CN110851724B - Article recommendation method based on self-media number grade and related products - Google Patents
Article recommendation method based on self-media number grade and related products Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 53
- 230000001174 ascending effect Effects 0.000 claims abstract description 37
- 230000000593 degrading effect Effects 0.000 claims abstract description 8
- 230000015556 catabolic process Effects 0.000 claims description 58
- 238000006731 degradation reaction Methods 0.000 claims description 58
- 230000036541 health Effects 0.000 claims description 28
- 238000004590 computer program Methods 0.000 claims description 11
- 230000000630 rising effect Effects 0.000 claims description 11
- 238000004364 calculation method Methods 0.000 claims description 10
- 230000008569 process Effects 0.000 claims description 9
- 238000013507 mapping Methods 0.000 claims description 8
- 238000012545 processing Methods 0.000 abstract description 7
- 238000010586 diagram Methods 0.000 description 10
- 230000006870 function Effects 0.000 description 8
- 230000006399 behavior Effects 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 4
- 238000007689 inspection Methods 0.000 description 4
- 230000005540 biological transmission Effects 0.000 description 3
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- 238000010168 coupling process Methods 0.000 description 3
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- 230000003287 optical effect Effects 0.000 description 2
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- 230000009286 beneficial effect Effects 0.000 description 1
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- 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
Abstract
The embodiment of the invention discloses an article recommending method based on self-media number grade and a related product, comprising the following steps: obtaining M self-media numbers and article information corresponding to the M self-media numbers respectively, and obtaining first ascending grades and first degrading grades corresponding to the M self-media numbers according to the M self-media numbers and the article information; processing the first ascending grades and the first degrading grades corresponding to the M self-media numbers respectively to obtain self-media number grades corresponding to the M self-media numbers respectively; when an article recommendation request sent by a first terminal is received, acquiring a user grade corresponding to the first terminal, and recommending articles corresponding to the self-media number grade matched with the user grade to the first terminal. The self-media number and the article information corresponding to the self-media number are rated, the grade corresponding to the self-media number is obtained, and then recommendation is carried out, so that the self-media number management system is more accurate.
Description
Technical Field
The present invention relates to the field of computer technologies, and in particular, to an article recommendation method, a server, an electronic device, and a storage medium based on a self-media number level.
Background
A WeChat looks at the product, which is to recommend information to the user in the form of information stream. All recommended information under the micro-letter ecology has a corresponding account number, namely a self-media number. In the prior art, quality control is generally performed directly on recommended information such as articles, and is not controlled from the latitude of the media number. Since the self-media number dimension is not considered, there will be articles that are recommended to the user's needs when recommending the articles.
Disclosure of Invention
The embodiment of the application provides an article recommendation method, a server, electronic equipment and a storage medium based on a self-media number grade, which can be used for recommending articles based on the self-media number grade and meet the requirements of users.
A first aspect of an embodiment of the present application provides an article recommendation method based on a self-media number level, including:
the method comprises the steps that a server obtains M self-media numbers and article information corresponding to the M self-media numbers respectively, wherein M is a positive integer;
the server obtains first ascending grades corresponding to the M self-media numbers and first degradation grades corresponding to the M self-media numbers according to the M self-media numbers and article information corresponding to the M self-media numbers respectively;
The server processes the first ascending grades and the first descending grades corresponding to the M self-media numbers respectively to obtain the self-media number grades corresponding to the M self-media numbers respectively;
when receiving an article recommendation request sent by a first terminal, the server acquires a user grade corresponding to the first terminal, and recommends articles corresponding to the self-media number grade matched with the user grade to the first terminal.
Optionally, the article information includes article originality information and article region coverage information, and the server obtains first ascending grades corresponding to the M self-media numbers according to the M self-media numbers and the article information corresponding to the M self-media numbers, respectively, including:
the server acquires authority degree information corresponding to the M self-media numbers respectively, and obtains first scores of the M self-media numbers respectively according to the authority degree information;
the server obtains second scores of the M self-media numbers according to the article originality information of the M self-media numbers;
the server obtains third scores of the M self-media numbers according to the article region coverage information of the M self-media numbers;
The server obtains fourth scores corresponding to the M self-media numbers according to the first score, the second score, the third score, the first weight corresponding to the first score, the second weight corresponding to the second score and the third weight corresponding to the third score, wherein the sum of the first weight, the second weight and the third weight is 1;
and the server obtains the first ascending grades corresponding to the fourth scores of the M self-media numbers according to the mapping relation between the preset scores and the ascending grades of the self-media numbers.
Optionally, the article information further includes article health degree information, and the server obtains first degradation levels corresponding to the M self-media numbers according to the M self-media numbers and the article information corresponding to the M self-media numbers, where the first degradation levels include:
the server respectively acquires first degradation levels corresponding to the M self-media numbers according to the article health degree information of the M self-media numbers, wherein when the health degree information meets a first preset condition, the first degradation level of the self-media number corresponding to the article health degree information is a first preset value; when the article health degree information meets a second preset condition, the first degradation level of the self-media number corresponding to the health degree information is a second preset value.
