Method, device, electronic device and storage medium for processing house source information

Abstract

The embodiment of the present application provides a method, device, electronic device and storage medium for processing house source information, the method comprising: obtaining multiple user behavior records for published house source information; sorting the multiple user behavior records in order of trigger time from earliest to latest to obtain a user behavior record sequence; calculating the behavior density parameters of each user behavior triggered by a user of each user role according to the multiple user behavior records and the user roles to which the user behavior records are derived; obtaining the dynamic score of the target house source indicated by the published house source information according to the first adjustment factor of each scoring item, the second adjustment factor and the basic score corresponding to each scoring item; calculating the comprehensive score of the target house source according to the dynamic score of the target house source and the static score of the target house source; processing the published house source information according to the comprehensive score of the target house source. This solution ensures the reliability of the obtained processing results.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, device, electronic device and storage medium for processing house information. Background Art

[0002] In the real estate transaction platform, the house score is generally calculated based on the static information of the house, such as the information introduction of the house (location, layout, area, price, etc.), and then the house information is processed according to the house score, which leads to the problem of low reliability of the processing results. Summary of the invention

[0003] The embodiments of the present application provide a method, device, electronic device and storage medium for processing housing information, which can at least to some extent solve the problem of low reliability of processing results existing in the related art.

[0004] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by the practice of the present application.

[0005] According to one aspect of an embodiment of the present application, a method for processing housing information is provided, comprising: obtaining a plurality of user behavior records for published housing information; the user behavior records indicating the user behaviors triggered for the published housing information and the corresponding triggering time; sorting the plurality of user behavior records in order of the triggering time from earliest to latest to obtain a user behavior record sequence, the user behavior record sequence being used to determine a first adjustment factor of a scoring item corresponding to each user behavior record; calculating a behavior density parameter of each user behavior triggered by a user of each user role according to the plurality of user behavior records and the user role to which the user behavior records are derived; the behavior density parameter being used to determine a second adjustment factor of a scoring item corresponding to each user behavior record; obtaining a dynamic score of a target housing indicated by the published housing information according to the first adjustment factor, the second adjustment factor and the basic score corresponding to each scoring item; calculating a comprehensive score of the target housing according to the dynamic score of the target housing and the static score of the target housing; the static score being determined according to the static information of the target housing; and processing the published housing information according to the comprehensive score of the target housing.

[0006] According to one aspect of an embodiment of the present application, a device for processing housing information is provided, including: a user behavior record acquisition module, used to acquire multiple user behavior records for published housing information; the user behavior record indicates the user behavior triggered for the published housing information and the corresponding trigger time; a user behavior record sequence determination module, used to sort the multiple user behavior records in order of trigger time from earliest to latest, to obtain a user behavior record sequence, the user behavior record sequence is used to determine a first adjustment factor for a scoring item corresponding to each user behavior record; a behavior density parameter calculation module, used to calculate a first adjustment factor for a scoring item corresponding to each user behavior record based on the multiple user behavior records and the user role to which the user behavior records are derived , calculate the behavior density parameters of each user behavior triggered by the user of each user role; the behavior density parameters are used to determine the second adjustment factor of the scoring item corresponding to each user behavior record; a dynamic score calculation module is used to obtain the dynamic score of the target house indicated by the published house information according to the first adjustment factor, the second adjustment factor of each scoring item and the basic score corresponding to each scoring item; a comprehensive score determination module is used to calculate the comprehensive score of the target house according to the dynamic score of the target house and the static score of the target house; the static score is determined according to the static information of the target house; a processing module is used to process the published house information according to the comprehensive score of the target house.

[0007] According to one aspect of an embodiment of the present application, an electronic device is provided, including: a processor; a memory, wherein computer-readable instructions are stored in the memory, and when the computer-readable instructions are executed by the processor, the method described above is implemented.

[0008] According to one aspect of an embodiment of the present application, a computer-readable storage medium is provided, on which computer-readable instructions are stored. When the computer-readable instructions are executed by a processor, the method described above is implemented.

[0009] In the scheme of the present application, the dynamic score of the target house is calculated based on the user behavior records for the published house information, and then the comprehensive score of the target house is calculated based on the dynamic score and the static score calculated based on the static information of the target house. The user behavior records that are dynamically updated over time reflect the attention paid to the published house information. Therefore, the scheme of the present application is equivalent to combining the dynamic score under the dynamic dimension that reflects the attention paid to the house and the static score under the static dimension to determine the comprehensive score of the house. Compared with scoring based only on the static dimension of the static information of the target house, the comprehensive score obtained by this scheme is more reliable and accurate; moreover, the dynamic user behavior records are mainly used to weaken the influence of static information on the score, so that the calculated comprehensive score can more comprehensively reflect the quality of the house.

[0010] Moreover, the comprehensive score of the target house is calculated by combining static information and dynamic user behavior records under the time dimension, and the dynamic user behavior records are mainly used to weaken the influence of static information on the score; the Bayesian probability superposition method is used to calculate the comprehensive score of the target house, and different user behavior records are independent of each other, and different user behavior records can corroborate each other. Even if there is missing data in the process of calculating the comprehensive score, the influence of the missing data on the comprehensive score can be weakened according to the newly added user behavior records. Therefore, the accuracy of the calculated comprehensive score can be guaranteed on the basis of the presence of missing data. On this basis, the published house information is processed based on the comprehensive score with high accuracy and reliability, which can ensure the reliability and accuracy of the processing results of the published house information.

[0011] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The drawings herein are incorporated into the specification and constitute a part of the specification, showing embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0013] Figure 1 A schematic diagram of an exemplary system architecture to which the technical solution of the embodiments of the present application can be applied is shown.

[0014] Figure 2 It is a flowchart of a method for processing housing information according to an embodiment of the present application.

[0015] Figure 3 FIG. 2 is a flowchart showing steps before step 240 according to an embodiment of the present application.

[0016] Figure 4 is a flowchart showing steps before step 240 according to another embodiment of the present application.

[0017] Figure 5 is a flowchart showing steps before step 240 according to another embodiment of the present application.

[0018] Figure 6 is a flowchart showing steps before step 250 according to an embodiment of the present application.

[0019] Figure 7It is a block diagram of a device for processing housing information according to an embodiment of the present application.

[0020] Figure 8 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0021] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this application will be more comprehensive and complete and fully convey the concept of the example embodiments to those skilled in the art.

[0022] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.

[0023] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0024] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0025] Figure 1 A schematic diagram of an exemplary system architecture to which the technical solution of the embodiments of the present application can be applied is shown.

[0026] like Figure 1 As shown, the system architecture may include a terminal device 101, a network 102, and a server 103. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 102. The network 102 may include various connection types, such as a wired communication link, a wireless communication link, and the like.

