CN113032671B - Content processing method, device, electronic equipment and storage medium - Google Patents
Content processing method, device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN113032671B CN113032671B CN202110288139.0A CN202110288139A CN113032671B CN 113032671 B CN113032671 B CN 113032671B CN 202110288139 A CN202110288139 A CN 202110288139A CN 113032671 B CN113032671 B CN 113032671B
- Authority
- CN
- China
- Prior art keywords
- content
- contents
- determining
- domain
- target
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000003672 processing method Methods 0.000 title claims abstract description 16
- 238000003860 storage Methods 0.000 title claims abstract description 16
- 238000004519 manufacturing process Methods 0.000 claims abstract description 18
- 238000012545 processing Methods 0.000 claims abstract description 16
- 238000000034 method Methods 0.000 claims description 26
- 238000009826 distribution Methods 0.000 claims description 17
- 238000013145 classification model Methods 0.000 claims description 6
- 230000010365 information processing Effects 0.000 abstract description 2
- 238000004590 computer program Methods 0.000 description 10
- 238000010586 diagram Methods 0.000 description 10
- 238000004891 communication Methods 0.000 description 9
- 230000006870 function Effects 0.000 description 5
- 238000013473 artificial intelligence Methods 0.000 description 4
- 238000011161 development Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 238000012544 monitoring process Methods 0.000 description 4
- 230000002349 favourable effect Effects 0.000 description 3
- 230000003993 interaction Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000009471 action Effects 0.000 description 2
- 238000001994 activation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000036541 health Effects 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000001737 promoting effect Effects 0.000 description 2
- 230000005856 abnormality Effects 0.000 description 1
- 230000004913 activation Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000003491 array Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 239000000969 carrier Substances 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 239000002537 cosmetic Substances 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000002143 encouraging effect Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000011835 investigation Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000005065 mining Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
- 230000001953 sensory effect Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000000007 visual effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
-
- 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
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Engineering & Computer Science (AREA)
- Databases & Information Systems (AREA)
- Theoretical Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The disclosure provides a content processing method, a content processing device, electronic equipment and a storage medium, and relates to the field of internet information processing. The specific implementation scheme is as follows: determining M knowledge fields related to the N contents based on the labels of the N contents produced by the target producer; determining the domain concentration of the N contents based on the number of the contents corresponding to the M knowledge domains in the N contents; the recommendation level of the target producer is determined based on the domain concentration of the N contents. The embodiments of the present disclosure facilitate accurate identification of the production capabilities of a target producer.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to the field of internet information processing.
Background
Due to the rapid development of the internet, knowledge propagation methods are evolving continuously. Currently, sharing knowledge in the internet knowledge community is the dominant knowledge propagation way. By evaluating the level of the content producers, the production capacity of each content producer in the knowledge community is quantified, so that the output of more high-quality content can be promoted, and the vigorous development of the knowledge community is maintained. Therefore, how to accurately identify the production capacity of content producers is a hotspot problem for knowledge community operations.
Disclosure of Invention
The disclosure provides a content processing method, a content processing device, an electronic device and a storage medium.
According to an aspect of the present disclosure, there is provided a content processing method including:
determining M knowledge fields related to the N contents based on the labels of the N contents produced by the target producer; wherein N and M are positive integers;
determining the domain concentration of the N contents based on the number of the contents corresponding to the M knowledge domains in the N contents;
the recommendation level of the target producer is determined based on the domain concentration of the N contents.
According to another aspect of the present disclosure, there is provided a content processing apparatus including:
the domain determining module is used for determining M knowledge domains related to the N contents based on the labels of the N contents produced by the target producer; wherein N and M are positive integers;
the concentration degree determining module is used for determining the domain concentration degree of the N pieces of content based on the quantity of the content corresponding to the M knowledge domains in the N pieces of content;
and the level determining module is used for determining the recommendation level of the target producer based on the domain concentration degree of the N contents.
According to another aspect of the present disclosure, there is provided an electronic device including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of the embodiments of the present disclosure.
According to another aspect of the present disclosure, there is provided a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method of any of the embodiments of the present disclosure.
According to another aspect of the present disclosure, there is provided a computer program product comprising a computer program which, when executed by a processor, implements the method in any of the embodiments of the present disclosure.
