CN111028044B - Renting method and device, electronic equipment and storage medium - Google Patents
Renting method and device, electronic equipment and storage medium Download PDFInfo
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- CN111028044B CN111028044B CN201911006143.2A CN201911006143A CN111028044B CN 111028044 B CN111028044 B CN 111028044B CN 201911006143 A CN201911006143 A CN 201911006143A CN 111028044 B CN111028044 B CN 111028044B
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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
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0613—Third-party assisted
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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
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0645—Rental transactions; Leasing transactions
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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
- 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 renting method, a renting device, electronic equipment and a storage medium, wherein the renting deviation of a first renting user is obtained, and a renting deviation vector of the first renting user is generated; obtaining lease bias vectors of all second lease subscribers; calculating the similarity between the first lessor and each second lessor by using the lessor deflection vectors of the first lessor and the lessor deflection vectors of all the second lessor; sorting all the second renting users from high to low according to the corresponding similarity, and selecting the first M second renting users; and if the first renting user and the renting users in the M second renting users reach the renting will, sending renting information. The method can efficiently and quickly find the renters with similar renting bias for the renters under the condition of saving cost, so as to avoid the problem of renting conflict caused by different renting bias.
Description
Technical Field
The present application relates to the field of internet house renting technologies, and in particular, to a method and apparatus for renting, an electronic device, and a storage medium.
Background
Because the price of the metropolitan area is high, many people adopt the way of renting to live in the metropolitan area, and most of renting users are not known each other before renting, even some conflicts caused by different renting bias occur after renting, the specific situation of the renting users can not be known by the existing renting intermediary company when introducing business.
Developed internet technology only provides the capability of finding houses on a home line, but does not solve the problem of rental conflict caused by different rental trends.
Disclosure of Invention
Aiming at the prior art, the embodiment of the application discloses a renting method and a renting device, which can efficiently and quickly find out renters with similar renting bias for renters under the condition of saving cost so as to avoid the problem of renting conflict caused by different renting bias.
In order to solve the technical problems, the technical scheme of the application is realized as follows:
in one embodiment, a renting method is provided, which is applied to an internet house renting system, and the method includes:
obtaining the lease bias of a first lease subscriber, and generating lease bias vectors of the first lease subscriber according to the lease bias of all second lease subscribers in the system and the lease bias of the first lease subscriber;
Obtaining lease bias vectors of all the second lease subscribers;
calculating the similarity between the first lessor and each second lessor by using the lessor deflection vectors of the first lessor and the lessor deflection vectors of all the second lessor;
sorting all the second renting users from high to low according to the corresponding similarity, and selecting the first M second renting users;
and if the first renting user and the renting users in the M second renting users reach the renting will, sending renting information.
Wherein the obtaining the lease bias vectors of all the second lease subscribers includes:
acquiring the position information of the first renter;
and acquiring the lease bias vectors of all second lease subscribers in a preset area range with the position information of the first lease subscriber as a center.
Wherein the generating a lease bias vector of the first lease according to lease bias of all second lease users and lease bias of the first lease users in the system comprises:
obtaining a preference value of each lease bias of the first lease user;
determining the weight of each lease bias of the first lease according to the lease bias of all second lease users and the lease bias of the first lease users; wherein the weight of any one of the rental metrics is a ratio of the total number of rental users for which the rental metric is selected to the sum of the total number of rental users for which each rental metric is selected;
Taking the product of the preference value of each lease bias and the corresponding weight as the preference degree of the lease bias;
and generating a lease bias vector of the first lease user by using the preference degree of all lease bias of the first lease user.
Wherein the method further comprises:
and updating the lease bias vectors of all lease subscribers in the system according to the lease bias periods selected by all lease subscribers.
Wherein M is not less than N-1; n is the number of rooms of the determined renting house source; the renting house source is a house source meeting the house source requirement of the first renting user.
Wherein if the first rental user and the second rental user reach the rental wish, sending the rental information, the method further comprises:
determining whether the number of the second spell users reaching the spell willingness with the first spell renter is less than N-1;
when the number of the second split renters achieving the split renting intention is smaller than N-1, determining whether the first split renters and the second split renters achieving the split renting intention and the M+1th to M+L+1th second split renters in the sequence achieve the split renting intention or not, until the number of the second split renters achieving the split renting intention with the first split renters is not smaller than N-1 or determining whether the number of times of achieving the split renting intention is larger than K, ending the acquisition of a new second split renter; wherein L is the number of users which do not reach the lease wish in the M second lease users; sending the renting information;
And when the number of the second lesson users achieving the lesson will not be smaller than N-1, sending lesson information.
