CN111967518A - Application labeling method, application labeling device and terminal equipment - Google Patents
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Abstract
The application labeling method provided by the application comprises the following steps: acquiring application sequences respectively corresponding to a plurality of users, wherein each application sequence comprises text information used for describing a first application related to the corresponding user; inputting each application sequence into a trained natural language processing model to obtain an output result of the trained natural language processing model based on each application sequence, wherein the output result comprises a feature vector corresponding to each first application; determining similarity between a basic application and other applications according to the feature vectors, wherein the basic application is a first application corresponding to a preset label, and the other applications are applications except the basic application in the first application; and determining application labels respectively corresponding to other applications according to the similarity and the preset labels of the basic applications. By the method, the accuracy of labeling the application can be improved.
Description
Technical Field
The present application belongs to the field of application technologies, and in particular, relates to an application labeling method, an application labeling apparatus, a terminal device, and a computer-readable storage medium.
Background
During the process of using the terminal device, the terminal device needs to continuously refresh the screen to provide dynamic display effect for the user.
Labeling applications plays an important role in many internet application scenarios. For example, in an application store, the application is accurately labeled, which is beneficial to improving the efficiency of searching the application by the user, and in addition, the associated application can be accurately recommended to the user according to the application label, so that the use experience of the user is improved.
Currently, common methods for acquiring application labels include manually labeling, extracting keywords from description information of an application to label the application, and the like. However, the method for labeling manually has low efficiency and strong subjectivity, and is difficult to ensure the accuracy of the applied labeling. The method for extracting keywords from the description information of the application to label the application depends on the description information of the application, but the description information is not necessarily accurate, and in addition, the description information may be too simple, so that it is difficult to extract a more accurate application label. Therefore, the accuracy of the current method for labeling the application is poor.
Disclosure of Invention
The embodiment of the application labeling method and device, the terminal device and the computer readable storage medium can improve the accuracy of labeling the application.
In a first aspect, an embodiment of the present application provides an application labeling method, including:
acquiring application sequences respectively corresponding to a plurality of users, wherein each application sequence comprises text information used for describing a first application related to the corresponding user;
inputting each application sequence into a trained natural language processing model to obtain an output result of the trained natural language processing model based on each application sequence, wherein the output result comprises a feature vector corresponding to each first application;
determining similarity between a basic application and other applications according to the feature vectors, wherein the basic application is a first application corresponding to a preset label, and the other applications are applications except the basic application in the first application;
and determining application labels respectively corresponding to other applications according to the similarity and the preset labels of the basic applications.
In a second aspect, an embodiment of the present application provides an application labeling apparatus, including:
the device comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring application sequences respectively corresponding to a plurality of users, and each application sequence comprises text information used for describing a first application related to the corresponding user;
the processing module is used for inputting each application sequence into a trained natural language processing model to obtain an output result of the trained natural language processing model based on each application sequence, wherein the output result comprises a feature vector corresponding to each first application;
a first determining module, configured to determine, according to the feature vectors, similarities between basic applications and other applications, where the basic applications are first applications corresponding to preset tags, and the other applications are applications, except the basic applications, in the first applications;
and the second determining module is used for determining the application label corresponding to each other application according to the similarity and the preset label of the basic application.
In a third aspect, an embodiment of the present application provides a terminal device, which includes a memory, a processor, a display, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the application labeling method according to the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where a computer program is stored, and the computer program, when executed by a processor, implements the application labeling method as described in the first aspect.
In a fifth aspect, an embodiment of the present application provides a computer program product, which, when running on a terminal device, causes the terminal device to execute the application labeling method described in the first aspect.
