CN112270589A - Online shopping mall commodity recommendation system based on cloud computing big data analysis - Google Patents

Online shopping mall commodity recommendation system based on cloud computing big data analysis Download PDF

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CN112270589A
CN112270589A CN202011383096.6A CN202011383096A CN112270589A CN 112270589 A CN112270589 A CN 112270589A CN 202011383096 A CN202011383096 A CN 202011383096A CN 112270589 A CN112270589 A CN 112270589A
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shopping
consumed
time
baby
commodity
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姚小彦
束振祺
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Yancheng Zhijuan Network Technology Co ltd
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    • G06Q30/00—Commerce
    • G06Q30/06—Buying, selling or leasing transactions
    • G06Q30/0601—Electronic shopping [e-shopping]
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Abstract

The invention discloses an online shopping mall commodity recommendation system based on cloud computing big data analysis. According to the method, the birth date of the baby and all historical shopping orders within the set time period under the user account are obtained through the user login account, consumption coefficients corresponding to the number of commodities consumed by the baby in the age group and consumed by the same commodity type are counted by the baby, the total predicted consumption time corresponding to the number of commodities consumed by the baby in the last shopping order and consumed by the same commodity type is calculated, and therefore the recommended time point is obtained.

Description

Online shopping mall commodity recommendation system based on cloud computing big data analysis
Technical Field
The invention belongs to the technical field of commodity recommendation, and relates to an online shopping mall commodity recommendation system based on cloud computing big data analysis.
Background
With the rapid development of economy, online shopping is more and more accepted by the public, and has become an important channel for people to consume, such as mother and infant commodities, mothers often shop on the internet with commodities related to babies, such as milk powder, diaper and other baby consumption commodities, whereas in the current online shopping mall, in order to improve the experience and the desire of online shopping of consumers, a personalized recommendation system is used to recommend commodities and time points to consumers, for time point recommendation, for example, the online shopping mall can calculate the standard consumption time of the current age of the baby after consuming the quantity of commodities in the last purchase record according to the quantity of the commodities purchased in the last purchase record and the current age of the baby, and then according to the corresponding purchase time of the last purchase record, so as to obtain the recommended time point, and then recommended to the user when the recommendation time point is reached. However, the time point calculation mode is too ideal, and the actual situation of each user baby is not considered, so that the matching degree of the time point recommended by the online shopping mall and the user is not high, and the recommendation requirement of the user with high matching degree is difficult to meet.
Disclosure of Invention
In order to solve the problems, the invention provides an online shopping mall commodity recommendation system with high matching degree based on cloud computing big data analysis.
The purpose of the invention can be realized by the following technical scheme:
an online shopping mall commodity recommendation system based on cloud computing big data analysis comprises a user baby basic information acquisition module, a historical shopping order screening module, a historical shopping order classification analysis module, a database, a consumed commodity actual consumption duration counting module, an analysis server and a recommendation terminal;
the user baby basic information acquisition module is used for acquiring the birth date of a baby according to a login account of a user in an online shopping mall and sending the acquired baby birth date to the actual consumed duration counting module for the consumed commodities;
the historical shopping order screening module is used for acquiring all historical shopping orders under a user account according to a login account of the user in the online shopping mall, screening all historical shopping orders in a set time period from all historical shopping orders under the user account according to a preset screening starting time point and the set time period, numbering the screened historical shopping orders according to the sequence of shopping time, and sequentially marking the historical shopping orders as 1,2.
The database is used for storing the commodity types corresponding to the infant consumable commodity types and storing the standard consumption duration corresponding to the unit commodity quantity of the commodity types consumed by the age groups corresponding to the infant consumable commodity types;
the historical shopping order classification analysis module is used for acquiring order parameters of each numbered historical shopping order, wherein the acquired order parameters comprise shopping time, commodity types and commodity quantity, and the order parameters corresponding to each acquired historical shopping order form a historical shopping order parameter set Rw(rw1,rw2,...,rwi,...,rwn)，rwi is a numerical value corresponding to a w-th order parameter of the ith historical shopping order, w is an order parameter, w is p1, p2, p3, p1, p2 and p3 are respectively represented as shopping time, commodity type and commodity quantity, further a commodity type corresponding to each historical shopping order is extracted from a formed historical shopping order parameter set, the commodity type corresponding to each historical shopping order is compared with each other, whether the same commodity type exists is analyzed, if the same commodity type exists, the number of the same commodity type is counted, meanwhile, the same commodity type is counted and marked as 1,2 Forming a shopping parameter set G of the same commodity type according to the shopping time and the commodity quantity in each historical shopping orderuq(guq1,,guq2...,guqa,...,guqk)，gwqa is a numerical value corresponding to the u-th shopping parameter corresponding to the a-th historical shopping order corresponding to the q-th same commodity type, u is a shopping parameter, and u is h1, h2, h1, h2 are shopping time, commodity number,the historical shopping order classification analysis module compares the commodity type name of each same commodity type in each same commodity type shopping parameter set with the commodity type corresponding to each infant consumption commodity type stored in the database, the same commodity type corresponding to each infant consumption commodity type is screened out from the commodity types corresponding to each same commodity type and is marked as the same commodity type consumed by each infant, and then the same commodity types consumed by each infant are numbered and are sequentially marked as 1,2u z(gu z1,,gu z2...,gu za,...,gu zk)，gu za represents a numerical value corresponding to the u-th shopping parameter of the a-th historical shopping order corresponding to the z-th infant consuming the same commodity type, z represents the serial number of the infant consuming the same commodity type, and z is 1,2.. b.. l, and the historical shopping order classification analysis module sends the shopping parameter set of the infant consuming the same commodity type to the actual consumption duration counting module of the consumed commodity type;