Optionally, the server processes the first ascending grades and the first degrading grades corresponding to the M self-media numbers respectively to obtain self-media number grades corresponding to the M self-media numbers respectively, including:
the server respectively acquires the number group grades of the M self-media numbers;
the server subtracts the first degradation grades corresponding to the M self-media numbers from the first rising grades corresponding to the M self-media numbers respectively, and subtracts the number group grades of the M self-media numbers to obtain the self-media number grades corresponding to the M self-media numbers respectively.
The server obtains the number group grades of the M self-media numbers respectively, and the method comprises the following steps:
the server respectively acquires the M management IP login accounts and/or operator accounts and/or main account numbers of the self-media numbers;
the server acquires N self-media number management IP login accounts and/or operator accounts and/or main body accounts from the M self-media number management IP login accounts and/or operator accounts and/or main body accounts, wherein N is not greater than M, and N is a positive integer;
The server sets the second degradation levels corresponding to the N self-media numbers as preset degradation levels, wherein the preset degradation levels are number group levels corresponding to the N self-media numbers respectively; the server sets the number group level of the other self-media numbers except for the N self-media numbers to 0.
Or, optionally, the server processes the first ascending grades and the first degrading grades corresponding to the M self-media numbers respectively to obtain the self-media number grades corresponding to the M self-media numbers respectively, which includes:
the server respectively acquires the M management IP login accounts and/or operator accounts and/or main account numbers of the self-media numbers;
the server acquires N self-media number management IP login accounts and/or operator accounts and/or main body accounts from the M self-media number management IP login accounts and/or operator accounts and/or main body accounts, wherein N is not greater than M, and N is a positive integer;
the server sets the second degradation levels corresponding to the N self-media numbers as preset degradation levels;
The server subtracts the first degradation levels corresponding to the M self-media numbers from the first rising levels corresponding to the M self-media numbers respectively to obtain the self-media number levels corresponding to the M self-media numbers respectively, and updates the self-media number levels corresponding to the N self-media numbers in the M self-media numbers according to the second degradation levels corresponding to the N self-media numbers respectively.
Optionally, the server obtains a user level corresponding to the first terminal, including:
the server acquires the identification information of the first terminal;
the server acquires the reading quantity of the historical articles corresponding to the identification information in preset time;
and the server obtains the user grade corresponding to the first terminal according to the mapping relation between the preset article reading quantity and the user grade.
Optionally, the server recommends an article corresponding to the self-media number level matched with the user level to the first terminal, including:
the server acquires the historical article reading type information corresponding to the identification information in preset time;
the server acquires a first article category corresponding to the identification information, wherein the first article category is the article category corresponding to the historical article reading type information with the maximum number of historical articles to read;
The server acquires a first self-media number grade matched with the user grade according to the user grade and the relation between the preset user grade and the self-media number grade;
the server acquires S self-media numbers corresponding to the first self-media number level and article categories corresponding to the S self-media numbers respectively, wherein S is not more than M, and S is a positive integer;
the server acquires a first self-media number corresponding to the first article category from the article categories respectively corresponding to the S self-media numbers;
and recommending the article corresponding to the first self-media number to the first terminal by the server.
A second aspect of the embodiments of the present application provides an article recommendation server, including:
the system comprises an acquisition module, a storage module and a storage module, wherein the acquisition module is used for acquiring M self-media numbers and article information corresponding to the M self-media numbers respectively, wherein M is a positive integer;
the first calculation module is used for obtaining first ascending grades corresponding to the M self-media numbers and first degradation grades corresponding to the M self-media numbers according to the M self-media numbers and article information corresponding to the M self-media numbers respectively;
the second calculation module is used for processing the first ascending grades and the first degrading grades corresponding to the M self-media numbers respectively to obtain self-media number grades corresponding to the M self-media numbers respectively;
And the recommending module is used for acquiring the user grade corresponding to the first terminal when receiving the article recommending request sent by the first terminal, and recommending articles corresponding to the self-media number grade matched with the user grade to the first terminal.
A third aspect of the embodiments of the present application provides an electronic device, including a processor, an input device, an output device, and a memory, where the processor, the input device, the output device, and the memory are connected to each other, and the memory is configured to store a computer program, where the computer program includes program instructions, and where the processor is configured to invoke the program instructions to perform the method.
A fourth aspect of the embodiments provides a computer readable storage medium storing a computer program for execution by a processor to implement the method.
The implementation of the embodiment of the application has at least the following beneficial effects:
according to the embodiment of the application, the self-media number grades respectively corresponding to the self-media numbers are obtained through obtaining the self-media numbers and the article information respectively corresponding to the self-media numbers; and then when a user request is received, recommending the article corresponding to the self-media number grade matched with the user grade by acquiring the user grade. By adopting the means, the self-media number and the article information corresponding to the self-media number are rated to obtain the grade corresponding to the self-media number, and then the recommendation is carried out, so that the self-media number management system is more accurate and improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Wherein:
fig. 1 is a schematic diagram of a network architecture according to an embodiment of the present invention;
FIG. 2 is a schematic view of a scenario of an article recommendation method based on a self-media number level according to an embodiment of the present invention;
FIG. 3 is a flowchart of an article recommendation method based on a self-media number level according to an embodiment of the present invention;
FIG. 4 is a flowchart of an article recommendation method based on a self-media number level according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of a method for determining a self-media number ranking based on multiple factors according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of an article recommendation method based on a self-media number ranking according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention;
Fig. 8 is a schematic structural diagram of an article recommendation device based on a self-media number level according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The terms first, second and the like in the description and in the claims of the present application and in the above-described figures, are used for distinguishing between different objects and not for describing a particular sequential order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly understand that the embodiments described herein may be combined with other embodiments.
