CN111143692A - Medicated diet recommendation electronic commerce platform based on user demands - Google Patents
Medicated diet recommendation electronic commerce platform based on user demands Download PDFInfo
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Abstract
The invention discloses a medicated diet recommendation e-commerce platform based on user requirements, which comprises a registration login module, a history acquisition module, a shop pre-recommendation module, a database, a shop information acquisition module, a data receiving module, a data processing module, a master control module and a recommendation display module; the system comprises a registration login module, a history record acquisition module, a retrieval module, a shop pre-recommendation module and a shop pre-recommendation module, wherein the registration login module is used for registering and logging in a platform by a user, the history record acquisition module is used for acquiring the past retrieval record information of an old user, the retrieval module is used for carrying out medicated diet retrieval by using a new user of the platform for the first time and acquiring the retrieval information of the new user, the shop pre-recommendation module is used for matching pre-recommended medicated diet shops according to the acquired past retrieval record information, and the shop pre-recommendation module is also used for matching pre; the invention has the beneficial effects that: the use and the login are more convenient, and the corresponding shop information can be more accurately recommended to the user.
Description
Technical Field
The invention relates to a protective device, in particular to a medicated diet recommendation electronic commerce platform based on user requirements, and belongs to the technical field of protective device application.
Background
The medicated diet is a delicious food with certain color, fragrance, taste and shape, which is prepared by matching traditional Chinese medicines with certain foods with medicinal values according to a medicated diet formula under the guidance of traditional Chinese medicine, culinary science and nutriology theories and adopting unique diet cooking technology and modern scientific methods in China. It is a product of traditional Chinese medical knowledge combined with cooking experience.
The Chinese patent with publication number CN107527274A discloses an information recommendation platform, device, system, method and terminal, relating to the technical field of computers. The information recommendation platform comprises: a receiver configured to obtain communication identifications of contacts of a recommended user; the processor is configured to search a user identifier corresponding to the communication identifier of the contact person, and acquire shopping information corresponding to the contact person according to the searched user identifier; and the transmitter is configured to recommend the shopping information corresponding to the contact person to the recommended user. The information recommendation platform provided by the invention can recommend shopping information for the recommended user according to the shopping condition of the contact of the recommended user, so that the click rate and the order conversion rate of the user can be improved, and the recommendation efficiency is improved; but the use and the login are not convenient enough, and differential push is not carried out on the new and the old users.
When the existing recommendation platform is used, the recommended information is not accurate enough, a user cannot accurately acquire recommended content meeting the mind of the user, and a new user cannot acquire accurate recommendation meeting the mind of the user when using the platform for the first time in the actual use process.
Disclosure of Invention
The invention aims to solve the problems that the recommendation information is not accurate enough, the user cannot accurately acquire the recommendation content meeting the mind of the user when the existing recommendation platform is used, the accurate recommendation meeting the mind of the new user cannot be acquired when the new user uses the platform for the first time in the actual use process, and meanwhile, the existing recommendation platform is not convenient to use and log in, and certain influence is brought to the use of the recommendation platform, and the medicated diet recommendation electronic commerce platform based on the user requirements is provided.
The purpose of the invention can be realized by the following technical scheme: the medicated diet recommendation e-commerce platform based on user requirements comprises a registration login module, a history acquisition module, a shop pre-recommendation module, a database, a shop information acquisition module, a data receiving module, a data processing module, a master control module and a recommendation display module;
the system comprises a registration login module, a history record acquisition module, a retrieval module, a shop pre-recommendation module and a shop pre-recommendation module, wherein the registration login module is used for a user to register and log in the platform, the history record acquisition module is used for acquiring the past retrieval record information of an old user, the retrieval module is used for carrying out medicated diet retrieval by using a new user of the platform for the first time and acquiring the retrieval information of the new user, the shop pre-recommendation module is used for matching pre-recommended medicated diet shops according to the acquired past retrieval record information, the shop pre-recommendation module is also used for matching pre-recommended medicated diet shops according to the retrieval information of the new user;
the method comprises the following steps that a medicinal food store information user obtains store information of a recommended medicinal food store, wherein the store information comprises the monthly average sales volume, the product average price, the good evaluation times, the medium evaluation times, the poor evaluation times and the pre-recommended times of the medicinal food store;
the data receiving module is used for receiving store information of recommended medical food stores and sending the received store information of the recommended medical food stores to the data processing module, the data processing module is used for processing the store information of the received recommended medical food stores and processing final recommended medical food stores and ranking information of the medical food stores, and the master control module controls the recommended display module to display the content of the recommended medical food stores and rank the medical food stores after the store information of the recommended medical food stores is generated.