the actual consumed duration counting module of the consumed commodities receives the birth date of the baby sent by the baby basic information acquisition module of the user, receives the shopping parameter set of the same commodity type consumed by the babies sent by the historical shopping order classification analysis module, extracts the shopping time corresponding to each historical shopping order from the received shopping parameter set of the same commodity type consumed by the babies, and forms a shopping time set G of the same commodity type consumed by the babiesh1 z(gh1 z1,,gh1 z2...,gh1 za,...,gh1 zk)，gh1 za represents the shopping time of the a-th historical shopping order corresponding to the z-th infant consuming the same commodity type, at this time, the shopping time set of the infant consuming the same commodity type is calculated by performing difference calculation on two adjacent items from the 2 nd item to obtain the shopping time difference corresponding to two adjacent historical shopping orders, and the adjacent shopping time difference set delta T of the infant consuming the same commodity type is formedz[Δtz1,Δtz2,...,Δtz(a-1),...,Δtz(k-1)],Δtz(a-1) the shopping time difference between the shopping time of the a-th historical shopping order corresponding to the z-th infant consuming the same commodity type and the shopping time of the a-1 th historical shopping order is expressed, and meanwhile, the actual consumed commodity duration counting module extracts the commodity quantity corresponding to each historical shopping order from the shopping parameter set of the same commodity type consumed by each infant to form a commodity quantity set G of the same commodity type consumed by each infanth2 z[gh2 z1,,gh2 z2...,gh2 z(a-1),gh2 za,...,gh2 z(k-1),gh2 zk]，gh2 za represents the number of the z-th baby consuming the same commodity type corresponding to the a-th historical shopping order, so that the actual consumption time length corresponding to the consumption unit commodity number in the shopping time difference of two adjacent shopping orders in the same commodity type consumed by each baby is counted according to the set of the adjacent shopping time differences of the same commodity type consumed by each baby and the set of the number of the commodities of the same commodity type consumed by each baby, and the baby age range corresponding to the shopping time of each historical shopping order corresponding to the same commodity type consumed by each baby is counted according to the birth date of the baby, thereby forming a baby age range set D for each baby consuming the same commodity typez(dz1,,dz2...,dza,...,dzk)，dza represents a baby age group corresponding to shopping time of a ith historical shopping order corresponding to the z th baby consuming the same commodity type, the consumed commodity type actual consumed time counting module sends each baby consuming the same commodity type shopping time set to the recommendation terminal, and simultaneously sends each baby consuming the same commodity type commodity quantity set, actual consumed time corresponding to consumed unit commodity quantity in the shopping time difference of two adjacent shopping orders in each baby consuming the same commodity type and each baby consuming the same commodity type baby age group set to the analysis server;
the analysis server receives the number set of the commodities with the same commodity type consumed by each baby and the consumption phase of each baby sent by the actual consumed time length counting module of the consumable commodity typeActual consumed time corresponding to the consumed unit commodity quantity in the shopping time difference of two adjacent shopping orders in the same commodity category and an infant age group set for each infant to consume the same commodity category are formed, and the actual consumed time corresponding to the consumed unit commodity quantity in the shopping time difference of two adjacent shopping orders in the same commodity category received by each infant forms an infant age group actual consumed time set T for each infant to consume the same commodity categoryz[Tz1,,Tz2...,Tza,...,Tz(k-1)]，Tza represents the actual consumption time corresponding to the unit commodity quantity consumed by the baby age group corresponding to the shopping time difference between the shopping time of the ith historical shopping order corresponding to the z th baby consuming the same commodity type and the shopping time of the (a-1) th historical shopping order, meanwhile, the analysis server compares the received baby age group set consuming the same commodity type by the babies with the standard consumption time corresponding to the unit commodity quantity consumed by the baby age group corresponding to the baby consumer type in the database, screens out the standard consumption time corresponding to the unit commodity quantity consumed by the corresponding age group in the baby age group set consuming the same commodity type by the babies, and forms the standard consumption time set T0 z[T0 z1,,T0 z2...,T0 za,...,T0 z(k-1),T0 zk],T0 za represents the standard consumption time corresponding to the consumption unit commodity quantity of the baby age group corresponding to the shopping time of the ith historical shopping order corresponding to the ith baby consumption same commodity type, the analysis server compares the actual consumption time set of the baby age group corresponding to the consumption of the same commodity type by each baby with the standard consumption time set of the baby age group corresponding to the consumption of the same commodity type by each baby, and further counts the consumption coefficient corresponding to the consumption unit commodity quantity of the baby age group corresponding to the consumption of the same commodity type by each baby, at the moment, the analysis server extracts the latest shopping order corresponding to the consumption of the same commodity type by each baby from the standard consumption time set of the baby age group corresponding to the consumption of the same commodity type by each babyThe shopping time corresponds to the standard consumption time corresponding to the unit commodity quantity of the commodity type consumed by the infant age group, namely T0 zk, extracting the commodity quantity of the latest shopping order corresponding to the same commodity type consumed by each baby, namely g, from the commodity quantity set of the same commodity type consumed by each babyh2 zk, counting the total standard consumed duration corresponding to the number of the commodities of the same commodity type consumed by each infant for the latest shopping order by integrating the above analysis servers, and sending the total predicted consumed duration corresponding to the number of the commodities of the same commodity type consumed by each infant for the latest shopping order to the recommendation terminal according to the consumption coefficient corresponding to the number of the commodities of the age group consumed by each infant for the same commodity type consumed by the same infant for the unit consumed and the total standard consumed duration corresponding to the number of the commodities of the same commodity type consumed by each infant for the latest shopping order;
the recommending terminal receives the shopping time set of the same commodity type consumed by each baby sent by the consumed commodity type actual consumed time counting module, receives the predicted total consumed time corresponding to the commodity quantity of the latest shopping order consumed by each baby of the same commodity type sent by the analyzing server, and further extracts the shopping time of the latest shopping order consumed by each baby, namely g, from the received shopping time set of the same commodity type consumed by each babyh1 zAnd k, adding the total predicted consumption time corresponding to the number of the commodities of the same commodity type consumed by the infants and the number of the commodities of the latest shopping order consumed by the infants to obtain a recommended time point corresponding to the consumption of the commodity number of the latest shopping order of the same commodity type consumed by the infants, tracking the current time point by the recommending terminal in real time, and recommending the commodities of the corresponding commodity type to the user at the recommended time point in time if the current time point reaches the recommended time point.
