CN112306251A - Input method, input device and input device - Google Patents
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- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/02—Input arrangements using manually operated switches, e.g. using keyboards or dials
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Abstract
The embodiment of the application discloses an input method, an input device and a device for inputting. An embodiment of the method comprises: counting historical behavior data of a user, and determining a click willingness value of the user for an association candidate item in input method application; determining a user gear of the user based on the click desire value; when the on-screen content to be associated is detected, determining a target association candidate item of the on-screen content, and determining a display strategy of the target association candidate item based on the user gear. The embodiment improves the click rate of the user on the association candidate.
Description
Technical Field
The embodiment of the application relates to the technical field of computers, in particular to an input method, an input device and an input device.
Background
When a user inputs content by using an input method application, candidates which may be associated with the content can be provided for the user in an associated manner according to the content input by the user or context content of a current cursor position, so as to save input cost of the user and improve input efficiency. For example, when the user is on the screen "now", association candidates such as "class", "back", etc. may be provided. Generally, the higher the click rate of the user on the association candidate item is, the better the pushing effect of the association candidate item is, and the stronger the convenience provided by the association candidate function to the user is.
The existing input method only supports the on or off of the association candidate function, and different display strategies cannot be customized for association candidate items aiming at users with different use habits, so that the click rate of the users on the association candidate items is low.
Disclosure of Invention
The embodiment of the application provides an input method, an input device and an input device, and aims to solve the technical problem that the click rate of a user on a joint candidate item is low in the prior art.
In a first aspect, an embodiment of the present application provides an input method, where the method includes: counting historical behavior data of a user, and determining a click willingness value of the user for an association candidate item in an input method application, wherein the click willingness value is used for representing the click willingness; determining a user gear of a user based on the click desire value; when the on-screen content to be associated is detected, determining a target association candidate item of the on-screen content, and determining a display strategy of the target association candidate item based on the user gear.
In a second aspect, an embodiment of the present application provides an input device, including: the statistical unit is configured to count historical behavior data of a user and determine a click willingness value of the user for an association candidate item in the input method application, wherein the click willingness value is used for representing the click willingness; a first determination unit configured to determine a user shift position of the user based on the click desire value; and the second determination unit is configured to determine a target association candidate item of the on-screen content when the on-screen content to be associated is detected, and determine a presentation strategy for the target association candidate item based on the user gear.
In a third aspect, an embodiment of the present application provides an apparatus for input, comprising a memory, and one or more programs, wherein the one or more programs are stored in the memory, and the one or more programs are configured to be executed by the one or more processors and include instructions for: counting historical behavior data of a user, and determining a click willingness value of the user for an association candidate item in an input method application, wherein the click willingness value is used for representing the click willingness; determining a user gear of a user based on the click desire value; when the situation that the user inputs information by using the input method application is detected, the display strategy of the target association candidate item corresponding to the input information is determined based on the gear of the user.
In a fourth aspect, embodiments of the present application provide a computer-readable medium on which a computer program is stored, which when executed by a processor, implements the method as described in the first aspect above.
According to the input method, the input device and the input device, the selection willingness value of the user on the association candidate item in the input method application is determined by counting the historical behavior data of the user; then, determining a user gear of the user based on the click desire value; and when the on-screen content to be associated is detected, determining a target association candidate item of the on-screen content, and accordingly determining a display strategy of the target association candidate item based on the gear of the user. Therefore, different display strategies can be customized for the association candidate items aiming at users with different using habits, so that the display strategies are more in line with the display habits of the users on the input method application, and the click rate of the users on the association candidate items is improved.
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Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is a flow diagram of one embodiment of an input method according to the present application;
FIG. 2 is a flow diagram of yet another embodiment of an input method according to the present application;
FIG. 3 is a schematic diagram of an embodiment of an input device according to the present application;
FIG. 4 is a schematic diagram of a structure of an apparatus for input according to the present application;
FIG. 5 is a schematic diagram of a server in accordance with some embodiments of the present application.
Detailed Description
The present application will be described in further detail with reference to the following drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
Referring to FIG. 1, a flow 100 of one embodiment of an input method according to the present application is shown. The input method can be operated in various electronic devices including but not limited to: a server, a smart phone, a tablet computer, an e-book reader, an MP3 (Moving Picture Experts Group Audio Layer III) player, an MP4 (Moving Picture Experts Group Audio Layer IV) player, a laptop, a car computer, a desktop computer, a set-top box, an intelligent tv, a wearable device, and so on.
