CN111782944A - Vehicle shopping recommendation method based on analytic hierarchy process - Google Patents

Vehicle shopping recommendation method based on analytic hierarchy process Download PDF

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CN111782944A
CN111782944A CN202010596557.1A CN202010596557A CN111782944A CN 111782944 A CN111782944 A CN 111782944A CN 202010596557 A CN202010596557 A CN 202010596557A CN 111782944 A CN111782944 A CN 111782944A
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vehicle
user
selectable
vehicles
attribute
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王秋伟
赵又群
徐瀚
张桂玉
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Nanjing University of Aeronautics and Astronautics
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9535—Search customisation based on user profiles and personalisation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/951—Indexing; Web crawling techniques
    • 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

Abstract

The invention discloses a vehicle selective purchasing recommendation method based on an analytic hierarchy process, which comprises the following steps: collecting information of all vehicles on the Internet; obtaining the vehicle selection limit of a user and the preference degree of the user to each attribute of the vehicle, and screening all the vehicles collected through the Internet according to the vehicle selection limit of the user to obtain all the vehicles selectable by the user; constructing a judgment matrix according to preference degrees of users for various attributes of vehiclesA(ii) a Constructing judgment matrixes of all vehicles selected by a user for all attributes; and calculating to obtain the recommended weight value of each vehicle selectable by the user according to the judgment matrix, wherein the larger the recommended weight value is, the vehicle is preferentially recommended to the user. The method of the invention avoids the problems of multiple options, complex data, incomplete analysis and consideration and the like in the traditional purchasing process, simultaneously has intelligent characteristic, and is based on the unique characteristics of the userThe specific preferred taste provides an accurate recommendation scheme in real time, time is saved, the efficiency is high, and physical and mental labor is saved.

Description

Vehicle shopping recommendation method based on analytic hierarchy process
Technical Field
The invention relates to a vehicle shopping recommendation method based on an analytic hierarchy process, and belongs to the technical field of vehicle shopping recommendation.
Background
At present, the conventional vehicle purchasing recommendation cannot realize the complete understanding of the intention of a client, so that the user has too much selectivity when selecting the vehicle, unnecessary time and energy waste is caused, the user often spends time and cannot find a vehicle suitable for the user, and the intelligent and convenient modern vehicle purchasing life is not facilitated.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the vehicle selective purchasing recommendation method based on the analytic hierarchy process can provide accurate technical consultation for a user when purchasing vehicles, and saves time and cost.
The invention adopts the following technical scheme for solving the technical problems:
a vehicle shopping recommendation method based on an analytic hierarchy process comprises the following steps:
step 1, collecting information of all vehicles on the Internet, wherein the information comprises vehicle types, vehicle brands, favorable rating, highest vehicle speed, fuel consumption rate, active safety series, maximum transfer rate and price;
step 2, obtaining the vehicle selection limit of the user and the preference degree of the user on each attribute of the vehicle, and screening all the vehicles collected through the Internet according to the vehicle selection limit of the user to obtain all the vehicles selectable by the user; the vehicle selection limit comprises a vehicle type and a vehicle brand, and each attribute of the vehicle comprises attractiveness, dynamic property, fuel economy, safety, comfort and price;
step 3, constructing a judgment matrix A according to preference degrees of the user on various attributes of the vehicle;
step 4, constructing a judgment matrix B of all vehicles selected by the user for the aesthetic property1Determination matrix B for dynamic property2Determination matrix B for fuel economy attributes3Judgment matrix B for safety attribute4A decision matrix B for comfort properties5And a judgment matrix B for the attribute of the price6；
Step 5, according to the judgment matrix A, B1、B2、B3、B4、B5And B6And calculating the weight vector of each vehicle which can be selected by the user for each attribute, finally calculating to obtain the recommendation weight of each vehicle which can be selected by the user, and sequencing all the recommendation weights from large to small, wherein the larger the recommendation weight is, the vehicle is preferentially recommended to the user.
As a preferred scheme of the present invention, in step 1, information of all vehicles on the internet is collected by a web crawler.