Further, the preset screening start time point should not be later than the birth date of the infant.
Further, the historical shopping order screening module screens out each historical shopping order in a set time period from all historical shopping orders under the user account according to a preset screening starting time point and the set time period, and the specific screening process comprises the following steps:
s1: counting the end time point of the screening according to a preset screening start time point and a set time period;
s2: acquiring shopping time corresponding to all historical shopping orders under the user account;
s3: and comparing the obtained shopping time corresponding to all historical shopping orders under the user account with the screening starting time point and the screening ending time point in sequence, and reserving the historical shopping orders with the shopping time between the screening starting time point and the screening ending time point, wherein the reserved historical shopping orders are the historical shopping orders in the set time period.
Further, the commodity types corresponding to the infant consumable commodity types comprise milk powder, paper diapers and infant supplementary food.
Further, the calculation formula of the actual consumption time corresponding to the consumption of the unit quantity of the unit commodity in the shopping time difference of the two adjacent shopping orders in the same commodity type consumed by each baby is
Figure BDA0002810137560000061
Tza represents the actual consumption time length corresponding to the unit commodity quantity consumed by the age group of the baby corresponding to the shopping time difference between the shopping time of the ith historical shopping order corresponding to the z th baby consuming the same commodity type and the shopping time of the (a-1) th historical shopping order, and gh2 z(a-1) the number of items in the a-1 th historical shopping order corresponding to the same item type consumed by the z-th baby.
Further, the formula for calculating the consumption coefficient corresponding to the number of the unit commodities consumed by the infants of the age group corresponding to the same commodity type is
Figure BDA0002810137560000071
ηz(a-1) shopping time for the ith baby consuming the same commodity category corresponding to the (a-1) th historical shopping order corresponds to the baby's age groupConsumption coefficient, T, corresponding to the number of units of merchandise consumed0 z(a-1) the standard consumption duration corresponding to the unit commodity quantity consumed by the age group of the baby corresponding to the shopping time of the a-1 th historical shopping order corresponding to the z th baby consuming the same commodity type is expressed.
Further, the calculation formula of the standard total consumption time corresponding to the number of the commodities of the same commodity type consumed by the infants for the latest shopping order is T0 zkGeneral assembly＝T0 zk*gh2 zk，T0 zkGeneral assemblyThe standard total elapsed time period corresponding to the number of items consumed by the latest shopping order for the z-th infant consuming the same item category.
Further, the calculation formula of the total consumption time forecast corresponding to the number of the same baby consuming the same commodity type consuming the latest shopping order is
Figure BDA0002810137560000072
TzkPredictionThe predicted total elapsed time period corresponding to the number of items in the latest purchase order for the z-th infant to consume the same item type.
The invention has the following beneficial effects:
the invention obtains the birth date of the baby of the user and all historical shopping orders in the set time period under the account of the user through the login account of the user, constructs a shopping parameter set for each baby to consume the same commodity type according to each historical shopping order, constructs an actual consumed time period set for each baby to consume the same commodity type in the baby age period, further counts the consumption coefficient corresponding to the unit commodity quantity consumed by each baby in the baby age period corresponding to the same commodity type, and calculates the total predicted consumed time period corresponding to the commodity quantity of the latest shopping order consumed by each baby in the same commodity type by combining the above steps, thereby obtaining the recommended time point for recommendation, realizing the intelligent recommendation of the recommended time point, improving the matching degree with the requirement of the user by the recommended time point, and overcoming the defect that the recommended time point of the online mall is not high in matching degree with the user, the consumption experience of the user is improved, and the recommendation requirement of the user with high matching degree is met.
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The invention is further illustrated by means of the attached drawings, but the embodiments in the drawings do not constitute any limitation to the invention, and for a person skilled in the art, other drawings can be obtained on the basis of the following drawings without inventive effort.
