CN112508649B - Recommendation method for vehicle leasing merchant - Google Patents
Recommendation method for vehicle leasing merchant Download PDFInfo
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- CN112508649B CN112508649B CN202011445049.XA CN202011445049A CN112508649B CN 112508649 B CN112508649 B CN 112508649B CN 202011445049 A CN202011445049 A CN 202011445049A CN 112508649 B CN112508649 B CN 112508649B
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- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000011156 evaluation Methods 0.000 claims abstract description 8
- 238000001914 filtration Methods 0.000 description 1
- 238000010030 laminating Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
Classifications
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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
-
- 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]
- G06Q30/0605—Supply or demand aggregation
-
- 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
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0639—Item locations
Abstract
The invention relates to a recommendation method of vehicle leasing merchants, which comprises the following steps of S110, comprehensively scoring the vehicle leasing merchants according to evaluation parameters, wherein the evaluation parameters comprise the distance from a store to a user pick-up point, pricing of the vehicle type of the store, inventory of the vehicle type of the store, historical demand satisfaction rate of the user of the store, whether the store supports free vehicle taking in a range, whether the store is operated for 24 hours, the leasing time of the store, the minimum preset time difference in advance and the type of the vehicle type of the store; and step S120, recommending a vehicle leasing merchant list to the user according to the grading level. The method solves the problems of high-quality sorting of all commercial establishments under a certain vehicle type and intelligent recommending of the commercial establishments based on user searching.
Description
Technical Field
The invention relates to the field of vehicle leasing, in particular to a recommendation method of a vehicle leasing merchant.
Background
When a user screens vehicle types through an APP after the current vehicle renting merchant joins in an aggregated taxi service line, sorting display is required for all merchants which can provide a certain vehicle type; and meanwhile, when a user inquires about a certain vehicle type, if the returned result is too small, the user needs to be given a proper store recommendation.
The current ordering is simply based on price or distance, and the current ordering is based on distance, so that the needs of partial users can be met, for example, the users can only judge according to the price information of the vehicle type and the distance information between the vehicle and the vehicle taking point provided by the system, and the store and the vehicle type information can not be known in more dimensions, so that the user is not full of the store or the vehicle type when arriving at the store to take the vehicle; recommending store and motorcycle type is only at present according to the distance carries out simple filtration, probably can not laminating user's true demand.
Disclosure of Invention
In view of the above, the present invention has been made to provide a vehicle rental business recommendation method that overcomes or at least partially solves the above-mentioned problems.
According to one aspect of the present invention, a recommendation method for a vehicle rental merchant is provided, comprising the steps of S110, comprehensively scoring the vehicle rental merchant according to evaluation parameters, wherein the evaluation parameters comprise a distance from a store to a user pick-up point, pricing of the vehicle type of the store, inventory of the vehicle type of the store, a historical demand satisfaction rate of a user of the store, whether the store supports free delivery of vehicles within a range, whether the store is operated for 24 hours, a store rental time, a minimum advance preset time difference, and a type of the vehicle type of the store; and step S120, recommending a vehicle leasing merchant list to the user according to the grading level.
In a possible implementation manner, before step S120, the method further includes: step S111, judging whether the merchant receives the complaint of the user.
In a possible implementation manner, before step S120, the method further includes: step S112, judging whether the predicted returning time of the user is within the business hours of the store.
In a possible implementation manner, before step S120, the method further includes: step S113, judging whether the predicted time length of the user is within the store stipulation range.
In a possible implementation manner, step S120 further includes: step S114, determining whether the predicted pickup time of the user is within a minimum advance predetermined time difference.
In one possible implementation, the scoring criteria for the distance of the store from the user pick-up point are as follows: the closer the store gets the point, the higher the score.
In one possible implementation, the store pricing for the model is scored as follows: the lower the pricing of a certain vehicle model is, the higher the score is, the system can calculate the market average price of the certain vehicle model according to big data, and the highest score is 30% lower than the market average price in a store.
In one possible embodiment, the store stores the vehicle model inventory as follows: 1 stock is given a score of 10, the more the stock is given a score of higher, and more than 10 stocks are given the highest score.
In one possible implementation, the store user historical demand satisfaction rate scoring criteria are as follows: according to the user feedback, whether the store history meets the user requirement or not does not meet the 10 points of the primary buckle.
In one possible implementation, the scoring criteria for whether a store supports in-range free pick-up cars are as follows: and supporting the free taking and delivering of the car and the adding of the car within a preset distance.
The method solves the problems of high-quality sorting of all commercial establishments under a certain vehicle type and intelligent recommending of the commercial establishments based on user searching.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for 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 invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a recommendation method of a vehicle rental business provided by an embodiment of the invention;
FIG. 2 is a flowchart illustrating another method for recommending a vehicle rental business according to an embodiment of the present invention;
FIG. 3 is a flowchart illustrating another method for recommending a vehicle rental business according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating another method for recommending a vehicle rental business according to an embodiment of the present invention;
fig. 5 is a flowchart of another method for recommending a vehicle rental business according to an embodiment of the present invention.
Detailed Description
The terms first, second and the like in the description and in the claims and drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion, such as a series of steps or elements. The method, system, article, or apparatus is not necessarily limited to those explicitly listed but may include other steps or elements not explicitly listed or inherent to such process, method, article, or apparatus.
The technical scheme of the invention is further described in detail below with reference to the accompanying drawings and the examples.
Referring to fig. 1, an embodiment of the present invention provides a recommendation method for a vehicle rental merchant, including:
step S110, comprehensively scoring a vehicle leasing merchant according to evaluation parameters, wherein the evaluation parameters comprise the distance from a store to a user pick-up point, the price of the vehicle type of the store, the stock of the vehicle type of the store, the historical demand satisfaction rate of the store user, whether the store supports free pick-up and delivery of vehicles in a range, whether the store is operated for 24 hours, the lease starting time of the store, the minimum advance preset time difference and the type of the vehicle type of the store;
step S120, recommending a vehicle leasing merchant list to the user according to the grading level;
in one example, as in fig. 2, step S120 further includes:
step S111, judging whether the merchant receives the complaint of the user.
If the merchant receives the complaint of the user, the merchant is not recommended to the user or is placed at the tail end of the vehicle rental merchant list.
In one example, as in fig. 3, step S120 further includes:
step S112, judging whether the predicted returning time of the user is within the business hours of the store.
If not, the merchant is not recommended to the user or placed at the end of the vehicle rental merchant list.
In one example, as shown in fig. 4, step S120 further includes:
step S113, judging whether the predicted time length of the user is within the store stipulation range.
If not, the merchant is not recommended to the user or placed at the end of the vehicle rental merchant list.
In one example, as in fig. 5, step S120 further includes:
step S114, determining whether the predicted pickup time of the user is within a minimum advance predetermined time difference.
If not, the merchant is not recommended to the user or placed at the end of the vehicle rental merchant list.
In one example, the store-to-user pick-up point distance scoring criteria are as follows: the closer the point of departure is to the user, the higher the score, and the highest score is within 1 km.
In one example, the scoring criteria for store pricing for the vehicle model are as follows: the lower the pricing of a certain vehicle model is, the higher the score is, the system can calculate the market average price of the certain vehicle model according to big data, and the highest score is 30% lower than the market average price in a store.
In one example, the store stores the scoring criteria for the model inventory as follows: 1 stock is given a score of 10, the more the stock is given a score of higher, and more than 10 stocks are given the highest score.
In one example, the store user historical demand satisfaction rate scoring criteria are as follows: according to the user feedback, whether the store history meets the user requirement or not does not meet the 10 points of the primary buckle. In addition, if the vehicle type is not satisfied but can be updated for the user without deduction. By way of example, a store may provide a model that is more expensive than the model selected by the user and does not receive additional user rental fees when there is no vehicle that meets the user's needs, at which time a discount to the store user's historical demand satisfaction rate may be eliminated.
In one example, the scoring criteria for whether a store supports in-range free pick-up cars are as follows: and supporting the free taking and delivering of the car and the adding of the car within a preset distance. Illustratively, the full share of the free pick-up and delivery vehicles within the range of 5 km is supported, the full share of the free pick-up and delivery vehicles within the range of the whole city is supported, and the full share is not supported.
In one example, the scoring criteria for whether a store is open for 24 hours are as follows: supported credits, unsupported no-credits.
In one example, the store rental period scoring criteria are as follows: the shorter the rental time length is, the higher the score is, and the highest score is that 1 day of rental and below can be rented in hours.
In one example, the scoring criteria for the minimum advance predetermined time difference are as follows: namely, the shortest allowed time length of the preset time and the vehicle taking time of the user, the shorter the time is, the higher the score is, and the highest score is below 30 minutes. By way of example, the current time is 12:00, store a sets the minimum advance preset time difference to 2 hours then the earliest time that the user can reserve the vehicle at store a is today 14:00, if the minimum advance preset time difference set by store B is 1 hour, then the earliest time that the user can preset the vehicle at store B is 13 today: 00. store B's minimum advance predetermined time difference is less than store A, then store B's score is higher than store A.
In one example, the scoring criteria for the type of store vehicle model are as follows: the item score of the store is determined according to the comfort, economy and luxury type vehicle stock proportion, and the specific rule can be dynamically adjusted according to the leasing condition of big data market research users on various types of vehicle types.
The foregoing detailed description of the invention has been presented for purposes of illustration and description, and it should be understood that the invention is not limited to the particular embodiments disclosed, but is intended to cover all modifications, equivalents, alternatives, and improvements within the spirit and principles of the invention.
Claims (7)
1. A vehicle rental merchant recommendation method, comprising:
step S110, comprehensively scoring a vehicle leasing merchant according to evaluation parameters, wherein the evaluation parameters comprise the distance from a store to a user pick-up point, the price of the vehicle type of the store, the stock of the vehicle type of the store, the historical demand satisfaction rate of the store user, whether the store supports free pick-up and delivery of vehicles in a range, whether the store is operated for 24 hours, the lease starting time of the store, the minimum advance preset time difference and the type of the vehicle type of the store;
step S120, recommending a vehicle leasing merchant list to the user according to the grading level;
the method further comprises, before step S120:
step S112, judging whether the predicted returning time of the user is within the business hours of a store;
the method further comprises, before step S120:
step S113, judging whether the predicted time length of the user is within the prescribed range of the store;
the step S120 further includes:
step S114, determining whether the predicted pickup time of the user is within a minimum advance predetermined time difference.
2. The recommendation method of a vehicle rental merchant according to claim 1, further comprising, prior to step S120:
step S111, judging whether the merchant receives the complaint of the user.
3. The recommendation method of a vehicle rental business according to claim 1, wherein the scoring criteria of the store-to-user pick-up point distance are as follows:
the closer the store gets the point, the higher the score.
4. The recommendation method for vehicle rental merchants of claim 1, wherein the scoring criteria for store vehicle type pricing are as follows:
the lower the pricing of a certain vehicle model is, the higher the score is, the system can calculate the market average price of the certain vehicle model according to big data, and the highest score is 30% lower than the market average price in a store.
5. The recommendation method of a vehicle rental merchant according to claim 1, wherein the scoring criteria of the store model inventory is as follows:
1 stock is given a score of 10, the more the stock is given a score of higher, and more than 10 stocks are given the highest score.
6. The recommendation method for vehicle rental merchants of claim 1, wherein the scoring criteria for the store user historical demand satisfaction rate are as follows:
according to the user feedback, whether the store history meets the user requirement or not does not meet the 10 points of the primary buckle.
7. The recommendation method for a vehicle rental merchant according to claim 1, wherein the scoring criteria for whether a store supports in-range free pick-up vehicles are as follows:
and supporting the free taking and delivering of the car and the adding of the car within a preset distance.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011445049.XA CN112508649B (en) | 2020-12-11 | 2020-12-11 | Recommendation method for vehicle leasing merchant |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202011445049.XA CN112508649B (en) | 2020-12-11 | 2020-12-11 | Recommendation method for vehicle leasing merchant |
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| CN112508649A CN112508649A (en) | 2021-03-16 |
| CN112508649B true CN112508649B (en) | 2024-03-15 |
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| CN114596666A (en) * | 2022-03-03 | 2022-06-07 | 浙江吉利控股集团有限公司 | Vehicle rental business processing method, device, system, server and storage medium |
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