CN113595860A - Data processing method and device, electronic equipment and computer storage medium - Google Patents

Data processing method and device, electronic equipment and computer storage medium Download PDF

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CN113595860A
CN113595860A CN202010368121.7A CN202010368121A CN113595860A CN 113595860 A CN113595860 A CN 113595860A CN 202010368121 A CN202010368121 A CN 202010368121A CN 113595860 A CN113595860 A CN 113595860A
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content
content object
comment
candidate
data
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CN113595860B (en
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刘俊
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Alibaba Group Holding Ltd
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Alibaba Group Holding Ltd
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L51/00—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail
    • H04L51/52—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail for supporting social networking services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L51/00—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail
    • H04L51/07—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail characterised by the inclusion of specific contents
    • H04L51/10—Multimedia information
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L51/00—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail
    • H04L51/07—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail characterised by the inclusion of specific contents
    • H04L51/18—Commands or executable codes
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Computing Systems (AREA)
  • Multimedia (AREA)
  • Information Transfer Between Computers (AREA)

Abstract

The embodiment of the invention provides a data processing method and device, electronic equipment and a computer storage medium. The data processing method comprises the following steps: acquiring content quality information and content freshness of the candidate content object; determining a target content object from the candidate content objects according to the content quality information and the content freshness of the candidate content objects; automatically generating comment data aiming at the target content object according to the attribute information of the target content object; commenting the target content object by using the comment data. By the embodiment of the invention, the richness of the comment modes can be improved.

Description

Data processing method and device, electronic equipment and computer storage medium
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to a data processing method and device, electronic equipment and a computer storage medium.
Background
With the development of internet technology, people increasingly share daily life or create content on the internet for other users to watch and comment, thereby realizing interaction and social interaction. The existing comment mode is single, for example, a reviewer manually reviews the content object published by the creator. The comment mode has a single function and cannot meet different comment requirements.
Disclosure of Invention
In view of the above, embodiments of the present invention provide a data processing scheme to solve some or all of the above problems.
According to a first aspect of the embodiments of the present invention, there is provided a data processing method, including: acquiring content quality information and content freshness of the candidate content object; determining a target content object from the candidate content objects according to the content quality information and the content freshness of the candidate content objects; automatically generating comment data aiming at the target content object according to the attribute information of the target content object; commenting the target content object by using the comment data.
According to a second aspect of the embodiments of the present invention, there is provided a data processing apparatus including: the acquisition module is used for acquiring the content quality information and the content freshness of the candidate content object; a first determining module, configured to determine a target content object from the candidate content objects according to content quality information and content freshness of the candidate content objects; the second determination module is used for automatically generating comment data aiming at the target content object according to the attribute information of the target content object; and the comment module is used for commenting the target content object by using the comment data.
According to a third aspect of embodiments of the present invention, there is provided an electronic apparatus, including: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus; the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the corresponding operation of the data processing method according to the first aspect.
According to a fourth aspect of embodiments of the present invention, there is provided a computer storage medium having stored thereon a computer program which, when executed by a processor, implements the data processing method according to the first aspect.
According to the data processing scheme provided by the embodiment of the invention, the target content data of the corresponding comment data which needs to be generated is determined according to the content quality information and the content freshness of the candidate content object, the target content object can be rapidly and accurately determined from the candidate content object, and the comment data is generated according to the attribute information of the target content object, so that the comment data object is used for commenting on the target content object. Therefore, automatic comment is realized, the comment forms are enriched, different comment requirements of users are met, the pertinence of the comment is strong, and the relevance of the comment and a target content object is better.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the embodiments of the present invention, and it is also possible for a person skilled in the art to obtain other drawings based on the drawings.
FIG. 1a is a flowchart illustrating steps of a data processing method according to a first embodiment of the present invention;
FIG. 1b is a diagram illustrating a usage scenario according to a first embodiment of the present invention;
FIG. 2 is a flowchart illustrating steps of a data processing method according to a second embodiment of the present invention;
FIG. 3a is a flowchart illustrating steps of a data processing method according to a third embodiment of the present invention;
FIG. 3b is a diagram illustrating a usage scenario according to a third embodiment of the present invention;
fig. 4 is a block diagram of a data processing apparatus according to a fourth embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic device according to a fifth embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments obtained by a person skilled in the art based on the embodiments of the present invention shall fall within the scope of the protection of the embodiments of the present invention.
The following further describes specific implementation of the embodiments of the present invention with reference to the drawings.
Example one
Referring to fig. 1a, a flowchart illustrating steps of a data processing method according to a first embodiment of the present invention is shown.