The input method application mentioned in the embodiment of the application can support various input methods. The input method may be an encoding method used for inputting various symbols to electronic devices such as computers and mobile phones, and a user may conveniently input a desired character or character string to the electronic devices using the input method application. It should be noted that, in the embodiment of the present application, in addition to the common chinese input method (such as pinyin input method, wubi input method, zhuyin input method, phonetic input method, handwriting input method, etc.), the input method may also support other language input methods (such as japanese hiragana input method, korean input method, etc.), and the input method and the language category of the input method are not limited at all.
The input method in this embodiment may include the following steps:
In this embodiment, the execution body of the input method may be pre-recorded with historical behavior data of the user. The historical behavior data may be behavior data generated by the user during the application of the input method during a certain historical time period (e.g., the previous month, the previous week, etc.). For example, the data of the on-screen content input by the user, the association candidate item associated with the on-screen content, the association candidate item clicked by the user, and the like can be included. Here, the association candidate may be a candidate (e.g., a word, phrase, etc.) associated with the already-on-screen content.
In this embodiment, the execution subject may count the historical behavior data, and determine a click intention value of the user for an association candidate in the input method application. Wherein, the click will value can be used for representing the click will. As an example, the click intention value may be a numerical value for characterizing a user's click intention on association candidates in the input method application, the click intention value being larger as the user's click intention is higher.
It will be appreciated that after displaying the already-displayed content on the screen, some users often want the input method application to associate with candidates that may be associated with the already-displayed content, so as to directly select the content to be displayed among the candidates, thereby improving the input efficiency. Therefore, during the process of using the input method application by the users, behavior data of more click association candidates are generated. In addition, because the input method application can generate some association candidates different from the user's intention, other users are not used to use the association candidates. During the process of using the input method application by the users, behavior data with fewer click association candidates can be generated. Since the historical behavior data records behavior data such as user clicking on the association candidate item, the execution main body can determine the click intention value of the user on the association candidate item in the input method application based on statistics and analysis of the historical behavior data.
Specifically, the execution main body may calculate the click desire value in various ways. As an example, the execution subject may count the number of times the user clicks the association candidate from the historical behavior data. The more times the user clicks on the associated candidate items, the stronger the user's will of clicking, and thus the value of the will of clicking can be determined according to the times.
As still another example, the execution subject may count a user's selection rate of the association candidates from the historical behavior data, and may use the selection rate as a user's selection willingness value for the association candidates in the input method application. The selection rate of the user for the association candidate items can be a ratio of the number of times the user selects the association candidate items to the number of times the association candidate items are presented.
In some optional implementations of the embodiment, the executing body may determine the click desire value of the user by:
the method comprises the steps of firstly, counting historical behavior data of a user, and determining the theoretical point selection rate and the actual point selection rate of the user on association candidate items in the input method application.
The actual click rate may be a ratio of the number of times the user clicks the association candidate to the number of times the association candidate is presented.
It will be appreciated that in some cases, after the input method application presents the association candidates of the content already on the screen, the user enters one of the association candidates in the input method application in a manual input manner instead of clicking on the association candidate. At this time, although the user does not click on the association candidate, the same effect is exhibited as when the user clicks on the association candidate, and therefore, in theory, this case can also be regarded as the case where the user clicks on the association candidate. Therefore, the execution main body can count the theoretical selection times of the association candidates by the user when the situation is included, and determine the ratio of the theoretical selection times to the presentation times of the association candidates as the theoretical selection rate.
The theoretical point selection rate considers the condition that a user manually inputs a certain association candidate item, so that data statistics can be more comprehensive, and the accuracy of the point selection intention value is improved.
And secondly, determining the ratio of the actual point selection rate to the theoretical point selection rate as the point selection willingness value of the user for the association candidate item in the input method application.
And 102, determining a user gear of the user based on the click desire value.
In this embodiment, the execution main body may determine the user's shift position based on the click desire value. Specifically, the execution main body may store, in advance, correspondence information between the click desire value and the user gear. After obtaining the selection will value, the execution main body may query, from the correspondence information, a user shift corresponding to the selection will value, so as to obtain the user shift of the user.
In some optional implementations of the present embodiment, the execution main body may be preset with a user gear comparison table. The user gear comparison table may record corresponding relationship information between the point selection will value and the user gear. The executing body may query the user gear corresponding to the selection will value determined in step 101 from the user gear comparison table.
As an example, the user gear may be divided into a gear a, a gear B, and a gear C in advance, and the value intervals of the click desire values corresponding to the respective gears may be set respectively. For example, the gear a corresponds to a value interval (0.8, 1), the gear B corresponds to a value interval (0.5, 0.8), and the gear C corresponds to a value interval [0,0.5 ].