According to the technical scheme, the corresponding domain concentration degree is determined according to the labels of the contents produced by the target producer. And determining the recommended level of the target producer based on the domain concentration, so that the recommended level can accurately embody the concentration degree of the target producer on the domain, and is favorable for accurately identifying the production capacity of the target producer.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure. Wherein:
FIG. 1 is a schematic diagram of a content processing method provided by one embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a content processing method provided by another embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a content processing method provided by a further embodiment of the present disclosure;
FIG. 4 is a schematic diagram of a recommended production task in one application example of the present disclosure;
FIG. 5 is a schematic diagram of a content processing apparatus provided by one embodiment of the present disclosure;
FIG. 6 is a schematic diagram of a content processing apparatus provided by another embodiment of the present disclosure;
fig. 7 is a schematic view of a content processing apparatus provided by a further embodiment of the present disclosure;
fig. 8 is a block diagram of an electronic device for implementing a content processing method of an embodiment of the present disclosure.
Detailed Description
Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and should be considered as merely exemplary. Accordingly, one of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 shows a schematic diagram of a content processing method provided in an embodiment of the present disclosure. As shown in fig. 1, the method includes:
step S110, determining M knowledge fields related to N contents based on the labels of the N contents produced by the target producer; wherein N and M are positive integers;
step S120, determining the domain concentration of N pieces of content based on the quantity of the N pieces of content corresponding to M knowledge domains respectively;
step S130, determining the recommendation level of the target producer based on the field concentration degree of the N contents.
In embodiments of the present disclosure, the target producer may include a content producer in a knowledge community, such as a question and answer platform, an encyclopedia platform, a library platform, etc., for which a recommendation level is to be determined. The content producer is, for example, a user who replies to a question in the question-answering platform, a user who uploads a document in the library platform, or the like.
For example, the content produced by the target producer for a certain period of time may be periodically acquired. For example, the content produced by the target producer in the previous week is acquired at the beginning of each week, or the content produced by the target producer in the previous two weeks is acquired at the beginning of each week. Accordingly, the content currently to be processed, i.e., the content newly acquired, may be determined as the above-described N contents. By periodically acquiring the content produced by the target producer and determining the recommendation level of the target producer based on the content, the problem of insensitivity to the change of the production capacity of the content producer can be overcome, and the production capacity of the content producer can be accurately identified.
In the embodiments of the present disclosure, content may refer to various carriers of information, such as knowledge documents, articles, answers to questions, and the like. In the knowledge community, tags may be determined for each content. For example, different tag sets may be set for different fields, and the tags of the content may be selected from the tag sets according to the related entity, topic, keyword, and the like of the content.
By establishing the corresponding relation between each tag and each knowledge domain, M knowledge domains related to N contents can be determined based on the tags of the N contents. Wherein each of the N content may have at least one tag relating to at least one knowledge domain. The fields related to each content are acquired and de-duplicated, so that M knowledge fields related to N content can be obtained.
Further, for each knowledge domain in the M knowledge domains, the number of the content corresponding to each knowledge domain in the N content may be determined, so as to obtain the distribution situation of the content produced by the target producer in each knowledge domain. Based on the number of contents of the N contents respectively corresponding to the M knowledge domains, the domain concentration degree of the N contents may be determined.
For example, the number ratio of the content corresponding to each of the M knowledge domains in the N contents may be calculated according to the number, and the sum of the number ratios corresponding to K, for example, one or two knowledge domains, with the highest number ratio may be used as the domain concentration of the N contents.
For example, content 1 of 4 contents relates to field 1, content 2 relates to field 1, content 3 relates to field 2, and content 4 relates to field 3. Based on this, the fields to which the contents 1 to 4 relate include fields 1 to 3. Among the 4 contents, the number of contents corresponding to the field 1 is 2, the number of contents corresponding to the field 2 is 1, and the number of contents corresponding to the field 3 is 1. It can be seen that 4 contents are mainly concentrated in field 1. Wherein, the content of the domain 1 has a ratio of 1/2 in the 4 contents, and the domain concentration of the 4 contents may be 1/2.