Wherein the method further comprises:
and when the number of times of determining whether to achieve the lease wish is larger than K and a second lease subscriber which achieves the lease with the first lease subscriber does not exist, the lease demand is abandoned, or the first lease subscriber is informed to initiate the lease request again, and the lease bias is reselected.
Wherein, the liquid crystal display device comprises a liquid crystal display device,
and when the similarity between the first lessor and each second lessor is calculated by using the lessor deflection vectors of the first lessor and the lessor deflection vectors of all the second lessor, respectively, a cosine similarity algorithm, a Jaccard similarity coefficient algorithm or a Pelson correlation coefficient algorithm is adopted.
In another embodiment, a rental device is provided, which is applied to an internet house renting system, and the device includes: the device comprises an acquisition unit, a generation unit, a calculation unit, a selection unit, a determination unit and a transmission unit;
the acquisition unit is used for acquiring the lease bias of the first lease subscriber; obtaining lease bias vectors of all the second lease subscribers;
the generation unit is used for generating a lease bias vector of the first lease according to lease bias of all second lease users in the system and the lease bias of the first lease user acquired by the acquisition unit;
The computing unit is used for respectively computing the similarity between the first renter and each second renter by using the renter deflection vector of the first renter generated by the generating unit and the renter deflection vectors of all the second renters acquired by the acquiring unit;
the selecting unit is used for sorting all the second renter sharing users calculated by the calculating unit from high to low according to the corresponding similarity, and selecting the first M second renter sharing users;
the determining unit is used for determining whether the first renter and the M second renters achieve a renting will or not;
the sending unit is configured to send the rental information if the determining unit determines that the first rental user and the renter of the M second rental users reach a rental wish.
Wherein, the liquid crystal display device comprises a liquid crystal display device,
the obtaining unit is configured to, when obtaining the lease bias vectors of all the second lease subscribers, include: acquiring the position information of the first renter; and acquiring the lease bias vectors of all second lease subscribers in a preset area range with the position information of the first lease subscriber as a center.
Wherein, the liquid crystal display device comprises a liquid crystal display device,
the generation unit is specifically configured to, when generating a lease bias vector of the first lease according to lease bias of all second lease subscribers and lease bias of the first lease subscriber in the system, include: obtaining a preference value of each lease bias of the first lease user; determining the weight of each lease bias of the first lease according to the lease bias of all second lease users and the lease bias of the first lease users; wherein the weight of any one of the rental metrics is a ratio of the total number of rental users for which the rental metric is selected to the sum of the total number of rental users for which each rental metric is selected; taking the product of the preference value of each lease bias and the corresponding weight as the preference degree of the lease bias; and generating a lease bias vector of the first lease user by using the preference degree of all lease bias of the first lease user.
Wherein, the liquid crystal display device comprises a liquid crystal display device,
the generating unit is further configured to update the lease bias vectors of all lease subscribers in the system according to the lease bias periods selected by all lease subscribers.
Wherein, the liquid crystal display device comprises a liquid crystal display device,
m is not less than N-1; n is the number of rooms of the determined renting house source; the renting house source is a house source meeting the house source requirement of the first renting user.
Wherein, the liquid crystal display device comprises a liquid crystal display device,
the determining unit is specifically configured to determine whether the number of the second rental users reaching the rental will with the first rental user is smaller than N-1; when the number of the second split renters achieving the split renting intention is smaller than N-1, determining whether the first split renters and the second split renters achieving the split renting intention and the M+1th to M+L+1th second split renters in the sequence achieve the split renting intention or not, until the number of the second split renters achieving the split renting intention with the first split renters is not smaller than N-1 or determining whether the number of times of achieving the split renting intention is larger than K, ending the acquisition of a new second split renter; wherein L is the number of users which do not reach the lease wish in the M second lease users; triggering the sending unit to send the renting information; and triggering the sending unit to send the lease information when the number of the second lease users achieving the lease wish is not less than N-1.
Wherein, the liquid crystal display device comprises a liquid crystal display device,
the determining unit is further configured to discard the lease requirement when it is determined that the number of times of achievement of lease intention is greater than K and there is no second lease subscriber who reaches the lease intention with the first lease subscriber, or trigger the sending unit to notify the first lease subscriber to initiate the lease request again, and reselect lease bias.