[0027] The terminal device 101 may be one or more of a smart phone, a tablet computer, a portable computer, and a desktop computer. It should be understood that Figure 1 The numbers of terminal devices 101, networks 102 and servers 103 shown in the figure are merely illustrative. For example, the server may be a single server or a server cluster consisting of multiple servers.

[0028] The terminal device 101 can run a client program for displaying published housing information, and the user interface of the client program provides controls for user interaction, such as controls for users to forward published housing information (such as forwarding to friends circle controls, forwarding to friends controls), collection controls for collecting published housing information, dialing the owner's phone or the real estate agent's phone, making an appointment control for making an appointment to visit the housing source, and editing controls for the housing source information, etc., which are not specifically limited here. Therefore, users, such as agents, owners, home buyers, etc., can trigger user actions on the displayed published housing information through controls in the user interface displayed by the terminal device 101.

[0029] After the client detects the triggering operation of the control on the page where the published house information is located, a corresponding user behavior record is generated, which indicates the user behavior triggered by the user and the triggering time. Furthermore, the user behavior record also indicates the user identification information that triggered the user behavior. In a specific embodiment, the triggered user behavior can be identified based on the triggered control. Therefore, the user behavior record can include the control information of the triggered control (such as the position information of the control in the page, the identification information of the control, etc.), and each control is used to indicate a user behavior. Therefore, the triggered user behavior can be identified based on the control information in the user behavior record.

[0030] In the scheme of the present application, after generating a user behavior record, the client reports the user behavior to the server 103. Thus, after receiving the user behavior records reported by many clients, the server 103 classifies the user behavior according to the published housing information to which the user behavior records target, obtains a set of user behavior records corresponding to each published housing information, and can calculate the comprehensive score of each housing source according to the method of the present application, and process the published housing information corresponding to the housing source.

[0031] It should be noted that the processing method for providing house source information in the present application is generally executed by the server 103, and accordingly, the processing device for house source information is generally set in the server 103. However, in other embodiments of the present application, the solution of the present application can also be implemented by the terminal device 101 interacting with the server 103, or by the terminal device 101 with processing capabilities.

[0032] The implementation details of the technical solution of the embodiment of the present application are described in detail below:

[0033] Figure 2 The following is a flow chart showing a method for processing house source information according to an embodiment of the present application. Figure 2 As shown, the method for processing the house source information includes at least steps 210 to 260, which are described in detail as follows:

[0034] Step 210, obtaining multiple user behavior records for the published housing information; the user behavior records indicate the user behaviors triggered for the published housing information and the corresponding triggering time.

[0035] The published property information is the information introduction of the property published on the page, which may include the geographical location of the property, price information, house layout information, property certificate number, building information of the community where the property is located, owner information, furniture information, supporting facilities information around the property, property information corresponding to the property, property video or property photos, etc., which are not specifically limited here.

[0036] User behaviors may be forwarding (forwarding to friends, forwarding to friends, forwarding to groups, forwarding to other social platforms, etc.), collecting, editing, calling the owner, making an appointment to view the house, confirming the house viewing, etc., which are not specifically limited here. In a specific embodiment, user behaviors can be identified based on the triggered controls. It is understandable that if the controls triggered in the interactive interface are different, the corresponding user behaviors are also different.

[0037] The server collects user behavior records from numerous clients, and then classifies the user behavior records according to the identifiers of the published housing information to which the user behavior records correspond, to obtain a user behavior record set corresponding to each published housing information, wherein the user behavior record set includes multiple user behavior records.

[0038] The user behavior triggered by the published housing information can reflect the user's attention to the target housing indicated by the published housing information, or the user's value assessment of the target housing indicated by the published housing information. Therefore, the user behavior record for the published housing information can reflect the quality of the corresponding target housing to a certain extent. It is based on this point that the inventor of the present application proposed the solution of the present application, that is, combining the dynamic user behavior record of the user for the published housing information to score the target housing indicated by the published housing information.

[0039] Step 220 , sorting the plurality of user behavior records in order of trigger time from earliest to latest to obtain a user behavior record sequence, wherein the user behavior record sequence is used to determine a first adjustment factor of a scoring item corresponding to each user behavior record.

[0040] The first adjustment factor refers to an adjustment factor determined for the scoring item according to the order of each user behavior sequence in the user behavior record sequence.

[0041] In the solution of the present application, each user behavior record uniquely corresponds to a scoring item, and the number of scoring items set can be set according to actual needs. In some embodiments of the present application, the scoring item can be related to the user role to which the user belongs and the triggered user behavior. For example, if the user role and user behavior of the user from which two user behavior records are derived are the same, then the scoring items corresponding to the two user behavior records are the same.

[0042] In some embodiments of the present application, the scoring items corresponding to each user behavior record can be determined according to the user behavior and the user role, that is: the scoring items corresponding to the user behavior record are determined according to the user behavior indicated by the user behavior record and the user role to which the user from which the user behavior record originates, and the scoring items are related to the user behavior and the user role to which the user belongs; the user roles include real estate agents, owners and home buyers.

[0043] In some embodiments of the present application, a first mapping relationship among user roles, user behaviors, and scoring items is pre-established, and on this basis, after determining the user behavior indicated by a user behavior record (for the convenience of description, referred to as target user behavior) and the user role to which the user from which the user behavior record originates (for the convenience of description, referred to as target user role), the scoring items associated with both the target user behavior and the target user role are determined in the first mapping relationship, and the scoring items determined to be associated with both the target user behavior and the target user role are the scoring items corresponding to the user behavior record.

[0044] In other embodiments, user roles can also be divided into finer granularity, for example, real estate agents are further divided into first-level real estate agents, second-level real estate agents, third-level real estate agents, etc. The level of real estate agents is directly related to the professionalism of real estate agents. Relatively speaking, real estate agents with higher professionalism have more accurate assessments of the quality (or value) of real estate, so that for user behaviors triggered by the same published real estate information, the first adjustment factor of the corresponding scoring item can be set to be greater than that of real estate agents with lower professionalism.

[0045] In some embodiments of the present application, the first adjustment factor of each scoring item can be determined according to the following process: obtaining adjustment strategy information, the adjustment strategy information including the adjustment strategy corresponding to each scoring item, the adjustment strategy indicating the reference user behavior record sequence segment and the corresponding adjustment factor of the corresponding scoring item; performing sequence segment matching in the user behavior record sequence according to the reference user behavior record sequence segment in the adjustment strategy; if the reference user behavior record sequence segment is matched in the user behavior record sequence, determining the adjustment factor in the adjustment strategy from which the matched reference user behavior record sequence segment comes as the first adjustment factor of the scoring item indicated by the adjustment strategy.