FIG. 1 is a block diagram of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the online shopping mall commodity recommendation system based on cloud computing big data analysis comprises a user baby basic information acquisition module, a historical shopping order screening module, a historical shopping order classification analysis module, a database, a consumed commodity actual consumption duration statistic module, an analysis server and a recommendation terminal, wherein the user baby basic information acquisition module is connected with the consumed commodity actual consumption duration statistic module, the historical shopping order screening module is connected with the historical shopping order classification analysis module, the historical shopping order classification analysis module is connected with the consumed commodity actual consumption duration statistic module, the consumed commodity actual consumption duration statistic module is respectively connected with the analysis server and the recommendation terminal, and the analysis server is connected with the recommendation terminal.
The user baby basic information acquisition module is used for acquiring the birth date of a baby according to a login account of a user in an online shopping mall and sending the acquired baby birth date to the consumed commodity actual consumption duration counting module.
The historical shopping order screening module is used for acquiring all historical shopping orders under a user account according to a login account of the user in an online shopping mall, screening all historical shopping orders in a set time period from all historical shopping orders under the user account according to a preset screening starting time point and the set time period, wherein the preset screening starting time point is not later than the birth date of an infant, and the specific screening process for screening all the historical shopping orders in the set time period comprises the following steps:
s1: counting the end time point of the screening according to a preset screening start time point and a set time period;
s2: acquiring shopping time corresponding to all historical shopping orders under the user account;
s3: comparing the obtained shopping time corresponding to all historical shopping orders under the user account with the screening starting time point and the screening ending time point in sequence, reserving the historical shopping orders with the shopping time between the screening starting time point and the screening ending time point, wherein the reserved historical shopping orders are the historical shopping orders in the set time period, and numbering the screened historical shopping orders according to the sequence of the shopping time, wherein the sequence is marked as 1,2.
The database is used for storing the commodity types corresponding to the infant consumption commodity types, wherein the commodity types corresponding to the infant consumption commodity types comprise milk powder, paper diapers and infant complementary food, and the standard consumption duration corresponding to the unit commodity quantity of the commodity types consumed by the infant consumption commodity types in all age periods is stored.
The historical shopping order classification analysis module is used for acquiring order parameters of each numbered historical shopping order, wherein the acquired order parameters comprise shopping time, commodity types and commodity quantity, and the order parameters corresponding to each acquired historical shopping order form a historical shopping order parameter set Rw(rw1,rw2,...,rwi,...,rwn)，rwi is a numerical value corresponding to the w-th order parameter of the ith historical shopping order, w is an order parameter, w is p1, p2, p3, p1, p2 and p3 are shopping time, commodity type and commodity quantity respectively, and furtherExtracting the commodity type corresponding to each historical shopping order from the formed historical shopping order parameter set, comparing the commodity types with each other, analyzing whether the same commodity type exists or not, counting the number of the same commodity type if the same commodity type exists, numbering the same commodity type, marking the same commodity type as 1,2.. j.. m in sequence, further counting the historical shopping order number corresponding to each same commodity type as q1, q2... qa... qk, qa representing the a-th historical shopping order corresponding to the q-th same commodity type, obtaining the shopping time and the commodity number of each historical shopping order corresponding to each same commodity type from the historical shopping order parameter set according to the historical shopping order number corresponding to each same commodity type, and further forming the same commodity type shopping parameter set Guq(guq1,,guq2...,guqa,...,guqk)，gwqa is a numerical value corresponding to the u-th shopping parameter of the qth historical shopping order corresponding to the qth same commodity type, u is a shopping parameter, u is h1, h2, h1 and h2 are shopping time and commodity quantity, the historical shopping order classification analysis module compares the commodity type name of each same commodity type in the shopping parameter set of each same commodity type with the commodity type corresponding to each infant consumer type stored in the database, the same commodity type corresponding to each infant consumer type is selected from the commodity types corresponding to each same commodity type and is recorded as the same commodity type consumed by each infant, and the same commodity type consumed by each infant is numbered and is sequentially marked as 1,2u z(gu z1,,gu z2...,gu za,...,gu zk)，gu za represents a numerical value corresponding to the u-th shopping parameter of the a-th historical shopping order corresponding to the z-th infant consuming the same commodity type, z represents a serial number of the infant consuming the same commodity type, and z is 1,2.
In the embodiment, the same commodity type consumed by each baby is screened out from the database according to the commodity type name corresponding to each same commodity type from the shopping parameter sets of the same commodity type, so that the shopping parameter sets of the same commodity type consumed by each baby are constructed, and a basis is provided for later counting the actual consumed duration corresponding to the consumed unit commodity quantity in the shopping time difference of two adjacent shopping orders of the same commodity type consumed by each baby.