In this embodiment, the data processing method is configured at a server (the server includes a server and/or a cloud), and the server automatically generates comment data corresponding to the target content object, so as to comment on the target content object using the comment data.
The data processing method of the embodiment comprises the following steps:
step S102: content quality information and content freshness of the candidate content object are obtained.
The candidate content object may be content published by a creator, such as video content, audio content, text content, image content, and the like, or may also be comment content published by a reviewer, such as comment content for content published by a creator, or comment content for comment content, and the like, which is not limited by this embodiment.
In this embodiment, the description is given taking an example in which the candidate content object is content posted by a creator, but it is needless to say that in other embodiments, the candidate content object may be comment content posted by a reviewer.
It should be noted that the creator and the reviewer in the present embodiment are different identity roles of the user, and each user may have one or more identity roles.
The content quality information is used to indicate the content quality status of each candidate content object. For example, the content quality information includes one of: high quality content, good content, normal content. Of course, the content included in the content quality information may be different according to different needs, and this embodiment does not limit this.
Content quality information may be obtained in any suitable manner by those skilled in the art. For example, the quality of each candidate content object is manually evaluated in advance, and corresponding content quality information is generated.
The content freshness is used to indicate the age and degree of attention of the corresponding candidate content object. The content freshness may be determined based on the posting time, number of existing comments, time of most recent comment, etc. of the candidate content object. This allows for a greater number of candidate content objects that are reviewed or have a newer time to be reviewed with greater content freshness. Of course, in other embodiments, those skilled in the art may also use other suitable rules to determine the content freshness, which is not limited by the embodiment.
Step S104: and determining a target content object from the candidate content objects according to the content quality information and the content freshness of the candidate content objects.
In a particular implementation, the target content object may be a content object with a number of existing comments less than the target number of comments. Of course, in other embodiments, the target content object may be determined in other manners, which is not limited by this embodiment.
The number of target comments may be determined according to content quality information and content freshness. For example, for each candidate content object, a reference value indicating the number of comments when the publication duration reaches a certain duration is determined from its content quality information. And then, adjusting the reference value according to the content freshness of each candidate content object, thereby obtaining the target comment number corresponding to each candidate content object.
Since the content freshness can indicate the aging and the attention degree of the corresponding candidate content object, the content freshness is considered in determining the target content object, so that the target content object having an appropriate distribution time, good aging, and an appropriate attention degree can be acquired from the candidate content objects. Similarly, since the content quality information can indicate the content quality of the candidate content objects, the content quality thereof is considered in determining the target content object, so that a good quality as the target content object can be obtained from the candidate content objects.
Step S106: and automatically generating comment data aiming at the target content object according to the attribute information of the target content object.
The attribute information includes at least one of: the content classification of the corresponding target content object, the content tag of the corresponding target content object, and the content field corresponding to the corresponding target content object, but not limited thereto, and the content classification may also include other information, which is not limited in this embodiment.
The target content object may have different content classifications according to different classification criteria, such as Chinese, Tang, Ming dynasty, and the like according to the dynasty related to the content, and male clothes, female clothes, and the like according to the gender of the person related to the content.
The content tag may be a descriptive tag set for the target content object, such as hanfang uniform, plum blossom, or a certain brand, a certain model, etc.
The content domain corresponding to the target content object may indicate its industry.
The comment data are generated according to the attribute information of the target content object, so that on one hand, automation can be achieved, the comment form is enriched, on the other hand, the generated comment data are high in association degree with the target content object, the comment data are used for commenting the target content object, the requirements of users can be better met, and the comment is closer to real manual comment.
The review data may be automatically generated by one skilled in the art in any suitable manner, for example, by a trained neural network model (e.g., generative model). Alternatively, comment data or the like corresponding to the target content object is selected from a preset comment database.
Step S108: commenting the target content object by using the comment data.
In a specific implementation, the comment data may be directly sent as a comment to the target content object, or one or more comment data may also be sent as comment data of the corresponding target content object at a required time as needed, which is not limited in this embodiment.
The following describes the data processing method with reference to a specific usage scenario:
as shown in fig. 1b, the server may obtain candidate content objects in the target application at intervals. The candidate content object may be a new content object in the interval time period, for example, content (such as video content, audio content, text content, and image-text content) released by the creator, or comment content (such as comment content for content released by the creator, or comment content for comment content) released by the user, and in this usage scenario, the new content object is the content released by the creator for example.
And aiming at each candidate content object, the content identification unit of the server side determines corresponding content quality information according to at least one of corresponding browsing data, audit state, recommendation level and sequencing information. For example, candidate content object a is premium content, candidate content object B is premium content, candidate content object C is normal content, and so on.