[0046] Sequence segment matching refers to detecting whether there is a sequence segment in the user behavior sequence segment that matches the reference user behavior record sequence segment. The reference user behavior sequence segment can be set according to actual needs. In some embodiments of the present application, if the order of the user behavior records in the two user behavior sequence segments is the same, the user behaviors indicated by the user behavior records are the same, and the user roles to which the user behavior records are derived are the same, then the two user behavior sequence segments are deemed to match.

[0047] Since the user behavior record is generated by the client when it detects the user's operation triggered by the published housing information, the user behavior record indicates the user ID corresponding to the user who triggered the user behavior. Based on the user ID and the user's portrait information, the user role to which the user belongs can be determined. The user's portrait information indicates the user role to which the user belongs.

[0048] In this embodiment, the adjustment strategy is equivalent to indicating the mapping relationship between the reference user behavior record sequence segment and the first adjustment factor corresponding to the scoring item.

[0049] In other embodiments of the present application, the specified conditions and adjustment factors of each scoring item may also be pre-set. If it is determined that the specified condition of the scoring item is met based on the user behavior record sequence, the adjustment factor set for the scoring item is determined as the first adjustment factor of the scoring item. The specified condition may be that the number of consecutive occurrences of the specified user behavior record reaches a set number, or one or more second specified user behavior records of the specified user behavior triggered by the specified user role exist before the first specified user behavior record. The specific setting may be based on actual needs.

[0050] Step 230, based on the multiple user behavior records and the user roles to which the user behavior records are derived, calculate the behavior density parameters of each user behavior triggered by a user of each user role; the behavior density parameters are used to determine a second adjustment factor for a scoring item corresponding to each user behavior record.

[0051] The behavior density parameter is used to reflect the density of user behavior. The density of user behavior can be the density in time or the density in information exposure. The density in time can be calculated by the number of user behaviors and the publishing time of the published property information; the density in information exposure can be calculated by the number of user behaviors and the exposure of the published property information.

[0052] As described above, users with different user roles have different accuracy in value assessment of the target property and / or degree of attention to the target property as represented by the same user behavior. Therefore, in the scheme of the present application, user behaviors are further divided according to user roles. For example, the user behavior of forwarding to friends can be further divided into: real estate agents forwarding to friends, home buyers forwarding to friends, and property owners forwarding to friends.

[0053] On this basis, multiple user behavior records of the published housing information are classified according to the user role from which the user behavior comes and the user behavior, and user behavior records of the same user behavior under the same user role are grouped together. The number of user behavior records corresponding to each user behavior under each user role is obtained by corresponding statistics. Then, the behavior density parameter corresponding to each user behavior under each user role is calculated according to the publishing time or exposure of the published housing information.

[0054] The second adjustment factor refers to the adjustment factor calculated based on the user behavior density parameter. In some embodiments of the present application, a second mapping relationship between the behavior density parameter of each user behavior under each user role and the second adjustment factor of the corresponding scoring item can be pre-set. On this basis, after determining the behavior density parameter of a user behavior under a user role, the second adjustment factor of the scoring item associated with the user role and the user behavior is determined according to the corresponding second mapping relationship.

[0055] Since the behavior density parameter reflects the density of user behaviors triggered by published housing information, it is understandable that if the density of a user behavior or a type of user behavior is higher, it indicates that the user pays more attention to the target housing indicated by the published housing information, thus indirectly reflecting that the quality or popularity of the target housing is higher. Therefore, in some embodiments of the present application, the second adjustment factor is positively correlated with the corresponding behavior density parameter, and the larger the behavior density parameter, the larger the corresponding second adjustment factor.

[0056] Step 240, obtaining a dynamic score of the target house indicated by the published house information according to the first adjustment factor of each scoring item, the second adjustment factor and the basic score corresponding to each scoring item.

[0057] The basic score may be a score initially set based on experience, or may be determined based on the initially set score after sample positive and negative feedback training and tuning.

[0058] The dynamic score refers to the score calculated based on the user behavior record of the published housing information corresponding to the housing source. It can be understood that the dynamic score is related to the user behavior triggered by the published housing information corresponding to the housing source, and as time goes by, the user behavior for the published housing information is also increasing. Correspondingly, it is necessary to repeat the above steps 210-240 to update the dynamic score of the target housing source corresponding to the published housing information. In other words, the dynamic score of the published housing information calculated according to this solution is related to time, accurately reflects the changes in the attention of the published housing information in the time dimension, and reflects the timeliness value of the published housing information.

[0059] In some embodiments of the present application, step 240 may include: determining a scoring coefficient for each scoring item based on a first adjustment factor and a second adjustment factor for each scoring item; performing a weighted calculation based on the scoring coefficient for each scoring item and a basic score corresponding to each scoring item to obtain a dynamic score for the target property indicated by the published property information.

[0060] In some embodiments of the present application, the first adjustment factor and the second adjustment factor corresponding to the scoring item may be added, and the result obtained by the addition may be used as the scoring coefficient of the scoring item. In another embodiment of the present application, the first adjustment factor and the second adjustment factor may be multiplied, and the result obtained may be used as the scoring coefficient of the scoring item.

[0061] In the process of weighted calculation, the basic scores of all the scoring items are weighted by taking the scoring coefficient of the scoring item as the weighting coefficient, and the weighted calculation result is the dynamic score of the target property indicated by the published property information. The weighted calculation can be a weighted sum or a weighted average.

[0062] Step 250, calculating a comprehensive score of the target house according to the dynamic score of the target house and the static score of the target house; the static score is determined according to the static information of the target house.

[0063] The static information of the target house source refers to the objective description information of the target house source and the surrounding of the target house source, which can be the geographical location, price information, house layout information, house source property certificate number, community building information (building height, community supporting facilities, building spacing, greening, etc.) of the house source described in the published house source information, owner information, furniture supporting information, supporting facilities information around the house source (such as shopping malls, hospitals, schools, stadiums, museums, subway stations, bus stops, etc.), property information corresponding to the house source (such as property management company), etc., which are basically unchanged. Of course, the above are only illustrative examples and cannot be considered as limiting the scope of use of this application.

[0064] In some embodiments of the present application, static scoring rules may be pre-set, and then the static score of the target property may be calculated in combination with the static information of the target property.

[0065] In some embodiments of the present application, the static score of the target property can be calculated according to the following process: obtaining static information of the target property, the static information including at least one of basic information of the target property and surrounding supporting facilities information of the target property; determining the static score of the target property based on the static information of the target property and preset static scoring rules.

[0066] The basic information of the target property may include the geographical location of the above-listed properties, price information (such as unit price), house layout information (such as house layout, house area, etc.), property certificate number of the property, building information of the community where the property is located (building height, community facilities, distance between buildings, greening, etc.), owner information, furniture supporting information, property information corresponding to the property, and other information.

[0067] The target property's surrounding facilities information includes, for example, shopping malls, hospitals, schools, stadiums, museums, subway stations, bus stops, etc., and the distance between the supporting facilities and the target property.

