Real estate information recommendation method and terminal device

Abstract

The present application provides a real estate information recommendation method and terminal device, the method is applied to a recommendation server, the method includes: obtaining a first recommended content uploaded by a primary user through a first user device; configuring a classification tag according to the title and content of the first recommended content; searching for multiple second recommended contents associated with the first recommended content according to the classification tag; sending the first recommended content and the multiple second recommended contents to a second user device of a target user. The present application labels the distributed content and provides a series of related recommended contents to the target user according to the label, so that the user can have a more comprehensive understanding of the relevant real estate information, avoid multiple interruptions, and speed up the transaction cycle.

Description

Technical Field

[0001] The present application relates to the field of real estate sales, and in particular to a real estate information recommendation method and terminal device. Background Art

[0002] At present, the existing technology for promoting and distributing house selling materials is basically that consultants distribute uniform house selling materials to potential buyers, and then conduct subjective analysis based on the potential buyers' reading data of the house selling materials to determine the customers' purchase intention, and then maintain the customers and slowly guide the customers to convert into real estate consumption.

[0003] However, this method has low distribution efficiency, requires a lot of manpower, and has a long transaction cycle.

[0004] Therefore, the existing technology still needs to be improved. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the purpose of this application is to provide a real estate information recommendation method and terminal device, aiming to solve the problems of low efficiency in real estate information distribution, large manpower consumption, and long transaction cycle.

[0006] In order to achieve the above objectives, this application adopts the following technical solutions:

[0007] In a first aspect, the present application provides a real estate information recommendation method, which is applied to a recommendation server, and the method includes: obtaining first recommended content uploaded by a primary user through a first user device; configuring a classification label according to the title and content of the first recommended content; searching for multiple second recommended content associated with the first recommended content according to the classification label; and sending the first recommended content and the multiple second recommended contents to a second user device of a target user.

[0008] This application labels the distributed content and provides a series of related recommended content to target users based on the labels, so that users can have a more comprehensive understanding of relevant real estate information, avoid multiple interruptions, and speed up the transaction cycle.

[0009] In a second aspect, the present application provides a terminal device, characterized in that it includes: a processor and a memory; the memory stores a computer-readable program that can be executed by the processor; when the processor executes the computer-readable program, it implements the steps in the method described above.

[0010] In a third aspect, the present application provides a computer-readable storage medium, characterized in that the computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps in the method described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1a The structural principle diagram of the recommended system provided for this application;

[0012] Figure 1b The structural principle diagram of the terminal device provided for this application;

[0013] Figure 2 A flow chart of the real estate information recommendation method provided in this application. DETAILED DESCRIPTION

[0014] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0015] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0016] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0017] "At least one" in this application refers to one or more, and "a plurality" refers to two or more. In this application, "and / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can represent: a, b, c, a and b, a and c, b and c, or a, b and c, where each of a, b, c can be an element itself, or a set containing one or more elements.

[0018] It should be pointed out that the equal to involved in the embodiments of the present application can be used in conjunction with greater than, and is applicable to the technical solution adopted when greater than, and can also be used in conjunction with less than, and is applicable to the technical solution adopted when less than. It should be noted that when equal to is used in conjunction with greater than, it is not used in conjunction with less than; when equal to is used in conjunction with less than, it is not used in conjunction with greater than. In the embodiments of the present application, "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be pointed out that when the distinction is not emphasized, the meanings to be expressed are consistent.

[0019] First, some nouns involved in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

[0020] 1. Recommended content: specifically refers to all electronic promotional materials of real estate developers, i.e. real estate information. In this application, it is represented by the first recommended content and the second recommended content, etc.

[0021] 2. User Equipment. UE is an important concept in mobile communications. In 3G and 4G networks, user equipment is called UE. User Equipment is an important concept in mobile communications. In 3G and 4G networks, user equipment is called UE, which is equivalent to MS in 2G networks. UE here includes mobile phones, smart terminals, multimedia devices, streaming media devices, etc. The user equipment in this application mainly refers to the user equipment used by the supervisor user and the target user.