As a preferred aspect of the present invention, in step 2, the preference degree of the user for each attribute of the vehicle is expressed as:
Figure BDA0002557437220000021
wherein q isiIndicating the user's preference for the ith attribute of the vehicle.
As a preferred embodiment of the present invention, the structure of the judgment matrix a in step 3 is as follows:
Figure BDA0002557437220000022
Figure BDA0002557437220000023
wherein, aijTo determine the elements of matrix A, qi、qjRespectively representing preference degrees of the user on ith and jth attributes of the vehicle;
if the preference degree of the user to a certain attribute of the vehicle is 0, the attribute is removed, and the remaining attribute is used for constructing the judgment matrix A, wherein the construction method is the same as the process.
As a preferred scheme of the invention, the judgment matrix B of all the vehicles for the aesthetic property, which is selectable by the user in the step 41The construction of (a) is as follows:
Figure BDA0002557437220000024
Figure BDA0002557437220000025
wherein, b1pqTo judge the matrix B1Element of (2), c1p、c1qNormalized evaluation scores of the aesthetic attribute for the p-th and q-th vehicles selectable by the user, respectively, n is the number of all vehicles selectable by the user, and
c1puser-selectable best rating of pth vehicle/user-selectable maximum good rating of all vehicles
Similarly, all vehicle pair dynamics attribute judgment matrix B selectable by the user2Determination matrix B for fuel economy attributes3Judgment matrix B for safety attribute4A decision matrix B for comfort properties5And a judgment matrix B for the attribute of the price6Construction method of (1) and judgment matrix B for aesthetic property1Is constructed in the same way, then
Figure BDA0002557437220000031
Figure BDA0002557437220000032
Figure BDA0002557437220000033
Figure BDA0002557437220000034
Figure BDA0002557437220000035
Figure BDA0002557437220000036
Figure BDA0002557437220000037
Figure BDA0002557437220000038
Figure BDA0002557437220000039
Figure BDA0002557437220000041
Wherein, b2pqTo judge the matrix B2Element of (2), c2p、c2qNormalized evaluation scores of the dynamic property attributes of the p-th vehicle and the q-th vehicle which can be selected by the user respectively; b3pqTo judge the matrix B3Element of (2), c3p、c3qThe normalized evaluation scores of the p and q vehicles for the fuel economy attribute are selectable by the user respectively; b4pqTo judge the matrix B4Element of (2), c4p、c4qThe normalized evaluation scores of the safety attributes of the p-th vehicle and the q-th vehicle which can be selected by the user are respectively; b5pqTo judge the matrix B5Element of (2), c5p、c5qThe p-th vehicle and the q-th vehicle which can be selected by the user respectively have normalized evaluation scores on the comfort attribute; b6pqTo judge the matrix B6Element of (2), c6p、c6qThe normalized evaluation scores of the price attributes of the p-th vehicle and the q-th vehicle which can be selected by the user are respectively; and is
c2pMaximum vehicle speed of the user-selectable pth vehicle/maximum vehicle speed of all the user-selectable vehicles
c3pUser-selectable fuel consumption rate of pth vehicle/user-selectable maximum fuel consumption rate of fuel consumption rates of all vehicles
c4pUser selectable active safety rating for pth vehicle/user selectable active safety for all vehiclesMaximum active safety number in number of numbers
c5pHighest transmission rate of pth user-selectable vehicle/highest transmission rate of all user-selectable vehicles
c6pUser-selectable price of the pth vehicle/maximum of the user-selectable prices of all vehicles.
As a preferred embodiment of the present invention, the step 5 comprises the following specific processes:
normalizing the column vectors of the judgment matrix A, namely regarding the judgment matrix A as a combination of the column vectors, normalizing the column vectors of the judgment matrix A, and summing each column to obtain a weight vector c;
according to the same method as above, the judgment matrixes B are respectively aligned1、B2、B3、B4、B5And B6Normalizing the column vector to obtain a new judgment matrix
Figure BDA0002557437220000042
And
Figure BDA0002557437220000043
then, the new judgment matrix is used
Figure BDA0002557437220000044
And
Figure BDA0002557437220000045
adding according to the column vector to obtain the corresponding weight vector wi，i＝1,2,...,6；
User-selectable recommendation weight K of p-th vehicle in n vehiclespThe calculation method comprises the following steps:
Figure BDA0002557437220000046
wherein, wi(p) represents a weight vector wiC (i) represents the ith element of the weight vector c;
and finally, sequencing all the recommended weights from large to small, wherein the larger the recommended weight is, the vehicle is preferentially recommended to the user.