And the freshness calculation unit determines the content freshness corresponding to the candidate content object according to the release time of the candidate content object and the number of the existing comments. For example, the content freshness is indicated by the number of existing comments per unit of posting time. The unit issue time may be 1 minute, 10 seconds, 5 minutes, etc.
And the comment quantity calculating unit calculates the target comment quantity according to the corresponding content quality information and content freshness of each candidate content object, and further determines the candidate target object with the comment quantity smaller than the target comment quantity as the target content object.
And for each target content object, the difference value between the number of the target comments and the number of the existing comments is the number of the comment data needing to be automatically generated.
In one mode, when automatically generating comment data, the comment content production unit may determine, from a plurality of preset candidate comment data, top N candidate comment data whose association satisfies a set condition (the set condition may be determined as needed, and may be, for example, an association greater than 70%, etc.) as the determined comment data according to attribute information of a target content object. N is larger than or equal to the difference value between the number of the target comments and the number of the existing comments.
Then, the comment sending task unit uses the comment data corresponding to each target content object to comment on it.
Because the comment data corresponding to the target content object are automatically generated according to the attribute information of each target content object, the comment data and the target content object have higher association degree, so that the comment data has better comment effect on the target content object, the comment forms are enriched, the requirements of users for comment in different modes are met, and the adaptability is improved.
According to the embodiment, the target content data of the corresponding comment data which needs to be generated is determined according to the content quality information and the content freshness of the candidate content object, the target content object can be rapidly and accurately determined from the candidate content object, and the comment data is generated according to the attribute information of the target content object, so that the comment data object is used for commenting on the target content object. Therefore, automatic comment is realized, the comment forms are enriched, different comment requirements of users are met, the pertinence of the comment is strong, and the relevance of the comment and a target content object is better.
The data processing method of the present embodiment may be performed by any suitable electronic device having data processing capabilities, including but not limited to: servers, mobile terminals (such as tablet computers, mobile phones and the like), PCs and the like.
Example two
Referring to fig. 2, a flowchart of steps of a data processing method according to a second embodiment of the present invention is shown.
The data processing method of the present embodiment includes the aforementioned steps S102 to S108.
In one specific implementation of the embodiment, in order to quickly and reliably obtain the content quality information and the content freshness, so as to improve the accuracy of subsequently determining the target content object, so as to well meet the user requirement, step S102 includes sub-step S1021 and sub-step S1022.
Sub-step S1021 is used for achieving the purpose of obtaining content quality information of the candidate content object, and sub-step S1022 is used for achieving the purpose of obtaining content freshness of the candidate content object, but in a specific application, the two steps may be executed without being sequentially performed, or may be executed in parallel. Specifically, the method comprises the following steps:
substep S1021: and determining content quality information corresponding to the candidate content object according to at least one of browsing data, audit state, recommendation level and sequencing information of the candidate content object.
In this embodiment, the content quality information includes at least one of: premium content, good content, and general content.
The browsing data includes at least one of: number viewed, number of comments made available, and number shared. The browsing data may be the sum of all the data it includes, or may be determined in any other suitable way depending on the data it includes. By browsing the data, the attention and interest level of the user on the candidate content object can be known.
The audit status is used to indicate whether the candidate content object passes the content audit or not.
The recommendation level can be determined according to needs, for example, the recommendation level is 1-5, and the higher the level is, the higher the possibility of recommending the user is. The ranking information is used to indicate the priority order of different candidate content objects at the same recommendation level.
Candidate content objects that do not meet the requirements can be filtered out on the one hand and the content quality of candidate content objects that meet the requirements can be rated on the other hand by means of the content quality information.
In a specific implementation, taking the candidate content object a as an example, the corresponding audit state is manual audit pass (which may be indicated by using corresponding quantized data, e.g., manual audit pass indicated by using the number "3"), the recommendation level is level 1, the ranking information is 10, and weighted summation is performed according to the browsing data, the audit state, the recommendation level, and the ranking information, so as to determine the content quality information of the candidate content object a according to the result of weighted summation, e.g., the content quality information of the candidate content object a is used to indicate that it is premium content.
The weighted values corresponding to the weighted summation browsing data, the audit state, the recommendation level and the sorting information may be determined as required, which is not limited in this embodiment. The corresponding relationship between the result of the weighted summation and the content quality level may also be determined according to the requirement, which is not limited in this embodiment.
Of course, in other embodiments, the content quality information for each candidate content object may be determined in other manners.
Substep S1022: and determining the content freshness corresponding to the candidate content object according to the publishing time and the number of the existing comments of the candidate content object.
In a feasible mode, for a certain candidate content object, the published time length is determined according to the published time and the current time, the number of comments in unit time length is determined according to the published time length and the number of existing comments, and the number of comments in unit published time is used as the freshness of the content.