Wherein, the liquid crystal display device comprises a liquid crystal display device,
the computing unit is specifically configured to compute, when computing the similarity between the first rental user and each second rental user by using the rental bias vectors of the first rental user and the rental bias vectors of all the second rental users, a cosine similarity algorithm, a Jaccard similarity coefficient algorithm, or a pearson correlation coefficient algorithm.
In another embodiment, there is also provided an electronic device including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the rental method when the program is executed.
In another embodiment, a computer-readable storage medium having stored thereon computer instructions that, when executed by a processor, implement steps in the rental method is also provided.
In summary, in the embodiment of the present application, by calculating the similarity between the rental deviation vector of the first rental user and the rental deviation vector of each second rental user, the second rental user with high similarity to the first rental user and with which the first rental user achieves the rental will is selected, and the rental information is sent. According to the scheme, under the condition of saving cost, the renters with similar renting bias can be efficiently and quickly found for the renters, so that the problem of renting conflict caused by different renting bias is avoided.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort to a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for implementing a rental method in an embodiment of the application;
FIG. 2 is a schematic diagram of a process for generating a lease bias vector for a first lease in an embodiment of the application;
FIG. 3 is a schematic diagram of a device applied to the above technology according to an embodiment of the present application;
fig. 4 is a schematic diagram of an entity structure of an electronic device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented, for example, in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those elements but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
The technical scheme of the application is described in detail below by specific examples. The following embodiments may be combined with each other, and some embodiments may not be repeated for the same or similar concepts or processes.
The embodiment of the application provides a renting method, which is applied to an Internet renting system, and is used for sending renting information when second renters with high similarity to a first renter and a renting wish with the first renter are selected by respectively calculating the similarity of the renting deflection vector of the first renter and the renting deflection vector of each second renter. According to the scheme, under the condition of saving cost, the renters with similar renting bias can be efficiently and quickly found for the renters, so that the problem of renting conflict caused by different renting bias is avoided.
The device for implementing the renting method in the embodiment of the application can be equipment with calculation processing capability, such as a PC.
The following describes in detail the process of implementing the rental in the embodiment of the present application with reference to the accompanying drawings:
example 1
Referring to fig. 1, fig. 1 is a flow chart illustrating a method for implementing a rental method according to an embodiment of the present application. The method comprises the following specific steps:
step 101, obtaining the lease bias of a first lease user, and generating lease bias vectors of the first lease user according to the lease bias of all second lease users in the system and the lease bias of the first lease user.
In the embodiment of the application, the lease bias of the first lease subscriber is acquired, and the lease bias can be triggered by the first lease subscriber sending a lease request or by a certain rule configured in an internet lease system, for example, the first lease subscriber is online, or the first lease subscriber is in a lease seeking state, and the second lease subscriber is recommended for the first lease subscriber periodically.
Where a second tenant may refer to a user other than the first tenant in the system, the first tenant and all second tenants theoretically constitute all users within the system unless otherwise specified by the second tenant.
In the embodiment of the application, the lease bias method for acquiring the first lease user can be used for receiving the current input or transmission of the first lease user, or can be stored in the Internet lease system in advance.
As receiving a first splice lessee input, the following implementations may be provided:
a lease bias selection interface is provided.
The lease bias selection interface provided herein may be a page, a document (word, excel, etc.), and there may be a plurality of lease bias options on the interface, and when the lease user selects which lease bias, the lease bias has a bias value of 1; when a certain rental preference is not selected, the preference value of the rental preference is 0.
The first lease user can directly input the lease bias.
In the step, the lease bias vector of the first lease user is generated according to the lease bias of all second lease users and the lease bias of the first lease user in the system, and the method is concretely implemented as follows:
referring to fig. 2, fig. 2 is a schematic flow chart of generating a rental bias vector of a first rental user according to an embodiment of the application. The method comprises the following specific steps:
step 201, obtain preference values for each rental bias of the first rental user.
Wherein, the first lease subscriber selects a lease bias, and the lease bias has a preference value of 1; otherwise, the preference value of the lease bias is 0;
here the first rental user selects a rental bias, i.e. the first rental user has the rental bias in the rental bias.
In this embodiment, if the rental renter inputs the rental bias by using the provided interface, the rental bias entries may be set according to actual needs, so long as it is ensured that the rental bias provided to each rental user in the same period is the same, and the provided rental bias may be: clean, the number of people living in each room, return home without strangers, gender, age group, whether pet care is allowed, etc.