The embodiment of the invention provides an article recommendation method based on self-media number grades, wherein the self-media number grades corresponding to the respective media numbers are obtained through obtaining the self-media numbers and article information corresponding to the self-media numbers respectively; and then when a user request is received, recommending the article corresponding to the self-media number grade matched with the user grade by acquiring the user grade. By adopting the means, the self-media number and the article information corresponding to the self-media number are rated to obtain the grade corresponding to the self-media number, and then the recommendation is carried out, so that the self-media number management system is more accurate and improved.
The self-media number in the embodiment of the application may be a public number, a header number, or the like.
Referring to fig. 1, fig. 1 is a schematic diagram of a network architecture according to an embodiment of the present application. The network architecture may include a plurality of servers and a plurality of terminal devices, as shown in fig. 1, specifically includes a terminal device 100a, a terminal device 100b, a terminal device 100c, a server 200a, and a server 200b, where the server 200a may perform data transmission with each terminal device through a network, each terminal device may install a reading information class application (such as a micro-letter look-up applet), and the server 200a may be a background server corresponding to the reading information class application, so that each terminal device may perform data transmission with the server 200a through a client corresponding to the reading information class application, such as the server 200a may send recommendation information to each terminal device, and the server 200b may be a data processing server, or may be referred to as an information recommendation server, that may determine different information recommendation data for each terminal device, and the server 200b may perform data transmission with the plurality of terminal devices through the server 200 a. The terminal device may include a cell phone, tablet, notebook, palm top, mobile internet device (mobile internet device, MID), wearable device (e.g., smart watch, smart bracelet, etc.). Each terminal device can display the recommended information stream, i.e. the recommended information, in the client corresponding to the read information class application.
The information contained in the recommended information stream displayed in each terminal device may be different, and specific information contained in the recommended information stream may be determined by a user history behavior corresponding to the terminal device, where the user history behavior may be represented as operations of clicking, reading time, downloading, etc. each time in a client corresponding to the information reading application before the current time. Referring to fig. 2, a schematic view of a scenario of an article recommendation method based on a self-media number level according to an embodiment of the present invention is shown. As shown in fig. 2, taking the terminal device 100a in the embodiment corresponding to fig. 1 as an example, the server 200 may include the server 200a and the server 200b in the embodiment corresponding to fig. 1, after opening a glance interface of a reading information application such as a WeChat, the terminal device 100a may first display a default first page of the WeChat for glance in a terminal display interface, in the first page, a plurality of function options may be displayed, such as "friend is looking at" option, "choice" option, and when the user selects the "choice" option 400, the user may jump to a presentation page corresponding to the "choice" option 400, at this time, no recommendation information is displayed in the display area 300a in the presentation page, so the terminal device 100a may respond to the selection operation of the user for the "choice" option 400, and send an information stream access request to the server 200 to request to obtain an information recommendation. The server 100 may determine, according to the information flow access request, a device number or user identification information corresponding to the requesting terminal device 100a, that is, determine a user to be requested, further obtain historical behavior data corresponding to the user, and determine historical browsing information and a user level corresponding to the user according to the historical behavior data, so that the self-media number level corresponding to the user level and an information article corresponding to the self-media number level obtained by calculation of the server 200 may be obtained from a media number level database and recommended to the terminal, where the recommendation field generated by the obtained information article may be displayed in the display area 300a, where the recommendation field includes a corresponding information title 301a and recommended website source information 302a. It should be noted that, the server 200 may perform the self-media number ranking calculation on all self-media numbers included in the reading information application, so as to obtain a self-media number ranking database, so as to perform recommendation of corresponding self-media number articles. The self-media number can be, for example, "reading has a track", "ten-point reading", etc.
Referring to fig. 3, fig. 3 is a flowchart of an article recommendation method based on a self-media number level according to an embodiment of the present application. As shown in fig. 3, it may include steps 301-304, which are specifically as follows:
301. the method comprises the steps that a server obtains M self-media numbers and article information corresponding to the M self-media numbers respectively, wherein M is a positive integer;
the server can acquire a large amount of self-media numbers and article information corresponding to the self-media numbers respectively so as to rate the self-media numbers. The article information may be originality, number of real users, geographical coverage, and the like. The number of users can be not only the number of vermicelli from media numbers, but also noise removal is needed to remove some zombie vermicelli introduced by cheating means, and the relative number of zombie vermicelli can be removed by calculating vermicelli bound with a bank card. The above region coverage may be a region distribution of media number fans, such as 6-level accounts in which the number of fans is qualified as the highest level only for nationwide accounts.
The server may obtain the self-media number from a data source, such as a self-media number corresponding to an article corresponding to a news channel including an entertainment channel, a science and technology channel, a sports channel, and the like.
302. The server obtains first ascending grades corresponding to the M self-media numbers and first degradation grades corresponding to the M self-media numbers according to the M self-media numbers and article information corresponding to the M self-media numbers respectively;
the server may input the M self-media numbers and the article information corresponding to the M self-media numbers respectively into a preset self-media number first calculation model, so as to calculate and obtain first ascending grades corresponding to the M self-media numbers respectively.