And 103, when the on-screen content to be associated is detected, determining a target association candidate item of the on-screen content, and determining a display strategy for the target association candidate item based on the user gear.
In this embodiment, when the execution subject detects the already-screened content to be associated, a target association candidate of the already-screened content may be determined.
In one scenario, when a user uses an input method application to input content, content that the user has input or content at a position where a current cursor is located may be used as a displayed content to be associated, and a target association candidate of the displayed content is determined.
In another scenario, when a user interacts with an opposite terminal (e.g., another user or an interactive system), the interactive content of the user and the opposite terminal may be used as the already-displayed content to be associated, so as to determine a target association candidate of the already-displayed content.
Here, various existing candidate association methods may be utilized to determine the target association candidate of the already-screened content. As an example, keywords in the already-on-screen content may be extracted first. Then, a predetermined word associated with the keyword can be found. And then, the searched words can be sorted, and a preset number of words are selected as target association candidate items of the on-screen content according to the sorting result.
It should be noted that the manner of determining the target association candidate of the on-screen content is not limited to the above example, and the association candidate may also be obtained in other manners, which is not limited herein.
In this embodiment, after obtaining the target association candidate of the on-screen content, the executing entity may determine a presentation policy for the target association candidate based on the user gear determined in step 102. As an example, different user gears may correspond to different preset presentation strategies. The execution main body can search the display strategy corresponding to the user gear, and display and control the target association candidate items based on the searched display strategy.
In practice, the presentation policy may be used to indicate that the target association candidate is presented or not presented; alternatively, the presentation policy may also be used to indicate the number of presentation target association candidates, and the like.
As an example, if the user gear is divided into the a gear and the B gear, the minimum value of the value interval of the click desire value corresponding to the a gear is greater than the maximum value of the value interval of the click desire value corresponding to the B gear. At this time, if the user gear is in the a gear, the presentation policy may be to present the target association candidate. If the user gear is in the B gear, the display strategy may be not to display the target association candidate.
As yet another example, if the user gear is in gear a, the presentation strategy may be to present a first preset number of target association candidates. If the user gear is in the B gear, the display strategy may be to display a second preset number of target association candidates. It is to be understood that, since the user in the a range is more accustomed to clicking on the association candidate and the user in the B range is less accustomed to clicking on the association candidate, the first preset number may be set to a value greater than the second preset number.
As another example, if the user gear is in the gear a, the display strategy may further be to display the target association candidate item when the sum of the conditional probabilities of the first screen association candidate items on the first screen is greater than a first preset threshold; and when the sum of the conditional probabilities of the first screen association candidate items is less than or equal to a first preset threshold value, not displaying the target association candidate items. If the gear of the user is in the gear B, the display strategy can also be that when the sum of the conditional probabilities of the first screen association candidate items positioned on the first screen is greater than a second preset threshold value, the target association candidate items are displayed; and when the sum of the conditional probabilities of the first screen association candidate items is less than or equal to a second preset threshold value, not displaying the target association candidate items. It can be understood that, since the user in the a-range is more used to click the association candidate items and the user in the B-range is less used to click the association candidate items, the target association candidate item can be displayed when the sum of the conditional probabilities of the first screen association candidate items is larger (i.e., when the first screen association candidate item has a stronger association with the already-displayed content), and therefore, the first preset threshold value can be set to be smaller than the second preset value.
It should be noted that the presentation policy may be preset as needed, and specific content of the presentation policy is not limited herein.
In some optional implementations of the embodiment, the executing entity may further determine a presentation policy for the target association candidate by:
firstly, acquiring a preset threshold corresponding to the user gear. Here, a threshold value is preset for each user gear. Different user gears may be preset with different preset thresholds.
And secondly, determining the sum of the word frequencies of the first screen association candidate items positioned on the first screen in the target association candidate items. Specifically, a first screen association candidate item located on a first screen in the target association candidate items can be determined at first; then, determining the word frequency of each first screen association candidate item; and finally, determining the sum of the word frequencies of the first screen association candidate items.
In practice, for a certain on-screen content, the association candidate item usually contains a plurality of items. Because the size of an input window of the input method application is limited, the input window usually only supports displaying part of the association candidates, so when the association candidates are displayed, the input method application usually ranks the association candidates according to the sequence of conditional probabilities from large to small, and displays the association candidates in multiple pages according to the ranking result. At this time, the association candidate item displayed on the home page is the home screen association candidate item located on the home screen.
It should be noted that, for a certain entry, the term frequency of the entry may be the number of the entry appearing in the statistical process. The word frequency of the first screen association candidate item can be the frequency of the first screen association candidate item appearing in the statistical process.