Further, the method comprises the following steps: the specific process for using the registration and login module to log in and register is as follows:
the method comprises the following steps: when a user registers by using the registration and login module for the first time, the user is required to set an account number, a password, a mobile phone number and face information, wherein the account number is any combination of pure numbers or combination of capital letters and numbers, and the password is any combination of numbers or combination of capital letters and numbers;
step two: when the user logs in the platform by using the selected account password, the error times of continuously inputting the password exceed three times, namely the user directly jumps to a face recognition login interface, and meanwhile, mobile phone login options are displayed on the face recognition login interface.
Further, the method comprises the following steps: the specific process of the user using the retrieval module to retrieve the product is as follows:
a retrieval sample plate is displayed on a retrieval interface of the retrieval module, the content of the retrieval sample plate is 'medicated food type + taste + price interval', and a user can input corresponding retrieval content in a retrieval frame of the retrieval module according to the retrieval sample plate to obtain an accurate retrieval result.
Further, the method comprises the following steps: the specific recommendation process of the shop pre-recommendation module is as follows:
the method comprises the following steps: when the user is the user using the platform for the first time, the store pre-recommendation module pulls the retrieval content input by the user on the retrieval module, and the medicated food store information matched with the retrieval content is called from the database according to the retrieval content of the user;
step two: when the user uses the platform twice or more, the shop pre-recommendation module pulls the previous search record and browsing record, extracts the number of times of each search content in the previous search record of the user, marks the number of times as Ki, i is … … n, and marks the browsing time of each product browsed by the user as Pi, i is … … n;
step three: ranking the times Ki of each search content from most to least according to the number of retrieval times, and extracting the first three medicated food shops with the largest retrieval times for recommendation;
step four: ranking the browsing duration Pi of each product browsed by the user from long to short according to the browsing duration, and extracting three first three products with the longest browsing duration for recommendation.
Further, the method comprises the following steps: the specific processing process of the final recommended medicated food shop and the specific processing process of the medicated food shop ranking information are as follows:
the method comprises the following steps: the method comprises the following steps of analyzing the monthly average sales volume and the product average price of the medicinal food store to calculate a first recommendation score of the medicinal food store, wherein the specific processing process of the first recommendation score is as follows:
s1: marking the monthly average sales volume of the medicated food store as Q1 and the average price of the products as Q2;
s2: when the monthly average sales Q1 exceeds a preset value, the monthly average sales Q1 is scored as A1, when the monthly average sales Q1 is within the preset score, the monthly average sales Q1 is scored as A2, when the monthly average sales Q1 is less than the preset value, the monthly average sales Q3838 is scored as A3, A1 is greater than A2 is greater than A3, and the final sales score of the medicated food shop is marked as W1;
s3: when the average product price Q2 is greater than the preset value, the score is B1, when the average product price Q2 is within the preset score, the score is B2, when the average product price Q2 is less than the preset value, the score is B3, B1 is greater than B2 is greater than B3, and the price score of the medicinal food store is marked as W2;
