CN111027999A - User sharing recommendation method and device, electronic equipment and storage medium - Google Patents

User sharing recommendation method and device, electronic equipment and storage medium Download PDF

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CN111027999A
CN111027999A CN201911005727.8A CN201911005727A CN111027999A CN 111027999 A CN111027999 A CN 111027999A CN 201911005727 A CN201911005727 A CN 201911005727A CN 111027999 A CN111027999 A CN 111027999A
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lease
user
sharing
weight vector
weight
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CN111027999B (en
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卫海波
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Beike Technology Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/02—Marketing; Price estimation or determination; Fundraising
    • G06Q30/0201—Market modelling; Market analysis; Collecting market data
    • G06Q30/0202—Market predictions or forecasting for commercial activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
    • G06Q30/0631—Item recommendations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0645—Rental transactions; Leasing transactions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/16—Real estate

Abstract

The application discloses a method and a device for recommending users sharing a rental book, electronic equipment and a storage medium. The method comprises the following steps: acquiring a first weight vector of a lease combining bias characteristic of a first lease combining user; acquiring second weight vectors of all second lease users in the system; and acquiring a corresponding lease-sharing recommended user according to the first weight vector and the second weight vector, and sending the lease-sharing recommended user to the first lease-sharing user. The method can efficiently and quickly recommend the leased sharing users with similar lease sharing deviation for the leased sharing users, and can avoid the lease sharing conflict problem caused by different lease sharing deviation.

Description

User sharing recommendation method and device, electronic equipment and storage medium
Technical Field
The application relates to the technical field of internet renting, in particular to a method and a device for recommending shareholder, electronic equipment and a storage medium.
Background
Because the house price of a large city is high, a plurality of people live in the large city in an renting mode, most of the renting users do not know each other before renting, conflicts caused by different renting preferences even occur after renting, and the specific conditions of the renting users cannot be known clearly when the existing renting intermediary company introduces services.
Developed internet technology only provides the capability of finding rooms on line for people, but does not solve the problem of lease sharing conflict caused by different lease sharing preferences.
Disclosure of Invention
In view of the prior art, the embodiment of the invention discloses a method and a device for recommending leased sharing users, electronic equipment and a storage medium, which can efficiently and quickly recommend leased sharing users with similar lease sharing bias for the leased sharing users, and can avoid the problem of lease sharing conflict caused by different lease sharing bias.
In order to solve the technical problem, the technical scheme of the application is realized as follows:
in one embodiment, a rental user recommendation method is provided, the method comprising:
acquiring a first weight vector of a lease combining bias characteristic of a first lease combining user;
acquiring second weight vectors of all second lease users in the system;
and acquiring a corresponding lease-sharing recommended user according to the first weight vector and the second weight vector, and sending the lease-sharing recommended user to the first lease-sharing user.
Wherein the step of obtaining the corresponding lease recommendation user according to the first weight vector and the second weight vector comprises:
judging whether the first weight vector has a lease-sharing deviation characteristic with the weight larger than a first preset threshold value;
if yes, judging whether the weight of the corresponding lease bias feature in the second weight vector is larger than the first preset threshold value or not;
when the weight of the corresponding lease biased characteristics in the second weight vector is judged to be larger than the first preset threshold value, determining the corresponding second lease user as a lease recommending user;
when the weight of the corresponding lease partial feature in the second weight vector is judged to be not more than the first preset threshold value, judging whether the difference value between the weight of the corresponding lease partial feature in the second weight vector and the weight of the lease partial feature of the first weight vector is smaller than a second preset threshold value, and if so, determining the corresponding second lease user as a lease recommendation user.
Wherein, the step of obtaining the corresponding lease-sharing recommendation user according to the first weight vector and the second weight vector further comprises:
calculating the similarity of a first sublist user and each second sublist user by using a first weight vector of the first sublist user and a second weight vector of each second sublist user;
sorting the second users according to the corresponding similarity from high to low;
and taking the first M second leased users as leased recommending users.
And when the similarity between the first sublettage user and each second sublettage user is calculated by using the first weight vector of the first sublettage user and the second weight vector of each second sublettage user, calculating by using a cosine similarity algorithm, a Jaccard similarity coefficient algorithm or a Pearson correlation coefficient algorithm.
Wherein before sorting the second users according to their corresponding similarities from high to low, the method further comprises:
and filtering out the second users with the similarity smaller than a fourth preset threshold.
After obtaining the second weight vectors of all the second leased users in the system, and before obtaining the corresponding leased recommended users according to the first weight vector and the second weight vector, the method further includes:
calculating the difference value of the weight values of corresponding lease preference characteristics of the first lease sharing user and each second lease sharing user;
and if a second sublet user with the calculated absolute value of the difference value larger than a third preset threshold value exists, deleting the sublet bias weight vector corresponding to the second sublet user.
Before obtaining the first weight vector of the lease preference feature of the first lease user, the method further includes:
determining whether the first leased user is a blacklist user, and if so, providing whole lease information for the first leased user; otherwise, acquiring a first weight vector of the lease combining preference characteristics of the first lease combining user.
