or equal to a preset rewarding value, carrying out degradation adjustment on the priority of the first sequencing weight in the sequencing model.
In this alternative embodiment, if the target prize value is greater than a preset prize value, the first ranking weight may be increased by indicating that the first ranking weight is better, and if the target prize value is less than or equal to the preset prize value, the first ranking weight may be decreased by indicating that the first ranking weight is not good enough.
S18, determining target sorting weights from the plurality of groups of sorting weights through the adjusted sorting model.
Wherein, the ranking model can output ranking weights with probability according to different priorities of different ranking weights, such as: if the priority of the A-ranking weight is 7, the A-ranking weight may be output with a probability of 70%.
S19, sorting the virtual buttons according to the target sorting weight.
Optionally, a set of importance vectors W (W1, W2, …) may be set according to the importance of the virtual buttons, and assuming that the vector corresponding to the target sorting weight is U, an inner product of the vector W and the vector U may be calculated, so as to obtain an inner product weight, and sorting is performed according to the size of the inner product weight corresponding to each virtual button from large to small. And if the importance vector is not set, sorting the virtual buttons directly according to the arrangement sequence corresponding to the target sorting weight.
As an alternative embodiment, the method further comprises:
resetting the first parameters of the trained sequencing model every other preset time period to obtain a first pre-training model;
retraining the first pre-trained model to obtain a first ranking model to reorder the virtual buttons through the first ranking model.
In this alternative embodiment, because the functional requirements may be different for different time periods, i.e., the virtual buttons that may be used may be different, the ranking model may be retrained based on the most current usage data at regular time, some of the parameters of the ranking model may be retained, only some of the parameters need to be adjusted, and the retrained speed is faster.
As an alternative embodiment, the method further comprises:
receiving feedback values for the trained ranking model;
judging whether the feedback value is smaller than a preset feedback value threshold value or not;
if the feedback value is smaller than a preset feedback value threshold, resetting the second parameter of the trained sequencing model to obtain a second pre-training model;
retraining the second pre-trained model to obtain a second ranking model.
In this alternative embodiment, as the user population changes, or the operation habit of the user changes, the arrangement order of the virtual buttons output by the ranking model may be better before and not required by the user later, so that a part of parameters of the ranking model need to be reset according to feedback of user operation, or at intervals, retraining is performed while the part of parameters are maintained, and the latest ranking model (the second ranking model) can be obtained faster.
In the method flow described in fig. 1, different environmental states of the target application program can be determined according to different environmental feature data of the target application program, and priorities of multiple sets of sorting weights corresponding to different environmental states are different, namely, sorting of the virtual buttons is different under different use environments, and the priorities of the sorting weights can be adjusted according to current operation data so as to continuously update the sorting model, so that the sorting model can always output optimal sorting, and the sorting effect of the virtual buttons is improved.
Referring to fig. 2, fig. 2 is a functional block diagram of a virtual button ordering apparatus according to a preferred embodiment of the present invention.
In some embodiments, the virtual button ordering apparatus operates in an electronic device. The virtual button ordering means may comprise a plurality of functional modules consisting of program code segments. Program code for each program segment in the virtual button ordering apparatus may be stored in memory and executed by at least one processor to perform some or all of the steps in the virtual button ordering method described in fig. 1.
In this embodiment, the virtual button ordering apparatus may be divided into a plurality of functional modules according to the functions performed by the virtual button ordering apparatus. The functional module may include: an acquisition module 201, a determination module 202, an adjustment module 203, and a ranking module 204. The module referred to in the present invention refers to a series of computer program segments capable of being executed by at least one processor and of performing a fixed function, stored in a memory.
The obtaining module 201 is configured to obtain environmental feature data of a target application program, and obtain multiple sets of sorting weights of virtual buttons of the target application program, where each set of sorting weights includes one sorting weight of each virtual button.
The environment characteristic data may include user basic information, virtual button scheduling click data, a use time of the corresponding function of the virtual button, and the like (i.e., historical behavior information).