And thirdly, determining a display strategy of the target association candidate item based on the comparison between the sum of the word frequencies and the preset threshold value. As an example, the presentation policy may include: when the sum of the word frequencies is larger than the preset threshold value, displaying the target association candidate item; and when the sum of the conditional word frequencies is less than or equal to the preset threshold value, not displaying the target association candidate item. Here, the presentation policy may be set as needed, and is not limited to the above example.
It should be noted that the implementation manner of determining the presentation strategy for the target association candidate based on the user gear is not limited to the implementation manner described above, and may also be implemented in other manners. For example, the presentation policy may also be determined based on a comparison between the number of long words (e.g., words with a number of characters greater than a preset value) in the target association candidate and a preset threshold. And will not be described in detail herein.
In some optional implementation manners of this embodiment, after determining the user gear of the user, the execution main body may further record the behavior data of the user in real time. Then, statistics can be performed on the behavior data recorded in real time to re-determine the click willingness value of the user for the association candidate in the input method application. In practice, the time interval of the user click desire value is determined to reach the preset time, or the behavior data is counted again when the data volume of the behavior data is greater than the preset value. After the new click desire value is obtained through statistics, if the click desire value is updated (that is, the new click desire value is different from the click desire value obtained through previous statistics), the execution main body may update the user gear of the user based on the updated click desire value.
It should be noted that, after updating the user gear of the user, the executing body may continue to execute the operation of step 103. Therefore, the display strategy is adjusted based on the clicking condition of the user on the association candidate items, and the display strategy can be suitable for the latest use habit of the user.
In the method provided by the embodiment of the application, the click willingness value of the user on the association candidate item in the input method application is determined by counting the historical behavior data of the user; then, determining the user gear of the user based on the click desire value; and when the on-screen content to be associated is detected, determining a target association candidate item of the on-screen content, and accordingly determining a display strategy of the target association candidate item based on the user gear. Therefore, different display strategies can be customized for the association candidate items aiming at users with different using habits, so that the display strategies are more in line with the display habits of the users on the input method application, and the click rate of the users on the association candidate items is improved.
With further reference to fig. 2, a flow 200 of yet another embodiment of an input method is shown. The process 200 of the input method comprises the following steps:
In this embodiment, the execution subject of the input method may count the historical behavior data of the user, and determine the theoretical click rate and the actual click rate of the user on the association candidate in the input method application. The actual click rate may be a ratio of the number of times the user clicks the association candidate to the number of times the association candidate is presented.
It will be appreciated that in some cases, after the input method application presents the association candidates of the content already on the screen, the user enters one of the association candidates in the input method application in a manual input manner instead of clicking on the association candidate. At this time, although the user does not click on the association candidate, the same effect is exhibited as when the user clicks on the association candidate, and therefore, in theory, this case can also be regarded as the case where the user clicks on the association candidate. Therefore, the execution main body can count the theoretical selection times of the association candidates by the user when the situation is included, and determine the ratio of the theoretical selection times to the presentation times of the association candidates as the theoretical selection rate.
In this embodiment, the execution subject may determine a ratio of the actual click rate to the theoretical click rate as a user's click desire value for the association candidate in the input method application.
The theoretical point selection rate considers the condition that a user manually inputs a certain association candidate item, so that data statistics can be more comprehensive, and the accuracy of the point selection intention value is improved.
In this embodiment, the execution body may be provided with a user gear comparison table in advance. The user gear comparison table may record corresponding relationship information between the point selection will value and the user gear. The executing body may query the user gear corresponding to the selection will value determined in step 101 from the user gear comparison table.
In this embodiment, different user gears may be preset with different preset thresholds. For example, the preset thresholds for gear a, gear B and gear C are 0.3, 0.5 and 0.8, respectively. The preset threshold corresponding to each user gear may be preset based on a large number of data statistics.
And step 206, determining the sum of the conditional probabilities of the first screen association candidate item positioned in the first screen in the target association candidate items.
In practice, for a certain on-screen content, the association candidate item usually contains a plurality of items. Because the size of an input window of the input method application is limited, the input window usually only supports displaying part of the association candidates, so when the association candidates are displayed, the input method application usually ranks the association candidates according to the sequence of conditional probabilities from large to small, and displays the association candidates in multiple pages according to the ranking result. At this time, the association candidate item displayed on the home page is the home screen association candidate item located on the home screen.
Here, the conditional probability refers to the probability of occurrence of an event a under the condition that another event B has occurred. For a certain word, the conditional probability of the word is the probability that the word is taken as the association candidate item of the content under the condition that the content is on the screen. It is understood that the greater the conditional probability of a word, the greater the probability that the word is a candidate for association with the already-displayed content, and the greater the association between the word and the already-displayed content.