The preset first calculation model for the self-media numbers may score according to the M self-media numbers and the article information corresponding to the M self-media numbers, and calculate first ascending grades corresponding to the M self-media numbers according to dimensions corresponding to different information.
Optionally, the article information may include article originality information and article region coverage information, and the server obtains first ascending grades corresponding to the M self-media numbers according to the M self-media numbers and the article information corresponding to the M self-media numbers, respectively, including steps 3021 to 3025:
3021. the server acquires authority degree information corresponding to the M self-media numbers respectively, and obtains first scores of the M self-media numbers respectively according to the authority degree information;
The server may obtain authority information corresponding to the respective media numbers by obtaining the authority information corresponding to the respective media numbers from the media number information, for example, by judging authority information corresponding to the respective media numbers based on the article authenticity information and the reliability information of the preset time. The server can respectively correspond to different scores according to different preset authority information, so as to obtain first scores of respective media numbers.
3022. The server obtains second scores of the M self-media numbers according to the article originality information of the M self-media numbers;
3023. the server obtains third scores of the M self-media numbers according to the article region coverage information of the M self-media numbers;
similarly, the server may obtain the second scores of the M self-media numbers according to the article originality information of the respective media numbers, and obtain the third scores of the M self-media numbers according to the article region coverage information of the self-media numbers, respectively.
3024. The server obtains fourth scores corresponding to the M self-media numbers according to the first score, the second score, the third score, the first weight corresponding to the first score, the second weight corresponding to the second score and the third weight corresponding to the third score, wherein the sum of the first weight, the second weight and the third weight is 1;
3025. And the server obtains the first ascending grades corresponding to the fourth scores of the M self-media numbers according to the mapping relation between the preset scores and the ascending grades of the self-media numbers.
The above embodiments are described by taking authority information, originality information of an article, and regional coverage information of an article from a media number as examples, and the present invention is not limited to the above information. The foregoing may also include information from the number of real users of the media number, the category of the media number, and so forth.
Further, the article information further includes article health degree information, and the server obtains first degradation levels corresponding to the M self-media numbers according to the M self-media numbers and the article information corresponding to the M self-media numbers, respectively, including:
the server respectively acquires first degradation levels corresponding to the M self-media numbers according to the article health degree information of the M self-media numbers, wherein when the health degree information meets a first preset condition, the first degradation level of the self-media number corresponding to the article health degree information is a first preset value; when the article health degree information meets a second preset condition, the first degradation level of the self-media number corresponding to the health degree information is a second preset value.
The article health information from the media number may include, but is not limited to, induced click, advertisement, pornography, colloquial, and the like. The present solution is not limited to the above article health information. The method can also obtain the number of vermicelli, for example, the number of vermicelli is too low, and the first degradation level can also be influenced.
Wherein the different health degree information corresponds to different degradation. Alternatively, the degradation of the self-media number corresponding to the preset health degree information may be set to the same level as the first rising level corresponding to the self-media number.
303. The server processes the first ascending grades and the first descending grades corresponding to the M self-media numbers respectively to obtain the self-media number grades corresponding to the M self-media numbers respectively;
the server may subtract the first degradation levels corresponding to the M self-media numbers from the first rising levels corresponding to the M self-media numbers, so as to obtain the self-media number levels corresponding to the M self-media numbers.
Or, the self-media number grades corresponding to the M self-media numbers can be obtained by calculating the weight occupied by the first ascending grades corresponding to the M self-media numbers and the weight occupied by the first descending grades corresponding to the M self-media numbers.
304. When receiving an article recommendation request sent by a first terminal, the server acquires a user grade corresponding to the first terminal, and recommends articles corresponding to the self-media number grade matched with the user grade to the first terminal.
The self-media number grade corresponding to the user grade can be obtained through a preset mapping relation between the user grade and the self-media number grade, and further an article corresponding to the self-media number grade is obtained and recommended to the terminal.
If the corresponding user level is the highest, only recommending the articles with the highest self-media number; for new users, only articles with the highest self-media number can be recommended for improving the retention; for a general user, articles of a general level of self media number, etc. may be recommended. In particular, based on the recommendation of friends in view, the scheme can be displayed without limitation of the self-media number level.
According to the embodiment of the application, the self-media number grades corresponding to the self-media numbers are obtained through obtaining the self-media numbers and the article information corresponding to the self-media numbers respectively; and then when a user request is received, recommending the article corresponding to the self-media number grade matched with the user grade by acquiring the user grade. By adopting the means, the self-media number and the article information corresponding to the self-media number are rated to obtain the grade corresponding to the self-media number, and then the recommendation is carried out, so that the self-media number management system is more accurate and improved. Further, the self-media number rating of the scheme can be dynamic and updated continuously in real time, so that accuracy of the self-media number rating is guaranteed, article recommendation accuracy is improved, and user experience is further improved.
Referring to fig. 4, fig. 4 is a flowchart of an article recommendation method based on a self-media number level according to an embodiment of the present application. As shown in fig. 4, it may include steps 401-407, which are specifically as follows:
401. the method comprises the steps that a server obtains M self-media numbers and article information corresponding to the M self-media numbers respectively, wherein M is a positive integer;
the server can acquire a large amount of self-media numbers and article information corresponding to the self-media numbers respectively so as to rate the self-media numbers. The article information may be originality, number of real users, geographical coverage, and the like.
