CN110851724A - 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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Abstract
The embodiment of the invention discloses an article recommendation method based on self-media number grades and a related product, wherein the article recommendation method comprises the following steps: acquiring article information corresponding to M self-media numbers and the M self-media numbers respectively, and acquiring a first ascending grade and a first degrading grade corresponding to the M self-media numbers respectively 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 the self-media number grades corresponding to the M self-media numbers respectively; when an article recommendation request sent by a first terminal is received, obtaining a user grade corresponding to the first terminal, and recommending an article corresponding to a self-media number grade matched with the user grade to the first terminal. The article information respectively corresponding to the self-media number and the self-media number is graded to obtain the grade corresponding to the self-media number, and then recommendation is performed, so that the method is more accurate, and the systematicness of self-media number management is improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to an article recommendation method based on self-media number grades, a server, electronic equipment and a storage medium.
Background
The Wechat can see the product at a glance and recommend information to the user in the form of information flow. All recommended information under the WeChat ecology has corresponding account numbers, namely self-media numbers. In the prior art, generally, the quality control is directly carried out on recommended information such as articles, and the control is not carried out from the latitude of a media number. Because the dimension of the self-media number is not considered, when articles are recommended, the articles which do not meet the requirements of the user can be recommended.
Disclosure of Invention
The embodiment of the application provides an article recommendation method, a server, an electronic device and a storage medium based on self-media number grades, which can be used for article recommendation based on the self-media number grades and better 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 rating, 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 descending grades corresponding to the M self-media numbers according to the article information corresponding to the M self-media numbers and 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;
when an article recommendation request sent by a first terminal is received, the server acquires a user grade corresponding to the first terminal and recommends an article corresponding to a 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, according to the M self-media numbers and the article information corresponding to the M self-media numbers respectively, first ascending grades 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 respectively obtains second scores of the M self-media numbers according to the article originality information of the M self-media numbers;
the server respectively obtains third scores of the M self-media numbers according to article region coverage information of the M self-media numbers;
the server obtains fourth scores corresponding to the M number of the self-media numbers respectively according to the first score, the second score and the third score as well as a first weight corresponding to the first score, a second weight corresponding to the second score and a 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 first ascending grades respectively corresponding to fourth scores of the M self-media numbers according to a mapping relation between preset scores and ascending grades of the self-media numbers.
Optionally, the article information further includes article health degree information, and the server obtains, according to the M self-media numbers and the article information corresponding to the M self-media numbers, first degradation levels corresponding to the M self-media numbers, respectively, including:
the server respectively acquires first degradation levels respectively 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; and 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 processing, by the server, the first ascending ranking and the first descending ranking that respectively correspond to the M self-media numbers to obtain respective self-media number rankings that respectively correspond to the M self-media numbers includes:
the server respectively acquires the number group grades of the M self-media numbers;
and the server subtracts the first ascending grades respectively corresponding to the M self-media numbers from the first descending grades respectively corresponding to the M self-media numbers, and subtracts the number group grades of the M self-media numbers to obtain the self-media number grades respectively corresponding to the M self-media numbers.
Wherein, the server respectively obtains the number group grades of the M number of the self-media numbers, and the method comprises the following steps:
the server respectively acquires the management IP login accounts and/or operator accounts and/or main account of the M self-media numbers;
the server acquires N self-media number management IP login accounts and/or operator accounts and/or main accounts from M self-media number management IP login accounts and/or operator accounts and/or main accounts, wherein the N self-media number management IP login accounts and/or operator accounts and/or main accounts are the same, N is not more than M, and N is a positive integer;
the server sets second degradation grades respectively corresponding to the N self-media numbers as preset degradation grades, wherein the preset degradation grades are number group grades respectively corresponding to the N self-media numbers; the server sets the number group rank of the other self-media numbers except the N self-media numbers to 0.
Or, optionally, the step of processing, by the server, the first ascending ranking and the first descending ranking respectively corresponding to the M self-media numbers to obtain the self-media number rankings respectively corresponding to the M self-media numbers includes:
the server respectively acquires the management IP login accounts and/or operator accounts and/or main account of the M self-media numbers;
the server acquires N self-media number management IP login accounts and/or operator accounts and/or main accounts from M self-media number management IP login accounts and/or operator accounts and/or main accounts, wherein the N self-media number management IP login accounts and/or operator accounts and/or main accounts are the same, N is not more than M, and N is a positive integer;
the server sets second degradation levels respectively corresponding to the N self-media numbers as preset degradation levels;
the server subtracts the first degradation grades respectively corresponding to the M self-media numbers from the first ascending grades respectively corresponding to the M self-media numbers to obtain the self-media number grades respectively corresponding to the M self-media numbers, and updates the self-media number grades respectively corresponding to the N self-media numbers in the M self-media numbers according to the second degradation grades respectively corresponding to the N self-media numbers.
