CN114115550A

CN114115550A - Method and device for processing association candidate

Info

Publication number: CN114115550A
Application number: CN202010881500.6A
Authority: CN
Inventors: 余天照; 崔欣
Original assignee: Beijing Sogou Technology Development Co Ltd
Current assignee: Beijing Sogou Technology Development Co Ltd
Priority date: 2020-08-27
Filing date: 2020-08-27
Publication date: 2022-03-01

Abstract

The embodiment of the invention provides a method and a device for processing association candidates and a device for processing the association candidates. The method specifically comprises the following steps: determining a sorting weight corresponding to the candidate type according to the use data of the current user aiming at the candidate type; and sorting the association candidates according to the sorting weight corresponding to the candidate type to which the association candidates belong. The embodiment of the invention can improve the matching degree between the sequencing result and the input intention of the current user, thereby improving the input efficiency.

Description

Method and device for processing association candidate

Technical Field

The present invention relates to the field of input technologies, and in particular, to a method and an apparatus for processing an association candidate, and an apparatus for processing an association candidate.

Background

For users in languages such as chinese, japanese, korean, etc., it is generally necessary to interact with the terminal through an input method. For example, a user can type an input string through a keyboard, and then the input string is converted into a candidate item of a corresponding language and displayed by an input method according to a preset standard mapping rule, so that the candidate item selected by the user is displayed on a screen.

The association function of the input method is an extended function of an input method program, and the occurrence of the association function reduces the times of active input and key pressing of a user and increases the intelligence of the input method. One implementation of the association function includes: and providing corresponding association candidates according to the last screen-on content. For example, if the last screen-on content is "sad", its corresponding association candidates may include: "too much", "expression of heart broken", "", etc.

Currently, the association candidates of different candidate types are usually sorted in a fixed order. For example, the association candidate of the text candidate type is usually ranked in front of the expression candidate type and the symbol candidate type. Different users have different preferences for the association candidates of different candidate types; therefore, the above uniformly ranks the association candidates of the text candidate type in front of the association candidates of other candidate types, so that the ranking result of the association candidates does not necessarily conform to the input intentions of all users.

Disclosure of Invention

The embodiment of the invention provides a processing method and device of an association candidate and a device for inputting, which can improve the matching degree between a sequencing result and the input intention of a current user, and further can improve the input efficiency.

In order to solve the above problem, an embodiment of the present invention discloses a method for processing a conjecture candidate, including:

determining a sorting weight corresponding to the candidate type according to the use data of the current user aiming at the candidate type;

and sorting the association candidates according to the sorting weight corresponding to the candidate type to which the association candidates belong.

On the other hand, the embodiment of the invention discloses a processing device for associating candidates, comprising:

the ranking weight determining module is used for determining the ranking weight corresponding to the candidate type according to the use data of the current user aiming at the candidate type; and

and the ranking module is used for ranking the association candidates according to the ranking weight corresponding to the candidate type to which the association candidates belong.

In yet another aspect, an embodiment of the present invention discloses an apparatus for processing association candidates, 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 configured to be executed by the one or more processors include instructions for:

In yet another aspect, embodiments of the invention disclose a machine-readable medium having instructions stored thereon, which when executed by one or more processors, cause an apparatus to perform a method of processing a suggested candidate as described in one or more of the preceding.

The embodiment of the invention has the following advantages:

in the embodiment of the present invention, the usage data of the current user for the candidate type may reflect preference information of the current user for the candidate type. According to the embodiment of the invention, the preference information is abstracted into the sorting weight corresponding to the candidate type, and the associated candidates are sorted according to the sorting weight, so that the matching degree between the sorting result and the preference information of the current user can be improved, and further the matching degree between the sorting result and the input intention of the current user can be improved. Under the condition of improving the matching degree between the sequencing result and the input intention of the current user, the embodiment of the invention can improve the selection efficiency of the user for the association candidate, and further can improve the input efficiency.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.

FIG. 1 is an illustration of an environment in which a method of processing a candidate for association according to an embodiment of the invention may be implemented;

FIG. 2 is a flow chart of steps of a method embodiment of a method of processing a candidate of interest of the present invention;

FIG. 3 is a block diagram of an embodiment of a processing device for a candidate for association according to the present invention;

FIG. 4 is a block diagram of an apparatus 800 for processing a suggested candidate in accordance with the present invention; and

fig. 5 is a schematic structural diagram of a server in some embodiments of the invention.