The actual consumed duration counting module of the consumed commodities receives the birth date of the baby sent by the baby basic information acquisition module of the user, receives the shopping parameter set of the same commodity type consumed by the babies sent by the historical shopping order classification analysis module, extracts the shopping time corresponding to each historical shopping order from the received shopping parameter set of the same commodity type consumed by the babies, and forms a shopping time set G of the same commodity type consumed by the babiesh1 z(gh1 z1,,gh1 z2...,gh1 za,...,gh1 zk)，gh1 za represents the shopping time of the a-th historical shopping order corresponding to the z-th infant consuming the same commodity type, at this time, the shopping time set of the infant consuming the same commodity type is calculated by performing difference calculation on two adjacent items from the 2 nd item to obtain the shopping time difference corresponding to two adjacent historical shopping orders, and the adjacent shopping time difference set delta T of the infant consuming the same commodity type is formedz[Δtz1,Δtz2,...,Δtz(a-1),...,Δtz(k-1)],Δtz(a-1) the shopping time difference between the shopping time of the a-th historical shopping order corresponding to the z-th infant consuming the same commodity type and the shopping time of the a-1 th historical shopping order is expressed, and meanwhile, the actual consumed commodity duration counting module extracts the commodity quantity corresponding to each historical shopping order from the shopping parameter set of the same commodity type consumed by each infant to form a commodity quantity set G of the same commodity type consumed by each infanth2 z[gh2 z1,,gh2 z2...,gh2 z(a-1),gh2 za,...,gh2 z(k-1),gh2 zk]，gh2 za represents the commodity quantity of the ith historical shopping order corresponding to the z th infant consuming the same commodity type, so that the actual consumption time length corresponding to the unit commodity quantity consumed in the shopping time difference of two adjacent shopping orders in the same commodity type consumed by each infant is counted according to the adjacent shopping time difference set of the same commodity type consumed by each infant and the commodity quantity set of the same commodity type consumed by each infant
Figure BDA0002810137560000111
Tza represents the actual consumption time length corresponding to the unit commodity quantity consumed by the age group of the baby corresponding to the shopping time difference between the shopping time of the ith historical shopping order corresponding to the z th baby consuming the same commodity type and the shopping time of the (a-1) th historical shopping order, and gh2 z(a-1) the number of products in the a-1 th historical shopping order corresponding to the z-th infant consuming the same product type is shown, and the age bracket of the infant corresponding to the shopping time of each historical shopping order corresponding to the same product type consumed by each infant is counted according to the birth date of the infant, so that an infant age bracket set D for each infant consuming the same product type is formedz(dz1,,dz2...,dza,...,dzk)，dza represents the age bracket of the baby corresponding to the shopping time of the a-th historical shopping order corresponding to the z-th baby consuming the same commodity category, the consumed commodity actual consumed time counting module sends the shopping time set of the same commodity category consumed by each baby to the recommending terminal, and simultaneously sends the actual consumed time corresponding to the consumed unit commodity number in the commodity quantity set of the same commodity category consumed by each baby, the shopping time difference of two adjacent shopping orders in the same commodity category consumed by each baby and the age bracket set of the same commodity category consumed by each baby to the analyzing server.
The analysis server receives the commodity quantity set of the same commodity type consumed by each baby sent by the actual consumed time counting module of the consumed commodity type and the corresponding consumed unit commodity quantity in the shopping time difference of two adjacent shopping orders in the same commodity type consumed by each babyThe actual consumed time length and the infant age group set of the same commodity type consumed by each infant are combined, and the actual consumed time length corresponding to the consumed unit commodity quantity in the shopping time difference of the two adjacent shopping orders in the same commodity type consumed by each infant forms the actual consumed time length set T of the infant age group of the same commodity type consumed by each infantz[Tz1,,Tz2...,Tza,...,Tz(k-1)]，Tza represents the actual consumption time corresponding to the unit commodity quantity consumed by the baby age group corresponding to the shopping time difference between the shopping time of the ith historical shopping order corresponding to the z th baby consuming the same commodity type and the shopping time of the (a-1) th historical shopping order, meanwhile, the analysis server compares the received baby age group set consuming the same commodity type by the babies with the standard consumption time corresponding to the unit commodity quantity consumed by the baby age group corresponding to the baby consumer type in the database, screens out the standard consumption time corresponding to the unit commodity quantity consumed by the corresponding age group in the baby age group set consuming the same commodity type by the babies, and forms the standard consumption time set T0 z[T0 z1,,T0 z2...,T0 za,...,T0 z(k-1),T0 zk],T0 za represents the standard consumption time corresponding to the consumption unit commodity quantity of the baby age group corresponding to the shopping time of the ith historical shopping order corresponding to the ith baby consumption same commodity type, the analysis server compares the actual consumption time set of the baby age group of the same baby consumption commodity type with the standard consumption time set of the baby age group of the same baby consumption commodity type, and then counts the consumption coefficient corresponding to the consumption unit commodity quantity of the baby age group of the same baby consumption commodity type
Figure BDA0002810137560000131
ηz(a-1) shopping time for the a-1 th historical shopping order for the z th infant consuming the same merchandise category corresponds to the infant's ageConsumption coefficient, T, corresponding to the number of unit commodities consumed by segments0 z(a-1) indicating that the standard consumed time corresponding to the unit commodity quantity consumed by the baby age group corresponding to the shopping time of the ith baby consuming the same commodity type corresponding to the (a-1) th historical shopping order is represented by the z-th baby, and at the moment, the analysis server extracts the standard consumed time corresponding to the unit commodity quantity consumed by the baby age group corresponding to the shopping time of the latest shopping order corresponding to the same commodity type consumed by the baby from the standard consumed time set of the baby age group corresponding to the baby consuming the same commodity type, namely T0 zk, extracting the commodity quantity of the latest shopping order corresponding to the same commodity type consumed by each baby, namely g, from the commodity quantity set of the same commodity type consumed by each babyh2 zk, thereby counting the total standard consumption time T corresponding to the number of the commodities of the same commodity type consumed by the infants and the latest shopping order consumed by the infants0 zkGeneral assembly＝T0 zk*gh2 zk，T0 zkGeneral assemblyThe standard total consumption time corresponding to the number of commodities for the latest shopping order consumed by the z-th infant consuming the same commodity type is expressed, and the predicted total consumption time corresponding to the number of commodities for the latest shopping order consumed by each infant consuming the same commodity type is counted by integrating the consumption coefficients corresponding to the number of commodities consumed by the infant age group consuming the same commodity type by the analysis server and the standard total consumption time corresponding to the number of commodities for the latest shopping order consumed by each infant consuming the same commodity type
Figure BDA0002810137560000132
TzkPredictionAnd the total consumption time which is expressed as the total consumption time corresponding to the number of the commodities of the latest shopping order of the same commodity type consumed by the z-th baby is sent to the recommendation terminal.