Wherein the plurality of sets of ranking weights may be used to represent various ranking orders. The virtual buttons can be ranked according to the corresponding weight, and the higher the ranking weight is, the earlier the ranking position of the virtual buttons is. Wherein, a set of ordering weights may be represented by a set of vectors, such as U (U1, U2, &. Cndot.).
In the embodiment of the invention, the virtual buttons have a plurality of groups of arrangement sequences, and the arrangement sequences are stored in the form of ordering weights.
A determining module 202, configured to determine a current environmental state according to the environmental characteristic data.
Wherein the current environmental state (S) comprises user basic information and historical behavior information.
The obtaining module 201 is further configured to obtain a priority corresponding to each set of the sorting weights in the current environmental state.
Wherein the same set of ordering weights may correspond to different priorities in different environmental states.
The determining module 202 is further configured to determine a first ranking weight from the plurality of sets of ranking weights according to the priority.
In the embodiment of the invention, the ranking weight with the largest priority can be determined as the first ranking weight from the plurality of groups of ranking weights, or a group of ranking weights can be randomly selected from the plurality of groups of ranking weights to be used as the first ranking weight, and the random selection probability corresponding to the ranking weight with the larger priority is larger. This ensures that the button ordering is most often the user's operating habit, but the user's operating habit may change, occasionally outputting a random button ordering, allowing faster acquisition of user-operated change data, and faster adjustment of parameters to accommodate.
The obtaining module 201 is further configured to obtain first operation data corresponding to the first ordering weight, where the first operation data refers to the number of times that the virtual button is clicked and the service time after being ordered according to the first ordering weight.
The first operation data may be operation data of the target application program by the user after the virtual buttons are ranked according to the first ranking weight, which includes, but is not limited to, the number of times the virtual buttons are clicked, the time when the functions corresponding to the virtual buttons are used, and the like.
The determining module 202 is further configured to determine, according to the first operation data, a target prize value corresponding to the first ranking weight, where the higher the target prize value is, the better the ranking effect of the virtual button for ranking according to the first ranking weight is indicated.
In the embodiment of the invention, for example: if the virtual button is detected to be clicked once, the prize value is increased by 5, if the function corresponding to the virtual button is completed once, the prize value is increased by 20, the function service time is converted into the prize value according to a preset proportion, for example, the service time is 10 minutes, and the converted prize value is 2 according to a proportion of 20%.
And the adjusting module 203 is configured to adjust the priority of the first ranking weight in the pre-trained ranking model according to the target prize value and the preset prize value.
The determining module 202 is further configured to determine, from the plurality of sets of ranking weights, a target ranking weight through the adjusted ranking model.
Wherein, the ranking model can output ranking weights with probability according to different priorities of different ranking weights, such as: if the priority of the A-ranking weight is 7, the A-ranking weight may be output with a probability of 70%.
And the sorting module 204 is configured to sort the virtual buttons according to the target sorting weight.
Optionally, a set of importance vectors W (W1, W2, …) may be set according to the importance of the virtual buttons, and assuming that the vector corresponding to the target sorting weight is U, an inner product of the vector W and the vector U may be calculated, so as to obtain an inner product weight, and sorting is performed according to the size of the inner product weight corresponding to each virtual button from large to small. And if the importance vector is not set, sorting the virtual buttons directly according to the arrangement sequence corresponding to the target sorting weight.
As an optional implementation manner, the obtaining module 201 is further configured to obtain historical operation data corresponding to the virtual button;
the determining module 202 is further configured to determine an activity weight of the virtual button according to the historical operation data;
the sorting module 204 is further configured to sort the virtual buttons according to the active weights, so as to obtain a first arrangement sequence.
In this alternative embodiment, an active weight may be set for each virtual button according to the historical operation data corresponding to the virtual button, where the more virtual buttons that are used, the greater the corresponding active weights. Then, virtual buttons with high active weights are arranged in front, so that the sorting effect can be improved.