[0068] In some embodiments of the present application, the static information of the target property may also include the property transaction heat information in the community where the target property is located, which may be the transaction volume of the property in the community where the target property is located within a unit time period (for example, one month), or the total historical transaction volume, etc.

[0069] The preset static scoring rule refers to the scoring rule set for static scoring. In some embodiments of the present application, the preset static scoring rule may include scoring rules for multiple static scoring items. Static scoring items include price scoring items, house layout scoring items, community environment scoring items, property scoring items, surrounding supporting facilities scoring items, etc. On this basis, according to the static information of the target house and the scoring rules under multiple static scoring items, the score of the target house under each static scoring item can be determined, and the comprehensive score of the target house under each static scoring item can correspond to the static score of the target house.

[0070] After obtaining the dynamic score of the target property and the static score of the target property, the comprehensive score of the target property is calculated according to the mapping relationship between the comprehensive score and the dynamic score and the static score. For example, the static score is added to the dynamic score. In other embodiments, other methods can be used to combine the dynamic score and the static score to determine the comprehensive score of the target property.

[0071] Step 260: Process the published housing information according to the comprehensive score of the target housing.

[0072] In some embodiments of the present application, the published house information can be pushed according to the comprehensive score of the target house. Step 260 further includes: screening the houses according to the comprehensive score of each house in the house set to determine the houses to be pushed; and pushing the published house information of the houses to be pushed.

[0073] The properties in the property collection are candidate properties to be pushed. Property screening can be performed by sorting properties or by setting a score threshold. In the process of screening by sorting properties, the properties in the property collection are first sorted in descending order of comprehensive scores, and the first set number of properties in the sorting are selected as properties to be pushed. Alternatively, properties with a comprehensive score greater than a set score threshold can be selected as properties to be pushed. The selected properties to be pushed can be one or more.

[0074] In the scheme of the present application, the dynamic score of the target house is calculated based on the user behavior records for the published house information, and then the comprehensive score of the target house is calculated based on the dynamic score and the static score calculated based on the static information of the target house. The user behavior records that are dynamically updated over time reflect the attention paid to the published house information. Therefore, the scheme of the present application is equivalent to combining the dynamic score under the dynamic dimension that reflects the attention paid to the house and the static score under the static dimension to determine the comprehensive score of the house. Compared with scoring based only on the static dimension of the static information of the target house, the comprehensive score obtained by this scheme is more reliable and accurate; moreover, the dynamic user behavior records are mainly used to weaken the influence of static information on the score, so that the calculated comprehensive score can more comprehensively reflect the quality of the house.

[0075] Moreover, the comprehensive score of the target house is calculated by combining static information and dynamic user behavior records under the time dimension. Although the comprehensive score is constrained by the user behavior records, since the user behavior records are constantly added, the impact of each user behavior record on the comprehensive score changes with time, which is equivalent to using the Bayesian probability superposition method to calculate the comprehensive score of the target house. Different user behavior records are independent of each other, and different user behavior records can corroborate each other. Even if there is missing data in the process of calculating the comprehensive score, the impact of the missing data on the comprehensive score can be weakened according to the newly added user behavior records. Therefore, the accuracy of the calculated comprehensive score can be guaranteed on the basis of the presence of missing data. On this basis, the processing of the published house information based on the comprehensive score with high accuracy and reliability can ensure the reliability and accuracy of the processing results of the published house information.

[0076] In some embodiments of the present application, the scoring item includes a first scoring item; the user behavior includes an information dissemination behavior belonging to a dissemination type; in this embodiment, before step 240, as Figure 3 As shown, the method also includes:

[0077] Step 310, obtaining propagation weight information and behavior density parameters of each information propagation behavior triggered by a user of each user role; the propagation weight information indicates the propagation weight associated with each user role and each information propagation behavior.

[0078] The propagation weight refers to the weight set for the information propagation behavior. The propagation weight is used to characterize the contribution of the corresponding information propagation behavior to the propagation of the published housing information. In this embodiment, since the contribution of the information propagation behavior of users with different roles to the propagation of the published housing information is also different, the propagation weight is set in combination with the user role and the information propagation behavior, that is, the propagation weight is associated with both the user role and the information propagation behavior.

[0079] Information dissemination behaviors may include forwarding behaviors (such as forwarding to Moments, forwarding to friends, forwarding to groups, forwarding to other social platforms, etc.), commenting on published housing information, etc.

[0080] Step 320, performing weighted calculation based on the dissemination weight associated with each user role and each information dissemination behavior, and the behavior density parameter of each information dissemination behavior triggered by the user of each user role, to obtain the behavior density parameter corresponding to the dissemination type.

[0081] In the embodiment of the present application, the behavior density parameters are weighted for the information dissemination behaviors of the dissemination types. The dissemination weight associated with each user role and each information dissemination behavior is used as a weighting coefficient to weight the behavior density parameters of each information dissemination behavior triggered by a user of each user role.

[0082] Step 330: Determine a second adjustment factor of the first scoring item according to the behavior density parameter corresponding to the propagation type.

[0083] In some embodiments of the present application, a mapping relationship between a behavior density parameter corresponding to a propagation type and a second adjustment factor can be pre-set, so that, after determining the behavior density parameter corresponding to the propagation type (for ease of description, referred to as the first target behavior density parameter), the second adjustment factor associated with the first target behavior density parameter is used as the second adjustment factor of the first scoring item.

[0084] For example, if the propagation weights corresponding to each user role and each forwarding behavior, and the calculated behavior density parameters are shown in Table 1 below.

[0085] Table 1

[0086] User roles Forwarding behavior Propagation weight Behavior density parameter Listing Agent Share to friends K1 L1 Homebuyers Share to Moments K2 L2 Listing Agent Share to Moments K3 L3 Homebuyers Forward Group K4 L4

[0087] Then, according to step 320, the behavior density parameter corresponding to the propagation type can be calculated: K1*L1+K2*L2+K3*L3+K4*L4.

[0088] In the scheme in this embodiment, it is equivalent to integrating the behavior density parameters related to the information dissemination behavior to obtain the parameters that overall reflect the density of all information dissemination behaviors, that is, the behavior density parameters corresponding to the dissemination types; compared with determining the second adjustment factor for each information dissemination behavior separately, determining the second adjustment factor of the first scoring item based on the behavior density parameter corresponding to the dissemination type reduces the amount of data processing.

[0089] In some embodiments of the present application, the scoring item includes a second scoring item; the user behavior includes an information maintenance behavior belonging to the information maintenance type; Figure 4 As shown, before step 240, the method further includes:

[0090] Step 410, obtaining maintenance weight information and behavior density parameters of each information maintenance behavior triggered by a user of each user role; the maintenance weight information indicates the maintenance weight associated with each user role and each information maintenance behavior.

