CN113761361A - House property information recommendation method and terminal equipment - Google Patents

House property information recommendation method and terminal equipment Download PDF

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Publication number
CN113761361A
CN113761361A CN202110861298.5A CN202110861298A CN113761361A CN 113761361 A CN113761361 A CN 113761361A CN 202110861298 A CN202110861298 A CN 202110861298A CN 113761361 A CN113761361 A CN 113761361A
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China
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recommended content
recommended
content
user
target user
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CN202110861298.5A
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Chinese (zh)
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王超
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Shenzhen Ideamake Software Technology Co Ltd
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Shenzhen Ideamake Software Technology Co Ltd
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Priority to CN202110861298.5A priority Critical patent/CN113761361A/en
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9536—Search customisation based on social or collaborative filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/01—Social networking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/16—Real estate

Abstract

The application provides a house property information recommendation method and terminal equipment, wherein the method is applied to a recommendation server and comprises the following steps: acquiring first recommended content uploaded by a supervisor user through first user equipment; configuring a classification label according to the title and the content of the first recommended content; searching out a plurality of second recommended contents associated with the first recommended contents according to the classification labels; and sending the first recommended content and the plurality of second recommended contents to a second user device of the target user. According to the method and the system, the distributed content is labeled, a series of associated recommended contents are provided for the target user according to the label, so that the user can comprehensively know the related house information, multiple disturbance is avoided, and the transaction cycle is accelerated.

Description

House property information recommendation method and terminal equipment
Technical Field
The application relates to the field of house property sales, in particular to a house property information recommendation method and terminal equipment.
Background
At present, for the popularization and distribution of house-selling materials in the prior art, basically, a consultant performs unified house-selling material distribution on potential house buyers, then performs subjective analysis according to reading data of the house-selling materials of the potential house buyers, determines the house-buying intention of a client, further performs client maintenance, and slowly guides the client to be converted into real estate consumption.
However, this method is inefficient in distribution, requires a large amount of manpower, and has a long transaction period.
Thus, the prior art remains to be improved.
Disclosure of Invention
In view of the defects of the prior art, the application aims to provide a house property information recommendation method and terminal equipment, and aims to solve the problems that house property information distribution efficiency is low, large manpower is required to be consumed, and a transaction period is long.
In order to achieve the purpose, the following technical scheme is adopted in the application:
in a first aspect, the present application provides a method for recommending property information, which is applied to a recommendation server, and the method includes: acquiring first recommended content uploaded by a supervisor user through first user equipment; configuring a classification label according to the title and the content of the first recommended content; searching out a plurality of second recommended contents associated with the first recommended contents according to the classification labels; and sending the first recommended content and the plurality of second recommended contents to a second user device of the target user.
According to the method and the system, the distributed content is labeled, a series of associated recommended contents are provided for the target user according to the label, so that the user can comprehensively know the related house information, multiple disturbance is avoided, and the transaction cycle is accelerated.
In a second aspect, the present application provides a terminal device, comprising: a processor and a memory; the memory has stored thereon a computer readable program executable by the processor; the processor, when executing the computer readable program, implements the steps in the method as described above.
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, which are executable by one or more processors to implement the steps in the method as described above.
Drawings
FIG. 1a is a schematic diagram of a recommendation system provided herein;
fig. 1b is a schematic structural diagram of a terminal device provided in the present application;
fig. 2 is a flowchart of a property information recommendation method provided in the present application.
Detailed Description
In order to make the technical solutions of the present application better understood, 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 herein 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 application. 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.
In the present application, "at least one" means one or more, and a plurality means two or more. In this application and/or, an association relationship of an associated object is described, which means that there may be three relationships, for example, a and/or B, which may mean: a alone, both A and B, and B alone, where A, B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of the singular or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, a and b, a and c, b and c, or a, b and c, wherein each of a, b, c may itself be an element or a set comprising one or more elements.