Compared with the prior art, the embodiment of the application has the advantages that: in the embodiment of the application, application sequences respectively corresponding to a plurality of users can be acquired, and each application sequence includes text information for describing a first application related to the corresponding user, so that the text information describing the associated application of each user can be acquired, each application sequence is input into a trained natural language processing model, an output result of the trained natural language processing model based on each application sequence is acquired, and feature vectors of each first application can be accurately and efficiently extracted through the trained natural language processing model; then, according to the feature vectors corresponding to the first applications, the similarity between the basic applications and other applications is determined, so that the application labels corresponding to the other applications can be determined according to the similarity and the preset labels of the basic applications. At the moment, the similarity between each basic application and other applications can be evaluated according to the feature vectors extracted accurately and efficiently, so that the labels of the similar other applications are determined accurately by combining the preset labels obtained by pre-labeling the basic applications, and the accuracy of labeling the applications is improved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flowchart of an application labeling method according to an embodiment of the present application;
fig. 2 is a schematic flowchart of another application labeling method according to an embodiment of the present application;
FIG. 3 is a schematic structural diagram of an application labeling apparatus according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a terminal device according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
The application labeling method provided by the embodiment of the application can be applied to terminal devices such as a server, a desktop computer, a mobile phone, a tablet computer, a wearable device, a vehicle-mounted device, an Augmented Reality (AR)/Virtual Reality (VR) device, a notebook computer, a super-mobile personal computer (UMPC), a netbook, and a Personal Digital Assistant (PDA), and the embodiment of the application does not limit the specific types of the terminal devices at all.
Specifically, fig. 1 shows a flowchart of an application labeling method provided in an embodiment of the present application, where the application labeling method can be applied to a terminal device.
As shown in fig. 1, the application labeling method may include:
step S101, obtaining application sequences respectively corresponding to a plurality of users, wherein each application sequence comprises text information used for describing a first application related to the corresponding user.
In the embodiment of the application, for each user, the first application related to the user may be an application used by the user within a specified time period and/or an installed application, and the like.
For example, the application sequence may include a name of the corresponding user and a name of the corresponding first application, and the application sequence may further include at least one of attribute information, application description information, and the like of the corresponding first application. The specific form of the application sequence is not limited herein.
In some embodiments, the obtaining the application sequences respectively corresponding to the plurality of users may include:
for each user, sequencing the users according to first applications used in sequence within a specified time period;
and generating an application sequence corresponding to the user according to the sequencing result.
For example, user a uses application a, application B, application C, application D, and application E in sequence during the day, and the application sequence corresponding to user a is as follows:
the user A: application A, application B, application C, application D, and application E.
Of course, in some examples, in the application sequence corresponding to the user a, the same first application may appear multiple times according to the use of the user.
According to the application sequence obtaining mode, the application sequences respectively corresponding to a plurality of users can be obtained as follows:
the user A: application a, application B, application C, application D, application E.
And a user B: application B, application C, application D, application F, application G.
And a user C: ...
At this time, the application sequence may include usage information of the application and the user for the application at different times within a specified time period (e.g., within a day), and also include some intrinsic association features between the applications. For example, for shopping applications, a user often needs to call a payment application to pay next when shopping using the shopping application. Therefore, the application sequence may include attribute characteristics of each first application and association information between each first application. So that the feature vector of each first application can be obtained from the application sequence by a subsequent natural language processing model.
Moreover, the acquisition mode of the application sequence in the embodiment of the application can quickly and efficiently acquire a large number of application sequences corresponding to users, the data processing efficiency is high, the form of the application sequence is clear, and the subsequent natural language processing model can be conveniently processed.
Step S102, inputting each application sequence into a trained natural language processing model, and obtaining an output result of the trained natural language processing model based on each application sequence, wherein the output result comprises a feature vector corresponding to each first application.
In this embodiment of the application, the natural language processing model may be configured to obtain a feature vector of each first application from a word, and corresponding context information in the application sequence. Illustratively, the natural language processing model may be a neural probabilistic language model, a g-gram model, a word2vec model, or the like.
The natural language processing model may be trained according to a preset data set. In one example, the preset data set may include a preset application and a truth label for the preset application. In another example, for example, if the natural language processing model is a Continuous Bag-of-words (cbow) model in the word2vec model, the natural language processing model may be obtained by training based on each of the application sequences, and after the training is completed, an output result of the trained natural language processing model based on each of the application sequences is obtained.
Taking a Continuous Bag-of-words (cbow) model in the word2vec model as an example, a principle of processing in the natural language processing model after each application sequence is input into the natural language processing model will be described.
In the CBOW model, conditional probabilities may be used to model to predict the first application in the application sequence. The target of model modeling is as formula 001:
P(wt|wt-c:wt+c)
wherein, wtFor the first application of prediction, wt-cInformation of the first c first applications being first applications in a corresponding sequence of applications, wt+cInformation of the last c first applications of the first application in the corresponding application sequence.
In a given application sequence w1,w2,w3...wtThe objective function of the CBOW model is a log-likelihood function that maximizes the formula 001, such as formula 002:
where T is the length of the corresponding application sequence, wtFor the first application of prediction, wt-cInformation of the first c first applications being first applications in a corresponding sequence of applications, wt+cInformation of the last c first applications of the first application in the corresponding application sequence.