Assume that the first splice renter has selected: the four lease biases for the four lease biases are 1 and the other lease biases are 0 when the lease biases are clean without strangers going home, sex and age.
Step 202, determining the weight of each lease bias of the first lease according to the lease bias of all second lease users and the lease bias of the first lease user.
Wherein the weight of any one of the rental metrics is a ratio of the total number of rental users for which the rental metric is selected to the sum of the total number of rental users for which each rental metric is selected.
In the calculation of the weight of each rental bias for the first rental user, only the weight of the rental bias whose preference value is not 0 may be calculated, and the weight of the rental bias whose preference value is 0 is not calculated, because the product of the preference value and the weight is used as the preference degree here, one of the parameters is 0, and the other parameter is not necessary to be recalculated, so that the calculation cost is saved.
If the weight corresponding to the lease bias (clean) of the first lease is calculated, the total number of all lease users selecting the lease bias to be clean in the system is obtained, and the sum of the total numbers of the lease users selecting each lease bias is obtained.
Assuming that there are a total of Q users in the system along with the first renter, there are a total of 6 renter bias (clean A, number of people living in each room B, return home without strangers C, gender D, age zone E, whether pet care is allowed or not)F) For example, the weight a for the "clean" lease bias is:wherein A is Q Selecting the total number of the renters who share the bias A for the system; b (B) Q Selecting the total number of the renters with the renter bias B in the system; c (C) Q Selecting the total number of the renters who share the price of C in the system; d (D) Q Selecting the total number of the renters who pay off D for the renter in the system; e (E) Q Selecting the total number of the renters who pay off the E for the renter in the system; f (F) Q The total number of rental subscribers in the system who opt for the rental bias F.
Here, taking the weight of the lease bias a as an example, the weights of other lease biases are similar to this, and no example is given.
Step 203, taking the product of the preference value of each lease bias and the corresponding weight as the preference degree of the lease bias.
The preference degree of the rental bias A is A1×a, wherein A1 is a preference value of the rental bias A, and a is a weight of the rental bias A.
Step 204, generating a rental bias vector for the first rental user using the preference levels of all rental biases for the first rental user.
Rental bias vector for first rental user
The above-mentioned method for generating the rental deflection vector of the first rental user is provided, and the rental deflection vector for each rental user in the system can be generated in the above-mentioned manner.
When the system is in concrete implementation, the lease bias of all lease subscribers can be recalculated once when the lease subscribers are newly added in the system, and the lease bias vectors of all lease subscribers in the system can be updated according to the lease bias period selected by all lease subscribers in the system.
All renters here refer to all users in the system that store a renting bias vector.
The period can be one day, one week, etc., and is determined according to actual needs when the method is specifically implemented.
Step 102, obtaining the lease bias vectors of all second lease subscribers.
The obtaining of the rental bias vectors of all the second rental subscribers in this step may include the following specific implementation:
acquiring position information of a first renter;
and acquiring the lease deflection vectors of all second lease subscribers in a preset area range centering on the position information of the first lease subscriber.
The position information of the renter can be obtained through GPS positioning, and can also be the position information input by the renter for renting.
The selected renter can improve the work efficiency of renting and promote the achievement of the renting will as soon as possible.
And step 103, calculating the similarity between the first lessor and each second lessor by using the lessor deflection vectors of the first lessor and the lessor deflection vectors of all the second lessor.
Assuming that two second renters exist, the corresponding renter deflection vectors of the two second renters are respectively Then calculate +.>And->Similarity of->And->As the similarity of (2)And the similarity between corresponding renters.
In the step, a preset similarity algorithm is adopted to calculate the similarity between the first lessee and each second lessee according to the lessee deflection vector of the first lessee and the lessee deflection vectors of all the second lessees.
The preset similarity algorithm is not limited in the embodiment of the application, so long as the algorithm of the similarity of the two vectors can be calculated.
The preset similarity algorithm can be a cosine similarity algorithm, a Jaccard similarity coefficient algorithm, a Pelson correlation coefficient algorithm and other similarity algorithms; taking the pearson correlation coefficient algorithm as an example, the procedure of calculating the similarity between the first tenant and the second tenant is given as follows:
assume that the rental bias weight vector of the first rental user isThe lease bias weight vector of the second lease subscriber is +.>The similarity of the first and second lessees can be calculated by the following formula:
wherein ρ is x,y Representation ofAnd->Correlation coefficient of>Representation->And->Covariance, sigma of x Sum sigma y Respectively indicate->And->Standard deviation.