Detailed Description

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Aiming at the technical problem that the ranking result of the association candidate in the traditional technology does not necessarily accord with the input intentions of all users, the embodiment of the invention provides a processing scheme of the association candidate, which can determine the ranking weight corresponding to the candidate type according to the use data of the current user aiming at the candidate type; and ranking the association candidates of the multiple candidate types according to the ranking weights corresponding to the candidate types to which the association candidates belong.

Conventional techniques uniformly rank the associated candidates of the text candidate type ahead of those of other candidate types, and different users have different preferences for the candidate types. For example, the user a likes to express the mood by expression, and thus prefers the expression candidate type. For another example, the user B is used to input a single word, and thus prefers the text candidate type so that the text candidate type can play a supplementary role for the single word, for example, the user B inputs "hurry" through multiple steps, for example, it inputs "hurry" and "hurry" in two steps, respectively, and the "hurry" in this case can play a supplementary role of "hurry".

According to the embodiment of the invention, the preference information of the current user for the candidate types is represented by the ranking weight obtained based on the use data, the preferred candidate types can be weighted according to the ranking weight, and the non-preferred candidate types can be weighted down. Therefore, the ranking position of the association candidate can be adjusted according to the ranking weight, so as to rank the candidate type preferred by the user at a position closer to the front, and further the selection cost of the user for the association candidate (such as the cost of searching for the required association candidate on the candidate page, or the cost of turning the page, etc.) can be reduced, and further the input efficiency can be improved.

Therefore, by applying the embodiment of the invention, under the condition of the same context, different ranking results of the associated candidates can be provided for users with different preferences.

For example, the above is "sad", and the preference provided for the user a may be an associative candidate of an expression candidate type, such as "heart-broken expression

"and the preference provided for user B may be an associative candidate of the text candidate type, such as" hard to cross "or the like.

The embodiment of the invention can be applied to input method programs of various input modes such as keyboard symbols, handwriting, voice and the like, namely, a user can input characters through the coding character string, and the input string can refer to the coding character string input by the user. In the field of input methods, for input method programs in, for example, chinese, japanese, korean, or other languages, an input string input by a user may be generally converted into a candidate for a corresponding language. Hereinafter, the description will be mainly given by taking Chinese as an example, and other languages such as Japanese and Korean may be referred to each other. It is to be understood that the chinese input method may include, but is not limited to, a full pinyin, a simple pinyin, a stroke, a five-stroke, etc., and the embodiment of the present invention is not limited to a specific input method program corresponding to a certain language.

In embodiments of the present invention, the context may include: above, and/or below. Alternatively, the context is typically the portion before the input cursor and the context is typically the portion after the input cursor.

According to an embodiment, the above may comprise: last or most recent times of on-screen content. According to another embodiment, the above may comprise: and under the communication scene, communicating the communication content sent by the opposite end. According to yet another embodiment, the above may comprise: and in the communication scene, transmitting the communication content to the correspondent node. It is to be understood that the embodiments of the present invention are not limited to the specific context.

Taking the input of chinese as an example, the types of the encoding character string may include: pinyin strings, character strings (such as pencils, etc.). Taking english input as an example, the types of the encoding strings may include: alphabetic strings, and the like.

The embodiment of the invention can be applied to an input method program of a voice input mode, and the voice input mode can give out corresponding candidates according to voice input by a user. Specifically, in the embodiment of the present invention, when the input mode of the input method program is a voice input mode, the embodiment of the present invention may provide a corresponding association candidate for the context.

The association candidate processing method provided by the embodiment of the present invention can be applied to the application environment shown in fig. 1, as shown in fig. 1, the client 100 and the server 200 are located in a wired or wireless network, and the client 100 and the server 200 perform data interaction through the wired or wireless network.

Optionally, the client 100 may run on a terminal, which specifically includes but is not limited to: smart phones, tablet computers, electronic book readers, MP3 (Moving Picture Experts Group Audio Layer III) players, MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop portable computers, car-mounted computers, desktop computers, set-top boxes, smart televisions, wearable devices, and the like. The client 100 may correspond to a website, or APP (Application).