It should be noted that, for a certain piece of displayed content, the conditional probability of the association candidate of the displayed content can be determined in various ways. For example, the determination may be based on a pre-trained association candidate prediction model. The association candidate prediction model may be based on a large amount of corpora and trained by using a machine learning method (e.g., a supervised learning method or an unsupervised learning method). In practice, the on-screen content may be input to the association candidate prediction model, so that the association candidate prediction model may output the conditional probability that each word in the lexicon is used as the association candidate of the on-screen content.
In this embodiment, for the content to be associated on the screen, the execution subject may first determine, from the target association candidates, the first screen association candidate located on the first screen according to the descending order of the conditional probabilities. Then, the sum of the conditional probabilities of the respective first screen association candidates can be calculated. The larger the sum of the conditional probabilities of the first screen association candidate items is, the stronger the relevance between the first screen association candidate items and the content already displayed on the screen is, and the better the selection effect of the first screen association candidate items is. Thus, the user can click on the association candidate more easily.
And step 207, determining a display strategy for the target association candidate item based on the comparison between the sum of the conditional probabilities and a preset threshold value.
In this embodiment, the execution subject may determine the presentation policy for the target association candidate based on a comparison between the sum of the conditional probabilities and the preset threshold.
Optionally, the presentation policy may include: when the sum of the conditional probabilities is greater than the preset threshold, displaying the target association candidate item; and when the sum of the conditional probabilities is less than or equal to the preset threshold, not displaying the target association candidate.
In practice, different user gears may be preset with different preset thresholds. For example, the larger the click desire value of the user is, the more the user is accustomed to clicking the association candidate. At this time, for the user gear with a larger click will value, the preset threshold value may be set to a smaller value to adapt to the habit of the user. Similarly, the smaller the click desire value of the user is, the less the user is used to click the association candidate. At this time, for the user gear with a smaller click intention value, the preset threshold value can be set to be a larger numerical value, so that when the sum of the conditional probabilities is larger than the numerical value (that is, when the relevance between the first screen association candidate item and the content already displayed on the screen is stronger), the click association candidate item is presented. Therefore, the user can more easily click the association candidate, and the click rate of the user on the association candidate is improved.
Optionally, the presentation policy may include: and when the sum of the conditional probabilities is greater than the preset threshold value, displaying a first preset number of target association candidate items. And when the sum of the conditional probabilities is less than or equal to the preset threshold, displaying a second preset number of target association candidates. The first preset number is larger than the second preset number. It can be understood that, when the sum of the conditional probabilities is larger, the relevance of the first screen association candidate to the already-screened content is stronger. At this time, more association candidates can be displayed, which is helpful for improving the click rate of the user on the association candidates. When the sum of the conditional probabilities is smaller, the relevance of the first screen association candidate and the content already displayed is weaker. At this time, fewer association candidates can be displayed, and interference to the user is avoided.
Optionally, after the execution main body determines the user gear of the user, the execution main body may record the behavior data of the user in real time. Then, statistics can be performed on the behavior data recorded in real time to re-determine the click willingness value of the user for the association candidate in the input method application. In practice, the time interval of the user click desire value is determined to reach the preset time, or the behavior data is counted again when the data volume of the behavior data is greater than the preset value. After the new click desire value is obtained through statistics, if the click desire value is updated (that is, the new click desire value is different from the click desire value obtained through previous statistics), the execution main body may update the user gear of the user based on the updated click desire value. It should be noted that, after updating the user gear of the user, the executing entity may continue to execute the operations of step 204 to step 207. Therefore, the display strategy is adjusted based on the clicking condition of the user on the association candidate items, so that the display strategy can be suitable for the latest use habit of the user, and the clicking rate of the user on the association candidate items is improved continuously.
As can be seen from fig. 2, compared with the corresponding embodiment of fig. 1, the flow 200 of the input method in the present embodiment involves a step of determining the click will value in combination with the theoretical click rate. The theoretical point selection rate considers the condition that a user manually inputs a certain association candidate item, so that data statistics can be more comprehensive, and the accuracy of the point selection intention value is improved. In addition, the flow 200 of the input method in this embodiment also involves a step of determining a presentation strategy for the target association candidate item based on a comparison between a sum of conditional probabilities of the first screen association candidate items and a preset threshold value. Therefore, the scheme described in this embodiment may perform presentation of the click association candidate when the sum of the conditional probabilities is greater than the value (i.e., when the relevance between the top screen association candidate and the already-displayed content is strong). Therefore, the user can more easily click the association candidate, and the click rate of the user on the association candidate is improved. In addition, the division of the user gears can be dynamically adjusted based on the clicking related operation information of the user in the input process, so that the display strategy is suitable for the latest use habit of the user, and the clicking rate of the user on the association candidate items is continuously improved.