The consumption coefficient corresponding to the number of unit commodities consumed by the baby age group corresponding to the same commodity type consumed by each baby is calculated in the embodiment, the comparison condition between the actual consumption duration of the unit commodities consumed by the baby age group corresponding to the same commodity type consumed by each baby and the standard consumption duration is visually shown, the closer the calculated consumption coefficient is to 1, the closer the actual consumption duration of the unit commodities consumed by the baby age group and the standard consumption duration is, and a reliable prediction basis is provided for later calculation of the total predicted consumption duration corresponding to the number of the latest shopping orders consumed by each baby consuming the same commodity type.
The recommending terminal receives the shopping time set of the same commodity type consumed by each baby sent by the consumed commodity type actual consumed time counting module, receives the predicted total consumed time corresponding to the commodity quantity of the latest shopping order consumed by each baby of the same commodity type sent by the analyzing server, and further extracts the shopping time of the latest shopping order consumed by each baby, namely g, from the received shopping time set of the same commodity type consumed by each babyh1 zAnd k, adding the total predicted consumption duration corresponding to the number of the commodities of the same commodity type consumed by the babies and the latest shopping order to obtain a recommended time point corresponding to the consumption of the number of the commodities of the same commodity type consumed by the babies and the latest shopping order, wherein the recommended time point improves the matching degree with the user requirement, overcomes the defect that the matching degree of the current online shopping recommendation time point and the user is not high, improves the user consumption experience, meets the high-matching recommendation requirement of the user, and the recommending terminal tracks the current time point in real time.
The foregoing is merely exemplary and illustrative of the present invention and various modifications, additions and substitutions may be made by those skilled in the art to the specific embodiments described without departing from the scope of the invention as defined in the following claims.

Claims (8)

1. The utility model provides an online shopping mall commodity recommendation system based on big data analysis of cloud, its characterized in that: the infant management system comprises a user infant basic information acquisition module, a historical shopping order screening module, a database, a historical shopping order classification analysis module, an actual consumption duration counting module of a consumable commodity class, an analysis server and a recommendation terminal;
the user baby basic information acquisition module is used for acquiring the birth date of a baby according to a login account of a user in an online shopping mall and sending the acquired baby birth date to the actual consumed duration counting module for the consumed commodities;
the historical shopping order screening module is used for acquiring all historical shopping orders under a user account according to a login account of the user in the online shopping mall, screening all historical shopping orders in a set time period from all historical shopping orders under the user account according to a preset screening starting time point and the set time period, numbering the screened historical shopping orders according to the sequence of shopping time, and sequentially marking the historical shopping orders as 1,2.
The database is used for storing the commodity types corresponding to the infant consumable commodity types and storing the standard consumption duration corresponding to the unit commodity quantity of the commodity types consumed by the age groups corresponding to the infant consumable commodity types;
the historical shopping order classification analysis module is used for acquiring order parameters of each numbered historical shopping order, wherein the acquired order parameters comprise shopping time, commodity types and commodity quantity, and the order parameters corresponding to each acquired historical shopping order form a historical shopping order parameter set Rw(rw1,rw2,...,rwi,...,rwn)，rwi represents a numerical value corresponding to a w-th order parameter of the ith historical shopping order, w represents an order parameter, w is p1, p2, p3, p1, p2 and p3 respectively represent shopping time, commodity type and commodity quantity, further a commodity type corresponding to each historical shopping order is extracted from a formed historical shopping order parameter set, the commodity types are compared with each other, whether the same commodity type exists or not is analyzed, if the same commodity type exists, the number of the same commodity type is counted, meanwhile, the same commodity type is counted, the number is sequentially marked as 1,2The historical shopping order numbers corresponding to the commodity types can be recorded as q1, q2... qa... qk, qa is expressed as the a-th historical shopping order corresponding to the q-th same commodity type, and at this time, the shopping time and the commodity number in each historical shopping order corresponding to each same commodity type are obtained from the historical shopping order parameter set according to the historical shopping order numbers corresponding to each same commodity type, so that a shopping parameter set G of each same commodity type is formeduq(guq1,,guq2...,guqa,...,guqk)，gwqa is a numerical value corresponding to the u-th shopping parameter of the qth historical shopping order corresponding to the qth same commodity type, u is a shopping parameter, u is h1, h2, h1 and h2 are shopping time and commodity quantity, the historical shopping order classification analysis module compares the commodity type name of each same commodity type in the shopping parameter set of each same commodity type with the commodity type corresponding to each infant consumer type stored in the database, the same commodity type corresponding to each infant consumer type is selected from the commodity types corresponding to each same commodity type and is recorded as the same commodity type consumed by each infant, and the same commodity type consumed by each infant is numbered and is sequentially marked as 1,2u z(gu z1,,gu z2...,gu za,...,gu zk)，gu za represents a numerical value corresponding to the u-th shopping parameter of the a-th historical shopping order corresponding to the z-th infant consuming the same commodity type, z represents the serial number of the infant consuming the same commodity type, and z is 1,2.. b.. l, and the historical shopping order classification analysis module sends the shopping parameter set of the infant consuming the same commodity type to the actual consumption duration counting module of the consumed commodity type;