In practical applications, for the input mode of keyboard symbols, a user may input the input string through a physical keyboard or a virtual keyboard. For example, for a terminal with a touch screen, a virtual keyboard may be set in the input interface to use input of an input string by triggering virtual keys included in the virtual keyboard. Optionally, examples of the virtual keyboard may include: a 9-key keyboard, a 26-key keyboard, etc. Moreover, it can be understood that, in addition to the virtual keys corresponding to the letters, the input interface may also be provided with symbol keys, numeric keys, and function keys such as a chinese-english switching key, or may also be provided with toolbar keys, and it can be understood that the specific keys included in the input interface are not limited in the embodiments of the present invention.

According to some embodiments, the input string may include, but is not limited to: a key symbol or a combination of a plurality of key symbols input by a user through a key. The key symbol may specifically include: pinyin, strokes, kana, etc.

In an embodiment of the invention, the candidates may be used to represent one or more characters provided by the input method program to be selected by the user. The candidates may correspond to the context, or the candidates may correspond to the input string and the context. The candidates may be characters of languages such as chinese characters, english characters, japanese characters, and the like, and the candidates may also be symbol combinations in the form of characters, pictures, and the like. The color text includes but is not limited to a drawing composed of lines, symbols and words, for example, examples of the color text may include: ": p ",": o ",": etc.

Candidates for embodiments of the present invention may include: a common candidate or a suggested candidate.

According to an embodiment, the input string may be searched in the lexicon to obtain a candidate corresponding to the input string, which may be referred to as a common candidate for short. For example, the word stock may include: the embodiment of the invention provides a system word stock, a user word stock, a cell word stock, a cloud word stock and the like, and the specific acquisition mode of the candidate items is not limited. For example, common candidates corresponding to the input string "nihao" may include: "hello", "your number", "fit", etc.

According to another embodiment, the multivariate relational data can be queried to obtain associative candidates according to context.

The multivariate relational data may include binary and more than binary relational data. A binary relationship, also known as a 2-gram, is used to represent the probability that two elements occur in succession. More than two-dimensional relationships are used to indicate the probability of two or more elements occurring in succession. The elements may include: words, letters, symbols, or expressions, etc.

In an alternative embodiment of the invention, the multivariate relational data can be characterized by a data model. Types of data models may include, but are not limited to: language models, neural network models, and the like. The data model described above may provide P (arbitrary element | context, …), i.e., the probability of an arbitrary element under certain context, etc. From this probability, the hit corresponding to the context can be determined. The corpus used by the data model may include: corpus under the condition of context and the like.

The mathematical model is a scientific or engineering model constructed by using a mathematical logic method and a mathematical language, and is a mathematical structure which is generally or approximately expressed by adopting the mathematical language aiming at the characteristic or quantity dependency relationship of a certain object system, and the mathematical structure is a relational structure which is described by means of mathematical symbols. The mathematical model may be one or a set of algebraic, differential, integral or statistical equations, and combinations thereof, by which the interrelationships or causal relationships between the variables of the system are described quantitatively or qualitatively. In addition to mathematical models described by equations, there are also models described by other mathematical tools, such as algebra, geometry, topology, mathematical logic, etc. Where the mathematical model describes the behavior and characteristics of the system rather than the actual structure of the system. The method can adopt methods such as machine learning and deep learning methods to train the mathematical model, and the machine learning method can comprise the following steps: linear regression, decision trees, random forests, etc., and the deep learning method may include: convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated cyclic units (GRU), and so on.

Optionally, the conditions of the data model may further include: and inputting the environmental characteristics. In this case, the data model may provide P (arbitrary element | context, input environment feature, …).

The input environment characteristics may be used to characterize the environment information in which the terminal is located at the time of the user input. The input environment characteristics can reflect the input intention of the user to a certain extent, so that the relation is established between the input environment characteristics and the input intention of the user, the input intention of the user can be indirectly identified, and the input efficiency of the user is improved.

In practical applications, the input environment features may include various types of features. Optionally, the input environment feature may include: at least one of a temporal environment feature, a location environment feature, a climate environment feature, an application environment feature, and a page environment feature.

Method embodiment one

Referring to fig. 2, a flowchart illustrating a first step of a first embodiment of a method for processing a candidate for association according to the present invention is shown, which may specifically include the following steps:

step 201, determining a ranking weight corresponding to a candidate type according to the use data of a current user for the candidate type;

step 202, ranking the associated candidates of multiple candidate types according to the ranking weights corresponding to the candidate types to which the associated candidates belong.

