CN112632370A

CN112632370A - Method, device and equipment for article pushing

Info

Publication number: CN112632370A
Application number: CN202011424645.XA
Authority: CN
Inventors: 胡百春
Original assignee: Qingdao Haier Technology Co Ltd; Haier Smart Home Co Ltd
Current assignee: Qingdao Haier Technology Co Ltd; Haier Smart Home Co Ltd
Priority date: 2020-12-08
Filing date: 2020-12-08
Publication date: 2021-04-09

Abstract

The application relates to the technical field of information, and discloses a method for pushing articles, which comprises the following steps: obtaining an article to be recommended from a preset alternative article set according to a preset rule; the alternative article set is obtained according to the user attribute information set and the article attribute information set; and pushing the article to be recommended to a user terminal. The method comprises the steps of obtaining an alternative article set through a user attribute information set and an article attribute information set, obtaining an article to be recommended from the preset alternative article set according to a preset rule, pushing the article to be recommended to a user, obtaining the article to be recommended by considering the user attribute and the article attribute and combining the preset article recommendation rule, carrying out personalized article pushing for different users, and improving user experience. The application also discloses a device and equipment for pushing the articles.

Description

Method, device and equipment for article pushing

Technical Field

The present application relates to the field of information technology, and for example, to a method, an apparatus, and a device for pushing an article.

Background

With the rapid development of the mobile internet and the wide popularization of the intelligent terminal, more and more people choose to read articles on the smart phone end or the tablet device, and the number of article information in the internet also increases rapidly. In the face of massive article information, a user can hardly find the content really interested by the user, or a great deal of time and energy are consumed to search the articles meeting the reading requirement of the user. In order to allow a user to accurately read an article of interest and to improve user stickiness, a service of recommending an article to a user is also gradually applied to various websites or software.

In the process of implementing the embodiments of the present disclosure, it is found that at least the following problems exist in the related art: in the prior art, personalized article pushing is not performed for different users.

Disclosure of Invention

The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview nor is intended to identify key/critical elements or to delineate the scope of such embodiments but rather as a prelude to the more detailed description that is presented later.

The embodiment of the disclosure provides a method, a device and equipment for article pushing, so that personalized article pushing can be performed for different users.

In some embodiments, the method comprises: obtaining an article to be recommended from a preset alternative article set according to a preset rule, wherein the alternative article set is obtained according to a user attribute information set and an article attribute information set; and pushing the article to be recommended to a user terminal.

In some embodiments, obtaining the candidate article set according to the user attribute information set and the article attribute information set includes: acquiring a user attribute information set and an article attribute information set, wherein the user attribute information set comprises user attributes, and the article attribute information set comprises article attributes; acquiring the matching degree between the user attribute and the article attribute in a preset matching relationship set; the matching degree between the user attribute and the article attribute is stored in the matching relation set; and acquiring a candidate article set according to the matching degree.

In some embodiments, the obtaining a set of candidate articles according to the matching degree includes: obtaining a matching result according to the matching degree; and selecting an alternative article set from a preset article database according to the matching result, wherein the article database comprises articles to be selected.

In some embodiments, selecting an alternative article set from a preset article database according to the matching result includes: obtaining a matching score according to the matching result; and acquiring a candidate article set according to the matching score.

In some embodiments, the obtaining a match score according to the match result includes: and carrying out normalization processing on the matching result to obtain a matching score.

In some embodiments, the obtaining a set of candidate articles according to the matching score includes: obtaining an article recommendation score according to the matching score; and selecting an alternative article set matched with the article recommendation score from a preset article database, wherein the article database comprises articles to be selected.

In some embodiments, said obtaining an article recommendation score from said match score comprises: and performing weighted calculation by using the matching score through a weighting algorithm to obtain an article recommendation score.

In some embodiments, the obtaining the article to be recommended from the preset candidate article set according to the preset rule includes: and taking the articles which are not recommended in the preset time period in the alternative article set as articles to be recommended.

In some embodiments, the apparatus comprises: a processor and a memory storing program instructions, the processor being configured to, when executing the program instructions, perform the method for article pushing described above.

In some embodiments, the apparatus includes the above-mentioned device for article pushing.