s4: in order to highlight the importance of the sales score, a final sales score W1 is now assigned a correction value u1, a price score W2 is assigned a correction value u2, u1 > u2, u1+ u2 is 1;
s5: obtaining a final first recommendation score T1T1 through a formula W1 u1+ W2 u2 ═ T1;
step two: and then processing the good evaluation times, the medium evaluation times and the poor evaluation times of the medicinal food stores to obtain a second recommendation score, wherein the specific processing process of the second recommendation score is as follows:
s1: marking the basic rating of the medicated food store as D, marking the number of times of good rating of the medicated food store as G1, marking the number of times of medium rating of the medicated food store as G2, and marking the number of times of bad rating of the medicated food store as G3;
s2: when the number of good scores G1 of the meal stores exceeds a preset value, namely the basic score is increased by H1, when the number of good scores G1 of the meal stores is within a preset value range, namely the basic score is increased by H2, when the number of good scores G1 of the meal stores is less than a preset value, namely the H3 is increased, H1 is greater than H2 is greater than H3, and the final good scores are marked as V1;
s3: when the number G2 of the medical and dietary stores exceeds a preset value, namely the basic score is increased by C1, when the number G2 of the medical and dietary stores is within a preset value range, namely the basic score is increased by C2, when the number G2 of the medical and dietary stores is less than a preset value, namely the basic score is increased by C3, and the final rating is marked as V2;
s4: when the poor rating times G3 of the diet store exceed a preset value, the basic score is deducted by Y1, when the poor rating times G3 of the diet store are within a preset value range, the basic score is deducted by Y2, when the poor rating times G3 of the diet store are less than a preset value, the Y3 is deducted, and the final poor rating deduction mark is V3;
s5: in order to highlight the importance of the medium score, a correction value r1 of the final good score V1, a correction value r2 of the final medium score V2, a correction value r3 of the final poor score V3, r2 > r3 > r1, and r2+ r3+ r1 are given as 1;
s6: obtaining a second recommended score T2 through a formula V1 × r1+ V2 × r2+ V3 × r3 ═ T2;
step three: and analyzing the recommendation times of the medicinal food shops to obtain a third recommendation score, wherein the specific processing process of the third recommendation score is as follows: marking the pre-recommendation times of the medicinal food shop as M, when the M is larger than a preset value, the pre-recommendation times are marked as L1, when the M is within the range of the preset value, the pre-recommendation times are marked as L2, when the M is smaller than the preset value, the pre-recommendation times are marked as L3, L1 is larger than L2 and larger than L3, and marking the final score of a third recommendation as T3;
step four: in order to highlight the importance of the first recommendation score and the third recommendation score, a correction value m1 is given to the first recommendation score T1, a correction value m2 is given to the second recommendation score T2, a correction value m3 is given to the third recommendation score T3, m1+ m2+ m3 is equal to 1, and m2 is larger than m3 and larger than m 1;
step five: by the formula T1 m1+ T2 m2+ T3 m3 ═ TPush awayGet the final recommendation score TPush away；
Step six: the final recommendation scores T of all the medicated food shopsPush awayRanking according to the rank from big to small to obtain the final ranking information of the medicinal food shop;
step seven: extracting a final recommendation score TPush awayThe top three corresponding medicated food shops are the final recommended medicated food shops.