[0022] 3. Supervisor User: In this application, it refers to real estate promoters, who are responsible for uploading real estate information.

[0023] 4. Target users: In this application, it refers specifically to people who are willing to buy a house.

[0024] At present, in the existing technology, the promotion and distribution of house sales materials is basically done by consultants distributing unified house sales materials to potential buyers, and then conducting subjective analysis based on the potential buyers' reading data of the house sales materials to determine the customer's purchase intention, and then maintaining the customer and slowly guiding the customer to convert into real estate consumption. However, this method is inefficient, requires a lot of manpower, and has a long transaction cycle.

[0025] In view of the above problems, the present application provides a real estate information recommendation method, which solves the problems of low efficiency in real estate information distribution, large manpower consumption, and long transaction cycle.

[0026] like Figure 1a As shown, Figure 1a An optional application scenario of a real estate information recommendation method provided in the present application is specifically a recommendation system, the recommendation system includes a first user device 11, a second recommendation and recommendation server 10, and the first user device 11 and the second user device 12 are both wirelessly connected to the recommendation server 10. The recommendation server 10 includes software, APP, etc. generated by the real estate information recommendation method. The recommendation server 10 includes a processing unit and a storage unit, and the storage unit stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps in the real estate information recommendation method. The first user device 11 uploads the first recommendation content to the recommendation server 10 through corresponding operations, and the recommendation server 10 sends the corresponding recommendation content to the second user device 12 (the recommendation content includes the first recommendation content and / or the second recommendation content).

[0027] The information query method provided by this application is described below with specific embodiments.

[0028] like Figure 2 As shown, the present application provides a real estate information recommendation method, which is applied to a recommendation server 10, and the method includes:

[0029] Step 101 : Obtain first recommended content uploaded by a supervisor user through a first user device 11 .

[0030] For example, the supervisor user uploads the first recommended content to the recommendation server 10 through the first user device 11. The first recommended content is the main content that the target user needs to read in this push. The first recommended content can be an article, picture, video or audio, etc., and is not limited to uniqueness.

[0031] Step 102: Configure a classification label according to the title and content of the first recommended content.

[0032] For example, after receiving the first recommended content, the recommendation server 10 detects the title or content of the first recommended content and configures a classification tag according to the title and content. Specifically, the classification tag is configured based on the title, and the keywords in the title are determined based on a pre-stored vocabulary, and then the classification tag is configured; the classification tag is configured based on the content, and the content is identified as an article, picture, video or audio, and the classification tag is configured according to the corresponding content type. It is understandable that the classification tag can also be manually configured by the competent user for the first recommended content, and no uniqueness limitation is made here.

[0033] In a possible example, before configuring the classification label according to the title and content of the first recommended content, the method further includes: presetting a plurality of classification labels; establishing and saving a correspondence between the plurality of classification labels and the corresponding title and / or content.

[0034] For example, the recommendation server 10 is pre-set with multiple classification tags and uniformly stored. The multiple classification tags may include title tags and content tags, and may be added or deleted by the competent user. For the title and content of the recommended content, a corresponding word library may be set, and the keywords or key words in the word library may be associated with the corresponding classification tags in the multiple classification tags, respectively, to obtain the corresponding relationship between the multiple classification tags and the corresponding titles and / or contents.

[0035] It can be seen that in this example, by pre-setting multiple classification tags in the recommendation server 10, and setting the vocabulary corresponding to the title and content to be associated with the tags, a corresponding relationship between the tags and the tags and / or the content is established, so that the recommendation server can automatically assign classification tags to the first recommended content.

[0036] In a possible example, after establishing and saving the correspondence between the plurality of classification tags and the corresponding titles and / or contents, the method further includes:

[0037] The multiple classification labels are divided into multiple recommendation levels.