The larger the calculated value of the correlation coefficient, the larger the similarity of the two vectors, that is, the greater the share bias similarity of the two sharing users.
And 104, sorting all the second lessees from high to low according to the corresponding similarity, and selecting the first M second lessees.
Here, M may be set to an integer greater than 0;
preferably, M is not less than N-1; n is the number of rooms of the determined rental property source.
Such restrictions are to agree on the corresponding house sources for the rental as soon as possible.
The specific implementation of determining the rental property source can be as follows:
and the house source meets the house source requirement of the first renting user.
The method is concretely realized as follows:
the first renter has a renting requirement, a suitable house source is screened on a house renting system, screening conditions comprise common areas, subways, house types, prices, building areas, house source features (such as full five years, near subways and the like), orientations, floors, decoration styles and purposes (such as commercial, common houses, villas, quadrangles and the like), the house renting system gives a suitable house source list, and the list can be ordered according to the degree of coincidence with the requirement of the user.
If the first renter selects a house source in the house source list, determining that the selected house source is the determined renter house source;
if the first house renter does not select a house in the house source list, the house renting system selects one house as the determined house of the house renter according to the preset rule, and the house listed in the first position in the list can be selected.
In the embodiment of the application, when the similarity between all the second renters and the first renters is obtained, the second renters with the similarity smaller than the first preset threshold value can be filtered, that is, only the second renters with the high similarity with the first renters are reserved, or only the second renters with the second preset threshold value are reserved directly.
Step 105, if the first renter and the renter in the M second renters reach a renting wish, sending renting information.
If the first lessee and the lessee of the M second lessees reach the lessee wish, further determination is needed:
whether the number of the second spell users reaching the spell will with the first spell renter is smaller than N-1;
when the number of the second split renters achieving the split renting intention is smaller than N-1, determining whether the first split renters and the second split renters achieving the split renting intention and the M+1th to M+L+1th second split renters in the sequence achieve the split renting intention or not, until the number of the second split renters achieving the split renting intention with the first split renters is not smaller than N-1 or determining whether the number of times of achieving the split renting intention is larger than K, ending the acquisition of a new second split renter; wherein L is the number of users which do not reach the lease wish in the M second lease users; sending the renting information;
When the number of the second lesson users achieving the lesson will not be less than N-1, sending lesson information;
and when the number of times of determining whether to achieve the lease wish is larger than K and a second lease subscriber which achieves the lease with the first lease subscriber does not exist, the lease demand is abandoned, or the first lease subscriber is informed to initiate the lease request again, and the lease bias is reselected.
If the fact that the first and M second renters do not reach the renting will is determined, selecting the M+1th to 2M second renters and the first renter in the sorting to continuously determine whether to reach the renting will;
or, abandoning the requirement of the split rental,
or notifying the first rental user to initiate the rental request again and reselecting the rental bias.
In the embodiment of the application, if only one room is selected in the lease bias of the first lease subscriber, M=N-1; if one room is not selected to hold only one in the rental bias of the first rental user, M is not greater than 2N-1, which is not necessarily limiting, but gives a preferred implementation.
When determining whether the first renting user and the second renting user reach the renting will, the embodiment of the application can form a chat group by the first renting user and a plurality of the second renting users, and determine whether the renting will can be reached in a chat mode.
In practical applications, a property broker may also be added to the chat group to learn whether the renter has achieved the realization of the willingness of the renter.
The house source introducer can be a worker of a house renting system, and the worker can know the exchange process of the renting user as soon as possible and prompt the agreement of the house renting will as soon as possible.
The following gives an implementation of how chat groups are formed by way of specific example, but is not limited to the following example implementations:
assuming that the lease bias similarity between 6 second lease subscribers acquired for the first lease subscriber and the first lease subscriber is higher, sorting the lease bias from high to low according to the similarity: second rental 1, second rental 2, second rental 3, second rental 4, second rental 5, second rental 6.
Assuming that the number of rooms of the determined tenant-renting room source is 3 and that the tenant-renting bias of the first tenant-renting user is that one room only holds one person, selecting a second tenant-renting user 1 and a second tenant-renting user 2 to form a chat group with the first tenant-renting user for the first time;
chat personnel in the chat group achieve the renting willingness through a chat mode;
determining the number of second renter users who achieve the renting will with the first renter users;
When it is determined that the number of second rental users who achieve a rental wish with the first rental user is 2 (second rental user 1 and second rental user 2), the first rental user, the second rental user 1, and the second rental user 2 are notified of the rental information, such as house watching on site, or the like.