And calculating the softmax function through a formula 003 according to the conditional probability, thereby constructing and obtaining the CBOW model based on the softmax function.
Wherein, the formula 003 is as follows:
wherein
In the embodiment of the application, when the natural language processing model is trained based on each application sequence, whether the natural language processing model is trained is determined according to a loss function and/or a preset iteration number. For example, it may be determined that the training of the natural language processing model is completed after the iteration number of the natural language processing model reaches a preset iteration number; furthermore, the determination may be based on a loss function of the natural language processing model. The loss function of the natural language processing model can adopt the existing loss function or the loss function generated in the future, and the selection of the loss function can be determined according to the actual requirement.
In the embodiment of the present application, the specific form of the feature vector may be various, and for example, the feature vector may be represented by a matrix, a vector, or the like. In some applications, the feature vector may be a Word embedding (Word embedding). The word vector may be referred to as word embedding. At this time, the names of the respective first applications in the input application sequence may be converted into the form of word vectors by the natural language processing model. The dimensions of the word vector may be set by a developer based on test results, etc. For example, the word vector corresponding to each first application may be an 8-dimensional vector.
Step S103, according to the feature vectors, determining similarity between a basic application and other applications respectively, wherein the basic application is a first application corresponding to a preset label, and the other applications are applications except the basic application in the first application.
In this embodiment of the application, the corresponding feature of the first application may be represented by each numerical value in the feature vector. For example, taking a Continuous Bag-of-words (CBOW) model in the word2vec model as an example, each numerical value in the feature vector output by the CBOW model for a certain first application may respectively represent a classification probability of each node of the first application in the Huffman tree of the CBOW model. The number of nodes may be the dimension of the feature vector.
The determination of the base application can be made in a variety of ways. For example, the base application may be determined from each first application according to information such as operation behavior (e.g., installation, uninstallation, usage, etc.) of the user on each first application, release time of the first application, and the like. It should be noted that the number of the basic applications may be determined according to an actual scenario, and is not limited herein. For example, the number of the base applications may be determined according to the number of the first applications, or may be predetermined by a developer. In some application scenarios, if there are one hundred thousand of the first applications, then there may be 1000 of the base applications. The preset tag of the basic application may be obtained in various ways. For example, it can be obtained by manual labeling; alternatively, the basic application may be obtained by extracting keywords from the description information of the basic application.
In some embodiments, the feature vector is an N-dimensional numeric vector, N being an integer greater than 1;
determining similarity between the basic application and other applications respectively according to the feature vectors includes:
calculating the inner product of the feature vector of the basic application and the feature vector of the other application aiming at any basic application and any other application;
and determining the similarity between the basic application and the other applications according to the inner product.
In this embodiment, the feature vector may be an N-dimensional digital vector, where the digital vector of each dimension may represent probability information of a corresponding first application in a certain feature dimension, and therefore, a similarity between the base application and the other applications may be determined by calculating an inner product of the feature vector of the base application and the feature vectors of the other applications.
For example, if the feature vector of application a is a ═ a1,a2,a3,...an]And the feature vector of application B is B ═ B1,b2,b3,...bn]Then the inner product of the feature vector of the base application and the feature vector of the other application is:
S＝a·b＝a1b1+a2b2+a3b3+...+anbn
in some examples, the inner product may be used as the similarity between the base application and the other applications, and of course, the similarity between the base application and the other applications may also be combined with the inner product and other comparison information between the base application and the other applications (e.g., comparison information between corresponding keywords, etc.).
In some embodiments, said determining a similarity between said base application and said other application from said inner product comprises:
taking the inner product as a first similarity between the base application and the other application;
determining a second similarity between the base application and the other applications according to the description information of the base application and the description information of the other applications;
determining a similarity between the base application and the other applications according to the first similarity, the second similarity, a third weight of the first similarity and a fourth weight of the second similarity.
In the embodiment of the present application, according to the description information of the base application and the description information of the other applications, the inherent attribute information of the base application and the other applications may be acquired, so that the second similarity between the base application and the other applications is determined according to the inherent attribute of the base application and the other applications.
For example, the keywords corresponding to the base application may be extracted from the description information of the base application, and the keywords corresponding to the other applications may be extracted from the description information of the other applications, so as to determine the second similarity between the base application and the other applications according to the keywords corresponding to the base application and the keywords corresponding to the other applications.