Optionally, the obtaining, by the server, the user level corresponding to the first terminal includes:
the server acquires identification information of the first terminal;
the server acquires the reading quantity of the historical articles corresponding to the identification information within preset time;
and the server obtains the user level corresponding to the first terminal according to the mapping relation between the preset article reading quantity and the user level.
Optionally, the recommending, by the server, the article corresponding to the self-media number rating matched with the user rating to the first terminal includes:
the server acquires reading type information of a historical article corresponding to the identification information within preset time;
the server acquires a first article category corresponding to the identification information, wherein the first article category is the article category of which the reading type information of the historical articles corresponds to the maximum reading quantity of the historical articles;
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 grade and article categories corresponding to the S self-media numbers respectively, wherein S is not more than M and is a positive integer;
the server acquires a first self-media number corresponding to the first article category from article categories respectively corresponding to the S self-media numbers;
and the server recommends the article corresponding to the first self-media number to the first terminal.
A second aspect of an embodiment of the present application provides an article recommendation server, including:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring M self-media numbers and article information corresponding to the M self-media numbers respectively, and M is a positive integer;
a first calculating module, configured to obtain, according to the article information corresponding to the M self-media numbers and the article information corresponding to the M self-media numbers, first ascending grades corresponding to the M self-media numbers respectively and first descending grades 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 the self-media number grades corresponding to the M self-media numbers respectively;
the recommendation module is used for acquiring the user grade corresponding to the first terminal when receiving an article recommendation request sent by the first terminal, and recommending the article 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, where the memory is used to store a computer program, and the computer program includes program instructions, and the processor is configured to call the program instructions to execute the method.
A fourth aspect of embodiments of the present application provides a computer-readable storage medium having a computer program stored thereon, the computer program being executable by a processor to implement the method.
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 respective media numbers are obtained by obtaining the self-media numbers and the article information respectively corresponding to the self-media numbers; and then recommending the article corresponding to the self-media number grade matched with the user grade by acquiring the user grade when receiving the user request. By adopting the method, the article information respectively corresponding to the self-media number and the self-media number is graded to obtain the grade corresponding to the self-media number, and then recommendation is carried out, so that the method is more accurate, and the systematicness of self-media number management is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Wherein:
fig. 1 is a schematic diagram of a network architecture according to an embodiment of the present invention;
FIG. 2 is a scene diagram of an article recommendation method based on self-media number ratings according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating an article recommendation method based on self-media number ratings according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating an article recommendation method based on self-media number ratings according to an embodiment of the present invention;
FIG. 5 is a diagram illustrating 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 diagram illustrating a method for article recommendation based on self-media number ratings 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 self-media number ratings according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively 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 can be included in at least one embodiment of the specification. The appearances of the phrase 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. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can 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 respectively corresponding to respective media numbers are obtained by obtaining article information respectively corresponding to the self-media numbers and the self-media numbers; and then recommending the article corresponding to the self-media number grade matched with the user grade by acquiring the user grade when receiving the user request. By adopting the method, the article information respectively corresponding to the self-media number and the self-media number is graded to obtain the grade corresponding to the self-media number, and then recommendation is carried out, so that the method is more accurate, and the systematicness of self-media number management is improved.
The self-media number in the embodiment of the present application may be a public number, or a headline number.
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 including 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 application (e.g., a small program for viewing WeChat), the server 200a may be a background server corresponding to the reading information application, and therefore, each terminal device may perform data transmission with the server 200a through a client corresponding to the reading information application, for example, the server 200a may send recommendation information to each terminal device, the server 200b may be a data processing server, which may also be referred to as an information recommendation server, that is, different information recommendation data may be determined for each terminal device, the server 200b can perform data transmission with a plurality of terminal apparatuses through the server 200 a. The terminal device may include a mobile phone, a tablet computer, a notebook computer, a palm computer, a Mobile Internet Device (MID), and a wearable device (e.g., a smart watch, a smart band, etc.). Each terminal device can display recommendation information flow, namely recommendation information, in the client corresponding to the reading information application.