Wherein the method further comprises:
and storing the sharing user in a blacklist according to the received evaluation information of the sharing user.
In another embodiment, there is provided a rental user recommendation apparatus, including: the device comprises an acquisition unit, a determination unit and a sending unit;
the obtaining unit is used for obtaining a first weight vector of the lease combining bias characteristic of a first lease combining user; acquiring second weight vectors of all second lease users in the system;
and the processing unit is used for acquiring the corresponding leased sharing recommendation user according to the first weight vector and the second weight vector acquired by the acquisition unit and sending the leased sharing recommendation user to the first leased sharing user.
Wherein the content of the first and second substances,
the processing unit is specifically configured to, when obtaining a corresponding lease recommendation user according to the first weight vector and the second weight vector, determine whether a lease biased feature whose weight is greater than a first preset threshold exists in the first weight vector; if yes, judging whether the weight of the corresponding lease bias feature in the second weight vector is larger than the first preset threshold value or not; when the weight of the corresponding lease biased characteristics in the second weight vector is judged to be larger than the first preset threshold value, determining the corresponding second lease user as a lease recommending user; when the weight of the corresponding lease partial feature in the second weight vector is judged to be not more than the first preset threshold value, judging whether the difference value between the weight of the corresponding lease partial feature in the second weight vector and the weight of the lease partial feature of the first weight vector is smaller than a second preset threshold value, and if so, determining the corresponding second lease user as a lease recommendation user.
Wherein the content of the first and second substances,
the processing unit is further configured to calculate, when obtaining corresponding leased recommendation users according to the first weight vector and the second weight vector, a similarity between a first leased user and each second leased user by using the first weight vector of the first leased user and the second weight vector of each second leased user; sorting the second users according to the corresponding similarity from high to low; and taking the first M second leased users as leased recommending users.
Wherein the content of the first and second substances,
the processing unit is specifically configured to use a cosine similarity algorithm, a Jaccard similarity coefficient algorithm, or a pearson correlation coefficient algorithm to calculate the similarity between the first sublet user and each of the second sublet users when calculating the similarity between the first sublet user and each of the second sublet users using the first weight vector of the first sublet user and the second weight vector of each of the second sublet users.
Wherein the content of the first and second substances,
the processing unit is further configured to filter out second users with similarity smaller than a fourth preset threshold before the second users with the same rank according to the corresponding similarity from high to low.
Wherein the content of the first and second substances,
the processing unit is further configured to perform difference calculation on the weight values of the corresponding lease biased features of the first lease user and each second lease user before acquiring the corresponding lease recommendation users according to the first weight vector and the second weight vector; and if a second sublet user with the calculated absolute value of the difference value larger than a third preset threshold value exists, deleting the sublet bias weight vector corresponding to the second sublet user.
Wherein the content of the first and second substances,
the obtaining unit is further configured to determine whether the first lease user is a blacklist user before obtaining a first weight vector of lease bias characteristics of the first lease user;
the processing unit is further configured to provide the entire lease information for the first leased sharing user when the obtaining unit determines that the first leased sharing user is a blacklist user;
the obtaining unit is further configured to obtain a first weight vector of the lease bias characteristics of the first lease sharing user when the first lease sharing user is determined not to be the blacklist user.
Wherein the content of the first and second substances,
the obtaining unit is further configured to store the sharing user in a blacklist according to the received evaluation information of the sharing user.
In another embodiment, an electronic device is also provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the user sharing recommendation method when executing the program.
In another embodiment, a computer-readable storage medium is also provided, having stored thereon computer instructions, which when executed by a processor, perform the steps of the rental user recommendation method.
In conclusion, by acquiring the lease bias weight of the lease users, lease users with similar lease bias are screened for one lease user and recommended to the lease users. The scheme can efficiently and quickly recommend the sharing renters with similar sharing rent deviation for the sharing renters, and can avoid the sharing rent conflict problem caused by different sharing rent deviation.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
Fig. 1 is a schematic flowchart illustrating a process of recommending a rental user in an embodiment of the present application;
fig. 2 is a schematic flowchart of a first process of obtaining a corresponding rental recommendation user according to a first weight vector and a second weight vector in an embodiment of the present application;
fig. 3 is a schematic flowchart of a second process of obtaining a corresponding rental recommendation user according to a first weight vector and a second weight vector in an embodiment of the present application;
fig. 4 is a schematic flowchart illustrating a process of recommending a rental user in the second embodiment of the present application;
FIG. 5 is a schematic diagram of an apparatus for implementing the above technique in an embodiment of the present application;
fig. 6 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are, for example, capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprising" and "having," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements explicitly listed, but may include other steps or elements not explicitly listed or inherent to such process, method, article, or apparatus.
The technical solution of the present invention will be described in detail with specific examples. Several of the following embodiments may be combined with each other and some details of the same or similar concepts or processes may not be repeated in some embodiments.
The embodiment of the application provides a method for recommending shared tenants, which is characterized in that shared tenants with similar shared tenants are screened for one shared tenant and recommended to shared tenants by acquiring shared tenants preference weights of the shared tenants. The scheme can efficiently and quickly recommend the sharing renters with similar sharing rent deviation for the sharing renters, and can avoid the sharing rent conflict problem caused by different sharing rent deviation.