For example, for application a, the description information of application a is:
1. the voice, the text message, the expression, the picture and the video can be sent and received, thousands of voices can be sent and received through a small amount of flow, and the electricity and the flow are saved; 2. a circle of friends, sharing the life infusion with friends; 3. shaking and checking nearby people, and no strangers exist in the world; 4. scanning, namely scanning commodity bar codes, book covers and CD covers, and even scanning English words to translate the English words into Chinese; 5. public accounts, which pay attention to stars, watch news and set reminders; 6. a game center for playing games with friends; 7. the expression store has interesting and amusing expressions.
The keywords from which the application a can be extracted include: voice, message, tag, picture, video, friend, etc.
After obtaining the keywords corresponding to the basic application and the keywords corresponding to the other applications, for example, the keywords corresponding to the basic application and the keywords corresponding to the other applications may be compared to determine a second similarity between the basic application and the other applications according to a ratio of the keywords corresponding to the basic application and the keywords corresponding to the other applications that are matched with each other.
Alternatively, the second similarity between the base application and the other application may be calculated by Cosine similarity (Cosine similarity). For example, the n-dimensional sample point a (x11, x12, …, x1n) of the basic application may be constructed according to the keyword corresponding to the basic application, and the n-dimensional sample point b (x21, x22, …, x2n) of the other application may be constructed according to the keyword corresponding to the other application, so that the second similarity may be calculated according to the calculation formula of cosine similarity, based on the a (x11, x12, …, x1n) and b (x21, x22, …, x2 n).
The cosine similarity is calculated by the following formula:
then, a similarity between the base application and the other application may be determined according to the first similarity, the second similarity, a third weight of the first similarity, and a fourth weight of the second similarity. For example, the third weight may be 0.7, and the second weight may be 0.3.
In the embodiment of the application, after the inner product is obtained, the similarity between the basic application and the other applications can be jointly judged by combining the description information of the basic application and the description information of the other applications, so that the similarity between the basic application and the other applications is more comprehensively evaluated by considering the similarity of the inherent attributes of the applications through the description information in addition to the use behavior of the application by the user, the judgment dimensionality is improved, and the accuracy of the similarity is improved.
In some embodiments, before step S103, the method further includes:
step S201, determining a basic application according to application operation data respectively corresponding to a plurality of users;
step S202, aiming at each basic application, at least two groups of initial label sets of the basic application are obtained, wherein each group of initial label set comprises at least one initial label;
step S203, using the same initial label among the initial label sets as a preset label of the basic application.
In this embodiment of the application, the application operation data may include data of operations such as installation, use, and uninstallation of the corresponding first application by the user. According to the application operation data respectively corresponding to the plurality of users, a relatively representative application can be determined from the first applications to serve as a basic application. For example, it may be determined that the Y first applications with the largest accumulated installation number are the basic applications in a certain period of time, or it may be determined that the Y first applications with the highest user usage frequency are the basic applications in a certain period of time, and so on.
After determining the base application, at least two sets of initial tag sets for the base application may be obtained. The specific obtaining mode of the initial label set may be multiple, and each initial label set may be generated by different manual annotators and/or different annotating modes. For example, the initial label sets obtained by manually labeling a certain basic application by a plurality of manual labelers may be obtained, in addition, a keyword may be extracted from the description information of the basic application to serve as a group of initial label sets, and then the same initial label among the groups of initial label sets is used as the preset label of the basic application.
By the method and the device, the multiple groups of initial label sets of the same basic application can be acquired, so that the same basic application can be cross-labeled, and the same initial labels are extracted to serve as the preset labels of the basic application. At the moment, due to the fact that information of a plurality of initial labels of the same basic application is combined, the labeling accuracy of the basic application can be greatly improved, and accurate labeling of the basic application is achieved.
In some embodiments, for application operation data of each user, the application operation data of the user includes a second application corresponding to the user, and a usage duration and a usage number of each used second application by the user in the preset time period, where the second application is a first application used by the user in the preset time period;
the determining the basic application according to the application operation data respectively corresponding to the plurality of users includes:
for each second application, determining the heat score of the second application according to the use duration and the use times of each user to the second application in the preset time period;
and determining a basic application from each second application according to the heat score of each second application.