Compared with the prior art, the invention has the beneficial effects that:
1. the invention can provide different pre-recommendation information for a new user and an old user who are used for the first time, so that the user can obtain better information recommendation of the medicated food shop, when the user is the user who uses the platform for the first time, the shop pre-recommendation module pulls the retrieval content input by the user on the retrieval module, the medicated food shop information matched with the retrieval content is called from the database according to the retrieval content of the user, when the user uses the platform for two times or more, the shop pre-recommendation module pulls the previous search record and browsing record, extracts the times of each search content in the previous search record of the user, marks the times of each search content as Ki and i as … … n, and marks the browsing duration of each product browsed by the user as Pi. Ranking the times Ki of each search content from more to less according to the number of retrieval times, extracting the first three medicated food shops with the largest retrieval times for recommendation, ranking the browsing duration Pi of each product browsed by a user from long to short according to the browsing duration, extracting the three first three products with the longest browsing duration for recommendation, and enabling the first user using the platform to obtain better use experience so that the platform is more worthy of popularization and use;
2. meanwhile, the invention collects store information of the medical food stores, a first recommendation score of the medical food stores is calculated by analyzing the monthly average sales volume and the product average price of the medical food stores, then the times of good evaluation, the times of medium evaluation and the times of poor evaluation of the medical food stores are processed to obtain a second recommendation score, then the times of recommendation of the medical food stores are analyzed to obtain a third recommendation score, and meanwhile, in order to highlight the importance of the first recommendation score and the third recommendation score, a correction value m1 is given to the first recommendation score T1, a correction value m2 is given to the second recommendation score T2, and a correction value m3 is given to the third recommendation score T3The value m3, and T1 m1+ T2 m2+ T3 m3Push awayGet the final recommendation score TPush awayThe final recommendation scores T of all the medicated food shopsPush awayRanking according to the rank from big to small to obtain the final ranking information of the medicinal food shop, and extracting the final recommendation score TPush awayThe top three corresponding medicinal food shops are the final recommended medicinal food shops, and the arrangement can lead the platform to recommend medicinal food shops with better quality for users, thus saving the trouble that the users frequently search in the platform and leading the platform to be more convenient to use;
3. meanwhile, when the platform has a plurality of different login modes and the user uses the registration and login module for the first time to register, the user is required to set an account number, a password, a mobile phone number and face information, wherein the account number is any combination of pure numbers or combination of upper and lower case letters and numbers, the password is any combination of numbers or combination of upper and lower case letters and numbers, when the user logs in by using the registration and login module for the second time, can select any one of account password login, face recognition login and mobile phone login to carry out platform login, when the user uses the selected account password to log in the platform and continuously inputs the password for more than three times, namely, directly jumping to a face recognition login interface, and simultaneously displaying a mobile phone login option on the face recognition login interface, the setting ensures that the platform is more convenient to log in, and saves the trouble that the user needs to modify the password before logging in again after forgetting the password.
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In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is a block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood 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 medicated diet recommendation e-commerce platform based on user needs includes a registration login module, a history acquisition module, a store pre-recommendation module, a database, a store information acquisition module, a data receiving module, a data processing module, a master control module and a recommendation display module;
the system comprises a registration login module, a history record acquisition module, a retrieval module, a shop pre-recommendation module and a shop pre-recommendation module, wherein the registration login module is used for a user to register and log in the platform, the history record acquisition module is used for acquiring the past retrieval record information of an old user, the retrieval module is used for carrying out medicated diet retrieval by using a new user of the platform for the first time and acquiring the retrieval information of the new user, the shop pre-recommendation module is used for matching pre-recommended medicated diet shops according to the acquired past retrieval record information, the shop pre-recommendation module is also used for matching pre-recommended medicated diet shops according to the retrieval information of the new user;
the method comprises the following steps that a medicinal food store information user obtains store information of a recommended medicinal food store, wherein the store information comprises the monthly average sales volume, the product average price, the good evaluation times, the medium evaluation times, the poor evaluation times and the pre-recommended times of the medicinal food store;
the data receiving module is used for receiving store information of recommended medicated food stores and sending the received store information of the recommended medicated food stores to the data processing module, the data processing module is used for processing the store information of the recommended medicated food stores and processing final recommended medicated food stores and ranking information of the medicated food stores, after the store information of the recommended medicated food stores is generated, the master control module controls the recommended display module to display the content of the recommended medicated food stores and rank the medicated food stores, and the recommended display module can be a display screen of a computer, a display screen of a user intelligent mobile terminal and the like.
The specific process for using the registration and login module to log in and register is as follows:
the method comprises the following steps: when a user registers by using the registration and login module for the first time, the user is required to set an account number, a password, a mobile phone number and face information, wherein the account number is any combination of pure numbers or combination of capital letters and numbers, and the password is any combination of numbers or combination of capital letters and numbers;
step two: when the user logs in the platform by using the selected account password, the error times of continuously inputting the password exceed three times, namely the user directly jumps to a face recognition login interface, and meanwhile, mobile phone login options are displayed on the face recognition login interface.