With further reference to fig. 3, as an implementation of the methods shown in the above figures, the present application provides an embodiment of an input device, which corresponds to the embodiment of the method shown in fig. 1, and which is particularly applicable to various electronic devices.
As shown in fig. 3, the input device 300 according to the present embodiment includes: a counting unit 301 configured to count historical behavior data of a user and determine a click will value of the user for an association candidate item in an input method application, where the click will value is used to represent a click will; a first determination unit 302 configured to determine a user gear of the user based on the click desire value; a second determining unit 303, configured to, when an already-screened content to be associated is detected, determine a target association candidate of the already-screened content, and determine a presentation policy for the target association candidate based on the user gear.
In some optional implementations of this embodiment, the statistical unit is further configured to: counting historical behavior data of a user, and determining the theoretical click rate and the actual click rate of the user on association candidate items in the input method application; and determining the ratio of the actual point selection rate to the theoretical point selection rate as the point selection willingness value of the user for the association candidate item in the input method application.
In some optional implementations of this embodiment, the first determining unit is further configured to: and inquiring a user gear corresponding to the selection will value from a preset user gear comparison table, wherein the user gear comparison table records the corresponding relation between the selection will value and the user gear.
In some optional implementations of this embodiment, the second determining unit is further configured to: acquiring a preset threshold corresponding to the user gear; determining the sum of conditional probabilities of first screen association candidate items positioned at the first screen in the target association candidate items; and determining a display strategy of the target association candidate item based on the comparison between the sum of the conditional probabilities and the preset threshold value.
In some optional implementations of this embodiment, the display policy includes: when the sum of the conditional probabilities is greater than the preset threshold, displaying the target association candidate item; and when the sum of the conditional probabilities is less than or equal to the preset threshold, not displaying the target association candidate.
In some optional implementations of this embodiment, the second determining unit is further configured to: acquiring a preset threshold corresponding to the user gear; determining the sum of the word frequencies of the first screen association candidate items positioned in the first screen in the target association candidate items; and determining a display strategy for the target association candidate item based on the comparison between the sum of the word frequencies and the preset threshold value.
In some optional implementations of this embodiment, the display policy includes: when the sum of the word frequencies is larger than the preset threshold value, displaying the target association candidate item; and when the sum of the conditional word frequencies is less than or equal to the preset threshold value, not displaying the target association candidate item.
In some optional implementations of this embodiment, the apparatus further includes: the recording unit is configured to record the behavior data of the user in real time; the third determining unit is configured to count the behavior data recorded in real time and re-determine the click intention value of the user on the association candidate item in the input method application; and the updating unit is configured to respond to the judgment that the clicking will value is updated, and update the user gear of the user based on the updated clicking will value.
The device provided by the embodiment of the application determines the click willingness value of the user on the association candidate item in the input method application by counting the historical behavior data of the user; then, determining the user gear of the user based on the click desire value; and when the on-screen content to be associated is detected, determining a target association candidate item of the on-screen content, and accordingly determining a display strategy of the target association candidate item based on the user gear. Therefore, different display strategies can be customized for the association candidate items aiming at users with different using habits, so that the display strategies are more in line with the display habits of the users on the input method application, and the click rate of the users on the association candidate items is improved.
Fig. 4 is a block diagram illustrating an apparatus 400 for input according to an example embodiment, where the apparatus 400 may be an intelligent terminal or a server. For example, the apparatus 400 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, an exercise device, a personal digital assistant, and the like.
Referring to fig. 4, the apparatus 400 may include one or more of the following components: processing components 402, memory 404, power components 406, multimedia components 408, audio components 410, input/output (I/O) interfaces 412, sensor components 414, and communication components 416.
The processing component 402 generally controls overall operation of the apparatus 400, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing element 402 may include one or more processors 420 to execute instructions to perform all or part of the steps of the methods described above. Further, the processing component 402 can include one or more modules that facilitate interaction between the processing component 402 and other components. For example, the processing component 402 can include a multimedia module to facilitate interaction between the multimedia component 408 and the processing component 402.
The memory 404 is configured to store various types of data to support operations at the apparatus 400. Examples of such data include instructions for any application or method operating on the device 400, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 404 may be implemented by any type or combination of volatile or non-volatile memory devices such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks.