The information contained in the recommended information stream displayed in each terminal device may be different, the specific information contained in the recommended information stream may be determined by the user history behavior corresponding to the terminal device, and the user history behavior may be represented as operations of each click, reading time, downloading, and the like in the client corresponding to the reading information application before the current time of the user. Referring to fig. 2, a scene diagram of an article recommendation method based on self-media number ratings 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 the terminal device 100a opens a viewing interface for reading information applications such as WeChat, a default first page for viewing WeChat may be displayed in the terminal display interface first, in the home page, several functional options may be displayed, such as the "friends are watching" option, "pick" option, etc., respectively, and when the user selects "pick" option 400, a jump may be made to the presentation page corresponding to the "pick" option 400, at which point no recommendation information has been displayed in the display area 300a of the presentation page, therefore, the terminal device 100a can respond to the user's selection operation of the "pick" option 400 to send a stream access request to the server 200 to request to obtain information recommendation. The server 100 may determine, according to the information stream access request, an apparatus number or user identification information corresponding to the terminal apparatus 100a that issues the request, that is, determine a user to be requested, the server 200 further obtains historical behavior data corresponding to the user, and determines, according to the historical behavior data, historical browsing information and a user rating corresponding to the user, so as to obtain, from a media number rating database, a self media number rating corresponding to the user rating, and recommend, to the terminal, information articles and the like corresponding to the self media number rating, which are calculated by the server 200, where the step of generating a recommendation column for the obtained information articles may be displayed in the display area 300a, where the recommendation column includes a corresponding information title 301a and recommended website source information 302 a. It should be noted that the server 200 may perform the self-media number ranking calculation on all the self-media numbers included in the reading information application to obtain the self-media number ranking database, so as to recommend the corresponding self-media number articles. The self-media number may be "read with track", "read ten" or the like.
Referring to fig. 3, fig. 3 is a flowchart illustrating an article recommendation method based on self-media number ratings according to an embodiment of the present application. As shown in fig. 3, it may include steps 301 and 304 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 obtain a large amount of article information corresponding to the self-media numbers and the self-media numbers respectively so as to grade the self-media numbers. The article information can be original creation degree, number of real users, region coverage and the like. The number of the users can be not only the number of fans of the media number, but also denoising is needed, some zombie fans introduced by cheating means are removed, and relative number of zombie fans can be removed by calculating fans bound with the bank card. The region coverage can be the region distribution of media number fans, for example, accounts with fan numbers distributed all over the country are qualified to become the highest 6-level accounts.
The server may obtain the self-media number from a data source, such as the self-media number corresponding to articles corresponding to various news channels including entertainment channels, science and technology channels, military channels, and sports channels.
302. The server obtains first ascending grades corresponding to the M self-media numbers and first descending grades corresponding to the M self-media numbers according to the article information corresponding to the M self-media numbers and the M self-media numbers respectively;
the server can input the M self-media numbers and the article information corresponding to the M self-media numbers to a preset first calculation model of the self-media numbers, so as to calculate and obtain first ascending grades corresponding to the M self-media numbers.
The preset self-media number first calculation model may be used to score according to the M self-media numbers and the article information corresponding to the M self-media numbers, and calculate the first ascending grades corresponding to the M self-media numbers according to the dimensions corresponding to different information.
Optionally, the article information may include article originality information and article region coverage information, and the server obtains the first ascending grades respectively corresponding to the M self-media numbers according to the M self-media numbers and the article information respectively corresponding to the M self-media numbers, including step 3021-3025:
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 can obtain authority degree information corresponding to each media number through the obtained media number information, for example, authority degree information corresponding to each media number is judged through article authenticity information and reliability information based on preset time. The server can respectively correspond to different scores according to preset different authority degree information, and then first scores of respective media numbers are obtained.
3022. The server respectively obtains second scores of the M self-media numbers according to the article originality information of the M self-media numbers;
3023. the server respectively obtains third scores of the M self-media numbers according to 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.
3024. The server obtains fourth scores corresponding to the M number of the self-media numbers respectively according to the first score, the second score and the third score as well as a first weight corresponding to the first score, a second weight corresponding to the second score and a 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 first ascending grades respectively corresponding to fourth scores of the M self-media numbers according to a mapping relation between preset scores and ascending grades of the self-media numbers.
The above embodiments are described only by taking authority information of the self-media number, original article creation information, and article region coverage information as examples, and the present solution is not limited to the above information. The above may also include information from the number of real users of the media number, from the category of the media number, etc.
Further, the article information further includes article health degree information, and the server obtains, according to the M self-media numbers and the article information corresponding to the M self-media numbers, first degradation levels corresponding to the M self-media numbers, respectively, including:
the server respectively acquires first degradation levels respectively 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; and 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, the effects of inducing clicks, advertising, pornography, vulgar, etc. The present solution is not limited to the above article health information. The scheme can also influence the first degradation level and the like by acquiring the number of fans, such as the number of fans is too low.