The device for implementing the method for recommending the user sharing in the embodiment of the application can be equipment with computing processing capacity, such as a PC.
The description below describes in detail the leasing user recommendation process in the embodiment of the present application, with reference to the accompanying drawings:
example one
Referring to fig. 1, fig. 1 is a schematic flowchart illustrating a process of recommending a rental user according to a first embodiment of the present application. The method comprises the following specific steps:
step 101, obtaining a first weight vector of an lease combining bias characteristic of a first lease combining user.
In the embodiment of the application, the first weight vector for acquiring the lease preference characteristics of the first lease user can be a locally stored first weight vector, and can also be acquired through information input by the first lease user;
the obtaining of the first weight vector through the information input by the first user may be:
providing an interface for inputting the weight value of the lease bias for the first lease user;
the interface can be provided when an lease-sharing request sent by a first lease-sharing user is received, or can be provided when a set user logs in a certain interface or performs a certain specific operation.
The interface for inputting the lease preference weight value can be a page and a document (word, excel and the like), a plurality of lease preference options are arranged on the interface, and when an lease user selects which lease preference, the lease preference weight value needs to be filled in according to the selected lease preference; the unselected sublets do not need to fill out sublet bias weighted values, and the system can default the corresponding sublet bias weighted value to be 0.
The lease-sharing deviation items on the interface provided by the embodiment of the application can be set according to actual needs, as long as the lease-sharing deviation items provided for each lease-sharing user in the same period are ensured to be the same, and the lease-sharing deviation items provided can be as follows: clean, basketball, going home without strangers, gender, age zone, whether pet care is allowed, etc.
Assume that the first user has selected: clean, without strangers going home, gender and age zone four-party lease bias, and respectively giving weight values as follows: 0.5, 0.2,0.1, 0.2.
The lease bias weight vector X generated for the first lease owner is [0.5,0,0.2,0.1,0.2,0 ]. The weight value of the unselected sublettage bias is marked as 0, and the sum of the weight values of the sublettage bias filled by the sublettage user is 1.
And if the lease bias weight value input by the first lease user is not received, enabling the first lease user to automatically select the lease user with the lease request on the system platform without recommending the lease user.
And 102, acquiring second weight vectors of all second leased users in the system.
The lease partial weight vectors of all the lease users are generated and stored, and when the lease partial weight vectors of all the lease users need to be obtained, the lease partial weight vectors can be directly obtained from the local, or the lease partial weight vectors of the lease users can be obtained from other equipment, and the limitation is not carried out.
In the embodiment of the application, a first lease combining user is a user for whom the lease combining user needs to be recommended; the second leased sharing users are all the leased sharing users with the weight vectors stored for the leased sharing users in the system.
And if the weight vectors of the lease-sharing preference characteristics of the lease-sharing users A, B and C are stored in the system, all the second lease-sharing users in the system are the lease-sharing users A, B and C.
In this embodiment of the application, after obtaining the second weight vectors of all the second leased users in the system, the method further includes:
calculating the difference value of the weight values of corresponding lease preference characteristics of the first lease sharing user and each second lease sharing user;
and if a second sublet user with the calculated absolute value of the difference value larger than a third preset threshold value exists, deleting the sublet bias weight vector corresponding to the second sublet user.
After the filtering of the lease bias weight vectors of the second lease users is performed, step 203 is performed.
If the first lease partial (clean) weight value of the first lease of a first lease user is 0.5, the first lease partial (clean) weight value of a certain second lease user is 0.1, and the absolute value of the difference value of the corresponding lease partial weight values is 0.4, whether the difference value is large or not is determined according to a set third preset threshold value, if the difference value is larger than the third preset threshold value, the difference value is determined to be large, the lease partial errors of the two lease users can generate conflict and are not suitable for lease, so that the second lease user can not be recommended, and the corresponding second lease user can be filtered.
Step 103, obtaining a corresponding lease joining recommendation user according to the first weight vector and the second weight vector, and sending the lease joining recommendation user to the first lease joining user.
In the embodiment of the present application, the implementation manner of obtaining the corresponding rental recommendation user according to the first weight vector and the second weight vector at least includes the following three manners:
the first method comprises the following steps:
referring to fig. 2, fig. 2 is a schematic flowchart of a first process of obtaining a corresponding lease recommendation user according to a first weight vector and a second weight vector in this embodiment of the present application. The method comprises the following specific steps:
step 201, judging whether the first weight vector has a lease combining bias characteristic with the weight larger than a first preset threshold value, if so, executing step 202; otherwise, the flow is ended.
Assuming that the first preset threshold is 0.45 and a weight value (0.5) greater than 0.45 exists in the lease bias weight vector of the first lease user, step 202 is executed.
Step 202, judging whether the weight of the corresponding lease partial feature in the second weight vector is greater than the first preset threshold value, if so, executing step 203; otherwise, step 204 is performed.