The method embodiment shown in fig. 2 may be executed by a client and/or a server, and it is understood that the specific execution subject of the method embodiment is not limited by the embodiment of the present invention.

In step 201, the current user may characterize the terminal user, that is, the user currently using the terminal. According to the embodiment of the invention, the user can be identified according to the user identification and/or the equipment identification, and the use data of the user aiming at the candidate type can be collected and stored. In this way, in the process of using the terminal by the current user, the usage data of the current user for the candidate types can be acquired so as to provide personalized services for the current user.

The usage data for the candidate types by the current user may include: in the case that the candidate is an association candidate, the current user uses the data of the candidate type; and/or, in the case that the candidate is a non-associative candidate (e.g., a normal candidate), the current user's usage data for the candidate type.

The usage data for the candidate types by the current user may include: candidate on-screen data for the current user for the candidate type of usage data. For example, the on-screen data may include: the number of times the user has been on the screen for the candidate type, etc., which may be updated as the usage data for the current user is updated. It should be noted that one candidate type may include multiple candidates, and the screen-up times corresponding to the candidate type may be obtained according to the screen-up times corresponding to the multiple candidates in the candidate type in the embodiment of the present invention.

The upper screen refers to an operation of displaying the content output in the preview window to the application window. Specifically to the input method program, the screen-up may refer to an operation of displaying a candidate output in the candidate window to the application window. For example, the association candidates provided by one input include: "hard to cross", "cardioverter expression", "what", "things", etc., if the user selects "hard to cross", the user's association candidates for the text candidate type may be updated, if the user selects "cardioverter expression", the user's association candidates for the expression candidate type may be updated, or if the user selects "", the user's association candidates for the symbol candidate type may be updated.

The usage data for the candidate type for the current user may reflect preference information for the candidate type for the current user. The embodiment of the invention can abstract the preference information into the sorting weight corresponding to the candidate type.

According to one embodiment, the relationship between the ranking weight and the number of times the user has been on-screen for the candidate type may be a forward relationship. Generally, the more the user screens on the candidate types, the higher the ranking weight; otherwise, the fewer the number of times the user has been on the screen for the candidate type, the lower the ranking weight.

In an optional embodiment of the present invention, the determining the ranking weight corresponding to the candidate type may specifically include: and determining the sorting weight corresponding to the candidate type according to the screen-on times of the user aiming at the candidate type and the selection probability of the candidate type.

The relationship between the ranking weight and the selection probability of the candidate type may be a negative relationship. The selection probabilities described above may ensure that in the case where multiple candidate types are provided, the selection probability for a particular candidate type is the majority of users. Generally, the greater the selection probability of the candidate type, the greater the probability that most users like the candidate type, and the preference information of the users for the candidate type is not necessarily reflected.

In the embodiment of the present invention, optionally, the ranking weight may be obtained according to a ratio of the screen-up times to the selection probability, so that the ranking weight objectively reflects preference information of the user for the candidate type.

In the embodiment of the present invention, optionally, the selection probability may be a preset value, where the preset value may be a value between 0 and 1. It will be appreciated that different candidate types may correspond to the same or different preset values.

Alternatively, the selection probability may be obtained according to the number of times of displaying the candidate type and the number of times of selecting the candidate type by the network user.

The number of times of presenting the candidate type may be the number of times of presenting the candidate type to the network user. The network users may be all or part of the users within the network, which may include: a current user and a non-current user.

Assuming that the number of times of displaying the candidate type is N, the number of times of selecting the candidate type by the network user is M, M, N may be a natural number, M may be smaller than N, and the selection probability may be obtained according to a ratio of M to N. It will be appreciated that M, N may be updated with updates to user data, and thus, the selection probabilities may also be updated with updates to the network user's input behavior data.

It should be noted that one candidate type may include multiple candidates, and the embodiment of the present invention may obtain the selection probability corresponding to the candidate type according to the selection probability corresponding to each of the multiple candidates in the candidate type. For example, the selection probabilities corresponding to a plurality of candidates under a candidate type may be averaged or weighted to obtain the selection probability corresponding to the candidate type.

In step 201, usage data within a preset time period may be determined, and according to the usage data, a ranking weight corresponding to the candidate type may be determined. The usage data may be updated with an update of the preset time period, and the ranking weight may be updated. The preset time period can be determined by those skilled in the art according to actual situations, for example, the preset time period is the last month, the last three months, the last half year, and the like.