As an optional implementation manner, the obtaining module 201 is further configured to sort all the virtual buttons according to the active weights by using the sorting module 204, and obtain multiple sets of random weights of the virtual buttons after obtaining a first arrangement order;
the determining module 202 is further configured to determine a plurality of second arrangements of the virtual buttons according to the plurality of sets of random weights and the active weights;
the obtaining module 201 is further configured to obtain second operation data corresponding to the second arrangement order;
the determining module 202 is further configured to determine a first prize value of each of the second arrangements according to the second operation data;
the virtual button ordering apparatus may further include:
the first judgment module is used for judging whether the first rewarding value is larger than a preset rewarding value threshold value or not;
and the initialization module is used for initializing parameters of the sorting model to be trained according to the second arrangement sequence if the first reward value is larger than a preset reward value threshold value to obtain an initial sorting model, wherein the initial sorting model comprises an action cost function.
The second operation data may be data such as the number of times the virtual button is clicked, the time when the function corresponding to the virtual button is used, and the like, in the second arrangement order of the virtual buttons.
The ranking model may determine an action with highest benefit in a certain state by using an action cost function; i.e. determining the optimal ranking of the virtual buttons in the current environmental state of the target application, such that the prize value obtained is highest in this optimal ranking.
The action cost function is also called as a Q function, and mainly comprises the steps of constructing an environment state and ordering weights into a table (Q-table), and storing the priority of each group of ordering weights in each environment state. A set of ranking weights for the maximum prize value benefit may be determined based on the priority, and the virtual buttons may then be ranked based on the set of ranking weights.
The method is characterized in that, assuming that the rewarding decay coefficient is gamma, the learning rate is alpha, and an update equation of the action cost function is as follows:
NewQ(s t ,a t )＝Q(s t ,a t )+α[r t+1 +γmaxQ(s t+1 ,a t )-Q(s t ,a t )]。
in the embodiment of the invention, for example: if the virtual button is detected to be clicked once, the prize value is increased by 5, if the function corresponding to the virtual button is completed once, the prize value is increased by 20, the function service time is converted into the prize value according to a preset proportion, for example, the service time is 10 minutes, and the converted prize value is 2 according to a proportion of 20%. If the first reward value is greater than a preset reward value threshold, the second ranking order is a better ranking, and parameters of the ranking model to be trained can be initialized according to the second ranking order, so that an initial ranking model is obtained.
As an optional implementation manner, the obtaining module 201 is further configured to initialize parameters of the sorting model to be trained according to the second arrangement order by using the initializing module, obtain an initial sorting model, and obtain third operation data, where the third operation data is the number of times that the virtual button is clicked and the service time after being sorted according to the sorting weight output by the initial sorting model;
the virtual button ordering apparatus may further include:
and the first training module is used for training the initial sequencing model according to the second reward value corresponding to the third operation data to obtain the trained sequencing model.
The third operation data may be operation data of the target application program by the user after the virtual buttons are ranked according to the ranking weight output by the initial ranking model, where the operation data includes, but is not limited to, the number of times that the virtual buttons are clicked, the time that the functions corresponding to the virtual buttons are used, and the like.
In this alternative embodiment, the parameters of the initial ranking model may be trained according to the second prize value, the priority parameters of the ranking order in the initial ranking model may be adjusted, if the prize value is high, the parameters of the priority of the second ranking order in the model may be increased, and if the prize value is low, the parameters of the priority of the second ranking order in the model may be decreased.
As an optional implementation manner, the adjusting module 203 adjusts the priority of the first ranking weight in the pre-trained ranking model according to the target prize value and the preset prize value specifically includes:
judging whether the target rewarding value is larger than a preset rewarding value or not;
if the target rewarding value is larger than a preset rewarding value, upgrading and adjusting the priority of the first sequencing weight in the sequencing model; or (b)
And if the target rewarding value is smaller than or equal to a preset rewarding value, carrying out degradation adjustment on the priority of the first sequencing weight in the sequencing model.
In this alternative embodiment, if the target prize value is greater than a preset prize value, the first ranking weight may be increased by indicating that the first ranking weight is better, and if the target prize value is less than or equal to the preset prize value, the first ranking weight may be decreased by indicating that the first ranking weight is not good enough.
As an alternative embodiment, the virtual button ordering apparatus may further include:
the first resetting module is used for resetting the first parameters of the trained sequencing model every other preset time period to obtain a first pre-training model;
And the second training module is used for retraining the first pre-training model to obtain a first ordering model so as to reorder the virtual buttons through the first ordering model.