When it is determined that only 1 second renter has reached a renting intention with the first renter, if the second renter has reached a renting intention with the second renter 1, determining that the number of times (1) of forming the chat group is smaller than K (assuming that the chat group is set to 3), forming the chat group with the first renter, the second renter 1 and the second renter 3;
chat personnel in the chat group achieve the renting willingness through a chat mode;
determining the number of second renter users who achieve the renting will with the first renter users;
when it is determined that the number of second rental users who achieve a rental wish with the first rental user is 2 (second rental user 1 and second rental user 3), the first rental user, the second rental user 1, and the second rental user 3 are notified of the rental information, such as house watching on site, or the like.
When it is determined that 1 second lessor has reached a lessor with the first lessor (assuming that the first lessor has reached a lessor with the second lessor 1 and the second lessor 3, respectively, but that three users have not reached a lessor, selecting a second lessor 1 with high similarity to continue to form a chat group), if it is determined that the second lessor 1 has reached a lessor, it is determined that the number of times (2) of forming the chat group is less than K (assuming that 3 is set), the first lessor, the second lessor 1, and the second lessor 4 are formed into the chat group;
Chat personnel in the chat group achieve the renting willingness through a chat mode;
determining the number of second renter users who achieve the renting will with the first renter users;
when it is determined that the number of second rental users who achieve a rental wish with the first rental user is 2 (second rental user 1 and second rental user 4), the first rental user, the second rental user 1, and the second rental user 4 are notified to view the house on site to perform the rental.
When it is determined that there are only 1 second lessons who have achieved a lesson with the first lesson, the first lessons are assumed to achieve a lesson with the second lessons 1 and 4, respectively, but the three lessons do not achieve a lesson agreement, the second lessons 1 with high similarity are selected to continue to form a chat group, if it is determined that the second lessons 1 are achieved as a lesson, it is determined that the number of times (3) of forming the chat group is not less than K (assuming that 3 is set), the need for the lesson is abandoned.
And when the number of the second spell users which achieve the spell willingness with the first spell renter is determined to be 0, giving up the spell demand, or informing the first spell renter to initiate the spell request again, and reselecting the spell deviation.
Thus, the processing of the user renting request is completed once.
The renting method provided by the embodiment of the application gives the renter the right to select the rented object before renting; the renters can roughly know and communicate with the roommates before renting, so that contradictions caused by unaware among the renters are reduced; the scheme can also lighten the work of house source introducers, namely reduces the cost of labor and time, and can simplify the multi-time house watching into one-time house watching after the splice renter agrees with the wish; meanwhile, rented house sources can collect rents uniformly, and follow-up renting is convenient.
Based on the same inventive concept, the embodiment of the application also provides a renting device which is applied to the Internet house renting system. Referring to fig. 3, fig. 3 is a schematic view of a device structure according to an embodiment of the present application, where the device structure is applied to the above technology. The device comprises: an acquisition unit 301, a generation unit 302, a calculation unit 303, a selection unit 304, a determination unit 305, and a transmission unit 306;
an obtaining unit 301, configured to obtain a rental bias of a first rental user; obtaining lease bias vectors of all second lease subscribers;
a generating unit 302, configured to generate a lease bias vector of a first lease according to lease biases of the first lease acquired by the lease bias and acquisition unit 301 of all second lease in the system;
A calculating unit 303, configured to calculate a similarity between the first rental user and each of the second rental users, using the rental bias vectors of the first rental user generated by the generating unit 302 and the rental bias vectors of all the second rental users acquired by the acquiring unit 301, respectively;
a selecting unit 304, configured to rank all the second lessees calculated by the calculating unit 303 from high to low according to the corresponding similarity, and select the first M second lessees;
a determining unit 305, configured to determine whether the first tenant and the second tenant of the M second tenants achieve a tenant intention;
a sending unit 306, configured to send the rental information if the determining unit 305 determines that the first rental user and the rental user of the M second rental users reach a rental wish.
Preferably, the method comprises the steps of,
the obtaining unit 301, when obtaining the lease bias vectors of all the second lease subscribers, includes: acquiring the position information of the first renter; and acquiring the lease bias vectors of all second lease subscribers in a preset area range with the position information of the first lease subscriber as a center.