In step 202, the association candidates are ranked according to the ranking weights, so that the matching degree between the ranking result and the preference information of the current user can be improved, and further the matching degree between the ranking result and the input intention of the current user can be improved. Under the condition of improving the matching degree between the sequencing result and the input intention of the current user, the embodiment of the invention can improve the selection efficiency of the user for the association candidate, and further can improve the input efficiency.

The association candidate may correspond to a plurality of candidate types, where the candidate types may include: at least two of a text candidate type, an expression candidate type, and a symbol candidate type. Wherein the expression candidate types may include: emoji (Emoji) expression, facial characters, emoticons, fighting drawings, and the like. The symbol candidate types may include: punctuation, graphical symbols, and the like.

Conventional techniques typically rank association candidates of different candidate types according to their candidate type priorities. The above priorities are typically: the priority of the text candidate type > the priority of the expression candidate type > the priority of the symbol candidate type, and the like. Therefore, it is common to rank the association candidates of the text candidate type at the top position, rank the association candidates of the expression candidate type and the symbol candidate type at the two-choice position and the three-choice position in order, and then rank the association candidates of different candidate types at positions subsequent to the three-choice position in order.

The embodiment of the invention can perform mixed ranking on the association candidates of various candidate types according to the ranking weight corresponding to the candidate type to which the association candidate belongs. Since the ranking weight can represent the preference information of the current user for the candidate type, the matching degree between the ranking result and the preference information of the current user can be improved.

In an optional embodiment of the present invention, the sorting the association candidates of multiple candidate types may specifically include: adjusting the score of the association candidate according to the ranking weight corresponding to the candidate type to which the association candidate belongs to obtain the adjusted score of the association candidate; and sorting the association candidates of multiple candidate types according to the adjustment scores of the association candidates.

According to the embodiment of the invention, the preference information of the current user for the candidate types is represented by the ranking weight obtained based on the use data, the preferred candidate types can be weighted according to the ranking weight, and the non-preferred candidate types can be weighted down. Therefore, the ranking position of the association candidate can be adjusted according to the ranking weight, so as to rank the candidate type preferred by the user at a position closer to the front, and further the selection cost (such as the cost of searching the required association candidate on the candidate page, the cost of the screen-up operation, or the cost of page turning) of the user for the association candidate can be reduced, and further the input efficiency can be improved.

According to one embodiment, the candidate types may include: the score may be obtained according to a selection probability of the associated candidate. The selection probability of the association candidate may be obtained according to the number of times N of presentation of the candidate type and the number of times M of selection of the candidate type by the network user.

According to another embodiment, the candidate types may include: and the score is obtained according to the occurrence probability of the association candidate under the condition of the context. For example, the probability of occurrence may be determined based on the multivariate relationship data.

Of course, for any candidate type, the score of the associated candidate can be determined by comprehensively using the selection probability and the occurrence probability according to the selection probability of the associated candidate and the occurrence probability of the associated candidate under the context condition, so that the accuracy of the score can be improved.

The embodiment of the invention can adopt a linear method to adjust the scores of the association candidates. The adjustment score may be derived, for example, based on the product of the score and the ranking weight.

Of course, a non-linear method may be used to adjust the score of the associated candidate. For example, the adjustment score may be obtained by a product or a sum between the score and the power i of the ranking weight, where i may be a positive integer.

After the sequencing result is obtained, the sequencing result can be displayed to the user, so that the user can display the required sequencing result on a screen.

In summary, in the method for processing the association candidate according to the embodiment of the present invention, the usage data of the current user for the candidate type may reflect the preference information of the current user for the candidate type. According to the embodiment of the invention, the preference information is abstracted into the sorting weight corresponding to the candidate type, and the associated candidates are sorted according to the sorting weight, so that the matching degree between the sorting result and the preference information of the current user can be improved, and further the matching degree between the sorting result and the input intention of the current user can be improved. Under the condition of improving the matching degree between the sequencing result and the input intention of the current user, the embodiment of the invention can improve the selection efficiency of the user for the association candidate, and further can improve the input efficiency.

In addition, the preference information of the current user for the candidate types is represented by the ranking weight obtained based on the use data, the preferred candidate types can be weighted according to the ranking weight, and the non-preferred candidate types can be weighted down. Therefore, the ranking position of the association candidate can be adjusted according to the ranking weight, so as to rank the candidate type preferred by the user at a position closer to the front, and further the selection cost of the user for the association candidate (such as the cost of searching for the required association candidate on the candidate page, or the cost of turning the page, etc.) can be reduced, and further the input efficiency can be improved.