[0091] Information maintenance behavior refers to the behavior of maintaining published property information, such as editing published property information (uploading property videos, property pictures, modifying property descriptions, modifying property prices, etc.), calling the owner, calling the property agent, etc.

[0092] Maintenance weight refers to the weight set for information maintenance behavior. Maintenance weight is used to characterize the contribution of the corresponding information maintenance behavior to the attention of the published property information. Since the information maintenance behavior of users with different roles also contributes to the attention of the published property information to different degrees, the maintenance weight is set in combination with the user role and information maintenance behavior, that is, the maintenance weight is associated with both the user role and the information maintenance behavior.

[0093] Step 420 , weighted calculation is performed based on the maintenance weight associated with each user role and each information maintenance behavior, and the behavior density parameter of each information maintenance behavior triggered by the user of each user role, to obtain the behavior density parameter corresponding to the information maintenance type.

[0094] The weighted calculation performed in step 420 may refer to the process of step 320 in the above embodiment, and will not be described in detail here.

[0095] Step 430: Determine a second adjustment factor of the second scoring item according to the behavior density parameter corresponding to the information maintenance type.

[0096] In some embodiments of the present application, a mapping relationship between a behavior density parameter corresponding to an information maintenance type and a second adjustment factor can be pre-set, so that, after determining the behavior density parameter corresponding to the information maintenance type (for ease of description, referred to as a second target behavior density parameter), the second adjustment factor associated with the second target behavior density parameter is used as the second adjustment factor of the second scoring item.

[0097] In the solution of this embodiment, the behavior density parameters related to the information maintenance behavior are integrated to obtain a parameter that reflects the behavior density of all information maintenance behaviors as a whole, that is, the behavior density parameter corresponding to the information maintenance type. Compared with determining the second adjustment factor for each information maintenance behavior separately, determining the second adjustment factor of the second scoring item based on the behavior density parameter corresponding to the maintenance type reduces the amount of data processing.

[0098] In some embodiments of the present application, the scoring item includes a third scoring item; the user behavior includes an access behavior belonging to an access type; Figure 5 As shown, before step 240, the method further includes:

[0099] Step 510, obtaining access weight information and obtaining behavior density parameters of each access behavior triggered by a user of each user role; the access weight information indicates the access weight associated with each user role and each access behavior.

[0100] The access weight is used to characterize the contribution of the corresponding information dissemination behavior to the attention of the published housing information, which can reflect the value of the target housing indicated by the published housing information from the side. In this embodiment, since the contribution of the access behavior of users with different roles to the attention of the published housing information is different, the access weight is set in combination with the user role and the access behavior, that is, the access weight is associated with both the user role and the access behavior.

[0101] Among them, the access behavior can be the access behavior to the published housing information, that is, the behavior of browsing the published housing information, and can also be the behavior of viewing the house. For example, if a control for booking a viewing or a control for confirming the viewing is provided in the interface of the published housing information, it can be determined whether there is a viewing behavior according to the triggering of the control for booking a viewing or the control for confirming the viewing. Specifically, the access behavior, such as the access behavior of the housing agent, the active access behavior of the home buyer, and the access behavior of the home buyer after passive access. The passive access of the home buyer can be the behavior of accessing the published housing information through a shared link, and can also be the behavior of viewing the house recommended by the housing agent. In a specific embodiment, the calculation of the behavior density parameter corresponding to each access behavior can be calculated by counting the number of visitors or the number of visits.

[0102] Step 520, weighted calculation is performed based on the access weight associated with each user role and each access behavior, and the behavior density parameter of each access behavior triggered by the user of each user role, to obtain the behavior density parameter corresponding to the access type. The weighted calculation performed in step 520 can refer to the process of step 320 in the above embodiment, and will not be repeated here.

[0103] Step 530: Determine a second adjustment factor of the third scoring item according to the behavior density parameter corresponding to the access type.

[0104] In some embodiments of the present application, a mapping relationship between a behavior density parameter corresponding to an access type and a second adjustment factor may be pre-set, so that, after determining the behavior density parameter corresponding to the access type (referred to as a third target behavior density parameter for ease of description), the second adjustment factor associated with the third target behavior density parameter is used as the second adjustment factor for the third scoring item.

[0105] In the solution of this embodiment, the behavior density parameters related to the access behavior are integrated to obtain a parameter that reflects the behavior density of all access behaviors as a whole, that is, the behavior density parameter corresponding to the access type. Compared with determining the second adjustment factor for each access behavior separately, determining the second adjustment factor of the third scoring item based on the behavior density parameter corresponding to the access type reduces the amount of data processing.

[0106] In the above Figure 3-Figure 5 In the corresponding embodiment, user behaviors are classified, and the corresponding categories include information dissemination behavior, information maintenance behavior and access behavior. In other embodiments, user behaviors can also be classified according to other classification principles, and then the second adjustment factor and the first adjustment factor of the scoring item corresponding to each type of user behavior are calculated accordingly. For example, information dissemination behavior and access behavior can be divided more finely. Figure 3-Figure 5 The classification of user behaviors in the corresponding embodiments cannot be regarded as limiting the scope of use of this application.

[0107] In some embodiments of the present application, before step 250, as Figure 6 As shown, the method also includes:

[0108] Step 610, obtaining price information of reference houses corresponding to the target house, wherein the reference houses corresponding to the target house include other houses in the community where the target house is located and houses of similar type to the target house in other communities.

[0109] In some embodiments of the present application, a distance range may be set for determining housing sources of the same type as a target housing source in other communities, that is, housing sources of similar types in communities whose distance from the community where the target housing source is located does not exceed a set distance threshold, and are used as reference housing sources corresponding to the target housing source.

[0110] In some embodiments of the present application, housing sources that are of a similar type to the target housing source can be determined based on housing source tags, such as apartment type tags, floor tags, orientation tags, building age tags, school-district housing tags, and other tags in one or more dimensions.

[0111] If all listings are labeled under one dimension, two listings with the same listing label can be considered as listings of similar types. If the listing labels include labels under at least two dimensions, two listings with the same listing label exceeding a set threshold are determined to be listings of similar types. The reference listings corresponding to the target listing can be one or more.

[0112] Step 620: Determine a static adjustment factor based on the price information of the reference housing source corresponding to the target housing source and the price information of the target housing source.

[0113] In some embodiments of the present application, the price information in step 610 may include unit price information, such as unit price per square meter. The static adjustment factor is then determined according to the unit price of the reference house corresponding to the target house and the unit price of the target house.

[0114] In some embodiments of the present application, a unit price difference calculation may be performed for a target property and each reference property of the target property, and a static adjustment factor may be determined based on the unit price difference.