In practical applications, other manners may be used to determine the domain concentration.
For example, the range of values of the domain concentration may be divided into a plurality of sub-ranges, each range corresponding to a recommendation level. For example, the range of the domain concentration is divided into two sub-ranges according to a preset threshold. If the field concentration is higher than a preset threshold, the recommended level of the target producer is one level; if the domain concentration is lower than the preset threshold, the recommended level of the target producer is two-level. In practice, more motivational resources may be put into a content producer with a first recommended level to promote the content producer to produce more quality content.
It can be seen that according to the method of the embodiment of the present disclosure, the corresponding domain concentration is determined according to the label of the content produced by the target producer. And determining the recommended level of the target producer based on the domain concentration, so that the recommended level can accurately embody the concentration degree of the target producer on the domain, and is favorable for accurately identifying the production capacity of the target producer. Further, the method is beneficial to promoting high-quality content producers to produce more high-quality content and keeping the good ecological development of knowledge communities.
In an exemplary embodiment, as shown in fig. 2, the step S120, based on the number of the N contents corresponding to the M knowledge domains, may include:
step S210, determining class entropy of N contents based on the ratio of the number of the contents corresponding to M knowledge fields in the N contents to N;
and step S220, determining the domain concentration degree of the N contents according to the class entropy of the N contents.
For example, for the ith knowledge field in the M knowledge fields, the number corresponding to the ith knowledge field in the N contents is X, and the ratio of the number to N is p=x/N. Wherein i is an integer of 1 or more and M or less. The class entropy entopy_cat of the N contents can be calculated according to the following formula:
based on the above-described category entropy_cat and the following formula, the domain concentration degree domain of N contents can be determined:
where tanh () represents a hyperbolic tangent function.
In the above embodiment, the domain concentration is determined according to the class entropy of the N contents, so that objectivity and accuracy of the domain concentration can be improved, and accuracy of the recommendation level of the target producer can be improved. The production capacity of the target producer can be accurately quantified.
In another exemplary embodiment, as shown in fig. 3, the step S120 may include, based on the number of the N pieces of content corresponding to the M knowledge domains, determining the domain concentration of the N pieces of content, including:
step S310, determining distribution information of the target knowledge domain based on the number of the N contents respectively corresponding to the M knowledge domains when the M knowledge domains comprise the target knowledge domain;
step S320, determining the domain concentration of N contents based on the distribution information of the target knowledge domain.
Illustratively, the target knowledge domain may be a knowledge domain having an association relationship with the target producer. For example, the target knowledge domain may be a knowledge domain to which the target producer belongs, which is determined according to a selection operation made by the target producer, or a knowledge domain that the target producer is most good at, which is determined according to the content that the target producer produces within a certain period of time, for example, half a year or one year.
For example, the distribution information of the target knowledge domain may be a number ratio of the content corresponding to the target knowledge domain in the N contents, or ranking information of the number ratio in the number ratio corresponding to each of the M domains, or the like. For example, if the target knowledge field of the target producer is artificial intelligence, the content of the artificial intelligence field produced by the target producer has a ratio of 0.6 among the N contents. The distribution information may be 0.6. Alternatively, the distribution information may be the first order if the number of contents of the artificial intelligence field in the N contents is the maximum number of contents of each field in the M fields in the N contents. Accordingly, the higher the distribution information or the earlier the ranking, the higher the domain concentration.
The domain concentration degree of N contents is determined based on the distribution information of the target knowledge domain, so that the concentration degree of the domain can more accurately reflect the concentration degree of a target producer on the target knowledge domain, and the accuracy of the recommendation level of the target producer is improved. The production capacity of the target producer can be accurately quantified.
The domain concentration determined according to the above embodiments is used to determine a recommendation level of a target producer. Specifically, the step S130 determines the recommendation level of the target producer based on the domain concentration levels of the N contents, including:
determining a content quality score for the target producer based on the N content and the classification model;
and determining the recommendation level of the target producer according to the domain concentration degree of the N contents and the content quality scores of the target producer.
Illustratively, the classification model may be used to classify the quality of the N content, and the quality categories of the N content are combined to obtain the content quality score of the target producer. And determining the recommended level of the target producer by combining the content quality score and the domain concentration.