Wherein the different health 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 ascending ranking corresponding to the self-media number described above.
303. 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;
the server can subtract the first ascending grades respectively corresponding to the M self-media numbers from the first descending grades respectively corresponding to the M self-media numbers to obtain the self-media number grades respectively corresponding to the M self-media numbers.
Or, the respective self-media number grades corresponding to the M self-media numbers can be obtained by calculating the weight occupied by the first ascending grade corresponding to the M self-media numbers respectively and the weight occupied by the first descending grade corresponding to the M self-media numbers respectively.
304. When an article recommendation request sent by a first terminal is received, the server acquires a user grade corresponding to the first terminal and recommends an article corresponding to a 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 mapping relation between a preset user grade and the self-media number grade, and then an article corresponding to the self-media number grade is obtained and recommended to the terminal.
If the corresponding user level is the highest high-end user, only recommending the article with the highest level self-media number; for a new user, in order to improve the retention, only the article with the highest level from the media number can be recommended; for a general user, general ratings of articles from media numbers, etc. may be recommended. Particularly, based on the recommendation of the friends when watching, the media number can be displayed without the limitation of the media number level.
According to the embodiment of the application, the self-media number grades respectively corresponding to the respective media numbers are obtained by obtaining the self-media numbers and the article information respectively corresponding to the self-media numbers; and then recommending the article corresponding to the self-media number grade matched with the user grade by acquiring the user grade when receiving the user request. By adopting the method, the article information respectively corresponding to the self-media number and the self-media number is graded to obtain the grade corresponding to the self-media number, and then recommendation is carried out, so that the method is more accurate, and the systematicness of self-media number management is improved. Furthermore, the self-media number rating of the scheme can be dynamic and can be continuously updated in real time, so that the accuracy of the self-media number rating is ensured, the article recommendation accuracy is improved, and the user experience is further improved.
Referring to fig. 4, fig. 4 is a flowchart illustrating an article recommendation method based on self-media number ratings according to an embodiment of the present application. As shown in fig. 4, it may include steps 401 and 407 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 obtain a large amount of article information corresponding to the self-media numbers and the self-media numbers respectively so as to grade the self-media numbers. The article information can be original creation degree, number of real users, region coverage and the like.
The server may obtain the self-media number from a data source, such as the self-media number corresponding to articles corresponding to various news channels including entertainment channels, science and technology channels, military channels, and sports channels.
402. The server obtains first ascending grades corresponding to the M self-media numbers and first descending grades corresponding to the M self-media numbers according to the article information corresponding to the M self-media numbers and the M self-media numbers respectively;
reference may be made to fig. 5, which is a schematic diagram illustrating a method for determining a self-media number ranking based on multiple factors according to an embodiment of the present application. In this scheme, the server obtains, according to the article information corresponding to the M self-media numbers and the M self-media numbers, first ascending grades corresponding to the M self-media numbers and first descending grades corresponding to the M self-media numbers, respectively, may refer to the description of the embodiment shown in fig. 3, and details are not repeated here.
403. The server respectively acquires the management IP login accounts and/or operator accounts and/or main account of the M self-media numbers;
404. the server acquires N self-media number management IP login accounts and/or operator accounts and/or main accounts from M self-media number management IP login accounts and/or operator accounts and/or main accounts, wherein the N self-media number management IP login accounts and/or operator accounts and/or main accounts are the same, N is not more than M, and N is a positive integer;
405. the server sets second degradation levels respectively corresponding to the N self-media numbers as preset degradation levels;
the server can acquire M management IP login accounts and/or operator accounts and/or main body accounts of the self-media numbers, and then acquire N self-media numbers with the same management IP login account and/or operator account and/or main body account. The N self-media numbers may all correspond to the same management IP login account and/or carrier account and/or subject account, or N1 of the N self-media numbers correspond to the first management IP login account and/or carrier account and/or subject account, N2 of the N self-media numbers correspond to the second management IP login account and/or carrier account and/or subject account, and the like, where N1 and N2 are positive integers not less than 2 and less than N.
The second degradation levels respectively 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 carrier account and/or main account, for example, the more the number of self-media numbers corresponding to the same management IP login account and/or carrier account and/or main account is, the larger the corresponding second degradation level is, and the like.
