CN113781174B - Recommendation method and system for improving preference commodity obtained by consumer - Google Patents
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Abstract
The invention discloses a recommendation method and a recommendation system for improving a consumer to obtain favorite commodities, wherein the method comprises the following steps: obtaining first historical consumption commodity information of a first user; obtaining first favorite commodity information according to the first historical consumption commodity information; constructing a first complementary commodity topological structure by taking the first favorite commodity information as a central node, wherein the first complementary commodity topological structure is a star-shaped topological structure; obtaining multi-complementary commodity information according to the first complementary commodity topological structure; acquiring real-time consumption data of the first favorite commodity; inputting the real-time consumption data of the first favorite commodity into an intelligent matching module to obtain first output information; and generating a first recommendation scheme according to the first output information. The technical problems that in the prior art, the traditional commodity recommending method cannot accurately meet the use requirement of a user, so that recommending performance is not perfect enough, and user satisfaction and experience cannot be effectively improved are solved.
Description
Technical Field
The invention relates to the field of electronic commerce, in particular to a recommendation method and a recommendation system for improving the acquisition of favorite commodities by consumers.
Background
With the high-speed development of the Internet and the popularization of intelligent terminals in China, the arrival of a big data age is quickened, the network environment is increasingly perfected, the electronic commerce mode is gradually mature, the consumption form of people starts to gradually change from off-line physical stores to on-line shopping websites, and the gradual expansion of the electronic commerce market scale provides richer choices for users. However, in the face of such various commodity information, how users can quickly and accurately select the required commodities becomes a topic of interest to users and electronic commerce.
However, in the process of implementing the technical scheme of the embodiment of the present application, the present inventors have found that the above-mentioned technology has at least the following technical problems:
in the prior art, the conventional commodity recommendation method cannot meet the use requirements of users, has imperfect recommendation performance, and cannot effectively improve the user satisfaction and experience.
Disclosure of Invention
The embodiment of the application solves the technical problems that the traditional commodity recommending method cannot accurately meet the use requirement of a user, the recommending performance is not perfect enough and the user satisfaction and experience cannot be effectively improved in the prior art by providing the recommending method and the system for improving the favorite commodity obtained by the consumer, and achieves the technical effects that the complementary structural analysis is carried out on the favorite commodity of the user, the intelligent matching is carried out according to the consumption complementarity of the user, and the accuracy and the intelligence of commodity recommending are effectively improved.
In view of the above problems, an embodiment of the present application provides a recommendation method and system for improving a consumer to obtain favorite goods.
In a first aspect, an embodiment of the present application provides a recommendation method for promoting a consumer to obtain a favorite commodity, where the method is applied to a recommendation system for promoting the consumer to obtain a favorite commodity, the system includes an intelligent matching module, and the method includes: obtaining first historical consumption commodity information of a first user; obtaining first favorite commodity information according to the first historical consumption commodity information; constructing a first complementary commodity topological structure by taking the first favorite commodity information as a central node, wherein the first complementary commodity topological structure is a star-shaped topological structure; obtaining multi-complementary commodity information according to the first complementary commodity topological structure; acquiring real-time consumption data of the first favorite commodity; inputting the real-time consumption data of the first favorite commodity into the intelligent matching module, and obtaining first output information according to the intelligent matching module, wherein the first output information is the matching consumption data of the multiple complementary commodities; and generating a first recommendation scheme according to the first output information.
On the other hand, the application also provides a recommendation system for improving the acquisition of favorite commodities by consumers, which comprises the following steps: the first obtaining unit is used for obtaining first historical consumption commodity information of a first user; the second obtaining unit is used for obtaining first favorite commodity information according to the first historical consumption commodity information; the first construction unit is used for constructing a first complementary commodity topological structure by taking the first favorite commodity information as a central node, wherein the first complementary commodity topological structure is a star-shaped topological structure; the third obtaining unit is used for obtaining multi-complementary commodity information according to the first complementary commodity topological structure; a fourth obtaining unit, configured to obtain real-time consumption data of the first favorite commodity; the first input unit is used for inputting the real-time consumption data of the first favorite commodity into the intelligent matching module, and obtaining first output information according to the intelligent matching module, wherein the first output information is the matching consumption data of the multiple complementary commodities; the first generation unit is used for generating a first recommendation scheme according to the first output information.
In a third aspect, the present application provides a recommendation system for promoting a consumer to obtain a favorite commodity, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of the first aspect when executing the program.
One or more technical solutions provided in the embodiments of the present application at least have the following technical effects or advantages:
the method comprises the steps of obtaining historical consumption commodity information of a user to analyze favorite commodities, further constructing a first complementary commodity topological structure by taking the favorite commodities as a central node, obtaining multi-complementary commodity information according to the first complementary commodity topological structure, further obtaining real-time consumption data of the favorite commodities of the user, inputting the real-time consumption data into an intelligent matching module to perform consumption matching, and further obtaining matching consumption data of the multi-complementary commodities corresponding to the favorite commodities.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
Drawings
FIG. 1 is a flowchart illustrating a recommendation method for enhancing a consumer to obtain a favorite commodity according to an embodiment of the present application;
FIG. 2 is a flowchart illustrating a method for improving a recommendation of a consumer to obtain favorite goods according to an embodiment of the present application;
FIG. 3 is a schematic diagram of a comparison flow of consumer matching for improving a recommendation method for consumers to obtain favorite goods according to an embodiment of the present application;
FIG. 4 is a schematic diagram of a compensation matching analysis flow for improving a recommendation method for a consumer to obtain favorite goods according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a process of analyzing the growth of consumers according to an embodiment of the present application;
FIG. 6 is a schematic diagram of a commodity complementation analysis process for improving a recommendation method for a consumer to obtain favorite commodities according to an embodiment of the present application;
FIG. 7 is a color label constraint flow chart of a recommendation method for improving a consumer to obtain a favorite commodity according to an embodiment of the present application;
FIG. 8 is a schematic diagram illustrating a recommendation system for enhancing a consumer to obtain a favorite product according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of an exemplary electronic device according to an embodiment of the present application.
Reference numerals illustrate: the device comprises a first obtaining unit 11, a second obtaining unit 12, a first constructing unit 13, a third obtaining unit 14, a fourth obtaining unit 15, a first input unit 16, a first generating unit 17, a computing device 90, a memory 91, a processor 92, and an input-output interface 93.
Detailed Description
The embodiment of the application solves the technical problems that the traditional commodity recommending method cannot accurately meet the use requirement of a user, the recommending performance is not perfect enough and the user satisfaction and experience cannot be effectively improved in the prior art by providing the recommending method and the system for improving the favorite commodity obtained by the consumer, and achieves the technical effects that the complementary structural analysis is carried out on the favorite commodity of the user, the intelligent matching is carried out according to the consumption complementarity of the user, and the accuracy and the intelligence of commodity recommending are effectively improved. Hereinafter, exemplary embodiments according to the present application will be described in detail with reference to the accompanying drawings. It should be apparent that the described embodiments are only some embodiments of the present application and not all embodiments of the present application, and it should be understood that the present application is not limited by the example embodiments described herein.
Summary of the application
With the high-speed development of the Internet and the popularization of intelligent terminals in China, the arrival of a big data age is quickened, the network environment is increasingly perfected, the electronic commerce mode is gradually mature, the consumption form of people starts to gradually change from off-line physical stores to on-line shopping websites, and the gradual expansion of the electronic commerce market scale provides richer choices for users. However, in the face of such various commodity information, how users can quickly and accurately select the required commodities becomes a topic of interest to users and electronic commerce. However, the conventional commodity recommendation method in the prior art cannot accurately meet the use requirements of users, so that the recommendation performance is not perfect enough, and the technical problems of user satisfaction and experience degree cannot be effectively improved.
Aiming at the technical problems, the technical scheme provided by the application has the following overall thought:
the embodiment of the application provides a recommendation method for improving the acquisition of favorite goods by a consumer, wherein the method is applied to a recommendation system for improving the acquisition of favorite goods by the consumer, the system comprises an intelligent matching module, and the method comprises the following steps: obtaining first historical consumption commodity information of a first user; obtaining first favorite commodity information according to the first historical consumption commodity information; constructing a first complementary commodity topological structure by taking the first favorite commodity information as a central node, wherein the first complementary commodity topological structure is a star-shaped topological structure; obtaining multi-complementary commodity information according to the first complementary commodity topological structure; acquiring real-time consumption data of the first favorite commodity; inputting the real-time consumption data of the first favorite commodity into the intelligent matching module, and obtaining first output information according to the intelligent matching module, wherein the first output information is the matching consumption data of the multiple complementary commodities; and generating a first recommendation scheme according to the first output information.
Having described the basic principles of the present application, embodiments of the present application are described below with reference to the accompanying drawings. As one of ordinary skill in the art can know, with the development of technology and the appearance of new scenes, the technical solution provided by the embodiment of the present application is applicable to similar technical problems.
Example 1
As shown in fig. 1, an embodiment of the present application provides a recommendation method for promoting a consumer to obtain a favorite commodity, where the method is applied to a recommendation system for promoting a consumer to obtain a favorite commodity, the system includes an intelligent matching module, and the method includes:
step S100: obtaining first historical consumption commodity information of a first user;
step S200: obtaining first favorite commodity information according to the first historical consumption commodity information;
specifically, the first historical consumption commodity information is obtained by collecting data of historical consumption information of a user, including information such as commodity names, commodity categories, commodity transaction amounts, commodity consumption time and the like, and further, by analyzing the preference degrees of all the historical consumption commodities, wherein the preference commodity information can be obtained by dividing the categories of all the commodities and calculating the consumption amount occupation ratio of each category of commodity in the process of obtaining the preference commodity information, and meanwhile, the preference commodity of the user is obtained by connecting browsing records of the user, the number of times of adding shopping carts and the like, all the information of the preference commodity is further extracted, interest labels are obtained according to user data tracking, the obtained interest labels are added into the first preference commodity information, and accurate basic data conditions are provided for subsequent recommendation.
Step S300: constructing a first complementary commodity topological structure by taking the first favorite commodity information as a central node, wherein the first complementary commodity topological structure is a star-shaped topological structure;
specifically, the first favorite commodity information is taken as a central node, and after being analyzed according to complementarity with the favorite commodity, a plurality of related complementary commodities are correspondingly generated as sub-nodes to construct the topological structure of the first complementary commodity, further, complementary analysis of using the complementary is carried out according to the use requirement of the commodity in the complementary analysis process, corresponding requirement labels of the plurality of complementary commodities are established according to the user favorite commodity, for example, when one of the favorite commodities of the user is pigment, the use of the user is analyzed according to the pigment to obtain a plurality of labels connected with the corresponding commodity, drawing pigment is taken as the central node, and the label is taken as the sub-node to construct the topological structure, so that a corresponding star topological structure is constructed according to the complementary labels and the central favorite commodity, wherein the constructed topological structure can abstract entities into 'points' irrelevant to the size and the shape of the complementary labels, the points contain commodity digital identifications, the lines of the connecting entities abstract into 'lines' contain complementary relations, and the relations between the points and the complementary commodities are represented in a graph form, and the star topological structure can be established with the high accuracy of the complementary structure and the favorite commodity, and the accuracy of the analysis is high.