In order to make the embodiment of the present invention better understood, the processing method of the associative candidates of the embodiment of the present invention is described herein by specific examples.

This example takes the usage data of user a as an example. In the process of inputting by the user A, after the user A inputs 'haha', the input method gives the following association candidates: "haha", "smiling face expression", "" not there is something, and "good bar", etc. Assuming that the user a selects the "smiling face expression" on-screen, the number of on-screen times corresponding to the expression candidate type may be updated, for example, by adding 1 to the number of on-screen times corresponding to the expression candidate type.

According to the processing mode, the screen-on times b of the user A aiming at the expression candidate types can be determined. Specifically, the screen-up times b corresponding to the expression candidate types may be obtained according to the screen-up times respectively corresponding to the multiple candidates under the expression candidate types.

The embodiment of the invention can also determine the selection probability a of the expression candidate type according to the use data of the network user aiming at the candidate of the expression candidate type. The selection probability corresponding to the candidate type can be obtained according to the selection probability corresponding to each of the plurality of candidates under the expression candidate type. For example, the selection probabilities respectively corresponding to a plurality of candidates under the expression candidate type may be averaged or weighted to obtain the selection probability a corresponding to the expression candidate type.

According to the embodiment of the invention, the ranking weight corresponding to the expression candidate type of the user A can be determined according to the screen-on times b corresponding to the expression candidate type and the selection probability a corresponding to the expression candidate type.

During a certain input process of the user a, assuming that the user a inputs "hurry", the association candidates provided by the input method may include: "difficult to pass", "heart broken expression", etc. Since the user a selects the expression candidate type multiple times within the preset time period, so that the ranking weight c corresponding to the expression candidate type is 3.2, assuming that the score of the "heart-broken expression" is 0.17, the adjustment score of the "heart-broken expression" is: 0.17 × 3.2 — 0.544, and the adjustment of the score may advance the ranking position of the expression candidate type preferred by the user.

Further, in the process of performing mixed ranking on the association candidates of the multiple candidate types according to the adjustment score, it is assumed that the adjustment score of the "cardioid expression" is greater than the adjustment score of the "refractory", so that the ranking position of the "cardioid expression" can be determined as a preferred position, so that the user a can conveniently screen the "cardioid expression". Since the selection cost of the user a for the expression association candidates (for example, the cost of finding a desired association candidate on a candidate page, the cost of a screen-up operation, or the cost of page turning) can be reduced, the input efficiency can be improved.

In the embodiment of the present invention, the ranking position of the expression association candidates preferred by the user a is adjusted to the preferred position, so that the cost of the screen-up operation can be greatly reduced.

It should be noted that, for simplicity of description, the method embodiments are described as a series of movement combinations, but those skilled in the art should understand that the present invention is not limited by the described movement sequence, because some steps can be performed in other sequences or simultaneously according to the present invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no moving act is required as an embodiment of the invention.

Device embodiment

Referring to fig. 3, a block diagram of an embodiment of a candidate association processing apparatus according to the present invention is shown, which may specifically include:

a ranking weight determining module 301, configured to determine, according to usage data of a current user for a candidate type, a ranking weight corresponding to the candidate type; and

the ranking module 302 is configured to rank the associated candidates according to ranking weights corresponding to the candidate types to which the associated candidates belong.

Optionally, the ranking weight determining module 301 is specifically configured to determine the ranking weight corresponding to the candidate type according to the screen-up times of the user for the candidate type and the selection probability of the candidate type.

Optionally, the selection probability is a preset value; or

The selection probability is obtained according to the display times of the candidate types and the selection times of the network user aiming at the candidate types.

Optionally, the sorting module 302 may include:

the adjusting module is used for adjusting the scores of the association candidates according to the ranking weights corresponding to the candidate types to which the association candidates belong so as to obtain the adjusted scores of the association candidates;

and the adjusted sorting module is used for sorting the association candidates according to the adjustment scores of the association candidates.

Optionally, the candidate types may include: and a non-text candidate type, wherein the score is obtained according to the selection probability of the association candidate.

Optionally, the candidate types may include: and the score is obtained according to the occurrence probability of the association candidate under the condition of the context.