Alternatively, in another possible manner, based on the number of comments per unit posting time obtained in the previous possible manner, the corresponding content freshness is determined according to a preset correspondence, for example, the content freshness is 90%, or the like.
Alternatively, in another possible approach, for a candidate content object, the content freshness may be obtained by performing a weighted summation of the published duration and the number of existing comments. The weight value in the weighted sum may be determined as needed, which is not limited in this embodiment. Of course, in other embodiments, a person skilled in the art may also determine the content freshness in other ways, which is not limited by the embodiment.
According to the embodiment, the target content data of the corresponding comment data which needs to be generated is determined according to the content quality information and the content freshness of the candidate content object, the target content object can be rapidly and accurately determined from the candidate content object, and the comment data is generated according to the attribute information of the target content object, so that the comment data object is used for commenting on the target content object. Therefore, automatic comment is realized, the comment forms are enriched, different comment requirements of users are met, the pertinence of the comment is strong, and the relevance of the comment and a target content object is better.
The data processing method of the present embodiment may be performed by any suitable electronic device having data processing capabilities, including but not limited to: servers, mobile terminals (such as tablet computers, mobile phones and the like), PCs and the like.
EXAMPLE III
Referring to fig. 3a, a flow chart of steps of a data processing method according to a third embodiment of the present invention is shown.
The data processing method of the present embodiment includes the aforementioned steps S102 to S108. Step S102 may be implemented by any of the foregoing embodiments. In order to determine the target content object more accurately, in the present embodiment, the step S104 includes the following sub-steps:
substep S1041: and determining the number of target comments corresponding to each candidate content object according to the content quality information and the content freshness of each candidate content object.
In one embodiment, the content quality information of the candidate content object a is taken as an example of the high-quality content. After determining the corresponding reference value a according to the content quality information of the candidate content object a, adjusting the reference value a according to the content freshness of the candidate content object a, for example, performing weighted summation of the content freshness and the reference value a, and obtaining the target number of comments. The weighted value of the weighted sum may be determined as needed, which is not limited in this embodiment.
Of course, in other embodiments, the target number of comments may be obtained in other manners, which is not limited in this embodiment.
Substep S1042: and determining the target content objects with the number of the existing comments smaller than the target comment number according to the target comment number and the existing comment number of each candidate content object.
If the number of the existing comments of the candidate content object is less than the target number of the comments, the candidate content object is indicated to need to be commented, and therefore, the candidate content object is determined as the target content object. Conversely, if the number of existing comments of the candidate content object is greater than or equal to the target number of comments, it indicates that the candidate content object does not need to be commented, and no action may be taken with respect to the candidate content object.
By the method, the content quality information and the content freshness information of the candidate content object are combined when the target content object is determined, so that the determined target content object is high in heat, the supercooled content object with overlong release time and low heat cannot be processed, and the accuracy of determining the target content object is improved.
Optionally, in this embodiment, when automatically generating comment data for the determined target content object, in order to ensure that the association degree between the generated comment data and the target content object can meet the requirement, the step S106 includes the following sub-steps:
substep S1061: and respectively determining the content association degrees of a plurality of preset candidate comment data and the current target content object according to the attribute information of the current target content object and the content of the existing comment for each target content object.
In this embodiment, the attribute information includes at least one of: the content classification of the current target content object, the content label of the current target content object and the content field corresponding to the current target content object.
Of course, in other embodiments, the attribute information may also include other information, which is not limited in this embodiment.
In a specific implementation manner, a plurality of candidate comment data are preset, and the comment data may be comment content data or comment identification data.
And each piece of comment content data has corresponding comment content attribute information, such as a corresponding content classification, a corresponding content tag, a corresponding content field and the like. Similarly, the reviewer identification data has corresponding reviewer attribute information, such as corresponding content categories, content tags, and corresponding content fields.
For the comment content data, the first content relevance degree can be calculated according to the comment content attribute information, the attribute information of the current target content object and the already-commented content of the current target content object.
For the reviewer identification data, the second content relevancy may be calculated according to the reviewer attribute information and the attribute information of the current target content object indicated by the reviewer identification data, and the already-reviewed content of the current target content object.
The content relevance may be calculated by any suitable method, such as by calculating an euclidean distance, or by using a trained neural network model with a relevance calculation function, which is not limited in this embodiment.
Substep S1062: and determining candidate comment data with the content relevance degree of the current target content object meeting a set threshold value as the comment data of the current target content object.
In a specific implementation, the sub-step S1062 may be implemented as: determining candidate comment data with the content relevance of the current target content object meeting a set threshold; judging whether the determined number of the candidate comment data meets a preset number or not; if so, selecting the candidate comment data of the preset number from the candidate comment data meeting the set threshold value as the comment data of the current target content object; otherwise, all the determined candidate comment data are determined as the comment data of the current target content object.