[0038] For example, according to different rules, the multiple classification tags can be divided into different recommendation levels. For example, the multiple classification tags are divided into three levels, namely basic knowledge, advanced knowledge and promotional content. The basic knowledge refers to the relevant real estate basic knowledge, which may include the explanation of real estate-related terms, the average price of relevant areas, etc.; the advanced knowledge refers to the interpretation of real estate-related policies, the advantages and disadvantages of relevant housing sources, how to select high-quality housing sources, etc.; the promotional content refers to the relevant housing sources to be pushed this time, which may include specific housing source information, housing source prices, preferential strategies, etc. It can be understood that the basic knowledge, advanced knowledge and promotional content include but are not limited to the above content; in addition, the division of the recommendation level may not be limited to the three recommendation levels of basic knowledge, advanced knowledge and promotional content, but may also be four recommendation levels, five recommendation levels, or even more or less recommendation levels; and the recommendation level may also be divided according to other rules, such as dividing the levels according to housing source information, supporting facilities and surrounding environment, which are not limited to uniqueness here.

[0039] It can be seen that in this example, by dividing the multiple classification tags into multiple recommendation levels, the multiple classification tags are classified, which facilitates subsequent calling and sending of recommended content.

[0040] Step 103: Search for a plurality of second recommended contents associated with the first recommended contents according to the classification tags.

[0041] For example, after configuring the classification tag of the first recommended content, the second recommended content stored in the recommendation server 10 is searched according to the classification tag. The second recommended content is associated with the first recommended content, and can be the upper content of the first recommended content, or the lower content of the first recommended content, or the extended content or progressive content of the first recommended content, or other related content, which is not limited to uniqueness. The second recommended content is real estate information pre-stored in the recommendation server 10, or the first recommended content uploaded before step 101.

[0042] In a possible example, the category label includes a first category label and a second category label, wherein the first category label is a category label of the first recommended content, and the second category label is a category label among the category labels except the first category label.

[0043] Specifically, searching for a plurality of second recommended contents associated with the first recommended contents according to the classification tags specifically includes:

[0044] According to the correspondence between the first classification tag and the second classification tag, the second classification tag associated with the first classification tag is searched out; and according to the second classification tag, the second recommended content corresponding to the second classification tag is searched out.

[0045] For example, the recommendation server 10 pre-stores the correspondence between classification tags. After the first classification tag is configured for the first recommended content, the second classification tag is searched out according to the correspondence between the first classification tag and the second classification tag. The correspondence between the first classification tag and the second classification tag is set according to the association; for example, the first classification tag is school district housing sources, and the second classification tag is basic knowledge of school district housing. The two are associated, so the first classification tag and the second classification tag are associated. The second recommended content is real estate information pre-stored in the recommendation server 10, and the second classification tag is configured according to the title and content of the second recommended content. Furthermore, the second recommended content can be searched through the second classification tag.

[0046] It can be understood that the second classification tag searched out according to the first classification tag and the second recommended content searched out according to the second classification tag can be displayed on the display screen of the first user device 11 at the same time.

[0047] It can be seen that in this example, by pre-setting the correspondence between the first classification label and the second classification label, after configuring the first classification label for the first recommended content, the second classification label associated with the first classification label is searched out according to the correspondence between the first classification label and the second classification label; and then the second recommended content corresponding to the second classification label is searched out according to the second classification label, so as to enrich the first recommended content with the second recommended content, so that the target user can obtain relatively complete and systematic real estate information.

[0048] Step 104: Send the first recommended content and the plurality of second recommended contents to the second user device 12 of the target user.

[0049] For example, after searching for the second recommended content, the first recommended content and the plurality of second recommended contents are sent to the second user device 12 of the target user.

[0050] Specifically, the first recommended content may be sent first, and then the second recommended content may be sent after the target user has finished reading the first recommended content; or the second recommended content may be sent first, and then the second recommended content may be sent after the target user has finished reading the second recommended content. The recommended content sent first may be immediately followed by the recommended content sent later. For example, when the target user has read the end of the recommended content sent first, a prompt window for the recommended content to be sent later may be displayed at the end of the recommended content sent first, so that the user can read selectively and avoid disturbing the user.