In the embodiment of the application, the popularity score may indicate the user usage liveness of the corresponding second application. The method for determining the heat score of the second application may be multiple ways according to the usage duration and the usage times of the second application within the preset time period by the user using the second application, for example, the usage duration and the usage times may be respectively processed through a normalized dimension change way, so as to add or multiply the normalized usage duration and the normalized usage times. In addition, the weights of the use duration and the use times can be set respectively to perform weighting calculation, so that the heat score of the second application is obtained.
According to the heat scores, the user use activity of each second application can be quantitatively evaluated, so that a basic application can be determined from each second application.
For example, a second application with a hot score greater than a preset score may be used as the base application, or R applications with the highest hot score (e.g., 1000 applications with the highest hot score) may be used as the base application.
In some embodiments, for each second application, determining the popularity score of the second application according to the usage duration and the usage number of the second application by the respective user in the preset time period includes:
for each second application, calculating a heat score for the second application according to a first formula, wherein the first formula is:
wherein S ishIs the heat score of a second application h, n is the number of users using the second application h in the preset time period, FiThe number of times of using the second application h by the user i in the preset time period TiThe using duration of the second application h in the preset time period for the user i, FmaxThe maximum number of times of use, T, corresponding to the second application in the preset time periodmaxIs the longest usage number, w, corresponding to the second application in the preset time periodfIs a first weight, wtIs the second weight.
In this embodiment of the application, the user i is the ith user of the n users using the second application h within the preset time period. Fi and Ti can be respectively normalized through Fmax and Tmax, so that the characteristics of two different dimensions can be subjected to weighted operation, and the hot score can be obtained.
Said wfAnd wtThe specific value of (a) can be determined according to a specific scenario. Illustratively, in some examples, the number of uses may be considered more important than the length of use, and then, wfMay be greater than wtE.g. wfCan be 0.65，wtMay be 0.35.
And step S104, determining application labels respectively corresponding to other applications according to the similarity and the preset labels of the basic applications.
In the embodiment of the application, since the preset tag of the basic application is obtained in advance, whether the type of the basic application is similar to that of other applications can be judged according to the similarity between the other applications, and if so, the application tags of the other applications can be determined according to the preset tag of the basic application.
For example, if application a is the basic application, the default tag of application a is social communication. And if the similarity between the application B belonging to other applications and the application A is detected to be larger than a preset similarity threshold (such as 60%), setting the application label of the application B as social communication. In some examples, the similarity between the application B and the base application a may be greater than a preset similarity threshold, and meanwhile, the similarity between the application B and the base application C is also greater than the preset similarity threshold, so that the application tag of the application B may include the preset tag of the base application a and the preset tag of the base application C.
In some embodiments, the determining, according to the similarity and the preset tag of the basic application, an application tag corresponding to each of the other applications includes:
for each basic application, taking a preset label of the basic application as at least part of application labels of M other applications with the highest similarity with the basic application, wherein M is a positive integer;
or, for each basic application, using the preset label of the basic application as at least part of application labels of other applications with the similarity between the application and the basic application being greater than a preset similarity threshold.
In the embodiment of the application, the preset labels of the basic applications are accurate labels related to the basic applications, so that the applications which are the same as the basic applications in type are determined according to the similarity, rapid classification of the first applications is achieved, and other classified applications are labeled according to the preset labels of the basic applications.
Therefore, according to the embodiment of the application, the application labels of a large number of applications can be quickly determined according to the preset labels of a small number of basic applications, so that the quick labeling of the large number of applications can be realized, and the labeling efficiency is high.
In some embodiments, after determining the application tags respectively corresponding to the other applications, the first application with the same corresponding tag (including the preset tag and the application tag) may be counted, and a statistical list may be output according to a statistical result, so that a fast manual review may be manually implemented according to the statistical list.
Of course, the signature review may be performed in other ways. For example, in some embodiments, after determining the application tag of each first application according to the similarity and the preset tag of the base application, the method further includes:
and for each other application, verifying the application label of the other application according to the description information of the other application.
For example, the application tags of the other applications may be compared with information such as keywords in the corresponding description information, and if the application tags of the other applications are matched with the information, it is determined that the application tags of the other applications are correct.
For example, for each other application, it may be determined whether at least X application tags exist in the application tags of the other applications, and the at least X application tags are respectively matched with the keywords extracted from the description information of the other applications, and if the at least X application tags match with the keywords, it may be determined that the application tags of the other applications are correct.