Preferably, the method comprises the steps of,
the generating unit 302 is specifically configured to, when generating a lease bias vector of the first lease according to lease bias of all second lease subscribers and lease bias of the first lease subscriber in the system, include: obtaining a preference value of each lease bias of the first lease user; wherein, the first lease subscriber selects a lease bias, and the lease bias has a preference value of 1; otherwise, the preference value of the lease bias is 0; determining the weight of each lease bias of the first lease according to the lease bias of all second lease users and the lease bias of the first lease users; wherein the weight of any one of the rental metrics is a ratio of the total number of rental users for which the rental metric is selected to the sum of the total number of rental users for which each rental metric is selected; taking the product of the preference value of each lease bias and the corresponding weight as the preference degree of the lease bias; and generating a lease bias vector of the first lease user by using the preference degree of all lease bias of the first lease user.
Preferably, the method comprises the steps of,
the generating unit 302 is further configured to update the rental bias vectors of all the rental subscribers in the system according to the rental bias periods selected by all the rental subscribers.
Preferably, the method comprises the steps of,
m is not less than N-1; n is the number of rooms of the determined renting house source; the renting house source is a house source meeting the house source requirement of the first renting user.
Preferably, the method comprises the steps of,
a determining unit 305, specifically configured to determine whether the number of second rental users who reach a rental wish with the first rental user is less than N-1; when the number of the second split renters achieving the split renting intention is smaller than N-1, determining whether the first split renters and the second split renters achieving the split renting intention and the M+1th to M+L+1th second split renters in the sequence achieve the split renting intention or not, until the number of the second split renters achieving the split renting intention with the first split renters is not smaller than N-1 or determining whether the number of times of achieving the split renting intention is larger than K, ending the acquisition of a new second split renter; wherein L is the number of users which do not reach the lease wish in the M second lease users; triggering the sending unit 306 to send the rental information; and when the number of the second lesson users achieving the lesson will not be less than N-1, triggering the sending unit 306 to send lesson information.
Preferably, the method comprises the steps of,
the determining unit 305 is further configured to, when it is determined whether the number of times of achievement of the lease intention is greater than K and there is no second lease subscriber who reaches the lease intention with the first lease subscriber, discard the lease requirement for the time, or trigger the sending unit 306 to notify the first lease subscriber to initiate the lease request again, and reselect the lease bias.
Preferably, the method comprises the steps of,
the calculating unit 303 is specifically configured to calculate, when calculating the similarity between the first rental user and each second rental user using the rental bias vectors of the functional first rental user and the rental bias vectors of all the second rental users, respectively, a cosine similarity algorithm, a Jaccard similarity coefficient algorithm, or a pearson correlation coefficient algorithm.
The units of the above embodiments may be integrated or may be separately deployed; can be combined into one unit or further split into a plurality of sub-units.
In another embodiment, there is also provided an electronic device including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of the rental method when the program is executed.
In another embodiment, a computer-readable storage medium having stored thereon computer instructions that, when executed by a processor, implement steps in the rental method is also provided.
Fig. 4 is a schematic diagram of an entity structure of an electronic device according to an embodiment of the present invention. As shown in fig. 4, the electronic device may include: processor 410, communication interface (Communications Interface) 420, memory 430 and communication bus 440, wherein processor 410, communication interface 420 and memory 430 communicate with each other via communication bus 440. The processor 410 may call logic instructions in the memory 430 to perform the following method:
when receiving a lease request sent by a first lease subscriber, providing a lease bias selection interface for the first lease subscriber;
when receiving the lease bias selected by the first lease user through the interface, generating lease bias vectors of the first lease user according to the lease bias of all lease users; and obtaining the lease bias vectors of all second lease subscribers;
calculating the similarity between the first renter and each second renter according to the renter deflection vectors of the first renter and the renter deflection vectors of all the second renters;
sorting all second renters from high to low according to the corresponding similarity, and selecting the first M second renters, the first renters and house source introducers to form a chat group;
And when the first and second renters in the chat group reach the renting wish, notifying the first and second renters to watch rooms on site to carry out renting.