The specific process of the user using the retrieval module to retrieve the product is as follows:
a retrieval sample plate is displayed on a retrieval interface of the retrieval module, the content of the retrieval sample plate is 'medicated food type + taste + price interval', and a user can input corresponding retrieval content in a retrieval frame of the retrieval module according to the retrieval sample plate to obtain an accurate retrieval result.
The specific recommendation process of the shop pre-recommendation module is as follows:
the method comprises the following steps: when the user is the user using the platform for the first time, the store pre-recommendation module pulls the retrieval content input by the user on the retrieval module, and the medicated food store information matched with the retrieval content is called from the database according to the retrieval content of the user;
step two: when the user uses the platform twice or more, the shop pre-recommendation module pulls the previous search record and browsing record, extracts the number of times of each search content in the previous search record of the user, marks the number of times as Ki, i is … … n, and marks the browsing time of each product browsed by the user as Pi, i is … … n;
step three: ranking the times Ki of each search content from most to least according to the number of retrieval times, and extracting the first three medicated food shops with the largest retrieval times for recommendation;
step four: ranking the browsing duration Pi of each product browsed by the user from long to short according to the browsing duration, and extracting three first three products with the longest browsing duration for recommendation.
The specific processing process of the final recommended medicated food shop and the specific processing process of the medicated food shop ranking information are as follows:
the method comprises the following steps: the method comprises the following steps of analyzing the monthly average sales volume and the product average price of the medicinal food store to calculate a first recommendation score of the medicinal food store, wherein the specific processing process of the first recommendation score is as follows:
s1: marking the monthly average sales volume of the medicated food store as Q1 and the average price of the products as Q2;
s2: when the monthly average sales Q1 exceeds a preset value, the monthly average sales Q1 is scored as A1, when the monthly average sales Q1 is within the preset score, the monthly average sales Q1 is scored as A2, when the monthly average sales Q1 is less than the preset value, the monthly average sales Q3838 is scored as A3, A1 is greater than A2 is greater than A3, and the final sales score of the medicated food shop is marked as W1;
s3: when the average product price Q2 is greater than the preset value, the score is B1, when the average product price Q2 is within the preset score, the score is B2, when the average product price Q2 is less than the preset value, the score is B3, B1 is greater than B2 is greater than B3, and the price score of the medicinal food store is marked as W2;
s4: in order to highlight the importance of the sales score, a final sales score W1 is now assigned a correction value u1, a price score W2 is assigned a correction value u2, u1 > u2, u1+ u2 is 1;
s5: obtaining a final first recommendation score T1T1 through a formula W1 u1+ W2 u2 ═ T1;
step two: and then processing the good evaluation times, the medium evaluation times and the poor evaluation times of the medicinal food stores to obtain a second recommendation score, wherein the specific processing process of the second recommendation score is as follows:
s1: marking the basic rating of the medicated food store as D, marking the number of times of good rating of the medicated food store as G1, marking the number of times of medium rating of the medicated food store as G2, and marking the number of times of bad rating of the medicated food store as G3;
s2: when the number of good scores G1 of the meal stores exceeds a preset value, namely the basic score is increased by H1, when the number of good scores G1 of the meal stores is within a preset value range, namely the basic score is increased by H2, when the number of good scores G1 of the meal stores is less than a preset value, namely the H3 is increased, H1 is greater than H2 is greater than H3, and the final good scores are marked as V1;
s3: when the number G2 of the medical and dietary stores exceeds a preset value, namely the basic score is increased by C1, when the number G2 of the medical and dietary stores is within a preset value range, namely the basic score is increased by C2, when the number G2 of the medical and dietary stores is less than a preset value, namely the basic score is increased by C3, and the final rating is marked as V2;
s4: when the poor rating times G3 of the diet store exceed a preset value, the basic score is deducted by Y1, when the poor rating times G3 of the diet store are within a preset value range, the basic score is deducted by Y2, when the poor rating times G3 of the diet store are less than a preset value, the Y3 is deducted, and the final poor rating deduction mark is V3;
s5: in order to highlight the importance of the medium score, a correction value r1 of the final good score V1, a correction value r2 of the final medium score V2, a correction value r3 of the final poor score V3, r2 > r3 > r1, and r2+ r3+ r1 are given as 1;
s6: obtaining a second recommended score T2 through a formula V1 × r1+ V2 × r2+ V3 × r3 ═ T2;
step three: and analyzing the recommendation times of the medicinal food shops to obtain a third recommendation score, wherein the specific processing process of the third recommendation score is as follows: marking the pre-recommendation times of the medicinal food shop as M, when the M is larger than a preset value, the pre-recommendation times are marked as L1, when the M is within the range of the preset value, the pre-recommendation times are marked as L2, when the M is smaller than the preset value, the pre-recommendation times are marked as L3, L1 is larger than L2 and larger than L3, and marking the final score of a third recommendation as T3;
step four: in order to highlight the importance of the first recommendation score and the third recommendation score, a correction value m1 is given to the first recommendation score T1, a correction value m2 is given to the second recommendation score T2, a correction value m3 is given to the third recommendation score T3, m1+ m2+ m3 is equal to 1, and m2 is larger than m3 and larger than m 1;
step five: by the formula T1 m1+ T2 m2+ T3 m3 ═ TPush awayGet the final recommendation score TPush away；
Step six: the final recommendation scores T of all the medicated food shopsPush awayRanking according to the rank from big to small to obtain the final ranking information of the medicinal food shop;
step seven: extracting a final recommendation score TPush awayHighest heightThe first three corresponding medicated food shops are the final recommended medicated food shops.
When the invention is used, when a user registers by using the registration and login module for the first time, the user is required to set an account number, a password, a mobile phone number and face information, the account number is any combination of pure numbers or combination of capital letters and numbers, the password is any combination of numbers or combination of capital letters and numbers, when the user logs in by using the registration and login module for the second time, the platform login can be performed by selecting any one of the mode of account number password login, face recognition login and mobile phone login, when the user logs in the platform by selecting the account number and the password for more than three times, the user directly jumps to the face recognition login interface, and simultaneously, the option of mobile phone login is displayed on the face recognition login interface, the setting ensures that the platform is more convenient to log in, and saves the trouble that the user can log in again after the user forgets the password and needs to modify the password, the platform can provide different pre-recommendation information for a new user and an old user who use the platform for the first time, so that the user can obtain better information recommendation of the medicated food shop, when the user uses the platform for the first time, the shop pre-recommendation module pulls retrieval contents input by the user on the retrieval module, the medicated food shop information matched with the retrieval contents is called from the database according to the retrieval contents of the user, when the user uses the platform for twice or more times, the shop pre-recommendation module pulls the previous search records and browsing records, extracts the times of each search content in the previous search records of the user, marks the times of each search content as Ki and i as … … n, and marks the browsing duration of each product browsed by the user as Pi. Ranking the times Ki of each search content from more than to less according to the number of retrieval times, extracting the first three medicated food shops with the largest retrieval times for recommendation, ranking the browsing duration Pi of each product browsed by a user from longer to shorter according to the length of the browsing duration, and extracting the first three products with the longest browsing duration for recommendationThe store information of the food stores is obtained by analyzing the monthly average sales volume and the product average price of the food stores to calculate a first recommendation score of the food stores, processing the times of good evaluation, the times of medium evaluation and the times of poor evaluation of the food stores to obtain a second recommendation score, analyzing the times of recommendation of the food stores to obtain a third recommendation score, and simultaneously giving a correction value m1 to the first recommendation score T1, a correction value m2 to the second recommendation score T2, and a correction value m3 to the third recommendation score T3 to highlight the importance of the first recommendation score and the third recommendation score, and obtaining the food stores with the formula T1 m1+ T2 m2+ T3 m 3-T3683 m3Push awayGet the final recommendation score TPush awayThe final recommendation scores T of all the medicated food shopsPush awayRanking according to the rank from big to small to obtain the final ranking information of the medicinal food shop, and extracting the final recommendation score TPush awayThe medical food shop corresponding to the first three highest medical food shops is the final recommended medical food shop, the platform can recommend the medical food shop with better quality for the user, the trouble that the user frequently searches in the platform is eliminated, and the platform is more convenient to use.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (5)