The multimedia component 408 includes a screen that provides an output interface between the device 400 and a user. In some embodiments, the screen may include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive an input signal from a user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundary of the touch or slide action but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 408 includes a front facing camera and/or a rear facing camera. The front-facing camera and/or the rear-facing camera may receive external multimedia data when the device 400 is in an operational mode, such as a shooting mode or a video mode. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.
The audio component 410 is configured to output and/or input audio signals. For example, audio component 410 includes a Microphone (MIC) configured to receive external audio signals when apparatus 400 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may further be stored in the memory 404 or transmitted via the communication component 416. In some embodiments, audio component 410 also includes a speaker for outputting audio signals.
The I/O interface 412 provides an interface between the processing component 402 and peripheral interface modules, which may be keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to: a home button, a volume button, a start button, and a lock button.
The sensor component 414 includes one or more sensors for providing various aspects of status assessment for the apparatus 400. For example, the sensor assembly 414 may detect an open/closed state of the device 400, the relative positioning of the components, such as a display and keypad of the apparatus 400, the sensor assembly 414 may also detect a change in the position of the apparatus 400 or a component of the apparatus 400, the presence or absence of user contact with the apparatus 400, orientation or acceleration/deceleration of the apparatus 400, and a change in the temperature of the apparatus 400. The sensor assembly 414 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 414 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 414 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 416 is configured to facilitate wired or wireless communication between the apparatus 400 and other devices. The apparatus 400 may access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 416 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 416 further includes a Near Field Communication (NFC) module to facilitate short-range communications. For example, the NFC module may be implemented based on Radio Frequency Identification (RFID) technology, infrared data association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.
In an exemplary embodiment, the apparatus 400 may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components for performing the above-described methods.
In an exemplary embodiment, a non-transitory computer-readable storage medium comprising instructions, such as the memory 404 comprising instructions, executable by the processor 420 of the apparatus 400 to perform the above-described method is also provided. For example, the non-transitory computer readable storage medium may be a ROM, a Random Access Memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, and the like.
Fig. 5 is a schematic diagram of a server in some embodiments of the present application. The server 500 may vary widely in configuration or performance and may include one or more Central Processing Units (CPUs) 522 (e.g., one or more processors) and memory 532, one or more storage media 530 (e.g., one or more mass storage devices) storing applications 542 or data 544. Memory 532 and storage media 530 may be, among other things, transient storage or persistent storage. The program stored on the storage medium 530 may include one or more modules (not shown), each of which may include a series of instruction operations for the server. Still further, the central processor 522 may be configured to communicate with the storage medium 530, and execute a series of instruction operations in the storage medium 530 on the server 500.
The server 500 may also include one or more power supplies 526, one or more wired or wireless network interfaces 550, one or more input-output interfaces 558, one or more keyboards 556, and/or one or more operating systems 541, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, etc.
A non-transitory computer readable storage medium having instructions therein which, when executed by a processor of an apparatus (smart terminal or server), enable the apparatus to perform an input method, the method comprising: counting historical behavior data of a user, and determining a click willingness value of the user for an association candidate item in an input method application, wherein the click willingness value is used for representing a click willingness; determining the user gear of the user based on the click desire value; when the on-screen content to be associated is detected, determining a target association candidate item of the on-screen content, and determining a display strategy of the target association candidate item based on the user gear.
Optionally, the counting historical behavior data of the user and determining a click intention value of the user for an association candidate in the input method application includes: counting historical behavior data of a user, and determining the theoretical click rate and the actual click rate of the user on association candidate items in the input method application; and determining the ratio of the actual point selection rate to the theoretical point selection rate as the point selection willingness value of the user for the association candidate item in the input method application.
Optionally, the determining the user gear of the user based on the click desire value includes: and inquiring a user gear corresponding to the selection will value from a preset user gear comparison table, wherein the user gear comparison table records the corresponding relation between the selection will value and the user gear.
Optionally, the determining, based on the user gear, a display strategy for the target association candidate includes: acquiring a preset threshold corresponding to the user gear; determining the sum of conditional probabilities of first screen association candidate items positioned at the first screen in the target association candidate items; and determining a display strategy of the target association candidate item based on the comparison between the sum of the conditional probabilities and the preset threshold value.
Optionally, the display policy includes: when the sum of the conditional probabilities is greater than the preset threshold, displaying the target association candidate item; and when the sum of the conditional probabilities is less than or equal to the preset threshold, not displaying the target association candidate.
Optionally, the determining a display strategy for the target association candidate based on the user gear includes: acquiring a preset threshold corresponding to the user gear; determining the sum of the word frequencies of the first screen association candidate items positioned in the first screen in the target association candidate items; and determining a display strategy for the target association candidate item based on the comparison between the sum of the word frequencies and the preset threshold value.