It should be noted that, in the embodiments of the present application, the term "equal to" may be used in conjunction with more than, and is applicable to the technical solution adopted when more than, and may also be used in conjunction with less than, and is applicable to the technical solution adopted when less than, and it should be noted that when equal to or more than, it is not used in conjunction with less than; when the ratio is equal to or less than the combined ratio, the ratio is not greater than the combined ratio. In the embodiments of the present application, "of", "corresponding" and "corresponding" may be sometimes used in combination, and it should be noted that the intended meaning is consistent when the difference is not emphasized.
First, partial terms referred to in the embodiments of the present application are explained so as to be easily understood by those skilled in the art.
1. Recommending contents: in particular to all the electronic propaganda materials of the house and local manufacturers, i.e. house information. The first recommended content, the second recommended content, and the like are expressed in the present application.
2. A user equipment. UE is an important concept in mobile communication, and in 3G and 4G networks, user equipment is called UE. User Equipment is an important concept in mobile communication, and in networks such as 3G and 4G, User Equipment is called UE, which is equivalent to an MS in a 2G network where the UE includes a mobile phone, an intelligent terminal, multimedia Equipment, streaming media Equipment, and the like. The user equipment in the present application mainly refers to user equipment used by a supervisor user and a target user.
3. The user is in charge. The application refers to real estate popularizing personnel, which are related personnel responsible for uploading real estate information.
4. A target user. The application refers specifically to persons with the desire to purchase a house.
At present, for the popularization and distribution of house-selling materials in the prior art, basically, a consultant performs unified house-selling material distribution on potential house buyers, then performs subjective analysis according to reading data of the house-selling materials of the potential house buyers, determines the house-buying intention of a client, further performs client maintenance, and slowly guides the client to be converted into real estate consumption. However, this method is inefficient in distribution, requires a large amount of manpower, and has a long transaction period.
In order to solve the problems, the application provides a house property information recommendation method, which solves the problems that the house property information distribution efficiency is low, large manpower is required to be consumed, and the transaction period is long.
As shown in fig. 1a, fig. 1a is a selectable application scenario of a property information recommendation method provided in the present application, and particularly is a recommendation system, where the recommendation system includes a first user equipment 11 and a second recommendation and recommendation server 10, and both the first user equipment 11 and the second user equipment 12 are wirelessly connected to the recommendation server 10. The recommendation server 10 includes software, APP, etc. generated by the property 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 property information recommendation method. The first user equipment 11 uploads the first recommended content to the recommendation server 10 through a corresponding operation, and the recommendation server 10 sends the corresponding recommended content (the recommended content includes the first recommended content and/or the second recommended content) to the second user equipment 12 according to the corresponding operation.
The following describes an information query method provided by the present application with specific embodiments.
As shown in fig. 2, the present application provides a property information recommendation method applied in a recommendation server 10, the method including:
step 101, obtaining a first recommended content uploaded by a supervisor user through a first user device 11.
Illustratively, a supervisor user uploads a first recommended content to the recommendation server 10 through the first user device 11, where the first recommended content is a main content that needs to be read by the target user in this push. The first recommended content may be an article, a picture, a video, an audio, or the like, and is not limited herein.
And 102, configuring a classification label according to the title and the content of the first recommended content.
For example, after receiving the first recommended content, the recommendation server 10 detects a title or content of the first recommended content, and configures a classification tag according to the title and content. Specifically, the classification label is configured based on the title, and the classification label can be configured by judging the keyword in the title according to a pre-stored word stock; the category labels are configured based on the content, but identify whether the content is an article, picture, video, or audio, and are configured according to the corresponding content type. It is to be understood that the category label configured for the first recommended content manually by the administrative user may also be used, and is not limited herein.
In one possible example, before configuring the category label according to the title and the content of the first recommended content, the method further includes: presetting a plurality of classification labels; and establishing and storing the corresponding relation between the plurality of classification labels and the corresponding titles and/or contents.
Illustratively, the recommendation server 10 is preset with a plurality of classification tags, and stores them in a unified manner. The plurality of category labels may include title labels and content labels, and may be added or deleted by the hosting user. For the title and the content of the recommended content, a corresponding word bank can be set, and keywords or keywords in the word bank are respectively associated with corresponding classification labels in the classification labels to obtain the corresponding relation between the classification labels and the corresponding title and/or content.