The method, the device and the equipment for pushing the article provided by the embodiment of the disclosure can realize the following technical effects: the method comprises the steps of obtaining an alternative article set through a user attribute information set and an article attribute information set, obtaining an article to be recommended from the preset alternative article set according to a preset rule, pushing the article to be recommended to a user, obtaining the article to be recommended by considering the user attribute and the article attribute and combining the preset article recommendation rule, carrying out personalized article pushing for different users, and improving user experience.

The foregoing general description and the following description are exemplary and explanatory only and are not restrictive of the application.

Drawings

One or more embodiments are illustrated by way of example in the accompanying drawings, which correspond to the accompanying drawings and not in limitation thereof, in which elements having the same reference numeral designations are shown as like elements and not in limitation thereof, and wherein:

fig. 1 is a schematic diagram of a method for pushing an article according to an embodiment of the present disclosure;

FIG. 2 is a schematic diagram of another method for article pushing provided by an embodiment of the present disclosure;

fig. 3 is a schematic diagram of an apparatus for article pushing provided by an embodiment of the present disclosure;

fig. 4 is a schematic diagram of a system for article pushing provided by an embodiment of the present disclosure.

Detailed Description

So that the manner in which the features and elements of the disclosed embodiments can be understood in detail, a more particular description of the disclosed embodiments, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. In the following description of the technology, for purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, one or more embodiments may be practiced without these details. In other instances, well-known structures and devices may be shown in simplified form in order to simplify the drawing.

The terms "first," "second," and the like in the description and in the claims, and the above-described drawings of embodiments of the present disclosure, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the data so used may be interchanged under appropriate circumstances such that embodiments of the present disclosure described herein may be made. Furthermore, the terms "comprising" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions.

The term "plurality" means two or more unless otherwise specified.

In the embodiment of the present disclosure, the character "/" indicates that the preceding and following objects are in an or relationship. For example, A/B represents: a or B.

The term "and/or" is an associative relationship that describes objects, meaning that three relationships may exist. For example, a and/or B, represents: a or B, or A and B.

With reference to fig. 1, an embodiment of the present disclosure provides a method for pushing an article, including:

step S101, obtaining an article to be recommended from a preset alternative article set according to a preset rule;

and step S102, pushing the article to be recommended to the user terminal.

The alternative article set is obtained according to the user attribute information set and the article attribute information set.

By adopting the method for pushing the articles, provided by the embodiment of the disclosure, the alternative article set is obtained through the user attribute information set and the article attribute information set, the articles to be recommended are obtained from the preset alternative article set according to the preset rules, then the articles to be recommended are pushed to the user, the articles to be recommended are obtained by considering the user attributes and the article attributes and combining the preset article recommendation rules, personalized article pushing can be performed for different users, and the user experience is improved.

Optionally, obtaining a set of alternative articles according to the user attribute information set and the article attribute information set includes: acquiring a user attribute information set and an article attribute information set, wherein the user attribute information set comprises user attributes, and the article attribute information set comprises article attributes; acquiring the matching degree between the user attribute and the article attribute in a preset matching relationship set; the matching degree between the user attribute and the article attribute is stored in the matching relation set; and acquiring a candidate article set according to the matching degree.

Optionally, the user attributes comprise a first user attribute and a second user attribute; the article attributes include a first article attribute and a second article attribute.

Optionally, the user attribute information set includes a first user attribute information set and a second user attribute information set; the article attribute information set comprises a first article attribute information set and a second article attribute information set.

Optionally, the first user attribute information set includes a first user attribute; the second set of user attribute information includes a second user attribute. Optionally, the first article attribute information set comprises a first article attribute; the second article attribute information set includes a second article attribute.

Optionally, the first user attribute and the second user attribute are obtained according to the user basic information.

Optionally, the first user attributes include: one or more of age, gender, industry, hobbies, and the like. Optionally, the user basic information includes: date of birth, sex, work industry, hobbies and interests, etc. Optionally, the first user attribute corresponding to the user basic information "birth date" is an age attribute, for example: the first user attribute corresponding to the ages of 10 to 16 is juvenile, the first user attribute corresponding to the ages of 17 to 34 is young, the first user attribute corresponding to the ages of 35 to 60 is middle-aged, and the first user attribute corresponding to the ages of 60 and above is elderly. The first user attribute corresponding to the user basic information "gender" is a gender attribute, for example: male or female. The first user attribute corresponding to the user basic information "work industry" is an industry attribute, for example: financial, IT, home, building materials, etc. The first user attribute corresponding to the user basic information "interest and hobby" is a hobby attribute, for example: one or more attributes of music, video, text history, sports, etc.