If the weight value in the lease-sharing deviation weight vector of the first lease-sharing user corresponds to the 1 st lease-sharing deviation, if the weight value corresponding to the 1 st lease-sharing deviation of the second lease-sharing user is also larger than the first preset threshold value, executing step 203; otherwise, step 204 is performed.
Step 203, determining the corresponding second lease user as a lease sharing recommending user; step 204 is performed.
After the execution of step 203 is completed, the flow may be terminated as it is, or step 204 may be executed.
Step 204, judging whether the difference value between the weight corresponding to the sublevel bias feature in the second weight vector and the weight of the sublevel bias feature in the first weight vector is smaller than a second preset threshold value, if so, executing step 205; otherwise, the flow is ended.
If the weight value of the corresponding lease partial bias not greater than the first preset threshold does not exist in the lease partial weight vector of the second lease user, the absolute value of the difference between the weight value of the corresponding lease partial bias and the weight value of the corresponding lease partial bias of the first lease user is considered, if the weight value of the corresponding lease partial bias (1 st lease partial bias) of the first lease user is 0.5, the weight value of the corresponding lease partial bias (1 st lease partial bias) of the second lease user is 0.4, the absolute value of the difference is 0.1, and if the second preset threshold is set to be 0.15, step 205 is executed; the value set for the second preset threshold is an example, and is set according to actual needs in practical applications, and is not limited here.
Step 205, determining the corresponding second lease user as an lease recommending user.
That is, in step 203 and step 205, the second leased user recommended to the first leased user is: and the matching refers to that the weight values of the lease bias corresponding to the maximum weight value of the first lease user are both larger than a first preset threshold value, or the absolute value of the difference value of the two is smaller than a second preset threshold value.
Through the steps, the shared users with similar shared lease preference can be recommended for the first shared user, so that the shared lease purpose can be achieved more quickly, and the shared lease preference conflict after shared lease is avoided.
And the second method comprises the following steps:
referring to fig. 3, fig. 3 is a schematic flowchart of a second process of obtaining a corresponding lease recommendation user according to a first weight vector and a second weight vector in this embodiment of the present application. The method comprises the following specific steps:
step 301, calculating the similarity between a first sublist user and each second sublist user by using the first weight vector of the first sublist user and the second weight vector of each second sublist user.
The algorithm for calculating the similarity comprises the following steps: cosine similarity algorithm, Jaccard similarity coefficient algorithm, or Pearson correlation coefficient algorithm;
assuming that three second leased users exist and the lease bias vectors corresponding to the three second leased users are Y, Z, M, N respectively, the similarity between X and Y, the similarity between X and Z, the similarity between X and M, and the similarity between X and N are calculated as the similarity between the corresponding leased users.
In the step, a preset similarity calculation method is adopted for calculating the similarity between the first sublist user and each second sublist user according to the sublist bias weight vector of the first sublist user and the sublist bias weight vectors of all the second sublist users.
In the embodiment of the present application, a preset similarity calculation method is not limited, as long as an algorithm capable of calculating the similarity between two vectors is used.
The preset similarity algorithm can be a cosine similarity algorithm, a Jaccard similarity coefficient algorithm, a Pearson correlation coefficient algorithm and other similarity algorithms; the process of calculating the similarity between the first sublet user and the second sublet user is given below by taking the pearson correlation coefficient algorithm as an example: .
Assuming that the lease bias weight vector of the first lease sharing user is X and the lease bias weight vector of the second lease sharing user is Y, the similarity between the first lease sharing user and the second lease sharing user can be calculated by the following formula:
Figure BDA0002242708180000101
wherein, taking the lease biased entry as n as an example, XiThe ith sublist of the first sublist user is biased to a corresponding weight value, YiThe ith sublist of the second sublist user is biased to a corresponding weight value; i is an integer of 1 to n;
Figure BDA0002242708180000102
represented is the average weight value of all lease bias for the first lease user,
Figure BDA0002242708180000103
representing the average weight value of all the sublets biased by the second sublet user; the numerator is the covariance of vector X and vector Y, and the denominator identifies the product of the respective standard deviations of X and Y.
And 302, sorting the second users according to the corresponding similarity from high to low.
Before the sorting the second users according to the corresponding similarity from high to low, the method further comprises:
and filtering out the second users with the similarity smaller than a fourth preset threshold.
Through step 104, after the similarity between the first leased-sharing user and each second leased-sharing user can be calculated, the corresponding second leased-sharing users are sorted from high to low according to the similarity, if the second leased-sharing users with the same similarity appear, several second leased-sharing users with the same similarity are randomly arranged, or the corresponding second leased-sharing users are preferentially sorted according to the lease-sharing deviation weight value of each second leased-sharing user, which is the largest, and the corresponding lease-sharing deviation weight value closest to the first leased-sharing user.
In specific implementation, the number of second users sharing the lease on the system may be more, and the first user sharing the lease does not need to pay attention to too many second users, so in the embodiment of the application, when the similarity between all the second users sharing the lease and the first user sharing the lease is obtained, the second users having the similarity smaller than a fourth preset threshold are filtered, that is, only the second users sharing the lease and having the similarity higher than that of the first user sharing the lease are reserved and recommended to the first user sharing the lease;
in specific implementation, the first K second users can also be recommended to the first user, i.e., the first K second users with relatively high similarity to the first user are recommended to the first user, and the other second users are not recommended.