Illustratively, the classification model may be an XGBoost (Extreme Gradient Boosting, extreme gradient lifting) model, which may improve accuracy of content quality scoring.
For example, the information of the N dimensions such as the domain concentration of the content, the content quality score, and the like may be used as input information of a predetermined algorithm or model, and the recommendation level of the target producer may be obtained based on the predetermined algorithm or model.
In practical application, the recommendation level of the target producer can be determined by combining information of other dimensions. For example, the recommendation level of the target producer may be determined in combination with information of each dimension of the liveness, user preference, authority, and the like of the target producer.
Wherein the liveness of the target producer may be determined based on an RFM (Recency-Frequency-Monetary) model. The liveness of the target producer is determined, for example, based on the quality or production time of the content that the target producer has last produced, the frequency with which the target producer has produced the content, and the number of contents that the target producer has produced.
User preference of the target producer may be determined based on a GBDT (Gradient Boosting Decison Tree, gradient lifting) regression model. For example, the number of actions such as browsing, praying, collecting and the like of each user on the content produced by the target producer is input into the GBDT model, and the user preference is obtained by calculating the GBDT model.
The authority of the target producer may be obtained based on the authentication information of the target producer.
It should be noted that, in the technical solution of the present disclosure, the acquisition, storage, application, etc. of the related content producer information or user information (including the content produced by the content producer and the information of each dimension mentioned above) all conform to the rules of the related laws and regulations, and do not violate the public welfare.
According to the embodiment, since the recommendation level of the target producer is determined by using the multidimensional information, the recommendation level of the target producer can be further improved, and the production capacity of the target producer can be more accurately identified.
The recommendation level determined by adopting the multidimensional information can be used for mining high-quality producers and perfecting a user incentive mechanism. For example, in a question-and-answer community, a high-quality producer with high concentration in the field can be mined based on information of each dimension of the target producer, and marks of the high-quality producer, such as a cosmetic man, a sports man, and the like, can be displayed on a client of the question-and-answer community. On one hand, the method has the encouraging effect, on the other hand, producers in the question-answering page can be personalized, interaction behaviors such as attention, communication and the like are caused among users of the knowledge community, and the activity of the knowledge community is improved.
As an example, the content processing method further includes:
and determining the recommended production task of the target producer according to the recommended level of the target producer.
For example, in the question-answer community, if the recommendation level of the target producer is higher, the domain concentration of the target producer is higher, and the list to be answered in the relevant domain is used as a recommended production task of the target producer, and is recommended to the target producer for answering, so that the target producer is prompted to produce answer content. As shown in fig. 4, on the target producer's client, a plurality of different types of question tabs may be included, such as a recommended question tab, an all-question tab, a video question tab. And displaying a list to be answered recommended to the target producer in the recommended question tab. Each question in the list is related to the field to which the target producer belongs, such as food, electronics, etc.
Therefore, based on the embodiment, the recommendation level of the target producer is used for knowledge content distribution, so that the quality of the content produced in the knowledge community can be effectively improved. In the knowledge community, core indexes such as the question answering rate and the like can be effectively improved.
In some examples, refined operation of the knowledge community may also be achieved based on information of dimensions of the target producer and the recommendation level. The following list a number of exemplary operations performed according to information for each dimension of the target producer and recommended levels:
(1) And monitoring the service core index. Such as periodically monitoring the number of content producers, the number of premium content producers, etc.
(2) Querying the producer representation. For example, a user may query the portrait tag of the content producer using the identification information of the content producer.
(3) Screening and analyzing the producer. For example, a user may utilize knowledge domain, domain concentration, content quality score to screen out content producers of interest.
(4) And analyzing the business health degree. For example, the knowledge community information is combined with the pull-up and activation data, and high-quality producers produced in the pull-up and activation processes are counted, so that comprehensive health degree analysis aiming at the knowledge community is obtained, and business investigation and decision making are supported.
(5) And managing the tasks. For example, the creation and management of batch query tasks is supported, meeting big data analysis requirements.