The set threshold may be determined as desired, e.g., 70%, 80%, 90%, etc.
When candidate comment data are determined, for candidate comment data (denoted as first candidate comment data) containing comment content data, comment content data larger than or equal to a set threshold may be determined according to a first content relevance degree corresponding to each comment content data, and the first candidate comment data corresponding to these comment content data are denoted as the determined first candidate comment data.
Similarly, for candidate comment data (denoted as second candidate comment data) containing comment person identification data, comment person identification data larger than or equal to a set threshold may be determined according to the second content relevancy corresponding to each comment person identification data, and the second candidate comment data corresponding to these comment person identification data are denoted as the determined second candidate comment data.
And if the determined first candidate comment data meet the preset number, determining the preset number of first candidate comment data from the determined first candidate comment data as the comment data of the current target content object. Otherwise, all the determined first candidate comment data are determined as the comment data of the current target content object.
Similarly, if the determined second candidate comment data meets the preset number, determining the preset number of second candidate comment data from the determined second candidate comment data as the comment data of the current target content object. Otherwise, all the determined second candidate comment data are determined as the comment data of the current target content object.
The preset number may be set by a person skilled in the art according to actual requirements, and may be a difference value between the target number of comments of the current target content object and the existing number of comments.
By the method, the comment data with high content association degree with the target content object in semantics and content can be acquired, so that the quality of subsequent comments is ensured.
After determining the comment data corresponding to each target content object, step S108 may be implemented as: and generating a corresponding comment task according to the comment data corresponding to the target content object and the time to be commented, wherein the comment task is used for publishing the comment data corresponding to the target content object when the time to be commented is reached.
In a particular implementation, the comment task corresponding to each target content object may be one or more. Each comment task has at least two corresponding comment data, namely first comment data including comment content data and second comment data including reviewer identification data. In addition, each comment task corresponds to a time to be commented on.
The following describes an implementation manner for determining the first comment data, the second comment data and the time to be commented in the comment task:
having determined the comment data corresponding to each target content object in step S106, a part of these comment data is the first comment data, and another part is the second comment data, in the present embodiment, for each first comment data, one task combination (i.e., determining the comment content and the reviewer) may be randomly matched from the second comment data to be composed with the first comment data, and each task combination corresponds to one comment task.
Of course, in other embodiments, the first comment data and the second comment data in the task combination may also be determined in other manners, which is not limited by this embodiment.
The time to be reviewed corresponding to the review task may be determined as needed. For example, a preset corresponding comment time period is determined for the target content object, such as 45 minutes, 1 hour, 1 day, etc. after the current time. And randomly matching a time for each comment task in the comment time period to serve as the time to be commented.
And then, generating a comment task according to each task combination and the time to be commented. Each target content object may correspond to one or more review tasks.
In order to improve automation, the comment task can be sent to a task queue, so that the comment task is automatically executed when the sending time is up, and comment data corresponding to the corresponding target content object is published.
It should be noted that the task queue may be any suitable queue, for example, the task queue may be a distributed queue based on a subscription model in order to reduce load and ensure operational reliability.
The following describes an implementation process of the method of this embodiment with reference to a specific usage scenario as follows:
as shown in fig. 3b, the server configured with the method regularly triggers a task of acquiring a candidate content object, for example, a task of requesting a candidate content object from a certain target application program by an existing task scheduler.
For each candidate content object, the content identification unit performs quality rating on the candidate content object according to browsing data, an audit state, a recommendation level, sorting information and the like to acquire content quality information. The content quality information includes one of a good content, and a general content. Wherein the browsing data is used to indicate the popularity of the corresponding candidate target content object.
Further, for each candidate content object, the freshness calculation unit calculates the corresponding content freshness according to its posting time and the number of existing comments.
For each candidate content object, the comment number calculation unit calculates through the content quality information and the content freshness, determines whether the current candidate content object needs to automatically generate comment data, and determines it as the target content object if necessary. Further, the comment number calculation unit may also determine the number of comment data required for each target content object.
Aiming at each target content object, the comment content production unit calculates a comment content index value through the attribute information (such as content classification, content tags and related industries) of the target content object and the existing comment content, and matches comment content data and comment person identification data from a preset comment content library according to the index value to serve as determined comment data.
And the comment sending task unit calculates corresponding sending time for each piece of determined comment data so as to achieve the purpose of sending different comment data in a plurality of time divisions. And when the sending time is up, the message queue sends the comment generated according to the comment data to an application program where the target content object is located, so that the function of commenting the comment is realized.