In this alternative embodiment, because the functional requirements may be different for different time periods, i.e., the virtual buttons that may be used may be different, the ranking model may be retrained based on the most current usage data at regular time, some of the parameters of the ranking model may be retained, only some of the parameters need to be adjusted, and the retrained speed is faster.
As an alternative embodiment, the virtual button ordering apparatus may further include:
the receiving module is used for receiving feedback values aiming at the trained sequencing model;
the second judging module is used for judging whether the feedback value is smaller than a preset feedback value threshold value or not;
the second resetting module is used for resetting the second parameters of the trained sequencing model if the feedback value is smaller than a preset feedback value threshold value to obtain a second pre-training model;
and the third training module is used for retraining the second pre-training model to obtain a second ordering model.
In this alternative embodiment, as the user population changes, or the operation habit of the user changes, the arrangement order of the virtual buttons output by the ranking model may be better before and not required by the user later, so that a part of parameters of the ranking model need to be reset according to feedback of user operation, or at intervals, retraining is performed while the part of parameters are maintained, and the latest ranking model (the second ranking model) can be obtained faster.
In the virtual button sorting device described in fig. 2, different environmental states of the target application program can be determined according to different environmental feature data of the target application program, and priorities of multiple groups of sorting weights corresponding to different environmental states are different, that is, sorting of the virtual buttons is different under different use environments, and the priorities of the sorting weights can be adjusted according to current operation data so as to continuously update the sorting model, so that the sorting model can always output optimal sorting, and sorting effects of the virtual buttons are improved.
Fig. 3 is a schematic structural diagram of an electronic device according to a preferred embodiment of the present invention for implementing the virtual button ordering method. The electronic device 3 comprises a memory 31, at least one processor 32, a computer program 33 stored in the memory 31 and executable on the at least one processor 32, and at least one communication bus 34.
It will be appreciated by those skilled in the art that the schematic diagram shown in fig. 3 is merely an example of the electronic device 3 and is not limiting of the electronic device 3, and may include more or less components than illustrated, or may combine certain components, or different components, e.g. the electronic device 3 may further include input-output devices, network access devices, etc.
The electronic device 3 further includes, but is not limited to, any electronic product that can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device, for example, a personal computer, a tablet computer, a smart phone, a personal digital assistant (Personal Digital Assistant, PDA), a game console, an interactive internet protocol television (Internet Protocol Television, IPTV), a smart wearable device, and the like. The network in which the electronic device 3 is located includes, but is not limited to, the internet, a wide area network, a metropolitan area network, a local area network, a virtual private network (Virtual Private Network, VPN), etc.
The at least one processor 32 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field-programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, transistor logic devices, discrete hardware components, or the like. The processor 32 may be a microprocessor or the processor 32 may be any conventional processor or the like, the processor 32 being a control center of the electronic device 3, the various interfaces and lines being used to connect the various parts of the entire electronic device 3.
The memory 31 may be used to store the computer program 33 and/or modules/units, and the processor 32 may implement various functions of the electronic device 3 by running or executing the computer program and/or modules/units stored in the memory 31 and invoking data stored in the memory 31. The memory 31 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data created according to the use of the electronic device 3 (such as audio data, etc.), and the like. In addition, the memory 31 may include a nonvolatile memory such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, and the like.
In connection with fig. 1, the memory 31 in the electronic device 3 stores a plurality of instructions to implement a virtual button ordering method, the processor 32 being executable to implement:
acquiring environmental characteristic data of a target application program, and acquiring multiple groups of sorting weights of virtual buttons of the target application program, wherein each group of sorting weights comprises one sorting weight of each virtual button;
Determining a current environment state according to the environment characteristic data;
acquiring priorities corresponding to each group of sorting weights in the current environment state;
determining a first sorting weight from the plurality of groups of sorting weights according to the priority;
acquiring first operation data corresponding to the first ordering weight, wherein the first operation data refers to the number of times that the virtual button is clicked and the service time after being ordered according to the first ordering weight;
determining a target rewarding value corresponding to the first sorting weight according to the first operation data, wherein the higher the target rewarding value is, the better the sorting effect of the virtual buttons according to the first sorting weight is indicated;
according to the target rewarding value and a preset rewarding value, adjusting the priority of the first sequencing weight in the pre-trained sequencing model;
determining target sorting weights from the plurality of groups of sorting weights through the adjusted sorting model;
and sorting the virtual buttons according to the target sorting weight.