406. The server subtracts first degradation grades respectively corresponding to the M self-media numbers from first ascending grades respectively corresponding to the M self-media numbers to obtain self-media number grades respectively corresponding to the M self-media numbers, and updates self-media number grades respectively corresponding to the N self-media numbers in the M self-media numbers according to second degradation grades respectively corresponding to the N self-media numbers;
the server subtracts the first degradation grades respectively corresponding to the M self-media numbers from the first ascending grades respectively corresponding to the M self-media numbers to obtain self-media number grades respectively corresponding to the M self-media numbers, and then updates the self-media number grades respectively corresponding to the N self-media numbers based on the obtained second degradation grades to further obtain self-media number grades respectively corresponding to the M self-media numbers.
As another optional implementation manner, the processing, by the server, the first ascending ranking and the first descending ranking that respectively correspond to the M self-media numbers to obtain self-media number rankings that respectively correspond to the M self-media numbers includes: the server respectively acquires the number group grades of the M self-media numbers; and the server subtracts the first ascending grades respectively corresponding to the M self-media numbers from the first descending grades respectively corresponding to the M self-media numbers, and subtracts the number group grades of the M self-media numbers to obtain the self-media number grades respectively corresponding to the M self-media numbers.
The server respectively acquires the number group grades of the M self-media numbers, and comprises the steps that the server respectively acquires management IP login accounts and/or operator accounts 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 accounts from M self-media number management IP login accounts and/or operator accounts and/or main accounts, wherein the N self-media number management IP login accounts and/or operator accounts and/or main accounts are the same, N is not more than M, and N is a positive integer; the server sets second degradation grades respectively corresponding to the N self-media numbers as preset degradation grades, wherein the preset degradation grades are number group grades respectively corresponding to the N self-media numbers; the server sets the number group level of other self-media numbers except the N self-media numbers to 0
That is, the present solution considers not only the first ascending level and the first descending level of the self-media number, but also the number group level, where the number group refers to a plurality of account numbers controlled by the same interest group. Such as accounts with the same principal, accounts with the same operator, or accounts with the same administrative IP login. In order to hit a content generator who breaks rules through raising numbers, making numbers and small numbers, the scheme does not consider a single account number singly, but considers the whole number group related to the account number. Number group rating may rate all account numbers of the same number group to the same rank.
The above embodiment only considers aspects of managing an IP login account, an operator account, a main account, and the like, and the scheme is not limited to the above description, and may also comprehensively consider factors of other dimensions. The specific implementation can refer to the above embodiments, and details are not repeated herein.
407. When an article recommendation request sent by a first terminal is received, the server acquires a user grade corresponding to the first terminal and recommends an article corresponding to a self-media number grade matched with the user grade to the first terminal.
The obtaining, by the server, the user level corresponding to the first terminal includes:
the server acquires identification information of the first terminal;
the server acquires the reading quantity of the historical articles corresponding to the identification information within preset time;
and the server obtains the user level corresponding to the first terminal according to the mapping relation between the preset article reading quantity and the user level.
The server may obtain the reading amount of the historical articles of the user from the following data sources, for example, to view historical data of various news channels including an entertainment channel, a science and technology channel, a military channel, a sports channel, and the like, or obtain the read 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 obtain the read article information from a social platform including a microblog, a sticker, a discussion group, and the like, which is specifically determined according to an actual application scenario, and 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 recommending, by the server, the article corresponding to the self-media number rating matched with the user rating to the first terminal includes:
the server acquires reading type information of a historical article corresponding to the identification information within preset time;
the server acquires a first article category corresponding to the identification information, wherein the first article category is the article category of which the reading type information of the historical articles corresponds to the maximum reading quantity of the historical articles;
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 grade and article categories corresponding to the S self-media numbers respectively, wherein S is not more than M and is a positive integer;
the server acquires a first self-media number corresponding to the first article category from article categories respectively corresponding to the S self-media numbers;
and the server recommends the article corresponding to the first self-media number to the first terminal.
If the server acquires that the first article category is a travel note article, the server acquires a first self-media number level corresponding to a user level, then acquires S self-media numbers corresponding to the first self-media number level and article categories corresponding to the S self-media numbers respectively, acquires the first self-media number of the corresponding travel note article from the article categories corresponding to the S self-media numbers respectively, and then acquires the article in the first self-media number and recommends the article 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 the self-media number grades corresponding to the same management IP login account and/or the carrier account and/or the main account are updated by further considering the number group factor; and then recommending the article corresponding to the self-media number grade matched with the user grade by acquiring the user grade when receiving the user request. By adopting the method, the article information respectively corresponding to the self-media number and the self-media number is graded to obtain the grade corresponding to the self-media number, and then recommendation is carried out, so that the method is more accurate, and the systematicness of self-media number management is improved.