At this time, the automatic verification of the application label can be realized by combining the description information of the other applications, and the verification of the application label can be performed by combining the multidimensional application information, so that the accuracy of the application label is verified from multiple dimensions, and the accuracy and efficiency of labeling the application are improved.
In the embodiment of the application, application sequences respectively corresponding to a plurality of users can be acquired, and each application sequence includes text information for describing a first application related to the corresponding user, so that the text information describing the associated application of each user can be acquired, each application sequence is input into a trained natural language processing model, an output result of the trained natural language processing model based on each application sequence is acquired, and feature vectors of each first application can be accurately and efficiently extracted through the trained natural language processing model; then, according to the feature vectors corresponding to the first applications, the similarity between the basic applications and other applications is determined, so that the application labels corresponding to the other applications can be determined according to the similarity and the preset labels of the basic applications. At the moment, the similarity between each basic application and other applications can be evaluated according to the feature vectors extracted accurately and efficiently, so that the labels of the similar other applications are determined accurately by combining the preset labels obtained by pre-labeling the basic applications, and the accuracy of labeling the applications is improved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Fig. 3 shows a structural block diagram of an application labeling apparatus provided in the embodiment of the present application, and for convenience of description, only the parts related to the embodiment of the present application are shown.
Referring to fig. 3, the application labeling apparatus 3 includes:
an obtaining module 301, configured to obtain application sequences corresponding to multiple users, where each application sequence includes text information for describing a first application related to a corresponding user;
a processing module 302, configured to input each of the application sequences into a trained natural language processing model, and obtain an output result of the trained natural language processing model based on each of the application sequences, where the output result includes feature vectors corresponding to each of the first applications;
a first determining module 303, configured to determine, according to the feature vectors, similarities between basic applications and other applications, respectively, where the basic applications are first applications corresponding to preset tags, and the other applications are applications, except the basic applications, in the first applications;
a second determining module 304, configured to determine, according to the similarity and the preset tag of the basic application, application tags corresponding to the other applications respectively.
Optionally, the application labeling apparatus 3 further includes:
the third determining module is used for determining the basic application according to the application operation data respectively corresponding to the multiple users;
the second acquisition module is used for acquiring at least two groups of initial label sets of each basic application, wherein each group of initial label set comprises at least one initial label;
and the setting module is used for taking the same initial label among the initial label sets as a preset label of the basic application.
Optionally, for application operation data of each user, the application operation data of the user includes a second application corresponding to the user, and a use duration and a use frequency of each used second application by the user in the preset time period, where the second application is a first application used by the user in the preset time period;
the third determining module includes:
the first determining unit is used for determining the heat score of each second application according to the use duration and the use times of each user to the second application in the preset time period;
and the second determining unit is used for determining the basic application from each second application according to the heat score of each second application.
Optionally, the first determining unit is specifically configured to:
for each second application, calculating a heat score for the second application according to a first formula, wherein the first formula is:
wherein S ishIs the heat score of a second application h, n is the number of users using the second application h in the preset time period, FiThe number of times of using the second application h by the user i in the preset time period TiThe using duration of the second application h in the preset time period for the user i, FmaxThe maximum number of times of use, T, corresponding to the second application in the preset time periodmaxIs the longest usage number, w, corresponding to the second application in the preset time periodfIs a first weight, wtIs the second weight.
Optionally, the feature vector is an N-dimensional digital vector, and N is an integer greater than 1;
the first determining module 303 includes:
a calculation unit, configured to calculate, for any one basic application and any one other application, an inner product of a feature vector of the basic application and a feature vector of the other application;
a third determining unit, configured to determine a similarity between the base application and the other applications according to the inner product.
Optionally, the third determining unit includes:
a processing subunit, configured to use the inner product as a first similarity between the base application and the other application;
a first determining subunit, configured to determine, according to the description information of the base application and the description information of the other applications, a second similarity between the base application and the other applications;
a second determining subunit, configured to determine a similarity between the base application and the other applications according to the first similarity, the second similarity, a third weight of the first similarity, and a fourth weight of the second similarity.
The second determining module 304 is specifically configured to:
for each basic application, taking a preset label of the basic application as at least part of application labels of M other applications with the highest similarity with the basic application, wherein M is a positive integer;
or, for each basic application, using the preset label of the basic application as at least part of application labels of other applications with the similarity between the application and the basic application being greater than a preset similarity threshold.