Optionally, the display policy includes: when the sum of the word frequencies is larger than the preset threshold value, displaying the target association candidate item; and when the sum of the conditional word frequencies is less than or equal to the preset threshold value, not displaying the target association candidate item.
Optionally, after determining the user gear of the user, the method further includes: recording the behavior data of the user in real time; counting the behavior data recorded in real time, and re-determining the click willingness value of the user for the association candidate item in the input method application; and updating the user gear of the user based on the updated click desire value in response to the fact that the click desire value is updated.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the application disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice in the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.
The input method, the input device and the device for inputting provided by the present application, as well as the method for updating the lexicon, the device for updating the lexicon and the device for updating the lexicon are introduced in detail above, and the principle and the implementation mode of the present application are explained by applying specific examples, and the description of the above examples is only used to help understand the method and the core ideas of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in view of the above, the content of the present specification should not be construed as a limitation to the present application.
Claims (11)
1. An input method, characterized in that the method comprises:
counting historical behavior data of a user, and determining a click willingness value of the user for an association candidate item in an input method application, wherein the click willingness value is used for representing a click willingness;
determining a user gear of the user based on the click desire value;
when the on-screen content to be associated is detected, determining a target association candidate item of the on-screen content, and determining a display strategy of the target association candidate item based on the user gear.
2. The method of claim 1, wherein the counting historical behavior data of the user and determining the user's will-click value for an association candidate in an input method application comprises:
counting historical behavior data of a user, and determining a theoretical click rate and an actual click rate of the user on association candidate items in input method application;
and determining the ratio of the actual point selection rate to the theoretical point selection rate as a point selection willingness value of the user for association candidate items in the input method application.
3. The method of claim 1, wherein determining the user gear of the user based on the click desire value comprises:
and inquiring a user gear corresponding to the selection will value from a preset user gear comparison table, wherein the user gear comparison table records the corresponding relation between the selection will value and the user gear.
4. The method of claim 1, wherein said determining a presentation strategy for said target association candidate based on said user gear comprises:
acquiring a preset threshold corresponding to the user gear;
determining the sum of conditional probabilities of first screen association candidate items positioned at the first screen in the target association candidate items;
and determining a display strategy of the target association candidate item based on the comparison between the sum of the conditional probabilities and the preset threshold value.
5. The method of claim 4, wherein the exposure policy comprises: when the sum of the conditional probabilities is larger than the preset threshold value, displaying the target association candidate item; and when the sum of the conditional probabilities is less than or equal to the preset threshold, not displaying the target association candidate.
6. The method of claim 1, wherein said determining a presentation strategy for said target association candidate based on said user gear comprises:
acquiring a preset threshold corresponding to the user gear;
determining the sum of the word frequencies of the first screen association candidate items positioned on the first screen in the target association candidate items;
and determining a display strategy of the target association candidate item based on the comparison between the sum of the word frequencies and the preset threshold value.
7. The method of claim 6, wherein the presentation policy comprises: when the sum of the word frequencies is larger than the preset threshold value, displaying the target association candidate item; and when the sum of the conditional word frequencies is less than or equal to the preset threshold value, not displaying the target association candidate item.
8. The method of claim 1, wherein after said determining the user gear of the user, the method further comprises:
recording the behavior data of the user in real time;
counting the behavior data recorded in real time, and re-determining the click willingness value of the user for association candidate items in the input method application;
and updating the user gear of the user based on the updated click desire value in response to determining that the click desire value is updated.
9. An input device, the device comprising:
the statistical unit is configured to count historical behavior data of a user and determine a click willingness value of the user for an association candidate item in an input method application, wherein the click willingness value is used for representing click willingness;
a first determination unit configured to determine a user shift position of the user based on the click desire value;
the second determining unit is configured to determine a target association candidate item of the on-screen content when the on-screen content to be associated is detected, and determine a presentation strategy for the target association candidate item based on the user gear.
10. An apparatus for input, comprising a memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for:
counting historical behavior data of a user, and determining a click willingness value of the user for an association candidate item in an input method application, wherein the click willingness value is used for representing a click willingness;
determining a user gear of the user based on the click desire value;
and when the situation that the user inputs information by using the input method application is detected, determining a display strategy of a target association candidate item corresponding to the input information based on the user gear.
11. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-8.
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| CN201910696194.6A CN112306251B (en) | 2019-07-30 | Input method, device and device for inputting |
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| CN201910696194.6A CN112306251B (en) | 2019-07-30 | Input method, device and device for inputting |
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