In the embodiment, the users with similar lease preference are screened for the users with the lease preference and recommended to the users with the lease preference input by the users with the lease preference. The scheme can efficiently and quickly recommend the shared rental users with similar shared rental bias for the shared rental users.
And 303, taking the M second lease users before the sorting as lease sharing recommendation users.
And the third is that:
the combination of the first and second modes:
the first combination mode:
and the union set of the leased sharing recommended users determined in the first mode and the leased sharing recommended users determined in the second mode is used as the determined leased sharing recommended users and is sent to the first leased sharing users.
The second combination mode is as follows:
and when the first mode does not determine the party-renting recommended user, determining the party-renting recommended user by using the second mode and sending the party-renting recommended user to the first party-renting user.
The third combination mode is as follows:
and determining the lease-sharing recommended users in the first mode, determining lease-sharing recommended users by using the second mode for lease-sharing users except the lease-sharing recommended users determined in the second lease-sharing users, and sending the lease-sharing recommended users determined in the two modes to the first lease-sharing user.
Example two
Referring to fig. 4, fig. 4 is a schematic flowchart illustrating a process of recommending a rental user in the second embodiment of the present application. The method comprises the following specific steps:
step 401, a blacklist is obtained.
The blacklist is set aiming at the sharing renters with bad habits, can be set by a system, can be updated, and is stored in the blacklist according to the received evaluation information of the sharing renters.
Here, it may be set that only poor evaluations are received, and all users of the received evaluation information are saved in the blacklist.
The user identification may be used in the blacklist to represent the users who are leased, such as a user name, a mobile phone number of the user, and the like.
Step 402, determining whether the first user is a blacklisted user, if yes, executing step 403, otherwise, executing step 404.
Matching the user identification of the first lease sharing user in the obtained blacklist, and executing step 403; otherwise, step 404 is performed.
Step 403, providing the entire lease information for the first user sharing lease, and ending the process.
In this case, no leased sharing user is recommended for the first leased sharing user, and only the entire lease information is provided.
Step 404, obtaining a first weight vector of the lease-sharing preference feature of the first lease-sharing user and second weight vectors of all second lease-sharing users in the system.
Step 405, obtaining a corresponding lease joining recommendation user according to the first weight vector and the second weight vector, and sending the lease joining recommendation user to the first lease joining user.
EXAMPLE III
Taking the example of obtaining the first weight vector of the first lease joining user in the interface providing manner, the determining process of the lease joining recommendation user is given as follows:
the method comprises the steps of firstly, acquiring a blacklist when an lease splicing request sent by a first lease splicing user is received.
The black list is set for users who have bad habits, can be set systematically, can be updated according to user feedback,
the user identification may be used in the blacklist to represent the users who are leased, such as a user name, a mobile phone number of the user, and the like.
And step two, determining whether the first user is a blacklist user, if so, executing the step three, otherwise, executing the step four.
Matching the user identification of the first lease sharing user in the acquired blacklist, and executing a third step; otherwise, executing the fourth step.
And step three, providing the whole lease information for the first leased user and finishing the process.
In this case, no leased sharing user is recommended for the first leased sharing user, and only the entire lease information is provided.
And fourthly, providing an interface for inputting the lease preference weight value for the first lease user.
The interface for inputting the lease preference weight value can be a page and a document (word, excel and the like), a plurality of lease preference options are arranged on the interface, and when an lease user selects which lease preference, the lease preference weight value needs to be filled in according to the selected lease preference; the unselected sublets do not need to fill out sublet bias weighted values, and the system can default the corresponding sublet bias weighted value to be 0.
And fifthly, when the lease partial weight value input by the first lease user is received, generating and storing the lease partial weight vector of the first lease user.
The lease-sharing deviation items on the interface provided by the embodiment of the application can be set according to actual needs, as long as the lease-sharing deviation items provided for each lease-sharing user in the same period are ensured to be the same, and the lease-sharing deviation items provided can be as follows: clean, basketball, going home without strangers, gender, age zone, whether pet care is allowed, etc.
Assume that the first user has selected: clean, without strangers going home, gender and age zone four-party lease bias, and respectively giving weight values as follows: 0.5, 0.2,0.1, 0.2.
The lease bias weight vector X generated for the first lease owner is [0.5,0,0.2,0.1,0.2,0 ]. The weight value of the unselected sublettage bias is marked as 0, and the sum of the weight values of the sublettage bias filled by the sublettage user is 1.
And if the lease bias weight value input by the first lease user is not received, enabling the first lease user to automatically select the lease user with the lease request on the system platform without recommending the lease user.
And sixthly, acquiring the lease allocation preference weight vectors of all second lease allocation users with lease allocation requirements.
The lease partial weight vectors of all the lease users are generated and stored, and when the lease partial weight vectors of all the lease users need to be obtained, the lease partial weight vectors can be directly obtained from the local, or the lease partial weight vectors of the lease users can be obtained from other equipment, and the limitation is not carried out.