As can be seen, in this example, a plurality of classification tags are preset in the recommendation server 10, and a lexicon corresponding to a title and a content is set to be associated with the tag, so as to establish a corresponding relationship between the tag and/or the content, so that the recommendation server can automatically assign the classification tag to the first recommended content.
In a possible example, after the establishing and storing the correspondence between the plurality of category labels and the corresponding titles and/or contents, the method further comprises:
the plurality of category labels are divided into a plurality of recommendation levels.
For example, the plurality of category labels may be classified into different recommendation levels according to different rules. For example, the plurality of category labels are divided into three levels, basic knowledge, advanced knowledge, and promotional content. The basic knowledge refers to related real estate basic knowledge and can comprise the explanation of related terms of real estate, the average price of related areas and the like; the advanced knowledge refers to the content of policy interpretation related to the property, the advantages and disadvantages of related property sources, how to select high-quality property sources and the like; the promotion content refers to the related house resources to be pushed at this time, and can include specific house resource information, house resource price, preferential strategies and the like. It is to be understood that the basic knowledge, the advanced knowledge and the promotional content include, but are not limited to, the above; in addition, the classification of the recommendation levels may not be limited to three recommendation levels of basic knowledge, advanced knowledge and promotion content, but may also be four recommendation levels, five recommendation levels, or even more or less recommendation levels; the recommended level may be classified according to other rules, for example, the recommended level may be classified by house source information, supporting facilities, and surrounding environment, which is not limited herein.
As can be seen, in this example, the classification tags are classified by dividing the classification tags into recommendation levels, so as to facilitate subsequent invocation and transmission of recommended content.
And 103, searching a plurality of second recommended contents associated with the first recommended contents according to the classification labels.
For example, after configuring the classification tag of the first recommended content, second recommended content stored in the recommendation server 10 is searched according to the classification tag, where the second recommended content is associated with the first recommended content, and may be upper content of the first recommended content, lower content of the first recommended content, extended content or progressive content of the first recommended content, or other associated content, which is not limited herein. The second recommended content is property information stored in the recommendation server 10 in advance, or is the first recommended content uploaded before step 101.
In one possible example, the classification tags include a first classification tag of the first recommended content and a second classification tag of the classification tags other than the first classification tag.
Specifically, the searching out a plurality of second recommended contents associated with the first recommended content according to the classification tag specifically includes:
searching out the second classification label associated with the first classification label according to the corresponding relation between the first classification label and the second classification label; and searching out second recommended content corresponding to the second classification label according to the second classification label.
For example, the recommendation server 10 stores a corresponding relationship between classification tags in advance, and searches for the second classification tag according to a previous corresponding relationship between the first classification tag and the second classification tag after configuring the first classification tag for the first recommended content. The corresponding relation between the first classification label and the second classification label is set according to the relevance; for example, if the first classification tag is a school district house source and the second classification tag is a school district house basic knowledge, and the first classification tag and the second classification tag have an association, the first classification tag and the second classification tag are associated. The second recommended content is property information pre-stored in the recommendation server 10, and a second classification tag is configured according to the title and the content of the second recommended content. Then, the second recommended content may be searched for through the second category tag.
It is understood that the second category label searched out according to the first category label and the second recommended content searched out according to the second category label may be simultaneously displayed on the display screen of the first user equipment 11.
As can be seen, in this example, by setting a correspondence between a first classification tag and a second classification tag in advance, after the first classification tag is configured for the first recommended content, the second classification tag associated with the first classification tag is searched out according to the correspondence between the first classification tag and the second classification tag; and searching out second recommended content corresponding to the second classification label according to the second classification label so as to enrich the first recommended content through the second recommended content, so that the target user can obtain complete and systematic property information.
And step 104, sending the first recommended content and the plurality of second recommended contents to the second user equipment 12 of the target user.
For example, after the second recommended content is searched out, the first recommended content and the plurality of second recommendations are sent to the second user equipment 12 of the target user.