According to the method, corresponding domain concentration degree is determined according to the labels of the contents produced by the target producer. And determining the recommended level of the target producer based on the domain concentration, so that the recommended level can accurately embody the concentration degree of the target producer on the domain, and is favorable for accurately identifying the production capacity of the target producer. Further, the method is beneficial to promoting high-quality content producers to produce more high-quality content and keeping the good ecological development of knowledge communities. In practical application, the method can be realized based on a depiction framework. In particular, the characterization frame may include:
the strategy execution module is used for executing the models for acquiring the dimension information uniformly;
the task scheduling module is used for scheduling and concatenating the acquisition sequence of various information and the execution sequence of various steps;
the identification matching module is used for realizing the association between the information of each dimension of the same content producer based on the unique identification of the content producer;
the model management module is used for processing the online, offline, configuration modification and the like of each model;
the data monitoring module is used for monitoring the ring ratio and the same-ratio expression of the data on which the models depend, timely finding out abnormality, reminding operators to process, and guaranteeing the effect of the models;
and the data updating module is used for updating each data according to the requirements.
As an implementation of the above methods, the present disclosure further provides a content processing apparatus, as shown in fig. 5, including:
a domain determining module 510, configured to determine M knowledge domains related to the N content based on the labels of the N content produced by the target producer; wherein N and M are positive integers;
a concentration determining module 520, configured to determine a domain concentration of the N content based on the number of content corresponding to the M knowledge domains, respectively, in the N content;
the level determining module 530 is configured to determine a recommendation level of the target producer based on the domain concentration levels of the N contents.
In an exemplary embodiment, as shown in fig. 6, the concentration determination module 520 includes:
a category entropy determining unit 610, configured to determine category entropies of the N pieces of content based on a ratio of a number of pieces of content corresponding to the M knowledge fields, respectively, to N of the N pieces of content;
the first concentration determining unit 620 is configured to determine the domain concentrations of the N contents according to the class entropies of the N contents.
In an exemplary embodiment, as shown in fig. 7, the concentration determination module 520 includes:
a distribution determining unit 720, configured to determine, when the M knowledge domains include the target knowledge domain, distribution information of the target knowledge domain based on the number of contents corresponding to the M knowledge domains, respectively, in the N contents;
and a second concentration determining unit 730 for determining the domain concentrations of the N contents based on the distribution information of the target knowledge domain.
Illustratively, as shown in fig. 7, the above apparatus further comprises:
the task recommendation module 710 is configured to determine a recommended production task of the target producer according to the recommendation level of the target producer.
Illustratively, as shown in FIG. 7, the level determination module 530 includes:
a quality determination unit 740 for determining a content quality score of the target producer based on the N pieces of content and the classification model;
the level determining unit 750 is configured to determine a recommendation level of the target producer according to the domain concentration of the N contents and the content quality score of the target producer.
The functions of each unit, module or sub-module in each apparatus of the embodiments of the present disclosure may be referred to the corresponding descriptions in the above method embodiments, which are not repeated herein.
According to embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium and a computer program product.
Fig. 8 illustrates a schematic block diagram of an example electronic device 800 that may be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 8, the electronic device 800 includes a computing unit 801 that can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM) 802 or a computer program loaded from a storage unit 808 into a Random Access Memory (RAM) 803. In the RAM 803, various programs and data required for the operation of the electronic device 800 can also be stored. The computing unit 801, the ROM 802, and the RAM 803 are connected to each other by a bus 804. An input output (I/O) interface 805 is also connected to the bus 804.
Various components in electronic device 800 are connected to I/O interface 805, including: an input unit 806 such as a keyboard, mouse, etc.; an output unit 807 such as various types of displays, speakers, and the like; a storage unit 808, such as a magnetic disk, optical disk, etc.; and a communication unit 809, such as a network card, modem, wireless communication transceiver, or the like. The communication unit 809 allows the electronic device 800 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The computing unit 801 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 801 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the respective methods and processes described above, for example, the content processing method. For example, in some embodiments, the content processing method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as the storage unit 808. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 800 via the ROM 802 and/or the communication unit 809. When a computer program is loaded into RAM 803 and executed by computing unit 801, one or more steps of the content processing method described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform the content processing method by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (10)
1. A content processing method, comprising:
determining M knowledge fields related to N contents based on labels of the N contents produced by a target producer; wherein N and M are positive integers;
determining the domain concentration of the N content based on the number of the content corresponding to the M knowledge domains in the N content;
determining a recommendation level of the target producer based on the domain concentrations of the N contents;
the determining the domain concentration of the N content based on the number of the content corresponding to the M knowledge domains, respectively, includes:
determining distribution information of the target knowledge domain based on the number of the N contents respectively corresponding to the M knowledge domains when the M knowledge domains comprise the target knowledge domain;
and determining the domain concentration degree of the N content based on the distribution information of the target knowledge domain.