And seventhly, determining whether the matching lease biased weight vector of the first matching lease user has a weight value larger than a first preset threshold value, if so, executing the eighth step, and otherwise, executing the eleventh step.
Assuming that the first preset weight value is 0.45 and a weight value (0.5) greater than 0.45 exists in the lease bias weight vector of the first lease user, the eighth step is executed.
Eighthly, determining whether a weight value corresponding to the lease bias exists in the obtained lease bias weight vector of the second lease user and is larger than a first preset threshold value, and if so, executing the tenth step; otherwise, executing the ninth step.
If the weighted value in the lease combining deviation weighted vector of the first lease combining user corresponds to the 1 st lease combining deviation, if the weighted value corresponding to the 1 st lease combining deviation of the second lease combining user is also larger than the first preset threshold value, executing the tenth step; otherwise, executing the ninth step.
Ninth, whether a weight value corresponding to the lease bias is not larger than a first preset threshold value and a weight value of a difference value with the weight value corresponding to the first lease user is smaller than a second preset threshold value exists in the obtained lease bias weight vector of the second lease user or not is determined, and if yes, the tenth step is executed; otherwise, the eleventh step is executed.
If the weight value of the corresponding lease partial bias which is not more than the first preset threshold value does not exist in the lease partial weight vector of the second lease user, the absolute value of the difference value between the weight value of the corresponding lease partial bias and the weight value of the corresponding lease partial bias of the first lease user is considered, if the weight value of the corresponding lease partial bias (1 st lease partial bias) of the first lease user is 0.5, the weight value of the corresponding lease partial bias (1 st lease partial bias) of the second lease user is 0.4, the absolute value of the difference value is 0.1, and if the second preset threshold value is set to be 0.15, the tenth step is executed; if the second preset threshold is set to 0.5, the eleventh step is executed, where the value set for the second preset threshold is an example, and is set according to actual needs in practical applications, and is not limited here.
And step ten, recommending the corresponding second party rental user to the first party rental user, and ending the process.
That is, in the tenth step, the second subletted user recommended to the first subletted user is: and the matching refers to that the weight values of the lease bias corresponding to the maximum weight value of the first lease user are both larger than a first preset threshold value, or the absolute value of the difference value of the two is smaller than a second preset threshold value.
Through the steps, the shared users with similar shared lease preference can be recommended for the first shared user, so that the shared lease purpose can be achieved more quickly, and the shared lease preference conflict after shared lease is avoided.
And step eleven, acquiring the weighted values of corresponding lease combining bias of the first lease combining user and the second lease combining user.
And step eleven, calculating the difference value of the obtained weighted values of the corresponding lease combining bias of the first lease combining user and each second lease combining user.
And thirteenth, if a second sublet user with the calculated difference absolute value larger than a third preset threshold exists, deleting the acquired sublet bias weight vector corresponding to the second sublet user.
If the first lease partial (clean) weight value of the first lease of a first lease user is 0.5, the first lease partial (clean) weight value of a certain second lease user is 0.1, and the absolute value of the difference value of the corresponding lease partial weight values is 0.4, whether the difference value is large or not is determined according to a set third preset threshold value, if the difference value is larger than the third preset threshold value, the difference value is determined to be large, the lease partial errors of the two lease users can generate conflict and are not suitable for lease, so that the second lease user can not be recommended, and the corresponding second lease user can be filtered.
And fourteenth, calculating the similarity between the first sublist users and each second sublist user according to the sublist deviation weight vectors of the first sublist users and the obtained sublist deviation weight vectors of all the second sublist users.
Assuming that three second leased users exist and the lease bias vectors corresponding to the three second leased users are Y, Z, M, N respectively, the similarity between X and Y, the similarity between X and Z, the similarity between X and M, and the similarity between X and N are calculated as the similarity between the corresponding leased users.
In the step, a preset similarity calculation method is adopted for calculating the similarity between the first sublist user and each second sublist user according to the sublist bias weight vector of the first sublist user and the sublist bias weight vectors of all the second sublist users.
In the embodiment of the present application, a preset similarity calculation method is not limited, as long as an algorithm capable of calculating the similarity between two vectors is used.
The preset similarity algorithm can be a cosine similarity algorithm, a Jaccard similarity coefficient algorithm, a Pearson correlation coefficient algorithm and other similarity algorithms; the process of calculating the similarity between the first sublet user and the second sublet user is given below by taking the pearson correlation coefficient algorithm as an example: .
Assuming that the lease bias weight vector of the first lease sharing user is X and the lease bias weight vector of the second lease sharing user is Y, the similarity between the first lease sharing user and the second lease sharing user can be calculated by the following formula:
Figure BDA0002242708180000161
wherein, taking the lease biased entry as n as an example, XiThe ith sublist of the first sublist user is biased to a corresponding weight value, YiThe ith sublist of the second sublist user is biased to a corresponding weight value; i is an integer of 1 to n;
Figure BDA0002242708180000162
represented is the average weight value of all lease bias for the first lease user,
Figure BDA0002242708180000163
representing the average weight value of all the sublets biased by the second sublet user; the numerator is the covariance of vector X and vector Y, and the denominator identifies the product of the respective standard deviations of X and Y.