Optionally, the second user attribute is obtained according to the user basic information. Optionally, matching the user basic information in a preset first tag database to obtain a tag corresponding to the user basic information, and using the matched tag as a second user attribute. The first label database stores the corresponding relation between the user basic information and the labels. Optionally, the second user attribute comprises: reticulum, elite, large V, etc.

Optionally, the first article attributes comprise: an article level 1 classification, an article level 2 classification, an article quality score, and the like. Optionally, the first article attribute is subjected to uniform coding processing and/or uniform unit processing, so as to specify the first article attribute.

Optionally, the second article attribute is obtained according to the article basic information. Optionally, the article basic information includes article title, article belonging field, author name, author gender, and the like. Optionally, the second article attributes include: reticulum, elite, and/or large V, etc.

Optionally, the second article attribute is obtained from the article basis information. Optionally, matching the article basic information in a preset second tag database to obtain a tag corresponding to the article basic information, and using the matched tag as a second article attribute. And the second label database stores the corresponding relation between the article basic information and the labels.

Optionally, the matching degree includes a first matching degree and a second matching degree. Optionally, obtaining a matching degree between the user attribute and the article attribute includes: acquiring a first matching degree between a first user attribute and a first article attribute; and acquiring a second matching degree between the second user attribute and the second article attribute.

Optionally, the obtaining a first matching degree between the first user attribute and the first article attribute includes: and obtaining a first matching degree between the first user attribute and the first article attribute by inquiring a preset matching relation set of the first user attribute and the first article attribute. As shown in table 1, table 1 is an example table of a matching relationship set R _ U _ B of a first user attribute and a first article attribute. Optionally, in a case that the matching relationship between the first user attribute and the first article attribute does not exist in the matching relationship set R _ U _ B, the first matching degree of the first user attribute and the first article attribute is zero. Optionally, the matching relationship set R _ U _ B of the first user attribute and the first article attribute needs to be initialized by the system.

TABLE 1

In some embodiments, as shown in table 1, the first user attribute 'youth' of user X corresponds to the first article attribute 1 of article 1 with a matching degree of "8"; the matching degree of the first article attribute 1 of the article 1 corresponding to the first user attribute ' IT ' of the user X is 8 '; the first user attribute 'male' of the user X corresponds to the matching degree of the first article attribute 1 of the article 1 being "9". The matching degree of the first article attribute 2 of the article 1 corresponding to the first user attribute 'youth' of the user X is '7'; the matching degree of the first article attribute 2 of the article 1 corresponding to the first user attribute 'IT' of the user X is "7"; the first user attribute 'male' of the user X corresponds to the matching degree of the first article attribute 2 of the article 1 being "8". The matching degree of the first article attribute 3 of the article 1 corresponding to the first user attribute 'youth' of the user X is '6'; the matching degree of the first article attribute 3 of the article 1 corresponding to the first user attribute 'IT' of the user X is "6"; the first user attribute 'male' of the user X corresponds to the matching degree of the first article attribute 3 of the article 1 being "7".

Optionally, obtaining a second matching degree between the second user attribute and the second article attribute includes: and obtaining a second matching degree between the second user attribute and the second article attribute by inquiring a preset matching relation set of the second user attribute and the second article attribute. Optionally, in a case that the matching relationship between the second user attribute and the second article attribute does not exist in the matching relationship set, the second matching degree of the second user attribute and the second article attribute is zero. Optionally, the set of matching relationships between the second user attribute and the second article attribute needs to be initialized by the system.

Therefore, the matching degree between the user attribute and the article attribute is obtained by obtaining the first matching degree between the first user attribute and the first article attribute and the second matching degree between the second user attribute and the second article attribute, the user attribute and the article attribute are considered, personalized article pushing can be performed for different users, and the experience of reading the article by the users is improved.

Optionally, obtaining a candidate article set according to the matching degree includes: obtaining a matching result according to the matching degree; selecting an alternative article set from a preset article database according to a matching result; the article database comprises articles to be selected.