2. The method of claim 1, wherein the determining the domain concentration of the N content based on the number of content of the N content that respectively correspond to the M knowledge domains further comprises:
determining class entropy of the N contents based on the ratio of the number of the contents corresponding to the M knowledge fields in the N contents to N;
and determining the domain concentration degree of the N contents according to the class entropy of the N contents.
3. The method of any of claims 1-2, further comprising:
and determining a recommended production task of the target producer according to the recommended level of the target producer.
4. The method of any of claims 1-2, wherein the determining the recommendation level for the target producer based on the domain concentrations of the N content comprises:
determining a content quality score for the target producer based on the N content and the classification model;
and determining the recommendation level of the target producer according to the domain concentration degree of the N contents and the content quality scores of the target producer.
5. A content processing apparatus comprising:
the domain determining module is used for determining M knowledge domains related to N contents based on the labels of the N contents produced by the target producer; wherein N and M are positive integers;
a concentration determining module, configured to determine a domain concentration of the N content based on the number of content corresponding to the M knowledge domains, respectively, in the N content;
the level determining module is used for determining the recommended level of the target producer based on the domain concentration degree of the N contents;
the concentration determination module includes:
a distribution determining unit, configured to determine, when the M knowledge domains include a target knowledge domain, distribution information of the target knowledge domain based on the number of contents corresponding to the M knowledge domains, respectively, in the N contents;
and a second concentration determining unit configured to determine the domain concentrations of the N contents based on the distribution information of the target knowledge domains.
6. The apparatus of claim 5, wherein the concentration determination module further comprises:
a category entropy determining unit, configured to determine category entropies of the N pieces of content based on ratios of the number of the N pieces of content corresponding to the M knowledge fields, respectively, to N;
and the first concentration determining unit is used for determining the domain concentration of the N contents according to the class entropy of the N contents.
7. The apparatus of any of claims 5-6, further comprising:
and the task recommending module is used for determining the recommended production task of the target producer according to the recommended level of the target producer.
8. The apparatus of any of claims 5-6, wherein the level determination module comprises:
a quality determination unit for determining a content quality score of the target producer based on the N pieces of content and the classification model;
and the level determining unit is used for determining the recommended level of the target producer according to the domain concentration degree of the N contents and the content quality scores of the target producer.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-4.