[0115] In some embodiments, a mapping relationship between the unit price difference and the static adjustment factor is preset, so that after determining the unit price difference between the target house and the reference house, the static adjustment factor corresponding to the unit price difference is determined according to the mapping relationship between the two. In some embodiments, when the unit price difference = the unit price of the reference house - the unit price of the target house, the unit price difference is positively correlated with the static adjustment factor, and the lower the unit price of the target house is compared to the unit price of the reference house, the larger the static adjustment factor is.

[0116] Step 630: Adjust the static score of the target property according to the static adjustment factor.

[0117] In some embodiments of the present application, when there are multiple reference properties for a target property, the average unit price in the community where the target property is located (referred to as the first average unit price for ease of description) and the average unit price of properties of the same type as the target property in other communities (referred to as the second average unit price for ease of description) can be calculated first, and then a first unit price difference between the first average unit price and the unit price of the target property, a second unit price difference between the second average unit price and the unit price of the target property are determined, and a static adjustment factor corresponding to the first unit price difference and a static adjustment factor corresponding to the second unit price difference are determined; on this basis, the static adjustment factor corresponding to the first unit price difference and the static adjustment factor corresponding to the second unit price difference can be calculated to obtain a total static adjustment factor, for example, the result of adding or multiplying the two static adjustment factors is used as the total static adjustment factor, and then the static score of the target property is adjusted according to the total static adjustment factor.

[0118] In other embodiments, the static score of the target housing source may be adjusted according to the static adjustment factor corresponding to the first unit price difference and the static adjustment factor corresponding to the second unit price difference, respectively.

[0119] In some embodiments of the present application, after step 250, the method further includes: determining an estimated transaction period of the target property based on the comprehensive score of the target property.

[0120] In the application plan, the comprehensive score of the target property is a combination of dynamic and static scores. The dynamic score of the target property reflects the attention of the target property to a certain extent, and the static score of the target property reflects the objective evaluation of the target property. Both the attention of the property and the objective evaluation of the property are important factors affecting the transaction cycle of the property. Therefore, the comprehensive score obtained by combining the dynamic and static scores can reflect the length of the transaction cycle of the property to a certain extent. In view of this, the expected effectiveness cycle of the target property can be determined by the comprehensive score of the target property.

[0121] In some embodiments of the present application, a mapping relationship between the comprehensive score and the expected transaction period can be set. For example, if the comprehensive score is within a first scoring range, the corresponding expected transaction period is within one week; if the comprehensive score is within a second scoring range, the corresponding expected transaction period is within one month, etc., wherein the comprehensive score within the first scoring range is higher than the comprehensive score within the second scoring range.

[0122] In some embodiments of the present application, the published housing information may also be pushed according to the estimated transaction period of the determined target housing, for example, the published housing information with a shorter estimated transaction period may be pushed preferentially.

[0123] The following describes an embodiment of the device of the present application, which can be used to execute the method for pushing house resources in the above embodiment of the present application. For details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the method for pushing house resources in the above embodiment of the present application.

[0124] Figure 7 A block diagram of a device for processing house source information according to an embodiment of the present application is shown. Figure 7 As shown, according to an embodiment of the present application, a device for processing housing information includes: a user behavior record acquisition module 710, which is used to acquire multiple user behavior records for published housing information; the user behavior record indicates the user behavior triggered for the published housing information and the corresponding trigger time; a user behavior record sequence determination module 720, which is used to sort the multiple user behavior records in order of trigger time from earliest to latest, and obtain a user behavior record sequence, and the user behavior record sequence is used to determine the first adjustment factor of the scoring item corresponding to each user behavior record; a behavior density parameter calculation module 730, which is used to calculate the user role of the user to which the user behavior record belongs according to the multiple user behavior records and the user role to which the user behavior record belongs. Calculate the behavior density parameters of each user behavior triggered by the user of each user role; the behavior density parameters are used to determine the second adjustment factor of the scoring item corresponding to each user behavior record; a dynamic score calculation module 740 is used to obtain the dynamic score of the target house indicated by the published house information according to the first adjustment factor, the second adjustment factor of each scoring item and the basic score corresponding to each scoring item; a comprehensive score determination module 750 is used to calculate the comprehensive score of the target house according to the dynamic score of the target house and the static score of the target house; the static score is determined according to the static information of the target house; a processing module 760 is used to process the published house information according to the comprehensive score of the target house.

[0125] In some embodiments of the present application, the device for processing housing information also includes: a scoring item determination module, which is used to determine the scoring item corresponding to the user behavior record based on the user behavior indicated by the user behavior record and the user role to which the user from which the user behavior record originates, wherein the scoring item is related to the user behavior and the user role to which the user belongs; the user roles include housing brokers, owners and home buyers.

[0126] In some embodiments of the present application, the scoring item includes a first scoring item; the user behavior includes information dissemination behavior belonging to a dissemination type; the housing information processing device also includes: a dissemination weight information acquisition module, used to obtain dissemination weight information, and obtain behavior density parameters of each information dissemination behavior triggered by a user of each user role; the dissemination weight information indicates the dissemination weight associated with each user role and each information dissemination behavior; a first behavior density parameter determination module, used to perform weighted calculation based on the dissemination weight associated with each user role and each information dissemination behavior, and the behavior density parameters of each information dissemination behavior triggered by a user of each user role, to obtain the behavior density parameter corresponding to the dissemination type; a first determination module, used to determine the second adjustment factor of the first scoring item based on the behavior density parameter corresponding to the dissemination type.

[0127] In some embodiments of the present application, the scoring item includes a second scoring item; the user behavior includes information maintenance behavior belonging to an information maintenance type; the housing information processing device also includes: a maintenance weight information acquisition module, used to obtain maintenance weight information, and obtain behavior density parameters of each information maintenance behavior triggered by a user of each user role; the maintenance weight information indicates the maintenance weight associated with each user role and each information maintenance behavior; a second behavior density parameter determination module, used to perform weighted calculation based on the maintenance weight associated with each user role and each information maintenance behavior, and the behavior density parameters of each information maintenance behavior triggered by a user of each user role, to obtain the behavior density parameter corresponding to the information maintenance type; a second determination module, used to determine the second adjustment factor of the second scoring item based on the behavior density parameter corresponding to the information maintenance type.

[0128] In some embodiments of the present application, the scoring item includes a third scoring item; the user behavior includes access behavior belonging to an access type; the housing information processing device also includes: an access weight information acquisition module, used to obtain access weight information, and obtain behavior density parameters of each access behavior triggered by users of each user role; the access weight information indicates the access weight associated with each user role and each access behavior; a third behavior density parameter determination module, used to perform weighted calculation based on the access weight associated with each user role and each access behavior, and the behavior density parameters of each access behavior triggered by users of each user role, to obtain the behavior density parameter corresponding to the access type; a third determination module, used to determine the second adjustment factor of the third scoring item based on the behavior density parameter corresponding to the access type.