Optionally, obtaining a matching result according to the matching degree includes: and obtaining a first matching result according to the first matching degree, and obtaining a second matching result according to the second matching degree.

Optionally, obtaining a first matching result according to the first matching degree includes: and calculating by using a first preset algorithm through a first matching degree between the ith first user attribute and each first article attribute of the article j to obtain a first matching result of the user and the article. Optionally, i is a positive integer and j is a positive integer.

Optionally, a first matching result is obtained by calculating a1 × a2.. Am + B1 × B2.. said Bm + … + C1.. C2... Cm, where Am is a first matching degree of the a-th first user attribute corresponding to the m-th first article attribute, Bm is a first matching degree of the B-th first user attribute corresponding to the m-th first article attribute, and Cm is a first matching degree of the C-th first user attribute corresponding to the m-th first article attribute. A. B, C, m are all positive integers.

In some embodiments, as shown in table 2, table 2 is a calculation process of calculating a first matching result between the user and the article by using the first matching degree in table 1. And calculating according to the calculation formula in the table 2 to obtain a calculation result in the table 3, namely a first matching result of the user and the article, wherein the table 3 is an example table of a first matching result set G _ U _ B of the user and the article. Optionally, in a case that the first matching result of the first user attribute and the first article attribute does not exist in the first matching result set G _ U _ B, the first matching result of the first user attribute and the first article attribute is zero.

TABLE 2

TABLE 3

In some embodiments, the first match result of user X with article 1 is "1176"; the first match result of user X with article 2 is "480"; the first match result of user X with article 3 is "686".

Optionally, obtaining a second matching result according to the second matching degree includes: and calculating by using a second preset algorithm through a second matching degree between the ith second user attribute and each second article attribute of the article j to obtain a second matching result of the user and the article.

Optionally, a second matching result is obtained by calculating a ' 1 × a ' 2.· a'm + B ' 1 × B ' 2.. B'm + … + C ' 1 × C ' 2.. C'm, where a'm is a second matching degree of the a ' th second user attribute corresponding to the m-th second article attribute, B'm is a second matching degree of the B ' th second user attribute corresponding to the m-th second article attribute, and C'm is a second matching degree of the C ' th second user attribute corresponding to the m-th second article attribute. A ', B ', C ' and m are positive integers.

Optionally, a second matching degree in a preset matching relationship set between a second user attribute and a second article attribute is calculated to obtain a second matching result set of the user and the article. Optionally, in a case that a second matching result of the second user attribute and the second article attribute does not exist in the second matching result set, the second matching result of the second user attribute and the second article attribute is zero.

Therefore, the matching result between the user attribute and the article attribute is obtained by obtaining the first matching degree between the first user attribute and the first article attribute and the second matching degree between the second user attribute and the second article attribute, the user attribute and the article attribute are considered, personalized article pushing can be performed for different users, and the experience of reading the article by the users is improved.

Optionally, selecting an alternative article set from a preset article database according to the matching result, including: obtaining a matching score according to a matching result; and acquiring a candidate article set according to the matching score.

Optionally, obtaining a matching score according to the matching result includes: and carrying out normalization processing on the matching result to obtain a matching score.

Optionally, obtaining a matching score according to the matching result includes: obtaining a first matching score according to the first matching result; and obtaining a second matching score according to the second matching result.

Optionally, the first matching result is normalized to obtain a first matching score. Optionally, the second matching result is normalized to obtain a second matching score.

Optionally, obtaining a first matching score according to the first matching result includes: and calculating by using a third preset algorithm according to the first matching result to obtain a first matching score. Optionally, a first matching score G _1 is obtained by calculating G _1 ═ ROUND (G _1 × 100/MAX (G _1, G _2, …, G _ n), 2); wherein, G _1 is a first matching score, G _1, G _2, …, and G _ n is a first matching result between the user X and n articles respectively. As shown in table 4, table 4 is an example table of the first matching score obtained from the first matching result.

TABLE 4

Optionally, obtaining a second matching score according to the second matching result includes: and calculating by using a fourth preset algorithm according to the second matching result to obtain a second matching score. Optionally, a second matching score F _1 is obtained by calculating F _1 ═ ROUND (F _1 × 100/MAX (F _1, F _2, …, F _ n), 2); wherein, F _1 is a second matching score, F _1, F _2, …, and F _ n are second matching results of the user X and n articles, respectively.

Optionally, obtaining a set of candidate articles according to the matching score includes: obtaining article recommendation scores according to the matching scores; selecting an alternative article set matched with the article recommendation score from a preset article database; the article database comprises articles to be selected.

Optionally, obtaining an article recommendation score according to the matching score includes: and performing weighted calculation by using the matching score through a weighting algorithm to obtain the article recommendation score.

Optionally, obtaining an article recommendation score w _ g by calculating w _ g (first match score, first attribute weight, second match score, second attribute weight)/2; wherein w _ g is the article recommendation score for user X and article j.

Optionally, selecting an alternative article set matching the article recommendation score from a preset article database, including: and selecting the articles with article recommendation scores larger than or equal to a set threshold value to obtain an alternative article set.

In this way, the article recommendation score is obtained by obtaining the first matching score between the first user attribute and the first article attribute and the second matching score between the second user attribute and the second article attribute, so that the alternative article set is obtained, the user attribute and the article attribute are taken into consideration, personalized article push can be performed for different users, and the article reading experience of the users is improved.

Optionally, obtaining an article to be recommended from a preset candidate article set according to a preset rule, including: and taking the articles which are not recommended in the preset time period in the alternative article set as articles to be recommended.

Optionally, the preset time period includes: eight am to ten am, twelve am to two pm, six pm to eight pm on the same day.

Optionally, an article that is not recommended within three days in the candidate article set is taken as the article to be recommended.

Optionally, the articles in the candidate article set that are not recommended in the same time period every day in the last seven days are taken as the articles to be recommended.

Optionally, articles in the alternative article set are sorted from large to small according to article recommendation scores; and selecting the article before the preset ranking as the article to be recommended, for example, the article ranked as the top 5 as the article to be recommended.

Table 5 is an example table of sorted candidate article sets W _ G.

TABLE 5

As shown in table 5, the article recommendation score of the user X and the article 1 is 96, and the article 1 is ranked as 3 in the alternative article set W _ G; the article recommendation score of the user X and the article 2 is 98, and the article 2 is ranked as 2 in the alternative article set W _ G; the article recommendation score of the user X and the article 3 is 99, and the article 3 is ranked as 1 in the alternative article set W _ G.

Optionally, the article to be recommended is pushed to the user. Optionally, the articles to be recommended are pushed to the user in the order of the article recommendation scores from large to small.

Therefore, the article to be recommended is obtained from the preset candidate article set according to the preset rule, so that the user cannot read repeated articles within the preset time period, the article push service can be more accurately and effectively provided for the user, and the experience of the user in reading the articles is improved.

With reference to fig. 2, an embodiment of the present disclosure provides a method for pushing an article, including:

step S201: and acquiring basic information of the user and basic information of the article.

Step S202: acquiring a first user attribute and a second user attribute according to the user basic information; and obtaining the first article attribute and the second article attribute according to the article basic information.

Step S203: obtaining a first matching degree according to the first user attribute and the first article attribute; and obtaining a second matching degree according to the second user attribute and the second article attribute.

Step S204: obtaining a first matching result according to the first matching degree; and obtaining a second matching result according to the second matching degree.

Step S205: obtaining a first matching score according to the first matching result; and obtaining a second matching score according to the second matching result.

Step S206: and obtaining an article recommendation score according to the first matching score and the second matching score.

Step S207: and selecting the articles with article recommendation scores larger than or equal to a set threshold value to obtain an alternative article set.

Step S208: and taking the articles which are not recommended in the preset time period in the alternative article set as articles to be recommended.

Step S209: and pushing the articles to be recommended to the user according to the sequence of the article recommendation scores from large to small.

By adopting the method for pushing the articles, which is provided by the embodiment of the disclosure, the alternative article set is obtained through the user attribute information set and the article attribute information set, the articles to be recommended are obtained from the preset alternative article set, and then the articles to be recommended are pushed to the user.

As shown in fig. 3, an apparatus for pushing an article according to an embodiment of the present disclosure includes a processor (processor)100 and a memory (memory)101 storing program instructions. Optionally, the apparatus may also include a Communication Interface (Communication Interface)102 and a bus 103. The processor 100, the communication interface 102, and the memory 101 may communicate with each other via a bus 103. The communication interface 102 may be used for information transfer. The processor 100 may call program instructions in the memory 101 to perform the method for article pushing of the above-described embodiment.

Further, the program instructions in the memory 101 may be implemented in the form of software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product.

The memory 101, which is a computer-readable storage medium, may be used for storing software programs, computer-executable programs, such as program instructions/modules corresponding to the methods in the embodiments of the present disclosure. The processor 100 executes functional applications and data processing by executing program instructions/modules stored in the memory 101, that is, implements the method for article pushing in the above-described embodiments.

The memory 101 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal device, and the like. In addition, the memory 101 may include a high-speed random access memory, and may also include a nonvolatile memory.

By adopting the device for pushing the articles, which is provided by the embodiment of the disclosure, the alternative article set is obtained through the user attribute information set and the article attribute information set, the articles to be recommended are obtained from the preset alternative article set according to the preset rules, then the articles to be recommended are pushed to the user, the articles to be recommended are obtained by considering the user attributes and the article attributes and combining the preset article recommendation rules, personalized article pushing can be performed for different users, and the article reading experience of the users is improved.

As shown in fig. 4, an embodiment of the present disclosure provides a system for pushing an article, including: the System comprises an information receiving device 41, a cloud server 42, a Hadoop (Distributed File System) big data platform 43, a message pushing server 44 and an information collecting device 45.

The information collecting device 45 and the information receiving device 41 are respectively bound with a user, so that an effective mapping relationship exists between the information collecting device 45 and the information receiving device 41. The user registers on the information collection device 45, and the information collection device 45 collects the user basic information and sends the user basic information to the cloud server 42. The cloud server 42 sends the user base information to the Hadoop big data platform 43. The Hadoop big data platform 43 stores data, processes the user basic information and the article basic information, obtains a first user attribute and a second user attribute according to the user basic information, and obtains a first article attribute and a second article attribute according to the article basic information; obtaining a first matching degree according to the first user attribute and the first article attribute; obtaining a second matching degree according to the second user attribute and the second article attribute; obtaining a first matching result according to the first matching degree; obtaining a second matching result according to the second matching degree; obtaining a first matching score according to the first matching result; obtaining a second matching score according to the second matching result; scoring by using the first matching score and the second matching score based on a weight scoring algorithm to obtain an article recommendation score; and obtaining the article to be recommended according to a preset article pushing rule. The Hadoop big data platform 43 sends the article to be recommended to the message pushing server 44. The message push server 44 sends the article to be recommended to the information acquisition device 45, so that personalized article push for different users is realized.

In this way, the user binding information acquisition device and the information receiving device acquire the basic information of the user through the information acquisition device, then send the basic information of the user to the Hadoop big data platform through the cloud server for data storage, process the basic information of the user and the basic information of the article on the Hadoop big data platform, acquire an alternative article set according to the attribute information set of the user and the attribute information set of the article, acquire the article to be recommended from the preset alternative article set, and then push the article to be recommended to the user.

The embodiment of the disclosure provides a device, which comprises the above device for article pushing.

Optionally, the apparatus comprises: a server or an intelligent terminal; optionally, the smart terminal includes a smart phone, a tablet, a smart home with a display screen, and the like.

Optionally, in the case that the device is a server, the server acquires user basic information and article basic information through the intelligent terminal; and the server pushes the article to be recommended to the user through the intelligent terminal.

The device acquires the alternative article set through the user attribute information set and the article attribute information set, acquires the article to be recommended from the preset alternative article set according to the preset rule, pushes the article to be recommended to the user, acquires the article to be recommended by considering the user attribute and the article attribute and combining the preset article recommendation rule, can perform personalized article pushing for different users, and improves the article reading experience of the users.

The disclosed embodiments provide a computer-readable storage medium storing computer-executable instructions configured to perform the above-described method for article pushing.

The embodiment of the disclosure provides a computer program product, which includes a computer program stored on a computer-readable storage medium, and the computer program includes program instructions, when the program instructions are executed by a computer, the computer executes the method for article pushing described above.

The computer-readable storage medium described above may be a transitory computer-readable storage medium or a non-transitory computer-readable storage medium.