Compared with the prior art, the invention adopting the technical scheme has the following technical effects:
according to the invention, through the design based on the analytic hierarchy process, the step flow of vehicle selection and purchase is simplified while the characteristics of the user are fully considered, the vehicle purchase scheme suggestion is accurately and efficiently provided, the time is saved, and the program is simple and effective.
Drawings
FIG. 1 is an overall architecture diagram of the vehicle shopping recommendation method based on analytic hierarchy process of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As shown in fig. 1, an overall architecture diagram of the vehicle shopping recommendation method based on the analytic hierarchy process of the present invention includes the following specific steps:
step 1, web crawlers are adopted to collect web pages from the internet and acquire vehicle information, wherein the information comprises the following steps: vehicle type, vehicle brand, goodness, maximum vehicle speed, fuel consumption rate, equipped with several levels of active safety devices, maximum transmissibility, vehicle price, etc.
Step 2, obtaining the vehicle selection limit of the user and the preference degree of each attribute of the vehicle, wherein the vehicle selection limit comprises the vehicle type and the vehicle brand, and the vehicle type comprises: the car selection method comprises the steps that car, sedan, suv, mpv, sports car, pickup and the like are adopted, the car brands comprise all car brands sold in the market at present, a user can select the car without filling in car selection limit, if all car brands are selected by default, all attributes of the car comprise six attributes of the car such as attractiveness, dynamic property, fuel economy, safety, comfort and price, and the preference degree q of the user of each attributei(i 1,2.., 6) is represented by a number between 0 and 1, with the number 1 representing the most interesting vehicle attribute and the number 0 representing the most interesting vehicle attribute for that vehicleThe attribute is not of much interest, the influence of the attribute on the vehicle selected by the user can be not considered, meanwhile, in order to prevent the situation that all the attributes of the user are set to be 1, and the condition that each attribute of the vehicle is considered occurs, the sum of preference degrees of the vehicle attributes configurable by the user is limited to be 3, the preference degrees of the user on each attribute of the vehicle can not be filled, if the preference degrees of the user on each vehicle attribute are not filled, the preference degrees are all 0.5, and the preference degrees of the vehicle attributes configured by the user can be expressed by the following formula:
Figure BDA0002557437220000061
wherein:
q1 degree of user's attention to vehicle beauty
q2 Degree of user's intention to vehicle dynamics
q3 User's degree of attention to vehicle fuel economy
q4 Degree of user's attention to vehicle safety
q5 User's level of attention to vehicle comfort
q6 User's degree of interest in vehicle price
And preliminarily screening according to the vehicle selection limit of the user to form a selectable vehicle database meeting the user requirement, and providing a selection space for vehicle recommendation of the user: vehicle 1, vehicle 2, … …, vehicle n.
Step 3, recommending the vehicle of the user based on an analytic hierarchy process, and constructing a judgment matrix A according to preference degrees of the user to six attributes of the vehicle, such as the beauty, the dynamic property, the fuel economy, the safety, the comfort and the price of the vehicle, wherein the construction method comprises the following steps:
Figure BDA0002557437220000062
Figure BDA0002557437220000063
if the preference degrees of the six attributes are 0, the judgment matrix A is constructed again according to the above mode without considering the influence of the attributes on the user car purchasing.