And fifteenth, sequencing all the second rental users from high to low according to the corresponding similarity, and recommending the second rental users to the first rental users.
Through the fourteenth step, after the similarity between the first leased-sharing user and each second leased-sharing user can be calculated, the corresponding second leased-sharing users are sorted from high to low according to the similarity, if the second leased-sharing users with the same similarity appear, a plurality of second leased-sharing users with the same similarity are randomly arranged, the corresponding second leased-sharing users can also be preferentially sorted according to the lease bias weight value of each second leased-sharing user, and the corresponding lease bias weight value closest to the first leased-sharing user is the largest.
In specific implementation, the number of second users sharing the lease on the system may be more, and the first user sharing the lease does not need to pay attention to too many second users, so in the embodiment of the application, when the similarity between all the second users sharing the lease and the first user sharing the lease is obtained, the second users having the similarity smaller than a fourth preset threshold are filtered, that is, only the second users sharing the lease and having the similarity higher than that of the first user sharing the lease are reserved and recommended to the first user sharing the lease;
in specific implementation, the first K second users can also be recommended to the first user, i.e., the first K second users with relatively high similarity to the first user are recommended to the first user, and the other second users are not recommended.
In the embodiment, the users with similar lease preference are screened for the users with the lease preference and recommended to the users with the lease preference input by the users with the lease preference. The scheme can efficiently and quickly recommend the shared rental users with similar shared rental bias for the shared rental users.
In this embodiment, the house source is used as a bridge between the communication users based on the on-line house source capability. Each lease sharing user can issue own lease sharing requirements on line and screen lease sharing deviation of existing house-source residents to find out a proper lease sharing object. The scheme can enable the sharing renter to efficiently and quickly find out a proper sharing object on line, shorten the house finding time, reduce the conflict of sharing and renting life and the like.
Based on the same inventive concept, the embodiment of the application also provides a user sharing recommendation device. Referring to fig. 5, fig. 5 is a schematic structural diagram of an apparatus applied to the above technology in the embodiment of the present application. The device includes: an acquisition unit 501 and a processing unit 502;
an obtaining unit 501, configured to obtain a first weight vector of an lease bias characteristic of a first lease sharing user; acquiring second weight vectors of all second lease users in the system;
the processing unit 502 is configured to obtain a corresponding leased-sharing recommended user according to the first weight vector and the second weight vector obtained by the obtaining unit 501, and send the leased-sharing recommended user to the first leased-sharing user.
Preferably, the first and second electrodes are formed of a metal,
the processing unit 502 is specifically configured to, when obtaining a corresponding lease recommendation user according to the first weight vector and the second weight vector, determine whether a lease biased feature whose weight is greater than a first preset threshold exists in the first weight vector; if yes, judging whether the weight of the corresponding lease bias feature in the second weight vector is larger than the first preset threshold value or not; when the weight of the corresponding lease biased characteristics in the second weight vector is judged to be larger than the first preset threshold value, determining the corresponding second lease user as a lease recommending user; when the weight of the corresponding lease partial feature in the second weight vector is judged to be not more than the first preset threshold value, judging whether the difference value between the weight of the corresponding lease partial feature in the second weight vector and the weight of the lease partial feature of the first weight vector is smaller than a second preset threshold value, and if so, determining the corresponding second lease user as a lease recommendation user.
Preferably, the first and second electrodes are formed of a metal,
the processing unit 502 is further configured to calculate, when obtaining corresponding leased recommendation users according to the first weight vector and the second weight vector, a similarity between a first leased user and each second leased user by using the first weight vector of the first leased user and the second weight vector of each second leased user; the algorithm for calculating the similarity comprises the following steps: cosine similarity algorithm, Jaccard similarity coefficient algorithm, or Pearson correlation coefficient algorithm; sorting the second users according to the corresponding similarity from high to low; and taking the first M second leased users as leased recommending users.
Preferably, the first and second electrodes are formed of a metal,
the processing unit 502 is specifically configured to use a cosine similarity algorithm, a Jaccard similarity coefficient algorithm, or a pearson correlation coefficient algorithm to calculate the similarity between the first leased user and each second leased user when calculating the similarity between the first leased user and each second leased user by using the first weight vector of the first leased user and the second weight vector of each second leased user.
Preferably, the first and second electrodes are formed of a metal,
the processing unit 502 is further configured to filter out second users with similarity smaller than a fourth preset threshold before the second users with the same rank according to the corresponding similarity from high to low.
Preferably, the first and second electrodes are formed of a metal,
the processing unit 502 is further configured to perform difference calculation on weight values of corresponding lease biased features of the first lease sharing user and each second lease sharing user before acquiring corresponding lease recommendation users according to the first weight vector and the second weight vector; and if a second sublet user with the calculated absolute value of the difference value larger than a third preset threshold value exists, deleting the sublet bias weight vector corresponding to the second sublet user.