According to the mode, automatic comment is achieved according to the needs of the user, the generated comment data are high in association degree with the target content object, the effect is better, the comment time can be freely controlled, a plurality of comment data can be generated for one target content object at a time, the execution efficiency is improved, and an appropriate target content object can be selected according to the needs of the user, such as the target content object with high popularity for comment.
By the method, a real comment scene can be simulated, and a better training effect can be obtained when comment data of a target content object is used as a training sample for training subsequently.
Or, in another usage scenario, the server configured with the method periodically triggers a task of acquiring the candidate content object, for example, periodically triggers a task of requesting the candidate content object from a certain network address (e.g., an address of a certain website) by using an existing task scheduler. The candidate content object may be teletext content published in a website by an author.
For each of the acquired candidate content objects, the content identifying unit obtains content quality information of each of the candidate content objects by calculation. The calculation may be the same as or different from that of the usage scenario described above. The content quality information includes one of good content (e.g., indicated by the content quality information being 1), good content (e.g., indicated by the content quality information being 0.7), and normal content (e.g., indicated by the content quality information being 0.3).
The corresponding content freshness is determined by the freshness calculation unit according to the posting time, the number of existing comments and the latest commented time, so as to indicate the aging and the degree of attention of the candidate content object by the content freshness. For example, the posting duration is determined according to the posting time, the latest comment posting duration is determined according to the latest commented time, and the posting duration, the number of existing comments and the latest comment posting duration are subjected to weighted summation to obtain the content freshness.
And determining a corresponding reference value according to the content quality information of the candidate content objects through a comment number calculation unit, and determining a target content object which needs to automatically generate comment data in each candidate content object according to the result of weighted summation of the content freshness and the reference value. Further, the comment number calculation unit may also determine the number of comment data required for each target content object.
For each target content object, the comment content production unit calculates a comment content index value according to the attribute information (such as content classification, content tag and related industry) of the target content object and the existing comment content, and matches comment content data and comment party identification data from a preset comment content library according to the index value to serve as determined comment data.
And the comment sending task unit calculates corresponding sending time for each piece of determined comment data so as to achieve the purpose of sending different comment data in a plurality of time divisions. And when the sending time is up, the message queue sends the comment generated according to the comment data to a website where the target content object is located, so that the function of commenting the comment is realized.
In the use scenario, in this way, automatic comment on content (such as posts issued by users) issued by creators in the website, comments issued by reviewers and the like can be realized.
According to the embodiment, the target content data of the corresponding comment data which needs to be generated is determined according to the content quality information and the content freshness of the candidate content object, the target content object can be rapidly and accurately determined from the candidate content object, and the comment data is generated according to the attribute information of the target content object, so that the comment data object is used for commenting on the target content object. Therefore, automatic comment is realized, the comment forms are enriched, different comment requirements of users are met, the pertinence of the comment is strong, and the relevance of the comment and a target content object is better.
The data processing method of the present embodiment may be performed by any suitable electronic device having data processing capabilities, including but not limited to: servers, mobile terminals (such as tablet computers, mobile phones and the like), PCs and the like.
Example four
Referring to fig. 4, a block diagram of a data processing apparatus according to a fourth embodiment of the present invention is shown.
The data processing apparatus of the present embodiment includes: an obtaining module 402, configured to obtain content quality information and content freshness of the candidate content object; a first determining module 404, configured to determine a target content object from the candidate content objects according to the content quality information and the content freshness of the candidate content objects; a second determining module 406, configured to automatically generate comment data for the target content object according to the attribute information of the target content object; a comment module 408, configured to comment on the target content object using the comment data.
Optionally, the obtaining module 402 is configured to, when obtaining content quality information of a candidate content object, determine content quality information corresponding to the candidate content object according to at least one of browsing data, an audit state, a recommendation level, and ranking information of the candidate content object, where the browsing data includes at least one of the following: number viewed, number of comments made available, and number shared.
Optionally, when the content freshness of the candidate content object is obtained, the obtaining module 402 determines the content freshness corresponding to the candidate content object according to the publishing time of the candidate content object and the number of existing comments.
Optionally, the first determining module 404 is configured to determine, according to content quality information and content freshness of each candidate content object, a target comment number corresponding to each candidate content object; and determining the target content objects with the number of the existing comments smaller than the target comment number according to the target comment number and the existing comment number of each candidate content object.
Optionally, the second determining module 406 is configured to determine, for each target content object, content association degrees between a plurality of preset candidate comment data and the current target content object according to attribute information of the current target content object and contents of existing comments; and determining candidate comment data with the content relevance degree of the current target content object meeting a set threshold value as the comment data of the current target content object.