the actual consumed duration counting module for the consumed commodities receives the birth date of the baby sent by the baby basic information acquisition module of the user, receives shopping parameter sets of the same commodity type consumed by the babies sent by the historical shopping order classification analysis module, and extracts each historical shopping parameter set of the same commodity type consumed by the babiesShopping time corresponding to shopping order forms shopping time set G for each infant consuming the same commodity typeh1 z(gh1 z1,,gh1 z2...,gh1 za,...,gh1 zk)，gh1 za represents the shopping time of the a-th historical shopping order corresponding to the z-th infant consuming the same commodity type, at this time, the shopping time set of the infant consuming the same commodity type is calculated by performing difference calculation on two adjacent items from the 2 nd item to obtain the shopping time difference corresponding to two adjacent historical shopping orders, and the adjacent shopping time difference set delta T of the infant consuming the same commodity type is formedz[Δtz1,Δtz2,...,Δtz(a-1),...,Δtz(k-1)],Δtz(a-1) the shopping time difference between the shopping time of the a-th historical shopping order corresponding to the z-th infant consuming the same commodity type and the shopping time of the a-1 th historical shopping order is expressed, and meanwhile, the actual consumed commodity duration counting module extracts the commodity quantity corresponding to each historical shopping order from the shopping parameter set of the same commodity type consumed by each infant to form a commodity quantity set G of the same commodity type consumed by each infanth2 z[gh2 z1,,gh2 z2...,gh2 z(a-1),gh2 za,...,gh2 z(k-1),gh2 zk]，gh2 za represents the number of the z-th baby consuming the same commodity type corresponding to the a-th historical shopping order, so that the actual consumption time length corresponding to the consumption unit commodity number in the shopping time difference of two adjacent shopping orders in the same commodity type consumed by each baby is counted according to the set of the adjacent shopping time differences of the same commodity type consumed by each baby and the set of the number of the commodities of the same commodity type consumed by each baby, and the baby age range corresponding to the shopping time of each historical shopping order corresponding to the same commodity type consumed by each baby is counted according to the birth date of the baby, thereby forming a baby age range set D for each baby consuming the same commodity typez(dz1,,dz2...,dza,...,dzk)，dza is expressed as the z-th baby infantConsuming infant age groups corresponding to shopping time of the a-th historical shopping order corresponding to the same commodity type, sending a shopping time set of the same commodity type consumed by each infant to a recommendation terminal by a consumed commodity actual consumption time counting module, and sending an actual consumption time corresponding to a unit commodity quantity consumed in a commodity quantity set of the same commodity type consumed by each infant, a shopping time difference between every two adjacent shopping orders in the same commodity type consumed by each infant and an infant age group set of the same commodity type consumed by each infant to an analysis server;
the analysis server receives the commodity quantity set of the same commodity type consumed by each baby, the actual consumed time corresponding to the consumed commodity quantity in the shopping time difference of two adjacent shopping orders in the same commodity type consumed by each baby and the baby age group set of the same commodity type consumed by each baby, which are sent by the actual consumed time counting module of the same commodity type consumed by each baby, and forms the actual consumed time set T of the baby age group in which each baby consumes the same commodity type by the actual consumed time corresponding to the consumed commodity quantity in the shopping time difference of two adjacent shopping orders in the same commodity type consumed by each babyz[Tz1,,Tz2...,Tza,...,Tz(k-1)]，Tza represents the actual consumption time corresponding to the unit commodity quantity consumed by the baby age group corresponding to the shopping time difference between the shopping time of the ith historical shopping order corresponding to the z th baby consuming the same commodity type and the shopping time of the (a-1) th historical shopping order, meanwhile, the analysis server compares the received baby age group set consuming the same commodity type by the babies with the standard consumption time corresponding to the unit commodity quantity consumed by the baby age group corresponding to the baby consumer type in the database, screens out the standard consumption time corresponding to the unit commodity quantity consumed by the corresponding age group in the baby age group set consuming the same commodity type by the babies, and forms the standard consumption time set T0 z[T0 z1,,T0 z2...,T0 za,...,T0 z(k-1),T0 zk],T0 za represents the standard consumption time corresponding to the unit commodity quantity consumed by the baby age group corresponding to the shopping time of the ith historical shopping order corresponding to the ith baby consumption same commodity type, the analysis server compares the actual consumption time set of the baby age group of each baby consumption same commodity type with the standard consumption time set of the baby age group of each baby consumption same commodity type, and further counts the consumption coefficient corresponding to the unit commodity quantity consumed by the baby age group corresponding to each baby consumption same commodity type, at the moment, the analysis server extracts the standard consumption time corresponding to the unit commodity quantity consumed by the baby age group corresponding to the shopping time of the latest shopping order corresponding to each baby consumption same commodity type from the standard consumption time set of the baby age group of each baby consumption same commodity type, namely T0 zk, extracting the commodity quantity of the latest shopping order corresponding to the same commodity type consumed by each baby, namely g, from the commodity quantity set of the same commodity type consumed by each babyh2 zk, counting the total standard consumed duration corresponding to the number of the commodities of the same commodity type consumed by each infant for the latest shopping order by integrating the above analysis servers, and sending the total predicted consumed duration corresponding to the number of the commodities of the same commodity type consumed by each infant for the latest shopping order to the recommendation terminal according to the consumption coefficient corresponding to the number of the commodities of the age group consumed by each infant for the same commodity type consumed by the same infant for the unit consumed and the total standard consumed duration corresponding to the number of the commodities of the same commodity type consumed by each infant for the latest shopping order;