[0051] In a possible example, after sending the first recommended content and the plurality of second recommended contents to the second user equipment 12 of the target user, the method further includes:

[0052] The first recommended content and the second recommended content are pushed in sequence according to the recommendation levels.

[0053] For example, according to the recommendation level corresponding to the first classification label of the first recommended content and the second classification label of the second recommended content, push is performed according to a preset rule. For example, basic knowledge is pushed first, then advanced knowledge is pushed, and finally promotional content is pushed. It is understandable that there can be multiple push orders; in addition, there can also be multiple push orders based on different recommendation level divisions, and no uniqueness is limited here.

[0054] It can be seen that in this example, different recommendation levels are used to push information according to preset rules, which helps the target user to gradually obtain real estate information. In addition, due to the correlation between the first recommended content and the second recommended content, the target user can obtain real estate information in a stable and efficient manner, thereby improving the push effect.

[0055] In a possible example, the first recommended content and the plurality of second recommended contents may be rearranged according to the recommendation level, combined into a third recommended content, and the third recommended content may be sent to the second user device 12. If the first recommended content and the second recommended content only include articles, the articles may be combined into one before being sent. If the first recommended content and the second recommended content include a plurality of articles, pictures, videos, and audios, the plurality of contents may be rearranged, for example, pictures, videos, and / or audios may be embedded in the same or related articles.

[0056] It can be seen that in this example, the recommended content only needs to be sent to the target once, thus avoiding interrupting the user twice or more times.

[0057] In a possible example, after sending the first recommended content and the plurality of second recommended contents to the second user equipment of the target user, the method further includes:

[0058] Acquire the reading data and consumption data of the target user; and modify the second recommended content sent subsequently according to the reading data and the consumption data.

[0059] For example, the recommendation system acquires the reading data and consumption data of the target user in real time. The reading data may be the access data for the first recommended content and the second recommended content, or the historical access data generated before the first recommended content and the second recommended content are sent; the consumption data may be the subscription data, payment data, collection data, etc. obtained by the target user from the related operations such as real estate consultation and real estate purchase, and the uniqueness is not limited here. Based on the reading data and consumption data, the preferences of the target user can be analyzed, and then the second recommended content to be sent subsequently can be adjusted according to the preferences of the target user, and the second recommended content associated with the preferences of the target user can be re-searched from the recommendation terminal or filtered from the existing second recommended content for sending.

[0060] It can be seen that in this example, by acquiring the reading data and consumption data of the target user in real time, and then correcting the second recommended content sent subsequently according to the reading data and consumption data, real-time adjustment of the recommended content is achieved, so that the real estate information obtained by the user is more in line with the needs.

[0061] In a possible example, after sending the first recommended content and the plurality of second recommended contents to the second user equipment of the target user, the method further includes:

[0062] Obtain the reading frequency and / or reading quantity of the first recommended content and / or the second recommended content by the target user within a preset time; and modify the second recommended content sent subsequently according to the reading frequency and / or reading quantity.

[0063] For example, according to the actual situation, the target user's reading time is uncertain, and the length of each reading is also uncertain. Generally speaking, when the user has a strong desire to buy a house, he will browse real estate information for a long time and at a high frequency. Therefore, this example obtains the reading frequency and number of readings of the target user in real time to determine the strength of the target user's desire to buy a house. If the reading frequency and / or the number of readings is greater than or equal to the first preset threshold, it is judged that the target user has a strong desire to buy a house. Therefore, more second recommended content related to housing sources is recommended to the target user, and finally the home purchase target is locked into a smaller range according to the user's needs, so as to increase the purchase transaction rate.

[0064] It can be seen that in this example, the target user's willingness to purchase a house is judged by obtaining the reading frequency and / or reading quantity of the first recommended content and / or the second recommended content by the target user within a preset time, and the second recommended content sent subsequently is corrected according to the reading frequency and / or reading quantity. Based on the corrected content, the home purchase target is finally locked into a smaller range according to the user's needs, so as to increase the home purchase transaction rate.