The server may obtain the self-media number from a data source, such as a self-media number corresponding to an article corresponding to a news channel including an entertainment channel, a science and technology channel, a sports channel, and the like.
402. The server obtains first ascending grades corresponding to the M self-media numbers and first degradation grades corresponding to the M self-media numbers according to the M self-media numbers and article information corresponding to the M self-media numbers respectively;
referring to fig. 5, a schematic diagram of a method for determining a self-media number ranking based on multiple factors according to an embodiment of the present application is shown. The means that the server obtains the first ascending grades corresponding to the M self-media numbers and the first degrading grades corresponding to the M self-media numbers according to the M self-media numbers and the article information corresponding to the M self-media numbers, respectively, may refer to the description of the embodiment shown in fig. 3, and will not be repeated herein.
403. The server respectively acquires the M management IP login accounts and/or operator accounts and/or main account numbers of the self-media numbers;
404. the server acquires N self-media number management IP login accounts and/or operator accounts and/or main body accounts from the M self-media number management IP login accounts and/or operator accounts and/or main body accounts, wherein N is not greater than M, and N is a positive integer;
405. the server sets the second degradation levels corresponding to the N self-media numbers as preset degradation levels;
the server may obtain the management IP login account number and/or the operator account number and/or the main account number of M self-media numbers, and then obtain N self-media numbers with the same management IP login account number and/or operator account number and/or main account number. The N self-media numbers may be all corresponding to the same management IP login account and/or operator account and/or main account, or N1 corresponding to the first management IP login account and/or operator account and/or main account, N2 corresponding to the second management IP login account and/or operator account and/or main account, and so on, where N1 and N2 are positive integers not less than 2 and less than N.
The second degradation levels corresponding to the N self-media numbers may be the same preset degradation level, or may be determined according to the number of self-media numbers of the same management IP login account and/or operator account and/or main account, for example, the greater the number of self-media numbers corresponding to the same management IP login account and/or operator account and/or main account, the greater the corresponding second degradation level, and so on.
406. The server subtracts the first degradation levels corresponding to the M self-media numbers from the first rising levels corresponding to the M self-media numbers respectively to obtain the self-media number levels corresponding to the M self-media numbers respectively, and updates the self-media number levels corresponding to the N self-media numbers in the M self-media numbers according to the second degradation levels corresponding to the N self-media numbers respectively;
the server subtracts the first degradation levels corresponding to the M self-media numbers from the first rising levels corresponding to the M self-media numbers respectively to obtain the self-media number levels corresponding to the M self-media numbers respectively, and updates the self-media number levels corresponding to the N self-media numbers respectively based on the obtained second degradation levels, thereby obtaining the self-media number levels corresponding to the M self-media numbers respectively.
As another optional implementation manner, the processing, by the server, the first ascending grades and the first downgrade corresponding to the M self-media numbers respectively to obtain self-media number grades corresponding to the M self-media numbers respectively includes: the server respectively acquires the number group grades of the M self-media numbers; the server subtracts the first degradation grades corresponding to the M self-media numbers from the first rising grades corresponding to the M self-media numbers respectively, and subtracts the number group grades of the M self-media numbers to obtain the self-media number grades corresponding to the M self-media numbers respectively.
The server respectively acquires the number group grades of the M self-media numbers, including that the server respectively acquires the management IP login account numbers and/or operator account numbers and/or main account numbers of the M self-media numbers; the server acquires N self-media number management IP login accounts and/or operator accounts and/or main body accounts from the M self-media number management IP login accounts and/or operator accounts and/or main body accounts, wherein N is not greater than M, and N is a positive integer; the server sets the second degradation levels corresponding to the N self-media numbers as preset degradation levels, wherein the preset degradation levels are number group levels corresponding to the N self-media numbers respectively; the server sets the number group level of the other self-media numbers except the N self-media numbers to 0
That is, the present solution considers not only the first rising level and the first degradation level of the self-media number, but also the number group level, where the number group refers to a plurality of accounts operated by the same benefit group. Such as an account with the same principal, an account with the same operator, or an account with the same administrative IP login. In order to hit against the content generator with the number, the number making and the violation by the small number, the scheme does not singly examine a single account, but examines the whole number group related to the account. The cluster rating may rate all accounts of the same cluster as the same level.
The above embodiment only considers the aspects of managing the IP login account, the operator account, the main account, and the like, and the scheme is not limited to the above description, and can comprehensively consider factors of other dimensions. The specific implementation of the present invention may refer to the above embodiments, and will not be described herein.
407. When receiving an article recommendation request sent by a first terminal, the server acquires a user grade corresponding to the first terminal, and recommends articles corresponding to the self-media number grade matched with the user grade to the first terminal.
The server obtaining the user grade corresponding to the first terminal includes:
The server acquires the identification information of the first terminal;
the server acquires the reading quantity of the historical articles corresponding to the identification information in preset time;
and the server obtains the user grade corresponding to the first terminal according to the mapping relation between the preset article reading quantity and the user grade.