The technical solution of the embodiments of the present disclosure may be embodied in the form of a software product, where the computer software product is stored in a storage medium and includes one or more instructions to enable 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 of the embodiments of the present disclosure. And the aforementioned storage medium may be a non-transitory storage medium comprising: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes, and may also be a transient storage medium.

The above description and drawings sufficiently illustrate embodiments of the disclosure to enable those skilled in the art to practice them. Other embodiments may incorporate structural, logical, electrical, process, and other changes. The examples merely typify possible variations. Individual components and functions are optional unless explicitly required, and the sequence of operations may vary. Portions and features of some embodiments may be included in or substituted for those of others. Furthermore, the words used in the specification are words of description only and are not intended to limit the claims. As used in the description of the embodiments and the claims, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. Similarly, the term "and/or" as used in this application is meant to encompass any and all possible combinations of one or more of the associated listed. Furthermore, the terms "comprises" and/or "comprising," when used in this application, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. Without further limitation, an element defined by the phrase "comprising an …" does not exclude the presence of other like elements in a process, method or apparatus that comprises the element. In this document, each embodiment may be described with emphasis on differences from other embodiments, and the same and similar parts between the respective embodiments may be referred to each other. For methods, products, etc. of the embodiment disclosures, reference may be made to the description of the method section for relevance if it corresponds to the method section of the embodiment disclosure.

Those of skill in the art would appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software may depend 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 disclosed embodiments. It can be clearly understood by the skilled person that, for convenience and brevity of description, the specific working processes of the system, the apparatus and the unit described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the embodiments disclosed herein, the disclosed methods, products (including but not limited to devices, apparatuses, etc.) may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units may be merely a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form. The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to implement the present embodiment. In addition, functional units in the embodiments of the present disclosure 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 flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. In the description corresponding to the flowcharts and block diagrams in the figures, operations or steps corresponding to different blocks may also occur in different orders than disclosed in the description, and sometimes there is no specific order between the different operations or steps. For example, two sequential operations or steps may in fact be executed substantially concurrently, or they may sometimes be executed in the reverse order, depending upon the functionality involved. Each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

Claims

1. A method for article pushing, comprising:

obtaining an article to be recommended from a preset alternative article set according to a preset rule; the alternative article set is obtained according to the user attribute information set and the article attribute information set;

and pushing the article to be recommended to a user terminal.

2. The method of claim 1, wherein obtaining the set of alternative articles according to a set of user attribute information and a set of article attribute information comprises:

acquiring a user attribute information set and an article attribute information set, wherein the user attribute information set comprises user attributes, and the article attribute information set comprises article attributes;

acquiring the matching degree between the user attribute and the article attribute in a preset matching relationship set; the matching degree between the user attribute and the article attribute is stored in the matching relation set;

and acquiring a candidate article set according to the matching degree.

3. The method of claim 2, wherein the obtaining a set of candidate articles according to the matching degree comprises:

obtaining a matching result according to the matching degree;

and selecting an alternative article set from a preset article database according to the matching result, wherein the article database comprises articles to be selected.

4. The method of claim 3, wherein selecting a set of alternative articles from a preset article database according to the matching result comprises:

obtaining a matching score according to the matching result;

and acquiring a candidate article set according to the matching score.

5. The method of claim 4, wherein obtaining a match score based on the match results comprises:

and carrying out normalization processing on the matching result to obtain a matching score.

6. The method of claim 4, wherein the obtaining a set of candidate articles according to the match score comprises:

obtaining an article recommendation score according to the matching score;

and selecting an alternative article set matched with the article recommendation score from a preset article database, wherein the article database comprises articles to be selected.

7. The method of claim 6, wherein obtaining an article recommendation score based on the match score comprises:

and performing weighted calculation by using the matching score through a weighting algorithm to obtain an article recommendation score.

8. The method according to any one of claims 1 to 7, wherein the obtaining of the article to be recommended from the preset candidate article set according to the preset rule comprises:

and taking the articles which are not recommended in the preset time period in the alternative article set as articles to be recommended.

9. An apparatus for article pushing comprising a processor and a memory storing program instructions, wherein the processor is configured to perform the method for article pushing of any one of claims 1 to 8 when executing the program instructions.

10. An apparatus, comprising the means for article pushing of claim 9.