Specifically, the specific implementation method of the above instructions by the processor 32 may refer to the description of the relevant steps in the corresponding embodiment of fig. 1, which is not repeated herein.
In the electronic device 3 described in fig. 3, different environmental states of the target application program can be determined according to different environmental feature data of the target application program, and priorities of multiple sets of sorting weights corresponding to different environmental states are different, that is, sorting of the virtual buttons is different under different use environments, and the priorities of the sorting weights can be adjusted according to current operation data so as to continuously update the sorting model, so that the sorting model can always output optimal sorting, and the sorting effect of the virtual buttons is improved.
The modules/units integrated in the electronic device 3 may be stored in a computer readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program code may be in the form of source code, object code, executable files, or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
In the several embodiments provided in the present invention, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned. Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. A plurality of units or means recited in the system claims can also be implemented by means of software or hardware by means of one unit or means. The terms second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (7)
1. A virtual button ordering method, characterized in that the virtual button ordering method comprises:
acquiring environmental characteristic data of a target application program, and acquiring multiple sets of sorting weights of virtual buttons of the target application program, wherein each set of sorting weights comprises one sorting weight of each virtual button, and before acquiring the environmental characteristic data of the target application program, the virtual button sorting method further comprises: acquiring historical operation data corresponding to the virtual button; determining the active weight of the virtual button according to the historical operation data; sequencing the virtual buttons according to the active weights to obtain a first sequencing order; acquiring a plurality of groups of random weights of the virtual buttons; determining a plurality of second arrangement orders of the virtual buttons according to the plurality of groups of random weights and the active weights; acquiring second operation data corresponding to the second arrangement sequence; determining a first prize value for each of the second arrangements according to the second operational data; judging whether the first rewarding value is larger than a preset rewarding value threshold value or not; if the first reward value is greater than a preset reward value threshold, initializing parameters of a sorting model to be trained according to the second arrangement sequence to obtain an initial sorting model, wherein the sorting model utilizes an action cost function to determine an action with highest benefit in an environmental state, the action cost function is to build the environmental state and the sorting weights into a table, and stores priority of each group of sorting weights in each environmental state, a group of sorting weights with the largest reward value benefit can be determined according to the priority, and the virtual buttons are sorted according to the group of sorting weights, wherein the initial sorting model comprises the action cost function; acquiring third operation data, wherein the third operation data is the clicked times and the service time of the virtual button after being sequenced according to the sequencing weight output by the initial sequencing model; training the initial sequencing model according to a second reward value corresponding to the third operation data to obtain a trained sequencing model;
Determining a current environment state according to the environment characteristic data;
acquiring the priority corresponding to each group of the sorting weights in the current environment state;
determining a first sorting weight from the plurality of groups of sorting weights according to the priority;
acquiring first operation data corresponding to the first ordering weight, wherein the first operation data refers to the number of times that the virtual button is clicked and the service time after being ordered according to the first ordering weight;
determining a target rewarding value corresponding to the first sorting weight according to the first operation data, wherein the higher the target rewarding value is, the better the sorting effect of the virtual buttons according to the first sorting weight is indicated;
according to the target rewarding value and a preset rewarding value, adjusting the priority of the first sequencing weight in the trained sequencing model;
determining target sorting weights from the plurality of groups of sorting weights through the adjusted trained sorting model;
and sorting the virtual buttons according to the target sorting weight.
2. The virtual button ranking method of claim 1 wherein the adjusting the priority of the first ranking weight in the trained ranking model according to the target prize value and a preset prize value comprises:
Judging whether the target rewarding value is larger than a preset rewarding value or not;
if the target rewarding value is larger than a preset rewarding value, upgrading and adjusting the priority of the first sequencing weight in the trained sequencing model; or (b)
And if the target rewarding value is smaller than or equal to a preset rewarding value, carrying out degradation adjustment on the priority of the first sequencing weight in the trained sequencing model.