the recommending terminal receives the shopping time set of the same commodity type consumed by each baby sent by the consumed commodity type actual consumed time counting module, receives the predicted total consumed time corresponding to the commodity quantity of the latest shopping order consumed by each baby of the same commodity type sent by the analyzing server, and further extracts the shopping time of the latest shopping order consumed by each baby, namely g, from the received shopping time set of the same commodity type consumed by each babyh1 zk, to be consumed by the same infantsAnd adding the total predicted consumption time corresponding to the commodity quantity of the latest shopping order consumed by the commodity types to obtain a recommended time point corresponding to the consumption of the commodity quantity of the latest shopping order of the same commodity type by each infant, tracking the current time point by the recommending terminal in real time, and recommending commodities of corresponding commodity types to the user at the recommended time point in time if the current time point reaches the recommended time point.
2. The online shopping mall commodity recommendation system based on cloud computing big data analysis as claimed in claim 1, wherein: the preset screening start time point should be no later than the birth date of the infant.
3. The online shopping mall commodity recommendation system based on cloud computing big data analysis as claimed in claim 1, wherein: the historical shopping order screening module screens all historical shopping orders in a set time period from all historical shopping orders under the user account according to a preset screening starting time point and the set time period, and the specific screening process comprises the following steps:
s1: counting the end time point of the screening according to a preset screening start time point and a set time period;
s2: acquiring shopping time corresponding to all historical shopping orders under the user account;
s3: and comparing the obtained shopping time corresponding to all historical shopping orders under the user account with the screening starting time point and the screening ending time point in sequence, and reserving the historical shopping orders with the shopping time between the screening starting time point and the screening ending time point, wherein the reserved historical shopping orders are the historical shopping orders in the set time period.
4. The online shopping mall commodity recommendation system based on cloud computing big data analysis as claimed in claim 1, wherein: the commodity types corresponding to the infant consumable commodity types comprise milk powder, paper diapers and infant complementary food.
5. The online shopping mall commodity recommendation system based on cloud computing big data analysis as claimed in claim 1, wherein: the calculation formula of the actual consumed time corresponding to the unit commodity quantity in the shopping time difference of two adjacent shopping orders in the same commodity type consumed by each baby is
Figure FDA0002810137550000061
Tza represents the actual consumption time length corresponding to the unit commodity quantity consumed by the age group of the baby corresponding to the shopping time difference between the shopping time of the ith historical shopping order corresponding to the z th baby consuming the same commodity type and the shopping time of the (a-1) th historical shopping order, and gh2 z(a-1) the number of items in the a-1 th historical shopping order corresponding to the same item type consumed by the z-th baby.
6. The online shopping mall commodity recommendation system based on cloud computing big data analysis as claimed in claim 1, wherein: the consumption coefficient corresponding to the consumption unit commodity number of the age groups of the babies consuming the same commodity type is calculated by the formula
Figure FDA0002810137550000062
ηz(a-1) a consumption coefficient, T, expressed as the number of commodities consumed by the infant age group corresponding to the shopping time of the ith-1 historical shopping order corresponding to the z th infant consuming the same commodity type0 z(a-1) the standard consumption duration corresponding to the unit commodity quantity consumed by the age group of the baby corresponding to the shopping time of the a-1 th historical shopping order corresponding to the z th baby consuming the same commodity type is expressed.
7. The online shopping mall commodity recommendation system based on cloud computing big data analysis as claimed in claim 1, wherein: the standard consumption total corresponding to the commodity quantity of the latest shopping order consumed by the same commodity type consumed by the babiesThe calculation formula of the time length is T0 zkGeneral assembly＝T0 zk*gh2 zk，T0 zkGeneral assemblyThe standard total elapsed time period corresponding to the number of items consumed by the latest shopping order for the z-th infant consuming the same item category.
8. The online shopping mall commodity recommendation system based on cloud computing big data analysis as claimed in claim 1, wherein: the calculation formula of the total consumption time corresponding to the commodity quantity of the latest shopping order consumed by the same commodity type consumed by the babies is
Figure FDA0002810137550000071
TzkPredictionThe predicted total elapsed time period corresponding to the number of items in the latest purchase order for the z-th infant to consume the same item type.
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CN113034238B (en) * 2021-04-13 2022-04-15 优优祝福(北京)科技有限公司 Commodity brand feature extraction and intelligent recommendation management method based on electronic commerce platform transaction
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CN116521936A (en) * 2023-06-30 2023-08-01 云南师范大学 Course recommendation method and device based on user behavior analysis and storage medium
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