Referring to fig. 6, fig. 6 is a schematic diagram of an article recommendation method based on self-media number ratings according to an embodiment of the present application. As shown in fig. 6, wherein the machine rating model may be as shown in fig. 5 by obtaining various information of respective media numbers and inputting the information into the machine rating model, thereby obtaining ratings of the respective media numbers. And then the server can send the high-level self-media numbers exceeding the preset level to a manual evaluation module for examination, and after the manual examination is finished, the corresponding self-media numbers are stored in an evaluated account pool. And the server also sends the low-level self-media numbers lower than the preset level to the manual evaluation module for examination, and stores the corresponding self-media numbers to the evaluated account number pool after the manual examination is finished. The method can be used for grading in a machine learning mode so as to guarantee the grading accuracy 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 media number rating is variable and not persistent. Which changes with the updating of the article from the media number and the respective information from the media number.
The quality control problem of the article recommendation ecology is solved by establishing a rating system of the self-media number, information feedback of all aspects can be quickly fused, dynamic rating is carried out on the self-media number in real time, the recommendation ecology which is seen at a glance is influenced by the rating system, and the exposure of the article can be increased for high-grade self-media numbers; for low-ranked self-media numbers, exposure of their articles is limited.
In accordance with the foregoing embodiments, please refer to fig. 7, fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application, and as shown in the drawing, the electronic device includes 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, where the memory is used to store a computer program, the computer program includes program instructions, the processor is configured to call the program instructions, and the program includes instructions for performing the following steps;
acquiring M self-media numbers and article information corresponding to the M self-media numbers respectively, wherein M is a positive integer;
obtaining first ascending grades corresponding to the M self-media numbers and first degrading grades corresponding to the M self-media numbers according to the article information corresponding to the M self-media numbers and 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 the self-media number grades corresponding to the M self-media numbers respectively;
when an article recommendation request sent by a first terminal is received, obtaining a user grade corresponding to the first terminal, and recommending an article corresponding to a 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 respective media numbers are obtained by obtaining the self-media numbers and the article information respectively corresponding to the self-media numbers; and then recommending the article corresponding to the self-media number grade matched with the user grade by acquiring the user grade when receiving the user request. By adopting the method, the article information respectively corresponding to the self-media number and the self-media number is graded to obtain the grade corresponding to the self-media number, and then recommendation is carried out, so that the method is more accurate, and the systematicness of self-media number management is improved.
The above description has introduced the solution of the embodiment of the present application mainly from the perspective of the method-side implementation process. It is understood that the terminal includes corresponding hardware structures and/or software modules for performing the respective functions in order to implement the above-described functions. Those of skill in the art will readily appreciate that the present application is capable of hardware or a combination of hardware and computer software implementing the various illustrative elements and algorithm steps described in connection with the embodiments provided herein. Whether a function is performed as hardware or computer software drives 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.
In the embodiment of the present application, the terminal may be divided into the functional units 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 into one processing unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. It should be noted that the division of the unit in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.
In accordance with the above, please refer to fig. 8, fig. 8 is a schematic structural diagram of an article recommendation device based on self-media number ratings according to an embodiment of the present application. The system comprises an acquisition module 801, a first calculation module 802, a second calculation module 803 and a recommendation module 804, wherein the following are specifically provided:
an obtaining module 801, configured to obtain M self-media numbers and article information corresponding to the M self-media numbers, where M is a positive integer;
a first calculating module 802, configured to obtain, according to the article information corresponding to the M self-media numbers and the article information corresponding to the M self-media numbers, first ascending grades corresponding to the M self-media numbers respectively and first descending grades corresponding to the M self-media numbers respectively;
a second calculating module 803, configured to process the first ascending ranking and the first descending ranking corresponding to the M self-media numbers, respectively, to obtain self-media number rankings corresponding to the M self-media numbers, respectively;
the recommending module 804 is configured to, when receiving an article recommending request sent by a first terminal, acquire a user rating corresponding to the first terminal, and recommend an article corresponding to a self-media number rating matched with the user rating to the first terminal.
It can be seen that, according to the embodiment of the present application, the article information corresponding to the self-media number and the self-media number respectively is obtained to obtain the self-media number grades corresponding to the respective media numbers; and then recommending the article corresponding to the self-media number grade matched with the user grade by acquiring the user grade when receiving the user request. By adopting the method, the article information respectively corresponding to the self-media number and the self-media number is graded to obtain the grade corresponding to the self-media number, and then recommendation is carried out, so that the method is more accurate, and the systematicness of self-media number management is improved.
Embodiments of the present application also provide a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any of the self-media number ranking-based article recommendation methods described in the above method embodiments.