Further, the logic instructions in the memory 430 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform 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, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Claims (8)
1. The renting method is applied to an Internet house renting system and is characterized by comprising the following steps of:
obtaining the lease bias of a first lease subscriber, and generating lease bias vectors of the first lease subscriber according to the lease bias of all second lease subscribers in the system and the lease bias of the first lease subscriber;
obtaining lease bias vectors of all the second lease subscribers;
calculating the similarity between the first lessor and each second lessor by using the lessor deflection vectors of the first lessor and the lessor deflection vectors of all the second lessor;
Sorting all the second renting users from high to low according to the corresponding similarity, and selecting the first M second renting users;
if the first renting user and the renting users in the M second renting users reach the renting will, sending renting information;
wherein the obtaining the lease bias vectors of all the second lease subscribers includes:
acquiring the position information of the first renter;
obtaining the lease deflection vectors of all second lease subscribers in a preset area range with the position information of the first lease subscriber as a center;
the generating of the lease bias vector of the first lease according to the lease bias of all second lease users and the lease bias of the first lease users in the system comprises the following steps:
obtaining a preference value of each lease bias of the first lease user;
determining the weight of each lease bias of the first lease according to the lease bias of all second lease users and the lease bias of the first lease users; wherein the weight of any one of the rental metrics is a ratio of the total number of rental users for which the rental metric is selected to the sum of the total number of rental users for which each rental metric is selected;
Taking the product of the preference value of each lease bias and the corresponding weight as the preference degree of the lease bias;
and generating a lease bias vector of the first lease user by using the preference degree of all lease bias of the first lease user.
2. The method according to claim 1, wherein the method further comprises:
and updating the lease bias vectors of all lease subscribers in the system according to the lease bias periods selected by all lease subscribers.
3. The method of claim 1, wherein the step of determining the position of the substrate comprises,
m is not less than N-1; n is the number of rooms of the determined renting house source; the renting house source is a house source meeting the house source requirement of the first renting user.
4. The method of claim 3, wherein the step of transmitting rental information if the first rental user reaches a rental wish with the one of the M second rental users comprises:
determining whether the number of the second spell users reaching the spell willingness with the first spell renter is less than N-1;
when the number of the second split renters achieving the split renting will is smaller than N-1, determining whether the first split renters and the second split renters achieving the split renting will and the M+1th to M+L+1th second split renters in the sequence achieve the split renting will, and ending the acquisition of the second split renters until the number of the second split renters achieving the split renting will with the first split renters is not smaller than N-1 or determining whether the number of times of achieving the split renting will is larger than K; wherein L is the number of users which do not reach the lease wish in the M second lease users; sending the renting information;
And when the number of the second lesson users achieving the lesson will not be smaller than N-1, sending lesson information.
5. The method according to claim 4, wherein the method further comprises:
and when the number of times of determining whether to achieve the lease wish is larger than K and a second lease subscriber which achieves the lease with the first lease subscriber does not exist, the lease demand is abandoned, or the first lease subscriber is informed to initiate the lease request again, and the lease bias is reselected.
6. A rental device applied to an internet rental system, comprising: the device comprises an acquisition unit, a generation unit, a calculation unit, a selection unit, a determination unit and a transmission unit;
the acquisition unit is used for acquiring the lease bias of the first lease subscriber; obtaining lease bias vectors of all second lease subscribers;
the generation unit is used for generating a lease bias vector of the first lease according to lease bias of all second lease users in the system and the lease bias of the first lease user acquired by the acquisition unit;
the computing unit is used for respectively computing the similarity between the first renter and each second renter by using the renter deflection vector of the first renter generated by the generating unit and the renter deflection vectors of all the second renters acquired by the acquiring unit;
The selecting unit is used for sorting all the second renter sharing users calculated by the calculating unit from high to low according to the corresponding similarity, and selecting the first M second renter sharing users;
the determining unit is used for determining whether the first renter and the M second renters achieve a renting will or not;
the sending unit is used for sending the lease information if the determining unit determines that the lease user in the first lease user and the M second lease users reaches the lease wish;
wherein, the liquid crystal display device comprises a liquid crystal display device,
the acquiring unit is specifically configured to acquire location information of the first lessor; obtaining the lease deflection vectors of all second lease subscribers in a preset area range with the position information of the first lease subscriber as a center;
the generation unit is specifically configured to obtain a preference value of each rental deviation of the first rental user; determining the weight of each lease bias of the first lease according to the lease bias of all second lease users and the lease bias of the first lease users; wherein the weight of any one of the rental metrics is a ratio of the total number of rental users for which the rental metric is selected to the sum of the total number of rental users for which each rental metric is selected; taking the product of the preference value of each lease bias and the corresponding weight as the preference degree of the lease bias; and generating a lease bias vector of the first lease user by using the preference degree of all lease bias of the first lease user.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any of claims 1-5 when the program is executed.
8. A computer readable storage medium having stored thereon computer instructions, which when executed by a processor, perform the steps of the method of any of claims 1-5.
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