Specifically, the sending may be to send the first recommended content first, and continue to send the second recommended content after the target user finishes reading the first recommended content; or the second recommended content is sent first, and the target user continues to send the second recommended content after reading the second recommended content. The first-sent recommended content may be immediately after the second-sent recommended content, for example, after the target user reads the end of the first-sent recommended content, a prompt window for prompting the second-sent recommended content is immediately followed at the end of the first-sent recommended content, so that the user can selectively read the first-sent recommended content, and the user is prevented from being disturbed.
In one possible example, after the sending the first recommended content and the plurality of second recommended contents to the second user device 12 of the target user, the method further includes:
and sequentially pushing the first recommended content and the second recommended content according to the recommendation level.
Illustratively, the pushing is performed according to a preset rule according to recommendation levels corresponding to a first classification tag of the first recommended content and a second classification tag of the second recommended content. For example, basic knowledge is pushed first, advanced knowledge is pushed, and promotional content is pushed last. It is understood that the order of pushing may be varied; in addition, based on different recommendation level divisions, there may be multiple push orders, which are not limited herein.
It can be seen that, in this example, the different recommendation levels are divided, and the pushing is performed according to the preset rule, so that the target user can obtain the property information step by step, and the target user can obtain the property information stably and efficiently due to the relevance between the first recommendation content and the second recommendation content, thereby improving the pushing effect.
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 sent to the second user equipment 12. And if the first recommended content and the second recommended content only comprise articles, combining the articles into one article, and then sending the article. If the first recommended content and the second recommended content comprise multiple articles, pictures, videos and audios, the multiple contents are rearranged, for example, the pictures, the videos and/or the audios are embedded into the articles with the same or related articles.
Therefore, the recommended content is only needed to be sent to the target once, and secondary or multiple disturbance to the user is avoided.
In one possible example, after the sending the first recommended content and the plurality of second recommended contents to the second user device of the target user, the method further includes:
acquiring reading data and consumption data of the target user; and correcting the second recommended content sent subsequently according to the reading data and the consumption data.
For example, the recommendation system obtains reading data and consumption data of the target user in real time, where the reading data may be access data for first recommended content and second recommended content, or historical access data generated before the first recommended content and the second recommended content are sent; the consumption data may be subscription data, payment data, collection data, and the like obtained by performing relevant operations on the house property consultation and house property purchase, and the like by the target user, which is not limited herein. And analyzing the preference of the target user based on the reading data and the consumption data, further adjusting subsequently sent second recommended content according to the preference of the target user, and searching again from the recommending terminal or screening out second recommended content associated with the preference of the target user from the existing second recommended content for sending.
Therefore, in this example, the reading data and the consumption data of the target user are obtained in real time, and the subsequently sent second recommended content is corrected according to the reading data and the consumption data, so that the recommended content is adjusted in real time, and the property information obtained by the user is more in line with the demand.
In one possible example, after the sending the first recommended content and the plurality of second recommended contents to the second user device of the target user, the method further includes:
acquiring the reading frequency and/or the reading quantity of the target user to the first recommended content and/or the second recommended content within preset time; and correcting the second recommended content which is sent subsequently according to the reading frequency and/or the reading number.
For example, according to the actual situation, the reading time of the target user is not fixed, and the time length of each reading is also not fixed. Generally, when the user has a strong desire to purchase a house, the user can browse the house information for a long time and at a high frequency. Therefore, in the present example, the reading frequency and the reading number of the target user are obtained in real time to determine the strength of the room purchasing desire of the target user. If the reading frequency and/or the reading quantity are/is larger than or equal to a first preset threshold value, the target user is judged to have a strong house purchasing intention, therefore, more second recommended contents related to house sources are recommended for the target user, and finally, the house purchasing target is locked in a smaller range according to the requirements of the user so as to improve the house purchasing rate.
As can be seen, in this example, the strength of the room purchasing desire of the target user is determined by obtaining the reading frequency and/or the reading number of the first recommended content and/or the second recommended content of the target user within a preset time, the second recommended content sent subsequently is modified according to the reading frequency and/or the reading number, and based on the modified content, the room purchasing target is finally locked within a smaller range according to the requirement of the user, so as to improve the room purchasing success rate.
In one possible example, a plurality of preset thresholds are set, each preset threshold being used to indicate the intensity of a purchase intention. For example, three preset thresholds (from small to large, respectively: a second preset threshold, a third preset threshold and a fourth preset threshold) are set, and the three preset thresholds correspond to the low, medium and high house purchasing wish strengths respectively. When the reading frequency and/or the reading quantity are greater than or equal to a second preset threshold and smaller than a third preset threshold, the intensity of the room purchasing intention of the target user is low; when the reading frequency and/or the reading quantity are greater than or equal to a third preset threshold and less than a fourth preset threshold, the intensity of the room purchasing intention of the target user is medium; and when the reading frequency and/or the reading quantity are/is greater than or equal to a fourth preset threshold value, the intensity of the room purchasing willingness of the target user is high.
Further, recommending a corresponding number of recommended contents (the recommended contents comprise first recommended contents and/or second recommended contents) for the target user at a corresponding frequency according to the house purchasing desire strength.
Therefore, in the example, a plurality of preset thresholds are set, so that more precise judgment on house purchasing will can be realized, and a more precise adjustment effect on recommended content can be achieved.
In one possible example, after the sending the first recommended content and the plurality of second recommended contents to the second user device of the target user, the method further includes:
obtaining a scoring result of the target user on the first recommended content and/or the second recommended content; and correcting the second recommended content which is sent subsequently according to the grading result.
Illustratively, a scoring mechanism is set for the recommended content, and when the user scores the first recommended content and/or the second recommended content, a scoring result of the target user on the first recommended content and/or the second recommended content is obtained in real time to determine the satisfaction degree of the user on the corresponding recommended content.
The scoring result includes a utility degree, a usability degree, an interest degree, and the like, the satisfaction degree of the target user on the first recommended content and/or the second recommended content is judged through multiple dimensions, and finally a final score is obtained, wherein the final score (i.e., the scoring result) is used for indicating the satisfaction degree.
For example, when the final score is greater than or equal to a fifth preset threshold, it is determined that the satisfaction of the target user is higher, so that more first recommended content with high satisfaction of the target user and/or second recommended content related to the second recommended content are recommended to the target user, and finally, a house purchasing target is locked in a smaller range according to the requirement of the user, so as to improve the house purchasing success rate.
Optionally, a plurality of preset thresholds may be set, each preset threshold being used to indicate a satisfaction. For example, three preset thresholds (from small to large, respectively: a sixth preset threshold, a seventh preset threshold, and an eighth preset threshold) are set, corresponding to the low, medium, and high satisfaction degrees, respectively. When the final score is greater than or equal to a sixth preset threshold and is smaller than a seventh preset threshold, the satisfaction degree of the target user is low; when the final score is greater than or equal to a seventh preset threshold and smaller than an eighth preset threshold, the satisfaction degree of the target user is medium; and when the final score is greater than or equal to an eighth preset threshold, the satisfaction degree of the target user is high.
Further, more first recommended contents with high satisfaction of the target user and/or second recommended contents related to the second recommended contents are recommended to the target user according to the satisfaction.
As can be seen, in this example, the satisfaction of the target user on the first recommended content and/or the second recommended content is determined by obtaining the scoring result of the target user on the first recommended content and/or the second recommended content, and then the second recommended content sent subsequently is modified according to the scoring result, and finally the house-buying target is locked in a smaller range according to the requirement of the user, so as to improve the house-buying rate.
To sum up, the method for recommending the house property information and the terminal device are applied to the recommendation server, and the method comprises the following steps: acquiring first recommended content uploaded by a supervisor user through first user equipment; configuring a classification label according to the title and the content of the first recommended content; searching out a plurality of second recommended contents associated with the first recommended contents according to the classification labels; and sending the first recommended content and the plurality of second recommended contents to a second user device of the target user. According to the method and the system, the distributed content is labeled, a series of associated recommended contents are provided for the target user according to the label, so that the user can comprehensively know the related house information, multiple disturbance is avoided, and the transaction cycle is accelerated.