The server may obtain the reading number of the historical articles of the user from the following data sources, for example, view the historical data of various news channels including entertainment channels, science and technology channels, sports channels, etc., or obtain the reading article information from a search log in a QQ browser, a search log in a TT browser, or any other browser or search engine, or from a social platform, which may include microblogs, bar tags, discussion groups, etc., and specifically, determine according to the actual application scenario, which is not limited herein.
The predetermined time may be, for example, one month, three months, one year, etc., and is not particularly limited herein.
Further, the server recommends an article corresponding to the self-media number level matched with the user level to the first terminal, including:
the server acquires the historical article reading type information corresponding to the identification information in preset time;
The server acquires a first article category corresponding to the identification information, wherein the first article category is the article category corresponding to the historical article reading type information with the maximum number of historical articles to read;
the server acquires a first self-media number grade matched with the user grade according to the user grade and the relation between the preset user grade and the self-media number grade;
the server acquires S self-media numbers corresponding to the first self-media number level and article categories corresponding to the S self-media numbers respectively, wherein S is not more than M, and S is a positive integer;
the server acquires a first self-media number corresponding to the first article category from the article categories respectively corresponding to the S self-media numbers;
and recommending the article corresponding to the first self-media number to the first terminal by the server.
If the server obtains that the first article category is the tour article, the server obtains the first self-media number corresponding to the tour article from the article category corresponding to the S self-media numbers by obtaining the first self-media number grade corresponding to the user grade, then obtaining the S self-media numbers corresponding to the first self-media number grade and the article category corresponding to the S self-media numbers respectively, and then obtaining the articles in the first self-media number and recommending the articles to the first terminal.
According to the embodiment of the application, the self-media number grades corresponding to the respective media numbers are obtained by obtaining the self-media numbers and the article information corresponding to the self-media numbers respectively, and meanwhile, the number group factors are further considered and the self-media number grades corresponding to the same management IP login account number and/or operator account number and/or main account number are updated; and then when a user request is received, recommending the article corresponding to the self-media number grade matched with the user grade by acquiring the user grade. By adopting the means, the self-media number and the article information corresponding to the self-media number are rated to obtain the grade corresponding to the self-media number, and then the recommendation is carried out, so that the self-media number management system is more accurate and improved.
Referring to fig. 6, fig. 6 is a schematic diagram of an article recommendation method based on a self-media number level according to an embodiment of the present application. As shown in fig. 6, by acquiring various information of the respective media numbers and inputting the information into a machine rating model, the machine rating model may be as shown in fig. 5, thereby obtaining the ratings of the respective media numbers. And then the server can send the high-level self-media number exceeding the preset level to the manual evaluation module for inspection, and when the manual inspection is completed, the corresponding self-media number is stored in the evaluated account number pool. The server also sends the low-level self-media number lower than the preset level to the manual evaluation module for inspection, and when the manual inspection is completed, the corresponding self-media number is also stored in the evaluated account number pool. Wherein, the rating can be performed by adopting a machine learning mode so as to ensure the accuracy of the rating of the self-media number. Further, the self-media number rating of embodiments of the present application may be dynamic, that is, the self-media number rating derived from the self-media number rating is variable, not constant. Which varies with the update of each information from the media number article and from the media number.
According to the embodiment of the application, the quality control problem of article recommendation ecology is solved by establishing a rating system of the self-media number, information feedback in all aspects can be fused rapidly, the self-media number is dynamically rated in real time, the recommendation ecology at a glance such as WeChat is influenced through the rating system, and the exposure of articles can be increased for high-grade self-media numbers; for low-level self-media numbers, exposure of their articles is limited.
In accordance with the foregoing embodiments, referring to fig. 7, fig. 7 is a schematic structural diagram of an electronic device provided in the embodiment of the present application, as shown in the fig. 7, including a processor, an input device, an output device, and a memory, where the processor, the input device, the output device, and the memory are connected to each other, and the memory is configured to store a computer program, where the computer program includes program instructions, where the processor is configured to invoke the program instructions, and where the program includes instructions for performing the following steps;
m self-media numbers and article information respectively corresponding to the M self-media numbers are acquired, wherein M is a positive integer;
obtaining first ascending grades corresponding to the M self-media numbers and first degradation grades corresponding to the M self-media numbers according to the M self-media numbers and article information corresponding to the M self-media numbers respectively;
Processing the first ascending grades and the first degrading grades corresponding to the M self-media numbers respectively to obtain self-media number grades corresponding to the M self-media numbers respectively;
when an article recommendation request sent by a first terminal is received, acquiring a user grade corresponding to the first terminal, and recommending articles corresponding to the self-media number grade matched with the user grade to the first terminal.
According to the embodiment of the application, the self-media number grades respectively corresponding to the self-media numbers are obtained through obtaining the self-media numbers and the article information respectively corresponding to the self-media numbers; and then when a user request is received, recommending the article corresponding to the self-media number grade matched with the user grade by acquiring the user grade. By adopting the means, the self-media number and the article information corresponding to the self-media number are rated to obtain the grade corresponding to the self-media number, and then the recommendation is carried out, so that the self-media number management system is more accurate and improved.