Embodiments of the present application also provide a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program causes a computer to execute some or all of the steps of any of the self-media number ranking-based article recommendation methods described in the above method embodiments.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may be implemented in the form of a software program module.
The integrated units, if implemented in the form of software program modules and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned memory comprises: various media capable of storing program codes, such as a usb disk, a read-only memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and the like.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash memory disks, read-only memory, random access memory, magnetic or optical disks, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. An article recommendation method based on self-media number ratings is characterized by comprising the following steps:
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 descending grades corresponding to the M self-media numbers according to the article information corresponding to the M self-media numbers and 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;
when an article recommendation request sent by a first terminal is received, the server acquires a user grade corresponding to the first terminal and recommends an article corresponding to a self-media number grade matched with the user grade to the first terminal.
2. The method of claim 1, wherein the article information includes article originality information and article region coverage information, and the obtaining, by the server, first ascending grades respectively corresponding to the M self-media numbers according to the M self-media numbers and the article information respectively corresponding to the M self-media numbers comprises:
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 respectively obtains second scores of the M self-media numbers according to the article originality information of the M self-media numbers;
the server respectively obtains third scores of the M self-media numbers according to article region coverage information of the M self-media numbers;
the server obtains fourth scores corresponding to the M number of the self-media numbers respectively according to the first score, the second score and the third score as well as a first weight corresponding to the first score, a second weight corresponding to the second score and a 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 first ascending grades respectively corresponding to fourth scores of the M self-media numbers according to a mapping relation between preset scores and ascending grades of the self-media numbers.
3. The method of claim 2, wherein the article information further includes article health information, and the obtaining, by the server, the first degradation levels respectively corresponding to the M self-media numbers according to the article information respectively corresponding to the M self-media numbers and the M self-media numbers comprises:
the server respectively acquires first degradation levels respectively 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; and 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.
4. The method according to any one of claims 1 to 3, wherein the server processes the first ascending ranking and the first descending ranking corresponding to the M self-media numbers respectively to obtain the self-media number rankings corresponding to the M self-media numbers respectively, and includes:
the server respectively acquires the number group grades of the M self-media numbers;
and the server subtracts the first ascending grades respectively corresponding to the M self-media numbers from the first descending grades respectively corresponding to the M self-media numbers, and subtracts the number group grades of the M self-media numbers to obtain the self-media number grades respectively corresponding to the M self-media numbers.
5. The method of claim 4, wherein the server obtains the number group ratings of the M self-media numbers respectively, and comprises:
the server respectively acquires the management IP login accounts and/or operator accounts and/or main account of the M self-media numbers;
the server acquires N self-media number management IP login accounts and/or operator accounts and/or main accounts from M self-media number management IP login accounts and/or operator accounts and/or main accounts, wherein the N self-media number management IP login accounts and/or operator accounts and/or main accounts are the same, N is not more than M, and N is a positive integer;
the server sets second degradation grades respectively corresponding to the N self-media numbers as preset degradation grades, wherein the preset degradation grades are number group grades respectively corresponding to the N self-media numbers; the server sets the number group rank of the other self-media numbers except the N self-media numbers to 0.
6. The method according to any one of claims 1 to 5, wherein the server obtaining the user level corresponding to the first terminal comprises:
the server acquires identification information of the first terminal;
the server acquires the reading quantity of the historical articles corresponding to the identification information within preset time;
and the server obtains the user level corresponding to the first terminal according to the mapping relation between the preset article reading quantity and the user level.
7. The method of claim 6, wherein the server recommending the article corresponding to the self-media number rating matching the user rating to the first terminal comprises:
the server acquires reading type information of a historical article corresponding to the identification information within preset time;
the server acquires a first article category corresponding to the identification information, wherein the first article category is the article category of which the reading type information of the historical articles corresponds to the maximum reading quantity of the historical articles;
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 grade and article categories corresponding to the S self-media numbers respectively, wherein S is not more than M and is a positive integer;
the server acquires a first self-media number corresponding to the first article category from article categories respectively corresponding to the S self-media numbers;
and the server recommends the article corresponding to the first self-media number to the first terminal.
8. An article recommendation server, comprising:
the system comprises an acquisition module, a processing module and a display module, wherein the acquisition module is used for acquiring M self-media numbers and article information corresponding to the M self-media numbers respectively, and M is a positive integer;
a first calculating module, configured to obtain, according to the article information corresponding to the M self-media numbers and the article information corresponding to the M self-media numbers, first ascending grades corresponding to the M self-media numbers respectively and first descending grades 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 the self-media number grades corresponding to the M self-media numbers respectively;
the recommendation module is used for acquiring the user grade corresponding to the first terminal when receiving an article recommendation request sent by the first terminal, and recommending the article corresponding to the self-media number grade matched with the user grade to the first terminal.