[0129] In some embodiments of the present application, the second adjustment factor is positively correlated with the corresponding behavior density parameter.

[0130] In some embodiments of the present application, the housing information processing device also includes: an adjustment strategy information acquisition module, which is used to acquire adjustment strategy information, the adjustment strategy information including the adjustment strategy corresponding to each scoring item, the adjustment strategy indicating the reference user behavior record sequence segment and the corresponding adjustment factor of the corresponding scoring item; a sequence segment matching module, which is used to perform sequence segment matching in the user behavior record sequence according to the reference user behavior record sequence segment in the adjustment strategy; and a first adjustment factor determination module, which is used to determine the adjustment factor in the adjustment strategy from which the matched reference user behavior record sequence segment comes as the first adjustment factor of the scoring item indicated by the adjustment strategy if the reference user behavior record sequence segment is matched in the user behavior record sequence.

[0131] In some embodiments of the present application, the housing information processing device also includes: a static information acquisition module, used to obtain the static information of the target housing, the static information including at least one of the basic information of the target housing and the surrounding supporting facilities information of the target housing; a static score determination module, used to determine the static score of the target housing based on the static information of the target housing and preset static scoring rules.

[0132] In some embodiments of the present application, the housing information processing device also includes: a price information acquisition module, which is used to acquire price information of reference housing sources corresponding to the target housing source, the reference housing sources corresponding to the target housing source including other housing sources in the community where the target housing source is located and housing sources of similar types to the target housing source in other communities; a static adjustment factor determination module, which is used to determine the static adjustment factor based on the price information of the reference housing sources corresponding to the target housing source and the price information of the target housing source; and a static score adjustment module, which is used to adjust the static score of the target housing source based on the static adjustment factor.

[0133] In some embodiments of the present application, the device for processing housing information further includes: an estimated transaction period determination module, which is used to determine the estimated transaction period of the target housing source based on the comprehensive score of the target housing source.

[0134] In some embodiments of the present application, the processing module includes: a unit for determining properties to be pushed, for screening properties according to the comprehensive score of each property in the property set, and determining the properties to be pushed; and a pushing unit, for pushing the published property information of the properties to be pushed.

[0135] Figure 8 A schematic diagram of the structure of a computer system suitable for implementing an electronic device of an embodiment of the present application is shown.

[0136] It should be noted that Figure 8The computer system 800 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0137] like Figure 8 As shown, the computer system 800 includes a central processing unit (CPU) 801, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 802 or the program loaded from the storage part 808 to the random access memory (RAM) 803, such as executing the method in the above embodiment. In RAM 803, various programs and data required for system operation are also stored. CPU 801, ROM 802 and RAM 803 are connected to each other through bus 804. Input / output (I / O) interface 805 is also connected to bus 804.

[0138] The following components are connected to the I / O interface 805: an input section 806 including a keyboard, a mouse, etc.; an output section 807 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 808 including a hard disk, etc.; and a communication section 809 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 809 performs communication processing via a network such as the Internet. A drive 810 is also connected to the I / O interface 805 as needed. A removable medium 811, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 810 as needed so that a computer program read therefrom is installed into the storage section 808 as needed.

[0139] In particular, according to an embodiment of the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication section 809, and / or installed from a removable medium 811. When the computer program is executed by a central processing unit (CPU) 801, various functions defined in the system of the present application are executed.

[0140] It should be noted that the computer-readable medium shown in the embodiment of the present application may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, - but not limited to - an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by an instruction execution system, device or device or used in combination with it. In the present application, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, wherein a computer-readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, which may send, propagate, or transmit programs for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0141] The flowchart and block diagram in the accompanying drawings illustrate the possible architecture, functions and operations of the system, method and computer program product according to various embodiments of the present application. Wherein, each box in the flowchart or block diagram can represent a module, a program segment, or a part of the code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0142] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. The names of these units do not, in some cases, constitute limitations on the units themselves.

[0143] As another aspect, the present application further provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments; or may exist independently without being assembled into the electronic device. The above computer-readable storage medium carries computer-readable instructions, and when the computer-readable storage instructions are executed by a processor, the method in any of the above embodiments is implemented.

[0144] According to one aspect of the present application, an electronic device is also provided, which includes: a processor; a memory, wherein computer-readable instructions are stored in the memory, and when the computer-readable instructions are executed by the processor, the method in any of the above embodiments is implemented.

[0145] According to one aspect of the embodiments of the present application, a computer program product or a computer program is provided, the computer program product or the computer program includes computer instructions, the computer instructions are stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the method in any of the above embodiments.

[0146] It should be noted that, although several modules or units of the equipment for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into being embodied by multiple modules or units.

[0147] Through the description of the above implementation methods, it is easy for those skilled in the art to understand that the example implementation methods described here can be implemented by software or by combining software with necessary hardware. Therefore, the technical solution according to the implementation methods of the present application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation methods of the present application.

[0148] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the embodiments disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.

[0149] It should be understood that the present application is not limited to the precise structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A method for processing house source information, characterized in that: include: Acquire multiple user behavior records for the published housing information; the user behavior records indicate the user behaviors triggered for the published housing information and the corresponding triggering times; Sorting the plurality of user behavior records in order of trigger time from earliest to latest to obtain a user behavior record sequence, wherein the user behavior record sequence is used to determine a first adjustment factor of a scoring item corresponding to each user behavior record; Calculate the behavior density parameter of each user behavior triggered by a user of each user role according to the publishing time or exposure of the published housing information, the multiple user behavior records, and the user role to which the user behavior record comes from; the behavior density parameter is used to determine the second adjustment factor of the scoring item corresponding to each user behavior record; Obtaining a dynamic score of the target property indicated by the published property information according to the first adjustment factor of each rating item, the second adjustment factor and the basic score corresponding to each rating item; Calculate the comprehensive score of the target house source according to the dynamic score of the target house source and the static score of the target house source; the static score is determined according to the static information of the target house source; Processing the published housing information according to the comprehensive score of the target housing; The scoring items include a first scoring item; the user behavior includes an information dissemination behavior belonging to a dissemination type; before obtaining a dynamic score of a target house indicated by the published house information according to the first adjustment factor of each scoring item, the second adjustment factor and the basic score corresponding to each scoring item, the method further includes: Acquire adjustment strategy information, the adjustment strategy information including the adjustment strategy corresponding to each scoring item, the adjustment strategy indicating the reference user behavior record sequence segment and the corresponding adjustment factor of the corresponding scoring item; According to the reference user behavior record sequence segment in the adjustment strategy, sequence segment matching is performed in the user behavior record sequence; If the reference user behavior record sequence segment is matched in the user behavior record sequence, the adjustment factor in the adjustment strategy from which the matched reference user behavior record sequence segment comes is determined as the first adjustment factor of the scoring item indicated by the adjustment strategy; Acquire propagation weight information, and acquire behavior density parameters of each information propagation behavior triggered by a user of each user role; the propagation weight information indicates the propagation weight associated with each user role and each information propagation behavior; Performing weighted calculation based on the dissemination weight associated with each user role and each information dissemination behavior, and the behavior density parameter of each information dissemination behavior triggered by the user of each user role, to obtain the behavior density parameter corresponding to the dissemination type; A second adjustment factor of the first scoring item is determined according to a behavior density parameter corresponding to the propagation type.