Selection space provided according to the preliminary screening: the vehicle 1, the vehicle 2, and the … … vehicle n calculate again the judgment matrix B of each selection plan for each attributej(j ═ 1,2.. 6), which is calculated as follows:
each vehicle is judged to be matrix B with beautiful attribute 11：
Figure BDA0002557437220000071
Figure BDA0002557437220000072
Wherein: c1p(p ═ 1,2.. n) is the normalized evaluation score for each vehicle for attribute 1 (beauty), e.g., the beauty normalized evaluation score for vehicle 1 is the good rating for vehicle 1 divided by the highest good rating of all vehicles. The following table shows the normalized evaluation scores of the vehicle attributesNumber calculation method:
Figure BDA0002557437220000073
similarly, the judgment matrix of each vehicle for other attributes is calculated according to the method, and the judgment matrix B of each attribute of the alternative vehicle can be obtained1、B2、B3、B4、B5、B6。
Then, the matrix B is judged1、B2、B3、B4、B5And B6For each decision matrix B, considered as a combination of column vectors1、B2、B3、B4、B5And B6The column vector is normalized (i.e. the original column vector is divided by its modulus) to obtain a new judgment matrix
Figure BDA0002557437220000074
And
Figure BDA0002557437220000075
then, the new judgment matrix is used
Figure BDA0002557437220000076
And
Figure BDA0002557437220000077
adding according to the column vector to obtain the corresponding weight vector wi(i＝1,2,...6)；
Similarly, carrying out column vector normalization on the matrix A, and summing according to the column vectors to obtain a weight vector c;
recommendation weight K for p-th vehicle in 1 … … n vehicles selectable by userpThe calculation method comprises the following steps:
Figure BDA0002557437220000081
where (p) denotes the p-th element of the corresponding vector and (i) denotes the i-th element of the corresponding vector.
And finally, sequentially arranging the recommended weights from large to small, wherein the larger the weight is, the vehicle is preferentially recommended to the user.
The above embodiments are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modifications made on the basis of the technical scheme according to the technical idea of the present invention fall within the protection scope of the present invention.

Claims (6)

1. A vehicle shopping recommendation method based on an analytic hierarchy process is characterized by comprising the following steps:
step 1, collecting information of all vehicles on the Internet, wherein the information comprises vehicle types, vehicle brands, favorable rating, highest vehicle speed, fuel consumption rate, active safety series, maximum transfer rate and price;
step 2, obtaining the vehicle selection limit of the user and the preference degree of the user on each attribute of the vehicle, and screening all the vehicles collected through the Internet according to the vehicle selection limit of the user to obtain all the vehicles selectable by the user; the vehicle selection limit comprises a vehicle type and a vehicle brand, and each attribute of the vehicle comprises attractiveness, dynamic property, fuel economy, safety, comfort and price;
step 3, constructing a judgment matrix A according to preference degrees of the user on various attributes of the vehicle;
step 4, constructing a judgment matrix B of all vehicles selected by the user for the aesthetic property1Determination matrix B for dynamic property2Determination matrix B for fuel economy attributes3Judgment matrix B for safety attribute4A decision matrix B for comfort properties5And a judgment matrix B for the attribute of the price6；
Step 5, according to the judgment matrix A, B1、B2、B3、B4、B5And B6Calculating the weight vector of each vehicle which can be selected by the user for each attribute, finally calculating the recommendation weight of each vehicle which can be selected by the user, sequencing all the recommendation weights from large to small, and recommending the weightsThe larger the value, the priority is given to recommending the vehicle to the user.
2. The analytic hierarchy process-based vehicle shopping recommendation method of claim 1, wherein in the step 1, information of all vehicles on the internet is collected through a web crawler.
3. The analytic hierarchy process-based vehicle shopping recommendation method of claim 1, wherein the user preference level for each attribute of the vehicle in step 2 is expressed as:
Figure FDA0002557437210000011
wherein q isiIndicating the user's preference for the ith attribute of the vehicle.
4. The analytic hierarchy process-based vehicle shopping recommendation method of claim 1, wherein the judgment matrix a of step 3 is constructed as follows:
Figure FDA0002557437210000012
Figure FDA0002557437210000021
wherein, aijTo determine the elements of matrix A, qi、qjRespectively representing preference degrees of the user on ith and jth attributes of the vehicle;
if the preference degree of the user to a certain attribute of the vehicle is 0, the attribute is removed, and the remaining attribute is used for constructing the judgment matrix A, wherein the construction method is the same as the process.