Preferably, the first and second electrodes are formed of a metal,
an obtaining unit 501, configured to determine whether a first lease user is a blacklist user before obtaining a first weight vector of lease bias characteristics of the first lease user;
the processing unit 502 is further configured to provide, when the obtaining unit 501 determines that the first leased sharing user is a blacklist user, complete lease information for the first leased sharing user;
the obtaining unit 501 is further configured to obtain a first weight vector of the lease preference feature of the first lease sharing user when it is determined that the first lease sharing user is not the blacklist user.
Preferably, the first and second electrodes are formed of a metal,
the obtaining unit 501 is further configured to store the leased-sharing user in a blacklist according to the received evaluation information of the leased-sharing user.
The units of the above embodiments may be integrated into one body, or may be separately deployed; may be combined into one unit or further divided into a plurality of sub-units.
In another embodiment, an electronic device is also provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the user sharing recommendation method when executing the program.
In another embodiment, a computer-readable storage medium is also provided, having stored thereon computer instructions, which when executed by a processor, perform the steps of the rental user recommendation method.
Fig. 6 is a schematic physical structure diagram of an electronic device according to an embodiment of the present invention. As shown in fig. 6, the electronic device may include: a processor (processor)610, a communication Interface (Communications Interface)620, a memory (memory)630 and a communication bus 640, wherein the processor 610, the communication Interface 620 and the memory 630 communicate with each other via the communication bus 640. The processor 610 may call logic instructions in the memory 630 to perform the following method:
acquiring a first weight vector of a lease combining bias characteristic of a first lease combining user;
acquiring second weight vectors of all second lease users in the system;
and acquiring a corresponding lease-sharing recommended user according to the first weight vector and the second weight vector, and sending the lease-sharing recommended user to the first lease-sharing user.
In addition, the logic instructions in the memory 630 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.

Claims (10)

1. A method for recommending users through sharing rental records is characterized by comprising the following steps:
acquiring a first weight vector of a lease combining bias characteristic of a first lease combining user;
acquiring second weight vectors of all second lease users in the system;
and acquiring a corresponding lease-sharing recommended user according to the first weight vector and the second weight vector, and sending the lease-sharing recommended user to the first lease-sharing user.
2. The method according to claim 1, wherein the step of obtaining the corresponding lease recommendation user according to the first weight vector and the second weight vector comprises:
judging whether the first weight vector has a lease-sharing deviation characteristic with the weight larger than a first preset threshold value;
if yes, judging whether the weight of the corresponding lease bias feature in the second weight vector is larger than the first preset threshold value or not;
when the weight of the corresponding lease biased characteristics in the second weight vector is judged to be larger than the first preset threshold value, determining the corresponding second lease user as a lease recommending user;
when the weight of the corresponding lease partial feature in the second weight vector is judged to be not more than the first preset threshold value, judging whether the difference value between the weight of the corresponding lease partial feature in the second weight vector and the weight of the lease partial feature of the first weight vector is smaller than a second preset threshold value, and if so, determining the corresponding second lease user as a lease recommendation user.
3. The method according to claim 1 or 2, wherein the step of obtaining the corresponding party recommendation user according to the first weight vector and the second weight vector further comprises:
calculating the similarity of a first sublist user and each second sublist user by using a first weight vector of the first sublist user and a second weight vector of each second sublist user;
sorting the second users according to the corresponding similarity from high to low;
and taking the first M second leased users as leased recommending users.
4. The method of claim 3, wherein before the ranking the second users according to their respective similarities from high to low, the method further comprises:
and filtering out the second users with the similarity smaller than a fourth preset threshold.
5. The method according to claim 1, wherein after obtaining second weight vectors of all second leased users in the system, and before obtaining corresponding leased recommended users according to the first weight vector and the second weight vector, the method further comprises:
calculating the difference value of the weight values of corresponding lease preference characteristics of the first lease sharing user and each second lease sharing user;
and if a second sublet user with the calculated absolute value of the difference value larger than a third preset threshold value exists, deleting the sublet bias weight vector corresponding to the second sublet user.
6. The method of claim 1, wherein before obtaining the first weight vector of the lease biased feature of the first lease user, the method further comprises:
determining whether the first leased user is a blacklist user, and if so, providing whole lease information for the first leased user; otherwise, acquiring a first weight vector of the lease combining preference characteristics of the first lease combining user.
7. The method of claim 6, further comprising:
and storing the sharing user in a blacklist according to the received evaluation information of the sharing user.
8. A rental user recommendation apparatus, comprising: the device comprises an acquisition unit, a determination unit and a sending unit;
the obtaining unit is used for obtaining a first weight vector of the lease combining bias characteristic of a first lease combining user; acquiring second weight vectors of all second lease users in the system;
and the processing unit is used for acquiring the corresponding leased sharing recommendation user according to the first weight vector and the second weight vector acquired by the acquisition unit and sending the leased sharing recommendation user to the first leased sharing user.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of any of claims 1 to 7 are implemented when the program is executed by the processor.
10. A computer-readable storage medium having stored thereon computer instructions, which when executed by a processor, perform the steps of the method of any one of claims 1-7.
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