Optionally, the second determining module 406 determines candidate comment data, of which the content association degree with the current target content object meets a set threshold, when the candidate comment data, of which the content association degree with the current target content object meets the set threshold, is determined as comment data of the current target content object; judging whether the determined number of the candidate comment data meets a preset number or not; if so, selecting the candidate comment data of the preset number from the candidate comment data meeting the set threshold value as the comment data of the current target content object; otherwise, all the determined candidate comment data are determined as the comment data of the current target content object.
Optionally, the attribute information includes at least one of: the content classification of the current target content object, the content label of the current target content object and the content field corresponding to the current target content object.
Optionally, the comment module 408 is configured to generate a corresponding comment task according to the comment data and the time to be commented corresponding to the target content object, where the comment task is configured to publish the comment data corresponding to the target content object when the time to be commented arrives.
The data processing apparatus of this embodiment is configured to implement the corresponding data processing method in the foregoing multiple method embodiments, and has the beneficial effects of the corresponding method embodiment, which are not described herein again. In addition, the functional implementation of each module in the data processing apparatus of this embodiment can refer to the description of the corresponding part in the foregoing method embodiment, and is not repeated here.
EXAMPLE five
Referring to fig. 5, a schematic structural diagram of an electronic device according to a fifth embodiment of the present invention is shown, and the specific embodiment of the present invention does not limit the specific implementation of the electronic device.
As shown in fig. 5, the electronic device may include: a processor (processor)502, a Communications Interface 504, a memory 506, and a communication bus 508.
Wherein:
the processor 502, communication interface 504, and memory 506 communicate with one another via a communication bus 508.
A communication interface 504 for communicating with other electronic devices such as a terminal device or a server.
The processor 502 is configured to execute the program 510, and may specifically perform relevant steps in the above-described data processing method embodiments.
In particular, program 510 may include program code that includes computer operating instructions.
The processor 502 may be a central processing unit CPU, or an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement an embodiment of the present invention. The electronic device comprises one or more processors, which can be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
And a memory 506 for storing a program 510. The memory 506 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 510 may specifically be used to cause the processor 502 to perform the following operations: acquiring content quality information and content freshness of the candidate content object; determining a target content object from the candidate content objects according to the content quality information and the content freshness of the candidate content objects; automatically generating comment data aiming at the target content object according to the attribute information of the target content object; commenting the target content object by using the comment data.
In an alternative implementation, the program 510 is further configured to, when obtaining content quality information of a candidate content object, determine content quality information corresponding to the candidate content object according to at least one of browsing data, an audit status, a recommendation level, and ranking information of the candidate content object, where the browsing data includes at least one of: number viewed, number of comments made available, and number shared.
In an optional implementation manner, the program 510 is further configured to, when the processor 502 obtains the content freshness of the candidate content object, determine the content freshness corresponding to the candidate content object according to the publishing time and the number of existing comments of the candidate content object.
In an alternative implementation, program 510 is further configured to enable processor 502, when determining a target content object from the candidate content objects according to the content quality information and the content freshness of the candidate content objects, to determine a target number of comments corresponding to each candidate content object according to the content quality information and the content freshness of each candidate content object; and determining the target content objects with the number of the existing comments smaller than the target comment number according to the target comment number and the existing comment number of each candidate content object.
In an optional implementation manner, the program 510 is further configured to, when automatically generating comment data for the target content object according to the attribute information of the target content object, for each target content object, respectively determine content association degrees of a plurality of preset candidate comment data and the current target content object according to the attribute information of the current target content object and contents of existing comments; and determining candidate comment data with the content relevance degree of the current target content object meeting a set threshold value as the comment data of the current target content object.
In an alternative embodiment, program 510 is further configured to cause processor 502 to determine candidate comment data whose content association with the current target content object satisfies a set threshold when determining candidate comment data whose content association with the current target content object satisfies a set threshold as comment data of the current target content object; judging whether the determined number of the candidate comment data meets a preset number or not; if so, selecting the candidate comment data of the preset number from the candidate comment data meeting the set threshold value as the comment data of the current target content object; otherwise, all the determined candidate comment data are determined as the comment data of the current target content object.
In an alternative embodiment, the attribute information includes at least one of: the content classification of the current target content object, the content label of the current target content object and the content field corresponding to the current target content object.
In an optional implementation manner, the program 510 is further configured to enable the processor 502, when the comment data is used to comment the target content object, to generate a corresponding comment task according to the comment data and the time to be commented, which correspond to the target content object, where the comment task is used to publish the comment data corresponding to the target content object when the time to be commented arrives.