The present invention also provides a computer-readable storage medium storing one or more programs, which are executable by one or more processors to implement the steps in the method described in the above embodiments.
The present invention also provides a terminal device 25, as shown in fig. 1b, comprising at least one processor (processor) 20; a display screen 21; and a memory (memory)22, and may further include a communication Interface (Communications Interface)23 and a bus 24. The processor 20, the display 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 user guidance interface preset in the initial setting mode. The communication interface 23 may transmit information. The processor 20 may call logic instructions in the memory 22 to perform the methods in the embodiments described above.
Optionally, the terminal device 25 may be the above-mentioned terminal device, or may be another terminal device, which is not limited herein.
Furthermore, the logic instructions in the memory 22 may be implemented in software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product.
The memory 22, which is a computer-readable storage medium, may be configured to store a software program, a computer-executable program, such as program instructions or modules corresponding to the methods in the embodiments of the present disclosure. The processor 20 executes the functional application and data processing, i.e. implements the method in the above-described embodiments, by executing the software program, instructions or modules stored in the memory 22.
The memory 22 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 25, and the like. Further, 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 disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk, may also be transient storage media.
In addition, the specific processes loaded and executed by the storage medium and the instruction processors in the mobile terminal are described in detail in the method, and are not stated herein.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions in the embodiments of the present application.

Claims (10)

1. A house property information recommendation method is applied to a recommendation server, and comprises the following steps:
acquiring first recommended content uploaded by a supervisor user through first user equipment;
configuring a classification label according to the title and the content of the first recommended content;
searching out a plurality of second recommended contents associated with the first recommended contents according to the classification labels;
and sending the first recommended content and the plurality of second recommended contents to a second user device of the target user.
2. The method of claim 1, wherein the category label comprises a first category label and a second category label, the first category label being a category label of the first recommended content, the second category label being a category label of the category labels other than the first category label;
the searching out a plurality of second recommended contents associated with the first recommended content according to the classification label comprises:
searching out the second classification label associated with the first classification label according to the corresponding relation between the first classification label and the second classification label;
and searching out second recommended content corresponding to the second classification label according to the second classification label.
3. The method of claim 1, wherein before configuring a category label according to the title and content of the first recommended content, the method further comprises:
presetting a plurality of classification labels;
and establishing and storing the corresponding relation between the plurality of classification labels and the corresponding titles and/or contents.
4. The method of claim 3, wherein after establishing and storing the correspondence between the plurality of category labels and the corresponding titles and/or content, the method further comprises:
the plurality of category labels are divided into a plurality of recommendation levels.
5. The method of claim 4, wherein the sending the first recommended content and the plurality of second recommended contents to a second user device of a target user comprises:
and sequentially pushing the first recommended content and the second recommended content according to the recommendation level.
6. The method of claim 1, wherein after sending the first recommended content and the plurality of second recommended contents to a second user device of a target user, the method further comprises:
acquiring reading data and consumption data of the target user;
and correcting the second recommended content sent subsequently according to the reading data and the consumption data.
7. The method of claim 1, wherein after sending the first recommended content and the plurality of second recommended contents to a second user device of a target user, the method further comprises:
acquiring the reading frequency and/or the reading quantity of the target user to the first recommended content and/or the second recommended content within preset time;
and correcting the second recommended content which is sent subsequently according to the reading frequency and/or the reading number.
8. The method of claim 1, wherein after sending the first recommended content and the plurality of second recommended contents to a second user device of a target user, the method further comprises:
obtaining a scoring result of the target user on the first recommended content and/or the second recommended content;
and correcting the second recommended content which is sent subsequently according to the grading result.
9. A terminal device, comprising: a processor and a memory; the memory has stored thereon a computer readable program executable by the processor; the processor, when executing the computer readable program, implements the steps in the method of any of claims 1-8.
10. A computer readable storage medium, storing one or more programs, the one or more programs being executable by one or more processors to perform the steps of the method of any one of claims 1-8.
CN202110861298.5A 2021-07-29 2021-07-29 House property information recommendation method and terminal equipment Pending CN113761361A (en)

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