3. The virtual button ordering method according to claim 1, further comprising:
resetting the first parameters of the trained sequencing model every other preset time period to obtain a first pre-training model;
retraining the first pre-trained model to obtain a first ranking model to reorder the virtual buttons through the first ranking model.
4. The virtual button ordering method according to claim 1, further comprising:
receiving feedback values for the trained ranking model;
judging whether the feedback value is smaller than a preset feedback value threshold value or not;
if the feedback value is smaller than a preset feedback value threshold, resetting the second parameter of the trained sequencing model to obtain a second pre-training model;
Retraining the second pre-trained model to obtain a second ranking model.
5. A virtual button ordering apparatus, characterized in that the virtual button ordering apparatus comprises:
the virtual button ordering method comprises the steps of obtaining environmental characteristic data of a target application program, and obtaining multiple sets of ordering weights of virtual buttons of the target application program, wherein each set of ordering weights comprises one ordering weight of each virtual button, and before obtaining the environmental characteristic data of the target application program and obtaining the multiple sets of ordering weights of the virtual buttons of the target application program, the virtual button ordering method further comprises: acquiring historical operation data corresponding to the virtual button; determining the active weight of the virtual button according to the historical operation data; sequencing the virtual buttons according to the active weights to obtain a first sequencing order; acquiring a plurality of groups of random weights of the virtual buttons; determining a plurality of second arrangement orders of the virtual buttons according to the plurality of groups of random weights and the active weights; acquiring second operation data corresponding to the second arrangement sequence; determining a first prize value for each of the second arrangements according to the second operational data; judging whether the first rewarding value is larger than a preset rewarding value threshold value or not; if the first reward value is greater than a preset reward value threshold, initializing parameters of a sorting model to be trained according to the second arrangement sequence to obtain an initial sorting model, wherein the sorting model utilizes an action cost function to determine an action with highest benefit in an environmental state, the action cost function is to build the environmental state and the sorting weights into a table, and stores priority of each group of sorting weights in each environmental state, a group of sorting weights with the largest reward value benefit can be determined according to the priority, and the virtual buttons are sorted according to the group of sorting weights, wherein the initial sorting model comprises the action cost function; acquiring third operation data, wherein the third operation data is the clicked times and the service time of the virtual button after being sequenced according to the sequencing weight output by the initial sequencing model; training the initial sequencing model according to a second reward value corresponding to the third operation data to obtain a trained sequencing model;
The determining module is used for determining the current environment state according to the environment characteristic data;
the acquisition module is further configured to acquire the priority corresponding to each group of the sorting weights in the current environmental state;
the determining module is further configured to determine a first ranking weight from the plurality of sets of ranking weights according to the priority;
the acquisition module is further configured to acquire first operation data corresponding to the first ordering weight, where the first operation data refers to the number of times that the virtual button is clicked and the service time after being ordered according to the first ordering weight;
the determining module is further configured to determine, according to the first operation data, a target prize value corresponding to the first ranking weight, where the higher the target prize value is, the better ranking effect of the virtual buttons according to the first ranking weight is indicated;
the adjustment module is used for adjusting the priority of the first sequencing weight in the trained sequencing model according to the target reward value and a preset reward value;
the determining module is further configured to determine a target ranking weight from the multiple sets of ranking weights through the adjusted trained ranking model;
And the sorting module is used for sorting the virtual buttons according to the target sorting weight.
6. An electronic device comprising a processor and a memory, the processor being configured to execute a computer program stored in the memory to implement the virtual button ordering method of any one of claims 1-4.
7. A computer readable storage medium storing at least one instruction which when executed by a processor implements the virtual button ordering method of any one of claims 1 to 4.
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| CN102087575A (en) * | 2009-12-03 | 2011-06-08 | 鸿富锦精密工业（深圳）有限公司 | Electronic device and method for dynamic arrangement of icons |
| CN103309661A (en) * | 2013-05-27 | 2013-09-18 | 深圳市金立通信设备有限公司 | Method and terminal for controlling interface application icons |
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