[0065] In a possible example, multiple preset thresholds are set, and each preset threshold is used to indicate a purchase intention strength. For example, three preset thresholds are set (from small to large: the second preset threshold, the third preset threshold, and the fourth preset threshold), corresponding to low, medium, and high purchase intention strengths. When the reading frequency and / or the number of readings is greater than or equal to the second preset threshold and less than the third preset threshold, the purchase intention strength of the target user is low; when the reading frequency and / or the number of readings is greater than or equal to the third preset threshold and less than the fourth preset threshold, the purchase intention strength of the target user is medium; when the reading frequency and / or the number of readings is greater than or equal to the fourth preset threshold, the purchase intention strength of the target user is high.

[0066] Furthermore, according to the strength of the purchase intention, a corresponding number of recommended contents (the recommended contents include the first recommended contents and / or the second recommended contents) are recommended to the target user at a corresponding frequency.

[0067] It can be seen that in this example, setting multiple preset thresholds can achieve a more precise determination of the purchase intention, thereby achieving a more accurate adjustment effect of the recommended content.

[0068] In a possible example, after sending the first recommended content and the plurality of second recommended contents to the second user equipment of the target user, the method further includes:

[0069] Obtaining the target user's rating result for the first recommended content and / or the second recommended content; and modifying the second recommended content sent subsequently according to the rating result.

[0070] For example, a rating mechanism is set for recommended content. When a user rates the first recommended content and / or the second recommended content, the rating result of the target user for the first recommended content and / or the second recommended content is obtained in real time to determine the user's satisfaction with the corresponding recommended content.

[0071] Among them, the scoring results include practicality, applicability, interest, etc., and the target user's satisfaction with the first recommended content and / or the second recommended content is judged through multiple dimensions, and finally a final score is obtained. The final score (i.e., the scoring result) is used to indicate the satisfaction.

[0072] For example, when the final score is greater than or equal to the fifth preset threshold, it is determined that the target user's satisfaction is high. Therefore, more first recommended content with high target user satisfaction and / or second recommended content related to the second recommended content are recommended to the target user. Ultimately, the home purchase target is locked into a smaller range based on the user's needs, so as to increase the home purchase transaction rate.

[0073] Optionally, multiple preset thresholds may be set, each of which is used to indicate a satisfaction level. For example, three preset thresholds are set (from small to large: the sixth preset threshold, the seventh preset threshold, and the eighth preset threshold), corresponding to low, medium, and high satisfaction levels, respectively. When the final score is greater than or equal to the sixth preset threshold and less than the seventh preset threshold, the target user's satisfaction level is low; when the final score is greater than or equal to the seventh preset threshold and less than the eighth preset threshold, the target user's satisfaction level is medium; when the final score is greater than or equal to the eighth preset threshold, the target user's satisfaction level is high.

[0074] Furthermore, more first recommended content with which the target user has high satisfaction and / or second recommended content related to the second recommended content are recommended to the target user according to the satisfaction.

[0075] It can be seen that in this example, the target user's satisfaction with the first recommended content and / or the second recommended content is determined by obtaining the target user's rating results for the first recommended content and / or the second recommended content, and the second recommended content sent subsequently is corrected according to the rating results. Finally, the home purchase target is locked into a smaller range according to the user's needs, so as to increase the home purchase transaction rate.

[0076] In summary, the present application provides a real estate information recommendation method and terminal device, the method is applied to a recommendation server, and the method includes: obtaining a first recommended content uploaded by a primary user through a first user device; configuring a classification label according to the title and content of the first recommended content; searching for multiple second recommended contents associated with the first recommended content according to the classification label; and sending the first recommended content and the multiple second recommended contents to the second user device of the target user. The present application labels the distributed content and provides a series of related recommended contents to the target user according to the label, so that the user can have a more comprehensive understanding of the relevant real estate information, avoid multiple interruptions, and speed up the transaction cycle.

[0077] The present invention also provides a computer-readable storage medium, which stores one or more programs. The one or more programs can be executed by one or more processors to implement the steps in the method described in the above embodiment.