In the embodiment of the application, application sequences respectively corresponding to a plurality of users can be acquired, and each application sequence includes text information for describing a first application related to the corresponding user, so that the text information describing the associated application of each user can be acquired, each application sequence is input into a trained natural language processing model, an output result of the trained natural language processing model based on each application sequence is acquired, and feature vectors of each first application can be accurately and efficiently extracted through the trained natural language processing model; then, according to the feature vectors corresponding to the first applications, the similarity between the basic applications and other applications is determined, so that the application labels corresponding to the other applications can be determined according to the similarity and the preset labels of the basic applications. At the moment, the similarity between each basic application and other applications can be evaluated according to the feature vectors extracted accurately and efficiently, so that the labels of the similar other applications are determined accurately by combining the preset labels obtained by pre-labeling the basic applications, and the accuracy of labeling the applications is improved.
It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and specific reference may be made to the part of the embodiment of the method, which is not described herein again.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned functions may be distributed as different functional units and modules according to needs, that is, the internal structure of the apparatus may be divided into different functional units or modules to implement all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
Fig. 4 is a schematic structural diagram of a terminal device according to an embodiment of the present application. As shown in fig. 4, the terminal device 4 of this embodiment includes: at least one processor 40 (only one is shown in fig. 4), a memory 41, and a computer program 42 stored in the memory 41 and executable on the at least one processor 40, wherein the steps in any of the above-described embodiments of the application labeling method are implemented when the computer program 42 is executed by the processor 40.
The terminal device 4 may be a server, a mobile phone, a wearable device, an Augmented Reality (AR)/Virtual Reality (VR) device, a desktop computer, a notebook, a desktop computer, a palmtop computer, or other computing devices. The terminal device may include, but is not limited to, a processor 40, a memory 41. Those skilled in the art will appreciate that fig. 4 is merely an example of the terminal device 4, and does not constitute a limitation of the terminal device 4, and may include more or less components than those shown, or combine some of the components, or different components, such as may also include input devices, output devices, network access devices, etc. The input device may include a keyboard, a touch pad, a fingerprint sensor (for collecting fingerprint information of a user and direction information of a fingerprint), a microphone, a camera, and the like, and the output device may include a display, a speaker, and the like.
The Processor 40 may be a Central Processing Unit (CPU), and the Processor 40 may also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field-Programmable Gate arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, and the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 41 may be an internal storage unit of the terminal device 4, such as a hard disk or a memory of the terminal device 4. In other embodiments, the memory 41 may also be an external storage device of the terminal device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like provided on the terminal device 4. Further, the memory 41 may include both an internal storage unit and an external storage device of the terminal device 4. The memory 41 is used for storing an operating system, an application program, a Boot Loader (Boot Loader), data, and other programs, such as program codes of the computer programs. The above-mentioned memory 41 may also be used to temporarily store data that has been output or is to be output.
In addition, although not shown, the terminal device 4 may further include a network connection module, such as a bluetooth module Wi-Fi module, a cellular network module, and the like, which is not described herein again.
In this embodiment, when the processor 40 executes the computer program 42 to implement the steps in any of the above application labeling method embodiments, it may obtain application sequences corresponding to a plurality of users, respectively, where each of the application sequences includes text information for describing a first application related to the corresponding user, and therefore, it may obtain text information describing a related application of each user, so as to input each of the application sequences into a trained natural language processing model, and obtain an output result of the trained natural language processing model based on each of the application sequences, so as to extract a feature vector of each of the first applications accurately and efficiently by using the trained natural language processing model; then, according to the feature vectors corresponding to the first applications, the similarity between the basic applications and other applications is determined, so that the application labels corresponding to the other applications can be determined according to the similarity and the preset labels of the basic applications. At the moment, the similarity between each basic application and other applications can be evaluated according to the feature vectors extracted accurately and efficiently, so that the labels of the similar other applications are determined accurately by combining the preset labels obtained by pre-labeling the basic applications, and the accuracy of labeling the applications is improved.
The embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps in the above method embodiments.
The embodiments of the present application provide a computer program product, which when running on a terminal device, enables the terminal device to implement the steps in the above method embodiments when executed.