The foregoing description of the embodiments of the present application has been presented primarily in terms of a method-side implementation. It will be appreciated that, in order to achieve the above-mentioned functions, the terminal includes corresponding hardware structures and/or software modules for performing the respective functions. Those of skill in the art will readily appreciate that the elements and algorithm steps described in connection with the embodiments disclosed herein may be embodied as hardware or a combination of hardware and computer software. Whether a function is implemented as hardware or computer software driven hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The embodiment of the application may divide the functional units of the terminal according to the above method example, for example, each functional unit may be divided corresponding to each function, or two or more functions may be integrated in one processing unit. The integrated units may be implemented in hardware or in software functional units. It should be noted that, in the embodiment of the present application, the division of the units is schematic, which is merely a logic function division, and other division manners may be implemented in actual practice.
In accordance with the foregoing, referring to fig. 8, fig. 8 is a schematic structural diagram of an article recommendation device based on a self-media number level according to an embodiment of the present application. The method comprises an acquisition module 801, a first calculation module 802, a second calculation module 803 and a recommendation module 804, wherein the specific steps are as follows:
an obtaining module 801, configured to obtain M self-media numbers and article information corresponding to the M self-media numbers respectively, where M is a positive integer;
a first calculation module 802, configured to obtain, according to the M self-media numbers and article information corresponding to the M self-media numbers, a first ascending level corresponding to the M self-media numbers and a first descending level corresponding to the M self-media numbers;
A second calculating module 803, configured to process the first ascending grades and the first descending grades corresponding to the M self-media numbers respectively to obtain self-media number grades corresponding to the M self-media numbers respectively;
and the recommending module 804 is configured to obtain a user level corresponding to a first terminal when receiving an article recommending request sent by the first terminal, and recommend an article corresponding to a self-media number level matched with the user level to the first terminal.
It can be seen that, according to the embodiment of the present application, the self-media number grades corresponding to the respective media numbers are obtained by obtaining the self-media numbers and the article information corresponding to the self-media numbers respectively; and then when a user request is received, recommending the article corresponding to the self-media number grade matched with the user grade by acquiring the user grade. By adopting the means, the self-media number and the article information corresponding to the self-media number are rated to obtain the grade corresponding to the self-media number, and then the recommendation is carried out, so that the self-media number management system is more accurate and improved.
The embodiment of the present application also provides a computer storage medium, where the computer storage medium stores a computer program for electronic data exchange, where the computer program causes a computer to perform some or all of the steps of any of the article recommendation methods based on self-media number ranking as described in the above method embodiments.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer-readable storage medium storing a computer program that causes a computer to perform some or all of the steps of any of the article recommendation methods based on self-media number ranking as described in the method embodiments above.
It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required in the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, such as the division of the units, merely a logical function division, and there may be additional manners of dividing the actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, or may be in electrical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present invention may be integrated in one processing unit, each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units described above may be implemented either in hardware or in software program modules.
The integrated units, if implemented in the form of software program modules, may be stored in a computer-readable memory for sale or use as a stand-alone product. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, including several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the method described in the embodiments of the present application. And the aforementioned memory includes: a U-disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be implemented by a program that instructs associated hardware, and the program may be stored in a computer readable memory, which may include: flash disk, read-only memory, random access memory, magnetic or optical disk, etc.
The foregoing has outlined rather broadly the more detailed description of embodiments of the present application, wherein specific examples are provided herein to illustrate the principles and embodiments of the present application, the above examples being provided solely to assist in the understanding of the methods of the present application and the core ideas thereof; meanwhile, as those skilled in the art will have modifications in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (7)
1. An article recommendation method based on self-media number level, comprising:
the method comprises the steps that a server obtains M self-media numbers and article information corresponding to the M self-media numbers respectively, wherein M is a positive integer; the article information comprises article originality information, article region coverage information and article health degree information;
The server obtains first ascending grades corresponding to the M self-media numbers and first degradation grades corresponding to the M self-media numbers according to the M self-media numbers and article information corresponding to the M self-media numbers respectively;
the server processes the first ascending grades and the first degrading grades corresponding to the M self-media numbers respectively to obtain the self-media number grades corresponding to the M self-media numbers respectively, and the method comprises the following steps: the server respectively acquires the number group grades of the M self-media numbers; the server subtracts the first degradation grades corresponding to the M self-media numbers from the first rising grades corresponding to the M self-media numbers respectively, and subtracts the number group grades of the M self-media numbers to obtain the self-media number grades corresponding to the M self-media numbers respectively;
when receiving an article recommendation request sent by a first terminal, the server acquires a user grade corresponding to the first terminal and recommends articles corresponding to the self-media number grade matched with the user grade to the first terminal;
the server obtains first ascending grades corresponding to the M self-media numbers according to the M self-media numbers and article information corresponding to the M self-media numbers respectively, and the first ascending grades comprise: the server acquires authority degree information corresponding to the M self-media numbers respectively, and obtains first scores of the M self-media numbers respectively according to the authority degree information; the server obtains second scores of the M self-media numbers according to the article originality information of the M self-media numbers; the server obtains third scores of the M self-media numbers according to the article region coverage information of the M self-media numbers; the server obtains fourth scores corresponding to the M self-media numbers according to the first score, the second score, the third score, the first weight corresponding to the first score, the second weight corresponding to the second score and the third weight corresponding to the third score, wherein the sum of the first weight, the second weight and the third weight is 1; the server obtains first ascending grades corresponding to the fourth scores of the M self-media numbers according to the mapping relation between the preset scores and the ascending grades of the self-media numbers;
The server obtains first degradation grades corresponding to the M self-media numbers according to the M self-media numbers and article information corresponding to the M self-media numbers respectively, and the first degradation grades comprise: the server respectively acquires first degradation levels corresponding to the M self-media numbers according to the article health degree information of the M self-media numbers, wherein when the article health degree information meets a first preset condition, the first degradation level of the self-media number corresponding to the article health degree information is a first preset value; when the article health degree information meets a second preset condition, the first degradation level of the self-media number corresponding to the article health degree information is a second preset value.