[0078] The present invention also provides a terminal device 25, such as Figure 1b As shown, it includes at least one processor (processor) 20; display screen 21; and memory (memory) 22, and may also include a communications interface (CommunicationsInterface) 23 and a bus 24. Among them, the processor 20, the display screen 21, the memory 22 and the communication interface 23 can communicate with each other through the bus 24. The display screen 21 is configured to display a preset user guide interface in the initial setting mode. The communication interface 23 can transmit information. The processor 20 can call the logic instructions in the memory 22 to execute the method in the above embodiment.

[0079] Optionally, the terminal device 25 may be the one described above, or other terminal devices, and is not limited to any uniqueness herein.

[0080] In addition, the logic instructions in the memory 22 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

[0081] The memory 22 is a computer-readable storage medium that can be configured to store software programs, computer executable programs, such as program instructions or modules corresponding to the methods in the embodiments of the present disclosure. The processor 20 executes functional applications and data processing by running the software programs, instructions or modules stored in the memory 22, that is, implementing the methods in the above embodiments.

[0082] The memory 22 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal device 25, etc. In addition, the memory 22 may include a high-speed random access memory and may also include a non-volatile memory. For example, a variety of media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, may also be a transient storage medium.

[0083] In addition, the specific process of loading and executing the plurality of instructions in the storage medium and the processor in the mobile terminal has been described in detail in the method above, and will not be described here one by one.

[0084] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A real estate information recommendation method, characterized in that: Applied to a recommendation server, the method includes: Acquire first recommended content uploaded by a supervisor user through a first user device; Configuring a classification label according to the title and content of the first recommended content includes: presetting a correspondence between a first classification label and a second classification label, and configuring the first classification label for the first recommended content; wherein the classification label includes a first classification label and a second classification label, the first classification label is a classification label of the first recommended content, and the second classification label is a classification label among the classification labels except the first classification label; Searching for multiple second recommended contents associated with the first recommended contents according to the classification tags includes: searching for the second classification tags associated with the first classification tags according to the corresponding relationship between the first classification tags and the second classification tags; searching for second recommended contents corresponding to the second classification tags according to the second classification tags; Sending the first recommended content and the plurality of second recommended contents to a second user device of a target user; Obtaining a rating result of the target user for the first recommended content and / or the second recommended content, wherein the rating result includes a final rating based on multiple dimensions of practicality, applicability, and interest, and the final rating is used to indicate the target user's satisfaction with the first recommended content and / or the second recommended content; revising the second recommended content that is subsequently sent according to the rating result; and then sending the revised second recommended content to a second user device of the target user.

2. The method according to claim 1, characterized in that Before configuring the classification label according to the title and content of the first recommended content, the method further includes: Pre-set multiple category labels.

3. The method according to claim 2, characterized in that The method further comprises: The multiple classification labels are divided into multiple recommendation levels.

4. The method according to claim 3, characterized in that The sending the first recommended content and the plurality of second recommended contents to the second user equipment of the target user includes: The first recommended content and the second recommended content are pushed in sequence according to the recommendation levels.

5. The method according to claim 1, characterized in that After sending the first recommended content and the plurality of second recommended contents to the second user equipment of the target user, the method further includes: Obtaining reading data and consumption data of the target user; The second recommended content sent subsequently is modified according to the reading data and the consumption data.

6. The method according to claim 1, characterized in that After sending the first recommended content and the plurality of second recommended contents to the second user equipment of the target user, the method further includes: Obtaining a reading frequency and / or a reading quantity of the first recommended content and / or the second recommended content by the target user within a preset time; The second recommended content sent subsequently is modified according to the reading frequency and / or the reading quantity.

7. A terminal device, characterized in that: include: A processor and a memory; the memory stores a computer-readable program that can be executed by the processor; when the processor executes the computer-readable program, the steps in the method according to any one of claims 1 to 6 are implemented.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to implement the steps in the method according to any one of claims 1 to 6.

Images