9. 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 configured 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 to 7.
10. 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 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111931073A (en) * | 2020-10-10 | 2020-11-13 | 腾讯科技（深圳）有限公司 | Content pushing method and device, electronic equipment and computer readable medium |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101686249A (en) * | 2008-09-27 | 2010-03-31 | 华为技术有限公司 | Subscription method and system of recommended information and recommended service server |
| CN101689075A (en) * | 2007-04-02 | 2010-03-31 | 纳珀企业有限责任公司 | Rating media item recommendations using recommendation paths and/or media item usage |
| KR20160019427A (en) * | 2013-06-10 | 2016-02-19 | 톰슨 라이센싱 | Method and system for recommending media to a user |
| US20170177717A1 (en) * | 2015-12-21 | 2017-06-22 | The Knife, LLC | Rating a level of journalistic distortion in news media content |
| CN107087235A (en) * | 2017-04-21 | 2017-08-22 | 腾讯科技（深圳）有限公司 | Media content recommendations method, server and client |
| US20170322947A1 (en) * | 2016-05-03 | 2017-11-09 | Echostar Technologies L.L.C. | Providing media content based on media element preferences |
| CN107343053A (en) * | 2017-08-01 | 2017-11-10 | 骆德轩 | A kind of content recommendation method |
| CN107566896A (en) * | 2017-08-17 | 2018-01-09 | 上海擎感智能科技有限公司 | Multimedia messages recommend method and device, storage medium, terminal |
| CN108509583A (en) * | 2018-03-29 | 2018-09-07 | 广东欧珀移动通信有限公司 | A kind of information-pushing method, server and computer readable storage medium |
| CN110334356A (en) * | 2019-07-15 | 2019-10-15 | 腾讯科技（深圳）有限公司 | Article matter method for determination of amount, article screening technique and corresponding device |
-
2019
- 2019-11-15 CN CN201911118450.XA patent/CN110851724B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101689075A (en) * | 2007-04-02 | 2010-03-31 | 纳珀企业有限责任公司 | Rating media item recommendations using recommendation paths and/or media item usage |
| CN101686249A (en) * | 2008-09-27 | 2010-03-31 | 华为技术有限公司 | Subscription method and system of recommended information and recommended service server |
| KR20160019427A (en) * | 2013-06-10 | 2016-02-19 | 톰슨 라이센싱 | Method and system for recommending media to a user |
| US20170177717A1 (en) * | 2015-12-21 | 2017-06-22 | The Knife, LLC | Rating a level of journalistic distortion in news media content |
| US20170322947A1 (en) * | 2016-05-03 | 2017-11-09 | Echostar Technologies L.L.C. | Providing media content based on media element preferences |
| CN107087235A (en) * | 2017-04-21 | 2017-08-22 | 腾讯科技（深圳）有限公司 | Media content recommendations method, server and client |
| US20190222899A1 (en) * | 2017-04-21 | 2019-07-18 | Tencent Technology (Shenzhen) Company Limited | Media content recommendation method, server, client, and storage medium |
| CN107343053A (en) * | 2017-08-01 | 2017-11-10 | 骆德轩 | A kind of content recommendation method |
| CN107566896A (en) * | 2017-08-17 | 2018-01-09 | 上海擎感智能科技有限公司 | Multimedia messages recommend method and device, storage medium, terminal |
| CN108509583A (en) * | 2018-03-29 | 2018-09-07 | 广东欧珀移动通信有限公司 | A kind of information-pushing method, server and computer readable storage medium |
| CN110334356A (en) * | 2019-07-15 | 2019-10-15 | 腾讯科技（深圳）有限公司 | Article matter method for determination of amount, article screening technique and corresponding device |
Non-Patent Citations (3)
| Title |
|---|
| 丁庆燊，李健伟，刘宁宁: "聚合信息客户端数据挖掘应用研究——以用户登录行为和文章推荐数据库为例", 《统计与信息论坛》, vol. 34, no. 2, pages 121 - 128 * |
| 曾洲, 宋顺林: "主动式知识获取模型", 计算机工程与设计, no. 10 * |
| 聂美星: ""今日头条"个性化推荐内容研究", 《中国优秀硕士学位论文全文数据库 (信息科技辑)》, vol. 2019, no. 01, pages 141 - 633 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111931073A (en) * | 2020-10-10 | 2020-11-13 | 腾讯科技（深圳）有限公司 | Content pushing method and device, electronic equipment and computer readable medium |
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|---|---|
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