The integrated unit may be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as a separate product. Based on such understanding, all or part of the processes in the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium and can implement the steps of the embodiments of the methods described above when the computer program is executed by a processor. The computer program includes computer program code, and the computer program code may be in a source code form, an object code form, an executable file or some intermediate form. The computer-readable medium may include at least: any entity or device capable of carrying computer program code to a photographing apparatus/terminal apparatus, a recording medium, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In certain jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and patent practice.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other ways. For example, the above-described apparatus/network device embodiments are merely illustrative, and for example, the division of the above modules or units is only one logical function division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. An application labeling method, comprising:
acquiring application sequences respectively corresponding to a plurality of users, wherein each application sequence comprises text information used for describing a first application related to the corresponding user;
inputting each application sequence into a trained natural language processing model to obtain an output result of the trained natural language processing model based on each application sequence, wherein the output result comprises a feature vector corresponding to each first application;
determining similarity between a basic application and other applications according to the feature vectors, wherein the basic application is a first application corresponding to a preset label, and the other applications are applications except the basic application in the first application;
and determining application labels respectively corresponding to other applications according to the similarity and the preset labels of the basic applications.
2. The application labeling method of claim 1, before determining the similarity between the base application in the first application and the other applications respectively according to the feature vector, further comprising:
determining a basic application according to application operation data respectively corresponding to a plurality of users;
for each basic application, acquiring at least two groups of initial label sets of the basic application, wherein each group of initial label set comprises at least one initial label;
and using the same initial label among the initial label sets as a preset label of the basic application.
3. The application labeling method according to claim 2, wherein for each user's application operation data, the user's application operation data includes a second application corresponding to the user, and a usage duration and a usage number of the user for each used second application in the preset time period, where the second application is a first application used by the user in the preset time period;
the determining the basic application according to the application operation data respectively corresponding to the plurality of users includes:
for each second application, determining the heat score of the second application according to the use duration and the use times of each user to the second application in the preset time period;
and determining a basic application from each second application according to the heat score of each second application.
4. The application labeling method of claim 3, wherein the determining the popularity score of each second application according to the usage duration and the usage times of the second application by the respective user in the preset time period comprises:
for each second application, calculating a heat score for the second application according to a first formula, wherein the first formula is:
wherein S ishIs the heat score of a second application h, n is the number of users using the second application h in the preset time period, FiThe number of times of using the second application h by the user i in the preset time period TiThe using duration of the second application h in the preset time period for the user i, FmaxThe maximum number of times of use, T, corresponding to the second application in the preset time periodmaxIs the longest usage number, w, corresponding to the second application in the preset time periodfIs a first weight, wtIs the second weight.
5. The application labeling method of claim 1, wherein the feature vector is an N-dimensional numeric vector, N being an integer greater than 1;
determining similarity between the basic application and other applications respectively according to the feature vectors includes:
calculating the inner product of the feature vector of the basic application and the feature vector of the other application aiming at any basic application and any other application;
and determining the similarity between the basic application and the other applications according to the inner product.
6. The application labeling method of claim 5, wherein said determining a similarity between the base application and the other applications according to the inner product comprises:
taking the inner product as a first similarity between the base application and the other application;
determining a second similarity between the base application and the other applications according to the description information of the base application and the description information of the other applications;
determining a similarity between the base application and the other applications according to the first similarity, the second similarity, a third weight of the first similarity and a fourth weight of the second similarity.
7. The application labeling method according to any one of claims 1 to 6, wherein the determining, according to the similarity and the preset label of the basic application, the application label corresponding to each of the other applications respectively comprises:
for each basic application, taking a preset label of the basic application as at least part of application labels of M other applications with the highest similarity with the basic application, wherein M is a positive integer;
or, for each basic application, using the preset label of the basic application as at least part of application labels of other applications with the similarity between the application and the basic application being greater than a preset similarity threshold.
8. An application labeling apparatus, comprising:
the device comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring application sequences respectively corresponding to a plurality of users, and each application sequence comprises text information used for describing a first application related to the corresponding user;
the processing module is used for inputting each application sequence into a trained natural language processing model to obtain an output result of the trained natural language processing model based on each application sequence, wherein the output result comprises a feature vector corresponding to each first application;
a first determining module, configured to determine, according to the feature vectors, similarities between basic applications and other applications, where the basic applications are first applications corresponding to preset tags, and the other applications are applications, except the basic applications, in the first applications;
and the second determining module is used for determining the application label corresponding to each other application according to the similarity and the preset label of the basic application.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the application tagging method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the application annotation method according to any one of claims 1 to 7.
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