2. The method of claim 1, wherein the server obtains the number group ranking of the M self-media numbers, respectively, comprising:
the server respectively acquires the M management IP login accounts and/or operator accounts and/or main account numbers of the self-media numbers;
the server acquires N self-media number management IP login accounts and/or operator accounts and/or main body accounts from the M self-media number management IP login accounts and/or operator accounts and/or main body accounts, wherein N is not greater than M, and N is a positive integer;
The server sets the second degradation levels corresponding to the N self-media numbers as preset degradation levels, wherein the preset degradation levels are number group levels corresponding to the N self-media numbers respectively; the server sets the number group level of the other self-media numbers except for the N self-media numbers to 0.
3. The method according to any one of claims 1 to 2, wherein the server obtaining the user class corresponding to the first terminal includes:
the server acquires the identification information of the first terminal;
the server acquires the reading quantity of the historical articles corresponding to the identification information in preset time;
and the server obtains the user grade corresponding to the first terminal according to the mapping relation between the preset article reading quantity and the user grade.
4. The method of claim 3, wherein the server recommending articles corresponding to the self-media number ratings matched to the user ratings to the first terminal, comprising:
the server acquires the historical article reading type information corresponding to the identification information in preset time;
the server acquires a first article category corresponding to the identification information, wherein the first article category is the article category corresponding to the historical article reading type information with the maximum number of historical articles to read;
The server acquires a first self-media number grade matched with the user grade according to the user grade and the relation between the preset user grade and the self-media number grade;
the server acquires S self-media numbers corresponding to the first self-media number level and article categories corresponding to the S self-media numbers respectively, wherein S is not more than M, and S is a positive integer;
the server acquires a first self-media number corresponding to the first article category from the article categories respectively corresponding to the S self-media numbers;
and recommending the article corresponding to the first self-media number to the first terminal by the server.
5. An article recommendation server, comprising:
the system comprises an acquisition module, a storage module and a storage module, wherein the acquisition module is used for acquiring M self-media numbers and article information corresponding to the M self-media numbers respectively, wherein M is a positive integer; the article information comprises article originality information, article region coverage information and article health degree information;
the first calculation module is used for obtaining first ascending grades corresponding to the M self-media numbers and first degradation grades corresponding to the M self-media numbers according to the M self-media numbers and article information corresponding to the M self-media numbers respectively; the server obtains first ascending grades corresponding to the M self-media numbers according to the M self-media numbers and article information corresponding to the M self-media numbers respectively, and the first ascending grades comprise: the server acquires authority degree information corresponding to the M self-media numbers respectively, and obtains first scores of the M self-media numbers respectively according to the authority degree information; the server obtains second scores of the M self-media numbers according to the article originality information of the M self-media numbers; the server obtains third scores of the M self-media numbers according to the article region coverage information of the M self-media numbers; the server obtains fourth scores corresponding to the M self-media numbers according to the first score, the second score, the third score, the first weight corresponding to the first score, the second weight corresponding to the second score and the third weight corresponding to the third score, wherein the sum of the first weight, the second weight and the third weight is 1; the server obtains first ascending grades corresponding to the fourth scores of the M self-media numbers according to the mapping relation between the preset scores and the ascending grades of the self-media numbers; the server obtains first degradation grades corresponding to the M self-media numbers according to the M self-media numbers and article information corresponding to the M self-media numbers respectively, and the first degradation grades comprise: the server respectively acquires first degradation levels corresponding to the M self-media numbers according to the article health degree information of the M self-media numbers, wherein when the article health degree information meets a first preset condition, the first degradation level of the self-media number corresponding to the article health degree information is a first preset value; when the article health degree information meets a second preset condition, the first degradation level of the self-media number corresponding to the article health degree information is a second preset value;
The second calculating module is configured to process the first rising levels and the first degradation levels corresponding to the M self-media numbers respectively to obtain self-media number levels corresponding to the M self-media numbers respectively, and includes: the server respectively acquires the number group grades of the M self-media numbers; the server subtracts the first degradation grades corresponding to the M self-media numbers from the first rising grades corresponding to the M self-media numbers respectively, and subtracts the number group grades of the M self-media numbers to obtain the self-media number grades corresponding to the M self-media numbers respectively;
and the recommending module is used for acquiring the user grade corresponding to the first terminal when receiving the article recommending request sent by the first terminal, and recommending articles corresponding to the self-media number grade matched with the user grade to the first terminal.
6. An electronic device comprising a processor, an input device, an output device and a memory, the processor, the input device, the output device and the memory being interconnected, wherein the memory is adapted to store a computer program comprising program instructions, the processor being configured to invoke the program instructions to perform the method of any of claims 1-4.
7. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program, which is executed by a processor to implement the method of any one of claims 1 to 4.
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