Optionally, the candidate types may include: a text candidate type, an expression candidate type, and a symbol candidate type.

For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.

The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.

With regard to the apparatus in the above-described embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated here.

An embodiment of the present invention provides an apparatus for processing association candidates, 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 configured to be executed by the one or more processors include instructions for: determining a sorting weight corresponding to the candidate type according to the use data of the current user aiming at the candidate type; and sorting the association candidates according to the sorting weight corresponding to the candidate type to which the association candidates belong.

Fig. 4 is a block diagram illustrating an apparatus 800 for processing a suggested candidate according to an example embodiment. For example, the apparatus 800 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 800 may include one or more of the following components: processing component 802, memory 804, power component 806, multimedia component 808, audio component 810, input/output (I/O) interface 812, sensor component 814, and communication component 816.

The processing component 802 generally controls overall operation of the device 800, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations. The processing elements 802 may include one or more processors 820 to execute instructions to perform all or a portion of the steps of the methods described above. Further, the processing component 802 can include one or more modules that facilitate interaction between the processing component 802 and other components. For example, the processing component 802 can include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.

The memory 804 is configured to store various types of data to support operation at the device 800. Examples of such data include instructions for any application or method operating on device 800, contact data, phonebook data, messages, pictures, videos, and so forth. The memory 804 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.

Power components 806 provide power to the various components of device 800. The power components 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the apparatus 800.

The multimedia component 808 includes a screen that provides an output interface between the device 800 and a user as described above. 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 808 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 800 is in an operating 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 810 is configured to output and/or input audio signals. For example, the audio component 810 includes a Microphone (MIC) configured to receive external audio signals when the apparatus 800 is in an operational mode, such as a call mode, a recording mode, and a voice input mode. The received audio signals may further be stored in the memory 804 or transmitted via the communication component 816. In some embodiments, audio component 810 also includes a speaker for outputting audio signals.

The I/O interface 812 provides an interface between the processing component 802 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 assembly 814 includes one or more sensors for providing various aspects of state assessment for the device 800. For example, the sensor assembly 814 may detect the open/closed state of the device 800, the relative positioning of components, such as a display and keypad of the apparatus 800, the sensor assembly 814 may also detect a change in position of the apparatus 800 or a component of the apparatus 800, the presence or absence of user contact with the apparatus 800, orientation or acceleration/deceleration of the apparatus 800, and a change in temperature of the apparatus 800. Sensor assembly 814 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 814 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 814 may also include an acceleration sensor, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

The communication component 816 is configured to facilitate communications between the apparatus 800 and other devices in a wired or wireless manner. The device 800 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 816 receives a broadcast signal or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the above-mentioned communication component 816 further comprises a Near Field Communication (NFC) module to facilitate short-range communication. 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 800 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 804 comprising instructions, executable by the processor 820 of the device 800 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 invention. The server 1900 may vary widely by configuration or performance and may include one or more Central Processing Units (CPUs) 1922 (e.g., one or more processors) and memory 1932, one or more storage media 1930 (e.g., one or more mass storage devices) storing applications 1942 or data 1944. Memory 1932 and storage medium 1930 can be, among other things, transient or persistent storage. The program stored in the storage medium 1930 may include one or more modules (not shown), each of which may include a series of instructions operating on a server. Still further, a central processor 1922 may be provided in communication with the storage medium 1930 to execute a series of instruction operations in the storage medium 1930 on the server 1900.

The server 1900 may also include one or more power supplies 1926, one or more wired or wireless network interfaces 1950, one or more input-output interfaces 1958, one or more keyboards 1956, and/or one or more operating systems 1941, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, etc.

A non-transitory computer readable storage medium, in which instructions are executed by a processor of an apparatus (server or terminal), so that the apparatus can perform the method of processing the association candidate shown in fig. 2 or 3.

A non-transitory computer readable storage medium having instructions therein which, when executed by a processor of an apparatus (server or terminal), enable the apparatus to perform a method of processing a conjoint candidate, the method comprising: determining a sorting weight corresponding to the candidate type according to the use data of the current user aiming at the candidate type; and sorting the association candidates according to the sorting weight corresponding to the candidate type to which the association candidates belong.

The embodiment of the invention discloses a1 and a method for processing a conjoint candidate, wherein the method comprises the following steps:

A2, according to the method in A1, the determining the ranking weight corresponding to the candidate type includes:

and determining the sorting weight corresponding to the candidate type according to the screen-on times of the user for the candidate type and the selection probability of the candidate type.