2. The method according to claim 1, characterized in that Before obtaining the dynamic score of the target house indicated by the published house information according to the first adjustment factor of each score item, the second adjustment factor and the basic score corresponding to each score item, the method further includes: According to the user behavior indicated by the user behavior record and the user role to which the user from which the user behavior record originates, a scoring item corresponding to the user behavior record is determined, wherein the scoring item is related to the user behavior and the user role to which the user belongs; the user roles include real estate agents, property owners and home buyers.

3. The method according to claim 1, characterized in that: The scoring item further includes a second scoring item; the user behavior includes an information maintenance behavior belonging to the information maintenance type; Before obtaining the dynamic score of the target house indicated by the published house information according to the first adjustment factor of each score item, the second adjustment factor and the basic score corresponding to each score item, the method further includes: Acquire maintenance weight information, and acquire behavior density parameters of each information maintenance behavior triggered by a user of each user role; the maintenance weight information indicates a maintenance weight associated with each user role and each information maintenance behavior; Performing weighted calculation based on the maintenance weight associated with each user role and each information maintenance behavior and the behavior density parameter of each information maintenance behavior triggered by a user of each user role, to obtain the behavior density parameter corresponding to the information maintenance type; A second adjustment factor of the second scoring item is determined according to a behavior density parameter corresponding to the information maintenance type.

4. The method according to claim 1, characterized in that: The scoring item further includes a third scoring item; the user behavior includes access behavior belonging to an access type; Before obtaining the dynamic score of the target house indicated by the published house information according to the first adjustment factor of each score item, the second adjustment factor and the basic score corresponding to each score item, the method further includes: Obtaining access weight information and obtaining behavior density parameters of each access behavior triggered by a user of each user role; the access weight information indicates the access weight associated with each user role and each access behavior; Performing weighted calculation based on the access weight associated with each user role and each access behavior, and the behavior density parameter of each access behavior triggered by the user of each user role, to obtain the behavior density parameter corresponding to the access type; A second adjustment factor of the third scoring item is determined according to the behavior density parameter corresponding to the access type.

5. The method according to any one of claims 1 to 4, characterized in that The second adjustment factor is positively correlated with the corresponding behavior density parameter.

6. The method according to claim 1, characterized in that Before calculating the comprehensive score of the target house source based on the dynamic score of the target house source and the static score of the target house source, the method further includes: Acquire static information of the target house, wherein the static information includes at least one of basic information of the target house and surrounding supporting facilities information of the target house; The static score of the target property is determined based on the static information of the target property and preset static scoring rules.

7. The method according to claim 1 or 6, characterized in that: Before calculating the comprehensive score of the target house source based on the dynamic score of the target house source and the static score of the target house source, the method further includes: Obtaining price information of reference housing sources corresponding to the target housing source, wherein the reference housing sources corresponding to the target housing source include other housing sources in the community where the target housing source is located and housing sources of similar types to the target housing source in other communities; Determining a static adjustment factor according to price information of a reference property corresponding to the target property and price information of the target property; The static score of the target property is adjusted according to the static adjustment factor.

8. The method according to claim 1, characterized in that After calculating the comprehensive score of the target house source according to the dynamic score of the target house source and the static score of the target house source, the method further includes: The estimated transaction period of the target property is determined according to the comprehensive score of the target property.

9. The method according to claim 1, characterized in that: The processing of the published housing information according to the comprehensive score of the target housing includes: Filter properties based on the comprehensive scores of each property in the property collection and determine the properties to be pushed; The published house information of the house to be pushed is pushed.

10. A device for processing house source information, characterized in that: include: A user behavior record acquisition module is used to acquire multiple user behavior records for published housing information; The user behavior record indicates the user behavior triggered by the published housing information and the corresponding triggering time; A user behavior record sequence determination module, used to sort the plurality of user behavior records in order of trigger time from earliest to latest, to obtain a user behavior record sequence, wherein the user behavior record sequence is used to determine a first adjustment factor of a scoring item corresponding to each user behavior record; A behavior density parameter calculation module is used to calculate the behavior density parameters of each user behavior triggered by a user of each user role according to the publishing time or exposure of the published housing information, the multiple user behavior records and the user role to which the user behavior record originates; the behavior density parameter is used to determine the second adjustment factor of the scoring item corresponding to each user behavior record; A dynamic score calculation module, used to obtain a dynamic score of the target house indicated by the published house information according to the first adjustment factor of each score item, the second adjustment factor and the basic score corresponding to each score item; A comprehensive score determination module, used to calculate the comprehensive score of the target house source according to the dynamic score of the target house source and the static score of the target house source; the static score is determined according to the static information of the target house source; A processing module, used for processing the published house information according to the comprehensive score of the target house; An adjustment strategy information acquisition module, used to acquire adjustment strategy information, wherein the adjustment strategy information includes an adjustment strategy corresponding to each scoring item, and the adjustment strategy indicates a reference user behavior record sequence segment and a corresponding adjustment factor of the corresponding scoring item; A sequence segment matching module, configured to perform sequence segment matching in the user behavior record sequence according to the reference user behavior record sequence segment in the adjustment strategy; A first adjustment factor determination module is configured to determine, if the reference user behavior record sequence segment is matched in the user behavior record sequence, the adjustment factor in the adjustment strategy from which the matched reference user behavior record sequence segment originates, as the first adjustment factor of the scoring item indicated by the adjustment strategy; The scoring item includes a first scoring item; the user behavior includes an information dissemination behavior belonging to a dissemination type; and the device further includes: A propagation weight information acquisition module, used to acquire propagation weight information and behavior density parameters of each information propagation behavior triggered by a user of each user role; the propagation weight information indicates the propagation weight associated with each user role and each information propagation behavior; A first behavior density parameter determination module is used to perform weighted calculation based on a propagation weight associated with each user role and each information propagation behavior, and a behavior density parameter of each information propagation behavior triggered by a user of each user role, to obtain a behavior density parameter corresponding to the propagation type; The first determination module is used to determine a second adjustment factor of the first scoring item according to a behavior density parameter corresponding to the propagation type.

11. An electronic device, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 9 is implemented.

12. A computer-readable storage medium having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by a processor, the method according to any one of claims 1 to 9 is implemented.

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