5. The analytic hierarchy process-based vehicle shopping recommendation method of claim 1, wherein the step 4 is a judgment matrix of aesthetic attribute for all vehicles selectable by the userB1The construction of (a) is as follows:
Figure FDA0002557437210000022
Figure FDA0002557437210000023
wherein, b1pqTo judge the matrix B1Element of (2), c1p、c1qNormalized evaluation scores of the aesthetic attribute for the p-th and q-th vehicles selectable by the user, respectively, n is the number of all vehicles selectable by the user, and
c1puser-selectable best rating of pth vehicle/user-selectable maximum good rating of all vehicles
Similarly, all vehicle pair dynamics attribute judgment matrix B selectable by the user2Determination matrix B for fuel economy attributes3Judgment matrix B for safety attribute4A decision matrix B for comfort properties5And a judgment matrix B for the attribute of the price6Construction method of (1) and judgment matrix B for aesthetic property1Is constructed in the same way, then
Figure FDA0002557437210000024
Figure FDA0002557437210000025
Figure FDA0002557437210000026
Figure FDA0002557437210000031
Figure FDA0002557437210000032
Figure FDA0002557437210000033
Figure FDA0002557437210000034
Figure FDA0002557437210000035
Figure FDA0002557437210000036
Figure FDA0002557437210000037
Wherein, b2pqTo judge the matrix B2Element of (2), c2p、c2qNormalized evaluation scores of the dynamic property attributes of the p-th vehicle and the q-th vehicle which can be selected by the user respectively; b3pqTo judge the matrix B3Element of (2), c3p、c3qThe normalized evaluation scores of the p and q vehicles for the fuel economy attribute are selectable by the user respectively; b4pqTo judge the matrix B4Element of (2), c4p、c4qThe normalized evaluation scores of the safety attributes of the p-th vehicle and the q-th vehicle which can be selected by the user are respectively; b5pqTo judge the matrix B5Element of (2), c5p、c5qThe p-th vehicle and the q-th vehicle which can be selected by the user respectively have normalized evaluation scores on the comfort attribute; b6pqTo judge the matrix B6Element of (2), c6p、c6qThe normalized evaluation scores of the price attributes of the p-th vehicle and the q-th vehicle which can be selected by the user are respectively; and is
c2pMaximum vehicle speed of the pth user-selectable vehicle/maximum vehicle speed of all the user-selectable vehiclesVehicle speed
c3pUser-selectable fuel consumption rate of pth vehicle/user-selectable maximum fuel consumption rate of fuel consumption rates of all vehicles
c4pUser-selectable active safety progression for the pth vehicle/maximum active safety progression of all user-selectable active safety progression for all vehicles
c5pHighest transmission rate of pth user-selectable vehicle/highest transmission rate of all user-selectable vehicles
c6pUser-selectable price of the pth vehicle/maximum of the user-selectable prices of all vehicles.
6. The analytic hierarchy process-based vehicle shopping recommendation method of claim 1, wherein the step 5 comprises the following specific processes:
normalizing the column vectors of the judgment matrix A, namely regarding the judgment matrix A as a combination of the column vectors, normalizing the column vectors of the judgment matrix A, and summing each column to obtain a weight vector c;
according to the same method as above, the judgment matrixes B are respectively aligned1、B2、B3、B4、B5And B6Normalizing the column vector to obtain a new judgment matrix
Figure FDA0002557437210000041
And
Figure FDA0002557437210000042
then, the new judgment matrix is used
Figure FDA0002557437210000043
And
Figure FDA0002557437210000044
adding according to the column vector to obtain the corresponding weight vector wi，i＝1,2,...,6；
User-selectable recommendation weight K of p-th vehicle in n vehiclespThe calculation method comprises the following steps:
Figure FDA0002557437210000045
wherein, wi(p) represents a weight vector wiC (i) represents the ith element of the weight vector c;
and finally, sequencing all the recommended weights from large to small, wherein the larger the recommended weight is, the vehicle is preferentially recommended to the user.
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CN108932644A (en) * 2017-05-26 2018-12-04 车伯乐（北京）信息科技有限公司 A kind of user selects vehicle method, apparatus, equipment and computer-readable medium
CN108647857A (en) * 2018-04-12 2018-10-12 成都达拓智通科技有限公司 A kind of automobile brand clue allocating method based on analytic hierarchy process (AHP)
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