1. The medicated diet recommendation e-commerce platform based on user requirements is characterized by comprising a registration login module, a historical record acquisition module, a shop pre-recommendation module, a database, a shop information acquisition module, a data receiving module, a data processing module, a master control module and a recommendation display module;
the system comprises a registration login module, a history record acquisition module, a retrieval module, a shop pre-recommendation module and a shop pre-recommendation module, wherein the registration login module is used for a user to register and log in the platform, the history record acquisition module is used for acquiring the past retrieval record information of an old user, the retrieval module is used for carrying out medicated diet retrieval by using a new user of the platform for the first time and acquiring the retrieval information of the new user, the shop pre-recommendation module is used for matching pre-recommended medicated diet shops according to the acquired past retrieval record information, the shop pre-recommendation module is also used for matching pre-recommended medicated diet shops according to the retrieval information of the new user;
the method comprises the following steps that a medicinal food store information user obtains store information of a recommended medicinal food store, wherein the store information comprises the monthly average sales volume, the product average price, the good evaluation times, the medium evaluation times, the poor evaluation times and the pre-recommended times of the medicinal food store;
the data receiving module is used for receiving store information of recommended medical food stores and sending the received store information of the recommended medical food stores to the data processing module, the data processing module is used for processing the store information of the received recommended medical food stores and processing final recommended medical food stores and ranking information of the medical food stores, and the master control module controls the recommended display module to display the content of the recommended medical food stores and rank the medical food stores after the store information of the recommended medical food stores is generated.
2. The user demand-based medicated diet recommendation e-commerce platform of claim 1, wherein the specific process for logging in and registering using the registration and login module is as follows:
the method comprises the following steps: when a user registers by using the registration and login module for the first time, the user is required to set an account number, a password, a mobile phone number and face information, wherein the account number is any combination of pure numbers or combination of capital letters and numbers, and the password is any combination of numbers or combination of capital letters and numbers;
step two: when the user logs in the platform by using the selected account password, the error times of continuously inputting the password exceed three times, namely the user directly jumps to a face recognition login interface, and meanwhile, mobile phone login options are displayed on the face recognition login interface.
3. The user demand-based medicated diet recommendation e-commerce platform as claimed in claim 1, wherein the specific process of the user using the search module for product search is as follows:
a retrieval sample plate is displayed on a retrieval interface of the retrieval module, the content of the retrieval sample plate is 'medicated food type + taste + price interval', and a user can input corresponding retrieval content in a retrieval frame of the retrieval module according to the retrieval sample plate to obtain an accurate retrieval result.
4. The user demand-based medicated diet recommendation e-commerce platform of claim 1, wherein the specific recommendation process of the shop pre-recommendation module is as follows:
the method comprises the following steps: when the user is the user using the platform for the first time, the store pre-recommendation module pulls the retrieval content input by the user on the retrieval module, and the medicated food store information matched with the retrieval content is called from the database according to the retrieval content of the user;
step two: when the user uses the platform twice or more, the shop pre-recommendation module pulls the previous search record and browsing record, extracts the number of times of each search content in the previous search record of the user, marks the number of times as Ki, i is … … n, and marks the browsing time of each product browsed by the user as Pi, i is … … n;
step three: ranking the times Ki of each search content from most to least according to the number of retrieval times, and extracting the first three medicated food shops with the largest retrieval times for recommendation;
step four: ranking the browsing duration Pi of each product browsed by the user from long to short according to the browsing duration, and extracting three first three products with the longest browsing duration for recommendation.
5. The medicated food recommendation e-commerce platform based on user needs as claimed in claim 1, wherein the specific processing of the final recommended medicated food store and the specific processing of the medicated food store ranking information are as follows:
the method comprises the following steps: the method comprises the following steps of analyzing the monthly average sales volume and the product average price of the medicinal food store to calculate a first recommendation score of the medicinal food store, wherein the specific processing process of the first recommendation score is as follows:
s1: marking the monthly average sales volume of the medicated food store as Q1 and the average price of the products as Q2;
s2: when the monthly average sales Q1 exceeds a preset value, the monthly average sales Q1 is scored as A1, when the monthly average sales Q1 is within the preset score, the monthly average sales Q1 is scored as A2, when the monthly average sales Q1 is less than the preset value, the monthly average sales Q3838 is scored as A3, A1 is greater than A2 is greater than A3, and the final sales score of the medicated food shop is marked as W1;
s3: when the average product price Q2 is greater than the preset value, the score is B1, when the average product price Q2 is within the preset score, the score is B2, when the average product price Q2 is less than the preset value, the score is B3, B1 is greater than B2 is greater than B3, and the price score of the medicinal food store is marked as W2;
s4: in order to highlight the importance of the sales score, a final sales score W1 is now assigned a correction value u1, a price score W2 is assigned a correction value u2, u1 > u2, u1+ u2 is 1;
s5: obtaining a final first recommendation score T1T1 through a formula W1 u1+ W2 u2 ═ T1;
step two: and then processing the good evaluation times, the medium evaluation times and the poor evaluation times of the medicinal food stores to obtain a second recommendation score, wherein the specific processing process of the second recommendation score is as follows:
s1: marking the basic rating of the medicated food store as D, marking the number of times of good rating of the medicated food store as G1, marking the number of times of medium rating of the medicated food store as G2, and marking the number of times of bad rating of the medicated food store as G3;
s2: when the number of good scores G1 of the meal stores exceeds a preset value, namely the basic score is increased by H1, when the number of good scores G1 of the meal stores is within a preset value range, namely the basic score is increased by H2, when the number of good scores G1 of the meal stores is less than a preset value, namely the H3 is increased, H1 is greater than H2 is greater than H3, and the final good scores are marked as V1;
s3: when the number G2 of the medical and dietary stores exceeds a preset value, namely the basic score is increased by C1, when the number G2 of the medical and dietary stores is within a preset value range, namely the basic score is increased by C2, when the number G2 of the medical and dietary stores is less than a preset value, namely the basic score is increased by C3, and the final rating is marked as V2;
s4: when the poor rating times G3 of the diet store exceed a preset value, the basic score is deducted by Y1, when the poor rating times G3 of the diet store are within a preset value range, the basic score is deducted by Y2, when the poor rating times G3 of the diet store are less than a preset value, the Y3 is deducted, and the final poor rating deduction mark is V3;
s5: in order to highlight the importance of the medium score, a correction value r1 of the final good score V1, a correction value r2 of the final medium score V2, a correction value r3 of the final poor score V3, r2 > r3 > r1, and r2+ r3+ r1 are given as 1;
s6: obtaining a second recommended score T2 through a formula V1 × r1+ V2 × r2+ V3 × r3 ═ T2;
step three: and analyzing the recommendation times of the medicinal food shops to obtain a third recommendation score, wherein the specific processing process of the third recommendation score is as follows: marking the pre-recommendation times of the medicinal food shop as M, when the M is larger than a preset value, the pre-recommendation times are marked as L1, when the M is within the range of the preset value, the pre-recommendation times are marked as L2, when the M is smaller than the preset value, the pre-recommendation times are marked as L3, L1 is larger than L2 and larger than L3, and marking the final score of a third recommendation as T3;
step four: in order to highlight the importance of the first recommendation score and the third recommendation score, a correction value m1 is given to the first recommendation score T1, a correction value m2 is given to the second recommendation score T2, a correction value m3 is given to the third recommendation score T3, m1+ m2+ m3 is equal to 1, and m2 is larger than m3 and larger than m 1;
step five: by the formula T1 m1+ T2 m2+ T3 m3 ═ TPush awayGet the final recommendation score TPush away；
Step six: the final recommendation scores T of all the medicated food shopsPush awayRanking according to the rank from big to small to obtain the final ranking information of the medicinal food shop;
step seven: extracting a final recommendation score TPush awayThe top three corresponding medicated food shops are the final recommended medicated food shops.
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