10. A non-transitory computer readable storage medium storing computer instructions for causing a computer to perform the method of any one of claims 1-4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110288139.0A CN113032671B (en) | 2021-03-17 | 2021-03-17 | Content processing method, device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110288139.0A CN113032671B (en) | 2021-03-17 | 2021-03-17 | Content processing method, device, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113032671A CN113032671A (en) | 2021-06-25 |
| CN113032671B true CN113032671B (en) | 2024-02-23 |
Family
ID=76472143
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110288139.0A Active CN113032671B (en) | 2021-03-17 | 2021-03-17 | Content processing method, device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113032671B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113468425A (en) * | 2021-06-30 | 2021-10-01 | 北京百度网讯科技有限公司 | Knowledge content distribution method and device, electronic equipment and storage medium |
| CN115375064B (en) * | 2022-02-11 | 2024-02-20 | 中国石油天然气集团有限公司 | Method and device for determining spatial distribution concentration of oil and gas upstream resources |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103577579A (en) * | 2013-11-08 | 2014-02-12 | 南方电网科学研究院有限责任公司 | Resource recommendation method and system based on potential demands of users |
| CN105069121A (en) * | 2015-08-12 | 2015-11-18 | 北京暴风科技股份有限公司 | Video pushing method based on video theme similarity |
| WO2015176652A1 (en) * | 2014-05-20 | 2015-11-26 | Tencent Technology (Shenzhen) Company Limited | Network service recommendation method and apparatus |
| CN105138508A (en) * | 2015-08-06 | 2015-12-09 | 电子科技大学 | Preference diffusion based context recommendation system |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108268619B (en) * | 2018-01-08 | 2020-06-30 | 阿里巴巴集团控股有限公司 | Content recommendation method and device |
| RU2731335C2 (en) * | 2018-10-09 | 2020-09-01 | Общество С Ограниченной Ответственностью "Яндекс" | Method and system for generating recommendations of digital content |
-
2021
- 2021-03-17 CN CN202110288139.0A patent/CN113032671B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103577579A (en) * | 2013-11-08 | 2014-02-12 | 南方电网科学研究院有限责任公司 | Resource recommendation method and system based on potential demands of users |
| WO2015176652A1 (en) * | 2014-05-20 | 2015-11-26 | Tencent Technology (Shenzhen) Company Limited | Network service recommendation method and apparatus |
| CN105138508A (en) * | 2015-08-06 | 2015-12-09 | 电子科技大学 | Preference diffusion based context recommendation system |
| CN105069121A (en) * | 2015-08-12 | 2015-11-18 | 北京暴风科技股份有限公司 | Video pushing method based on video theme similarity |
Non-Patent Citations (3)
| Title |
|---|
| 一种知识驱动的个性化文献推荐方法;郐弘智;闫健卓;陈建辉;;计算机应用研究(12);全文 * |
| 基于Item-CF算法在推荐系统中多样性的研究与应用;夏赵建;中国优秀硕士学位论文全文数据库(第12期);全文 * |
| 考虑标签情绪信息的图书资源个性化推荐方法研究;马晓悦;马昊;;情报理论与实践(09);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113032671A (en) | 2021-06-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Tsui et al. | Knowledge-based extraction of intellectual capital-related information from unstructured data | |
| CN112966081B (en) | Method, device, equipment and storage medium for processing question and answer information | |
| CN113032671B (en) | Content processing method, device, electronic equipment and storage medium | |
| CN105159910A (en) | Information recommendation method and device | |
| CN111966904A (en) | Information recommendation method based on multi-user portrait model and related device | |
| CN114118287A (en) | Sample generation method, sample generation device, electronic device and storage medium | |
| CN112417121A (en) | Client intention recognition method and device, computer equipment and storage medium | |
| CN108920617A (en) | A kind of decision-making system and method, information data processing terminal of data acquisition | |
| CN110990627A (en) | Knowledge graph construction method and device, electronic equipment and medium | |
| CN116109373A (en) | Recommendation method and device for financial products, electronic equipment and medium | |
| US20150294257A1 (en) | Techniques for Reducing Employee Churn Rate | |
| CN109345201A (en) | Human Resources Management Method, device, electronic equipment and storage medium | |
| CN112052297A (en) | Information generation method and device, electronic equipment and computer readable medium | |
| Ying et al. | Effective dynamic dispatching rule and constructive heuristic for solving single-machine scheduling problems with a common due window | |
| CN109934631A (en) | Question and answer information processing method, device and computer equipment | |
| EP2613275A1 (en) | Search device, search method, search program, and computer-readable memory medium for recording search program | |
| CN113344723A (en) | User insurance cognitive evolution path prediction method and device and computer equipment | |
| JP2020057206A (en) | Information processing device | |
| CN108319721B (en) | Application program recommendation method and device, user terminal and server | |
| US20230237076A1 (en) | Automatically drawing infographics for statistical data based on a data model | |
| CN110472140B (en) | Object word recommendation method and device and electronic equipment | |
| CN115034762A (en) | Post recommendation method and device, storage medium, electronic equipment and product | |
| CN113987260A (en) | Video pushing method and device, electronic equipment and storage medium | |
| CN113407827A (en) | Information recommendation method, device, equipment and medium based on user value classification | |
| US20170351747A1 (en) | Data analysis system, data analysis method, and data analysis program |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |