CN112508649A - Recommendation method for vehicle rental commercial tenants - Google Patents
Recommendation method for vehicle rental commercial tenants Download PDFInfo
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- CN112508649A CN112508649A CN202011445049.XA CN202011445049A CN112508649A CN 112508649 A CN112508649 A CN 112508649A CN 202011445049 A CN202011445049 A CN 202011445049A CN 112508649 A CN112508649 A CN 112508649A
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- vehicle rental
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- 238000000034 method Methods 0.000 title claims abstract description 29
- 238000011156 evaluation Methods 0.000 claims abstract description 8
- 238000012163 sequencing technique Methods 0.000 abstract description 2
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006116 polymerization reaction Methods 0.000 description 1
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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 for vehicle rental merchants, which comprises the following steps of S110, carrying out comprehensive scoring on the vehicle rental merchants according to evaluation parameters, wherein the evaluation parameters comprise the distance from a store to a user car taking point, pricing of a vehicle type of the store, inventory of the vehicle type of the store, the historical demand satisfaction rate of the user of the store, whether the store supports free car taking and sending within a range, whether the store is 24-hour business, the time length of renting the store, the minimum advance preset time difference 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 grade. The method and the system solve the problems of high-quality sequencing of stores of various merchants under a certain vehicle type and intelligent recommendation of stores after user search.
Description
Technical Field
The invention relates to the field of vehicle leasing, in particular to a recommendation method for a vehicle leasing merchant.
Background
After a current vehicle leasing commercial tenant joins in a 'polymerization car leasing' service line, when a user screens car types through an APP, all commercial tenants capable of providing a certain car type need to be sequenced and displayed; meanwhile, when the user inquires a certain vehicle type, if the returned result is too few, the user needs to give a suitable store recommendation.
The current sorting is only simple to sort and recommend according to price or distance, and also only the distance is recommended during recommendation, so that the requirements of part of users can be met, for example, the users can only judge according to vehicle type price information and vehicle type distance information provided by a system, and can not know the information of stores and vehicle types through more dimensions, so that the users are not satisfied with the store or the vehicle type when getting to the store and taking the vehicle; the recommended stores and vehicle types are simply filtered according to the distance at present, and the real requirements of users cannot be met.
Disclosure of Invention
In view of the above, the present invention has been made to provide a recommendation method for a vehicle rental merchant that overcomes or at least partially solves the above problems.
According to one aspect of the invention, a recommendation method for vehicle rental merchants is provided, which comprises a step S110 of carrying out comprehensive scoring on the vehicle rental merchants according to evaluation parameters, wherein the evaluation parameters comprise the distance from a store to a user car taking point, pricing of a car type of the store, inventory of the car type of the store, a historical demand satisfaction rate of a user of the store, whether the store supports free car taking and sending within a range, whether the store is operated for 24 hours, the time for renting the store, the minimum preset time difference in advance, and the type of the car type of the store; and step S120, recommending a vehicle leasing merchant list to the user according to the grade.
In a possible implementation, before step S120, the method further includes: step S111, determining whether the merchant has received the complaint from the user.
In a possible implementation, before step S120, the method further includes: step S112, determining whether the expected car returning time of the user is within the store business time range.
In a possible implementation, before step S120, the method further includes: in step S113, it is determined whether the expected car-use duration of the user is within the store-specified range.
In a possible implementation, step S120 further includes, before: and step S114, judging whether the predicted vehicle taking time of the user is within the minimum advanced preset time difference range.
In one possible embodiment, the scoring criteria for the store-to-user pick-up point distance are as follows: the store scores higher according to the closer the user pick-up point is.
In one possible embodiment, the scoring criteria for pricing a store for that model are as follows: the lower the pricing of a certain vehicle model is, the higher the score is, the market average price of the certain vehicle model can be calculated by the system according to big data, and the highest score is obtained when stores are 30% lower than the market average price.
In one possible embodiment, the scoring criteria for the store inventory for that vehicle type are as follows: the score of 1 stock is 10, the more stocks, the higher the score, and the highest score is more than 10 stocks.
In one possible embodiment, 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 requirements or not does not meet the primary deduction of 10 points.
In one possible embodiment, the scoring criteria for whether a store supports free delivery in range are as follows: and free vehicle taking and sending and scoring within a preset distance are supported.
The method and the system solve the problems of high-quality sequencing of stores of various merchants under a certain vehicle type and intelligent recommendation of stores after user search.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, 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 invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flowchart of a recommendation method for a vehicle rental merchant according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of a method for recommending a vehicle rental merchant according to another embodiment of the present invention;
fig. 3 is a schematic flowchart of a method for recommending a vehicle rental merchant according to an embodiment of the present invention;
fig. 4 is a schematic flowchart of a method for recommending a vehicle rental merchant according to an embodiment of the present invention;
fig. 5 is a flowchart illustrating a method for recommending a vehicle rental merchant according to another embodiment of the present invention.
Detailed Description
The terms "first," "second," and the like in the description and in the claims and in the drawings of the present invention are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. Furthermore, the terms "comprises" and "comprising," as well as any variations thereof, are intended to cover a non-exclusive inclusion, such as a list of steps or elements. A method, system, article, or apparatus 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, system, article, or apparatus.
The technical solution of the present invention is further described in detail with reference to the accompanying drawings and embodiments.
As shown in fig. 1, an embodiment of the present invention provides a method for recommending a vehicle rental merchant, including:
step S110, comprehensively scoring the vehicle rental commercial tenants according to evaluation parameters, wherein the evaluation parameters comprise the distance from a store to a user car taking point, pricing of the car type of the store, inventory of the car type of the store, historical demand satisfaction rate of the user of the store, whether the store supports free car taking and sending within a range, whether the store is 24-hour in service, the length of time for renting the store, the minimum advance preset time difference and the type of the car type of the store;
step S120, recommending a vehicle leasing commercial tenant list to the user according to the grade;
in one example, as shown in fig. 2, step S120 further includes, before:
step S111, determining whether the merchant has received the complaint from 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 shown in fig. 3, step S120 further includes, before:
step S112, determining whether the expected car returning time of the user is within the store business time 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 shown in fig. 4, step S120 further includes, before:
in step S113, it is determined whether the expected car-use duration of the user is within the store-specified 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 shown in fig. 5, step S120 further includes, before:
and step S114, judging whether the predicted vehicle taking time of the user is within the minimum advanced preset time difference 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, the scoring criteria for store-to-user pick-up distance are as follows: the store has higher score according to the closer car taking point of the user, and the score is the highest score within the range of 1 kilometer.
In one example, the scoring criteria for pricing a model for a store are as follows: the lower the pricing of a certain vehicle model is, the higher the score is, the market average price of the certain vehicle model can be calculated by the system according to big data, and the highest score is obtained when stores are 30% lower than the market average price.
In one example, the scoring criteria for the model inventory of the department store are as follows: the score of 1 stock is 10, the more stocks, the higher the score, and the highest score is more than 10 stocks.
In one example, 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 requirements or not does not meet the primary deduction of 10 points. In addition, if the vehicle type is not satisfied, the user can be upgraded without charge. For example, when the store does not have a vehicle meeting the vehicle type required by the user, the store can provide a vehicle type with a price higher than the price of the vehicle type selected by the user, and the fee for renting the vehicle is not charged, so that the deduction of the historical demand satisfaction rate of the user of the store can be avoided once.
In one example, the scoring criteria for whether a store supports free pickup in range is as follows: and free vehicle taking and sending and scoring within a preset distance are supported. Illustratively, the credit for free vehicle taking and sending within a range of 5 kilometers is supported, the full credit for free vehicle taking and sending within a range of the whole city is supported, and the non-credit is not supported.
In one example, the scoring criteria for whether a store is 24 hours open are as follows: supported bonus points and unsupported non-deduction points.
In one example, the scoring criteria for the length of time a store has been rented are as follows: the score is higher when the length of the initial lease is shorter, and the score is the highest when the 1-day initial lease and the following hourly lease are realized.
In one example, the scoring criteria for the minimum predetermined time difference in advance is as follows: namely, the shortest allowable time between the preset time of the user and the car taking time is the shortest time, the score is higher when the time is shorter, and the score is the highest when the time is less than 30 minutes. Illustratively, the current time is 12: 00, store a sets a minimum lead time difference of 2 hours-the earliest time that the user can book a vehicle at store a is today 14: 00, if store B sets a minimum lead time difference of 1 hour, then the earliest time that the user can book a vehicle at store B is today 13: 00. store B has a smaller minimum predetermined time difference in advance than store a, and then store B has a higher score than store a.
In one example, the scoring criteria for store vehicle type categories are as follows: the score of the item of the shop is determined according to the inventory ratio of the comfortable, economical and luxurious vehicle types, and the specific rules can be dynamically adjusted according to the leasing conditions of the big data market research user for various vehicle types.
The above embodiments are provided to further explain the objects, technical solutions and advantages of the present invention in detail, it should be understood that the above embodiments are merely exemplary embodiments of the present invention and are not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (10)
1. A recommendation method for a vehicle rental merchant is characterized by comprising the following steps:
step S110, comprehensively scoring the vehicle rental commercial tenants according to evaluation parameters, wherein the evaluation parameters comprise the distance from a store to a user car taking point, pricing of the car type of the store, inventory of the car type of the store, historical demand satisfaction rate of the user of the store, whether the store supports free car taking and sending within a range, whether the store is 24-hour in service, the length of time for renting the store, the minimum advance preset time difference and the type of the car type of the store;
and step S120, recommending a vehicle leasing merchant list to the user according to the grade.
2. The method for recommending vehicle rental merchants according to claim 1, further comprising, before step S120:
step S111, determining whether the merchant has received the complaint from the user.
3. The method for recommending vehicle rental merchants according to claim 1, further comprising, before step S120:
step S112, determining whether the expected car returning time of the user is within the store business time range.
4. The method for recommending vehicle rental merchants according to claim 1, further comprising, before step S120:
in step S113, it is determined whether the expected car-use duration of the user is within the store-specified range.
5. The method for recommending vehicle rental merchants according to claim 1, wherein step S120 is preceded by:
and step S114, judging whether the predicted vehicle taking time of the user is within the minimum advanced preset time difference range.
6. The recommendation method for the vehicle rental merchant according to claim 1, wherein the scoring criteria of the store-to-user pick-up point distance are as follows:
the store scores higher according to the closer the user pick-up point is.
7. The recommendation method for vehicle rental merchants according to claim 1, wherein the scoring criteria for pricing the model of the store are as follows:
the lower the pricing of a certain vehicle model is, the higher the score is, the market average price of the certain vehicle model can be calculated by the system according to big data, and the highest score is obtained when stores are 30% lower than the market average price.
8. The recommendation method for vehicle rental merchants according to claim 1, wherein the scoring criteria of the vehicle type inventory of the store are as follows:
the score of 1 stock is 10, the more stocks, the higher the score, and the highest score is more than 10 stocks.
9. The recommendation method for the vehicle rental merchant according to claim 1, wherein the scoring criteria of the historical demand satisfaction rate of the store users are as follows:
according to the user feedback, whether the store history meets the user requirements or not does not meet the primary deduction of 10 points.
10. The recommendation method for vehicle rental merchants according to claim 1, wherein the scoring criteria whether the store supports free delivery of vehicles in the range are as follows:
and free vehicle taking and sending and scoring within a preset distance are supported.
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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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| CN202011445049.XA CN112508649B (en) | 2020-12-11 | 2020-12-11 | Recommendation method for vehicle leasing merchant |
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| CN112508649A true CN112508649A (en) | 2021-03-16 |
| CN112508649B CN112508649B (en) | 2024-03-15 |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003091669A (en) * | 2001-09-19 | 2003-03-28 | Hitachi Ltd | Rental car reservation system |
| US20070276720A1 (en) * | 2006-05-26 | 2007-11-29 | Campusl, Inc. | Indexing of a focused data set through a comparison technique method and apparatus |
| CN107705190A (en) * | 2017-10-25 | 2018-02-16 | 深圳市国电科技通信有限公司 | A kind of vehicle leasing information recommendation method |
| CN109409982A (en) * | 2018-09-18 | 2019-03-01 | 杭州的蓝科技有限公司 | Shared vehicle recommended method and device |
| CN109635986A (en) * | 2018-12-28 | 2019-04-16 | 上汽通用五菱汽车股份有限公司 | Shops's method for pushing, device, equipment and storage medium |
| CN109711875A (en) * | 2018-12-19 | 2019-05-03 | 口碑(上海)信息技术有限公司 | Content recommendation method and device |
| CN109816206A (en) * | 2018-12-25 | 2019-05-28 | 深圳市元征科技股份有限公司 | A kind of vehicle recommended method and device |
| CN110415038A (en) * | 2019-07-31 | 2019-11-05 | 携程旅游网络技术（上海）有限公司 | It hires a car the screening technique and system of shops |
| US10685395B1 (en) * | 2017-09-12 | 2020-06-16 | Rodo Inc. | Graphical user interfaces facilitating vehicle leasing |
| CN111695954A (en) * | 2019-03-11 | 2020-09-22 | 新疆丝路大道信息科技有限责任公司 | Vehicle screening method and system of automobile leasing platform and electronic equipment |
| CN111695955A (en) * | 2019-03-11 | 2020-09-22 | 新疆丝路大道信息科技有限责任公司 | User management system of automobile leasing platform |
| CN111815394A (en) * | 2020-05-20 | 2020-10-23 | 北京嘀嘀无限科技发展有限公司 | Commodity scheme recommendation method for taxi hiring of online taxi appointment, electronic equipment and storage medium |
-
2020
- 2020-12-11 CN CN202011445049.XA patent/CN112508649B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003091669A (en) * | 2001-09-19 | 2003-03-28 | Hitachi Ltd | Rental car reservation system |
| US20070276720A1 (en) * | 2006-05-26 | 2007-11-29 | Campusl, Inc. | Indexing of a focused data set through a comparison technique method and apparatus |
| US10685395B1 (en) * | 2017-09-12 | 2020-06-16 | Rodo Inc. | Graphical user interfaces facilitating vehicle leasing |
| CN107705190A (en) * | 2017-10-25 | 2018-02-16 | 深圳市国电科技通信有限公司 | A kind of vehicle leasing information recommendation method |
| CN109409982A (en) * | 2018-09-18 | 2019-03-01 | 杭州的蓝科技有限公司 | Shared vehicle recommended method and device |
| CN109711875A (en) * | 2018-12-19 | 2019-05-03 | 口碑(上海)信息技术有限公司 | Content recommendation method and device |
| CN109816206A (en) * | 2018-12-25 | 2019-05-28 | 深圳市元征科技股份有限公司 | A kind of vehicle recommended method and device |
| CN109635986A (en) * | 2018-12-28 | 2019-04-16 | 上汽通用五菱汽车股份有限公司 | Shops's method for pushing, device, equipment and storage medium |
| CN111695954A (en) * | 2019-03-11 | 2020-09-22 | 新疆丝路大道信息科技有限责任公司 | Vehicle screening method and system of automobile leasing platform and electronic equipment |
| CN111695955A (en) * | 2019-03-11 | 2020-09-22 | 新疆丝路大道信息科技有限责任公司 | User management system of automobile leasing platform |
| CN110415038A (en) * | 2019-07-31 | 2019-11-05 | 携程旅游网络技术（上海）有限公司 | It hires a car the screening technique and system of shops |
| CN111815394A (en) * | 2020-05-20 | 2020-10-23 | 北京嘀嘀无限科技发展有限公司 | Commodity scheme recommendation method for taxi hiring of online taxi appointment, electronic equipment and storage medium |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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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