For specific implementation of each step in the program 510, reference may be made to corresponding steps and corresponding descriptions in units in the foregoing data processing method embodiments, which are not described herein again. It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described devices and modules may refer to the corresponding process descriptions in the foregoing method embodiments, and are not described herein again.
It should be noted that, according to the implementation requirement, each component/step described in the embodiment of the present invention may be divided into more components/steps, and two or more components/steps or partial operations of the components/steps may also be combined into a new component/step to achieve the purpose of the embodiment of the present invention.
The above-described method according to an embodiment of the present invention may be implemented in hardware, firmware, or as software or computer code storable in a recording medium such as a CD ROM, a RAM, a floppy disk, a hard disk, or a magneto-optical disk, or as computer code originally stored in a remote recording medium or a non-transitory machine-readable medium downloaded through a network and to be stored in a local recording medium, so that the method described herein may be stored in such software processing on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware such as an ASIC or FPGA. It will be appreciated that the computer, processor, microprocessor controller or programmable hardware includes memory components (e.g., RAM, ROM, flash memory, etc.) that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the data processing methods described herein. Further, when a general-purpose computer accesses code for implementing the data processing method shown herein, execution of the code converts the general-purpose computer into a special-purpose computer for executing the data processing method shown herein.
Those of ordinary skill in the art will appreciate that the various illustrative elements and method 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 embodiments.
The above embodiments are only for illustrating the embodiments of the present invention and not for limiting the embodiments of the present invention, and those skilled in the art can make various changes and modifications without departing from the spirit and scope of the embodiments of the present invention, so that all equivalent technical solutions also belong to the scope of the embodiments of the present invention, and the scope of patent protection of the embodiments of the present invention should be defined by the claims.

Claims (11)

1. A method of data processing, comprising:
acquiring content quality information and content freshness of the candidate content object;
determining a target content object from the candidate content objects according to the content quality information and the content freshness of the candidate content objects;
automatically generating comment data aiming at the target content object according to the attribute information of the target content object;
commenting the target content object by using the comment data.
2. The method of claim 1, wherein obtaining content quality information for candidate content objects comprises:
determining content quality information corresponding to the candidate content object according to at least one of browsing data, an audit state, a recommendation level and sequencing information of the candidate content object, wherein the browsing data comprises at least one of the following: number viewed, number of comments made available, and number shared.
3. The method of claim 1, wherein obtaining the content freshness of the candidate content object comprises:
and determining the content freshness corresponding to the candidate content object according to the publishing time and the number of the existing comments of the candidate content object.
4. The method of claim 1, wherein said determining a target content object from the candidate content objects based on content quality information and content freshness of the candidate content objects comprises:
determining the number of target comments corresponding to each candidate content object according to the content quality information and the content freshness of each candidate content object;
and determining the target content objects with the number of the existing comments smaller than the target comment number according to the target comment number and the existing comment number of each candidate content object.
5. The method of claim 4, wherein the automatically generating comment data for the target content object in accordance with the attribute information of the target content object comprises:
for each target content object, respectively determining the content association degrees of a plurality of preset candidate comment data and the current target content object according to the attribute information of the current target content object and the content of the existing comment;
and determining candidate comment data with the content relevance degree of the current target content object meeting a set threshold value as the comment data of the current target content object.
6. The method of claim 5, wherein the determining candidate comment data having a content association with the current target content object that satisfies a set threshold as comment data of the current target content object comprises:
determining candidate comment data with the content relevance of the current target content object meeting a set threshold;
judging whether the determined number of the candidate comment data meets a preset number or not;
if so, selecting the candidate comment data of the preset number from the candidate comment data meeting the set threshold value as the comment data of the current target content object; otherwise, all the determined candidate comment data are determined as the comment data of the current target content object.
7. The method of claim 5 or 6, wherein the attribute information comprises at least one of: the content classification of the current target content object, the content label of the current target content object and the content field corresponding to the current target content object.
8. The method of claim 1, wherein said commenting on the target content object using the commenting data comprises:
and generating a corresponding comment task according to the comment data corresponding to the target content object and the time to be commented, wherein the comment task is used for publishing the comment data corresponding to the target content object when the time to be commented is reached.
9. A data processing apparatus comprising:
the acquisition module is used for acquiring the content quality information and the content freshness of the candidate content object;
a first determining module, configured to determine a target content object from the candidate content objects according to content quality information and content freshness of the candidate content objects;
the second determination module is used for automatically generating comment data aiming at the target content object according to the attribute information of the target content object;
and the comment module is used for commenting the target content object by using the comment data.
10. An electronic device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction causes the processor to execute the corresponding operation of the data processing method according to any one of claims 1-8.
11. A computer storage medium, on which a computer program is stored which, when being executed by a processor, carries out the data processing method of any one of claims 1 to 8.
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