A3, according to the method A2, the selection probability is a preset value; or

The selection probability is obtained according to the display times of the candidate types and the selection times of the network users aiming at the candidate types.

A4, the ranking the associative candidates according to the method of any one of A1 to A3, comprising:

adjusting the score of the association candidate according to the ranking weight corresponding to the candidate type to which the association candidate belongs to obtain the adjusted score of the association candidate;

and sorting the association candidates according to the adjustment scores of the association candidates.

A5, the method of A4, the candidate types comprising: a non-text candidate type, the score being obtained according to a selection probability of the associated candidate.

A6, the method of A4, the candidate types comprising: a text candidate type, wherein the score is obtained according to the occurrence probability of the association candidate under the condition of context.

A7, the method of any one of A1 to A3, the candidate types comprising: a text candidate type, an expression candidate type, and a symbol candidate type.

The embodiment of the invention discloses B8 and a processing device for associating candidates, which comprises:

B9, the device according to B8, the ranking weight determining module is specifically configured to determine the ranking weight corresponding to the candidate type according to the screen-up times of the user for the candidate type and the selection probability of the candidate type.

B10, according to the device of B9, the selection probability is a preset value; or

B11, the apparatus of any of B8-B10, the ranking module comprising:

B12, the apparatus of B11, the candidate types comprising: a non-text candidate type, the score being obtained according to a selection probability of the associated candidate.

B13, the apparatus of B11, the candidate types comprising: a text candidate type, wherein the score is obtained according to the occurrence probability of the association candidate under the condition of context.

B14, the apparatus according to any one of B8-B10, the candidate types comprising: a text candidate type, an expression candidate type, and a symbol candidate type.

The embodiment of the invention discloses C15, an apparatus for processing association candidates, 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 configured to be executed by the one or more processors comprise instructions for:

C16, the determining the ranking weight corresponding to the candidate type according to the apparatus of C15, comprising:

C17, according to the device of C16, the selection probability is a preset value; or

C18, the ranking the associative candidates according to any of C15 to C17, comprising:

C19, the apparatus of C18, the candidate types comprising: a non-text candidate type, the score being obtained according to a selection probability of the associated candidate.

C20, the apparatus of C18, the candidate types comprising: a text candidate type, wherein the score is obtained according to the occurrence probability of the association candidate under the condition of context.

C21, the apparatus according to any of C15 to C17, the candidate types comprising: a text candidate type, an expression candidate type, and a symbol candidate type.

Embodiments of the present invention disclose D22, a machine-readable medium having instructions stored thereon, which when executed by one or more processors, cause an apparatus to perform a method of processing a suggested candidate as described in one or more of a 1-a 7.

Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the invention and including such departures from the present disclosure as come within known or customary practice within 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 invention being indicated by the following claims.

It will be understood that the invention 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 invention is limited only by the appended claims.

The above description is only exemplary of the present invention and should not be taken as limiting the invention, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

The method for processing the association candidate, the apparatus for processing the association candidate, and the apparatus for processing the association candidate provided by the present invention have been described in detail above, and specific examples are applied herein to illustrate the principles and embodiments of the present invention, and the above description of the embodiments is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, 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 invention.

Claims

1. A method for processing a conjoint candidate, the method comprising:

2. The method of claim 1, wherein determining the ranking weight corresponding to the candidate type comprises:

3. The method of claim 2, wherein the selection probability is a preset value; or

4. The method according to any one of claims 1 to 3, wherein said ranking said associative candidates comprises:

5. The method of claim 4, wherein the candidate types comprise: a non-text candidate type, the score being obtained according to a selection probability of the associated candidate.

6. The method of claim 4, wherein the candidate types comprise: a text candidate type, wherein the score is obtained according to the occurrence probability of the association candidate under the condition of context.

7. The method of any of claims 1-3, wherein the candidate types comprise: a text candidate type, an expression candidate type, and a symbol candidate type.

8. A device for processing a candidate for association, comprising:

9. An apparatus for processing associative candidates, comprising a memory, and one or more programs, wherein the one or more programs are stored in the memory, and wherein execution of the one or more programs by one or more processors comprises instructions for:

10. A machine-readable medium having stored thereon instructions, which when executed by one or more processors, cause an apparatus to perform a method of processing associative candidates according to one or more of claims 1 to 7.