Step S400: obtaining multi-complementary commodity information according to the first complementary commodity topological structure;
specifically, the first complementary commodity topological structure is a structure constructed according to the central favorite commodity and the demand complementarity of the user, so that complementary commodities corresponding to each label can be obtained according to the sub-node labels in the structure, for example, when one of the favorite commodities of the user is pigment, the painting pigment is used as a central node, and complementary commodities such as a painting board, a painting brush, painting gloss oil, a positioning adhesive tape and the like are obtained one by one based on the usage label after the user demand analysis, so that corresponding information acquisition is carried out on all the complementary commodities according to the information category of the favorite commodity, the acquired information is stored in an information analysis library, and when the corresponding information is required to be called, the calling instruction is carried out, so that the information pool data of basic information is improved, and the commodity recommendation accuracy and reliability are correspondingly improved.
Step S500: acquiring real-time consumption data of the first favorite commodity;
specifically, the real-time consumption data of the first favorite commodity is obtained by collecting real-time consumption data in a historical purchase record of the first user, wherein the real-time consumption data comprises real-time consumption data such as favorite commodity purchase frequency, purchase time, purchase quantity, transaction amount and the like in a certain period, and corresponding calculation is completed by further storing and analyzing the real-time consumption data of the first favorite commodity, so that analysis of complementary commodity consumption is further completed by the real-time consumption data, and basic data is provided for recommending commodities.
Step S600: inputting the real-time consumption data of the first favorite commodity into the intelligent matching module, and obtaining first output information according to the intelligent matching module, wherein the first output information is the matching consumption data of the multiple complementary commodities;
step S700: and generating a first recommendation scheme according to the first output information.
Specifically, the real-time consumption data of the first favorite commodity is input into the intelligent matching module, wherein the matching process is to correspondingly match corresponding consumption data of the complementary commodity according to the purchase quantity and the purchase index of the favorite commodity by a user, and the matching consumption data is corresponding consumption data obtained by matching according to the consumption data of the favorite commodity. For example, when the frequency and the number of the drawing pigments purchased by the user are both large, the user has a high purchase demand for drawing demands, so that the use demand of the corresponding complementary commodity is increased, the intelligent matching model performs proportional analysis on the demand relationship between the favorite commodity and the corresponding complementary commodity in a certain period of history, and compares the real-time consumption of the favorite commodity of the current user according to the matching proportion, so as to generate corresponding real-time demand matching consumption data, and further complete complex data operation according to the first output information output by the intelligent matching model, so as to generate a corresponding commodity recommendation scheme through a built computer platform, improve the usability performance of the system recommending the favorite commodity, and further achieve the technical effects of performing intelligent matching according to the consumer complementarity through performing complementary structural analysis on the user favorite commodity, and further effectively improving the accuracy and the intelligence of commodity recommendation.
Further, as shown in fig. 2, the step S100 of obtaining the first historical consumer goods information of the first user further includes:
step S110: obtaining a first consumption index by analyzing the price of the first favorite commodity;
step S120: obtaining a comprehensive consumption index by analyzing the price of the first historical consumption commodity;
step S130: determining a first recommended consumption level according to the duty ratio information of the first consumption index to the comprehensive consumption index;
step S140: and generating a first constraint condition according to the first recommended consumption level, wherein the first constraint condition is used for constraining the complementary commodity.
Specifically, the consumption index of the first user on the favorite commodity is determined by analyzing the prices of all the favorite commodity, further, the comprehensive consumption index of the user is obtained by analyzing the prices of the historical consumption commodity, so that the expense occupation ratio of the first user on the favorite commodity is determined according to the occupation ratio between the favorite commodity and the comprehensive consumption index, and the expense consumption grade of the user is determined. Further, the market consumption grade analysis is performed on the consumption index of the favorite commodity and the market consumption data of the similar commodity, and then the first recommended consumption grade is determined according to the expenditure consumption grade and the market consumption grade, wherein the consumption grade is divided into a plurality of grades, so that constraint conditions are generated according to the first consumption grade to be used for constraining the recommendation of the complementary commodity, such as C grade of the recommended consumption grade in A, B, C, D, E, and the corresponding recommended complementary commodity is subjected to C grade commodity screening.
Further, as shown in fig. 3, before inputting the real-time consumption data of the first favorite commodity into the intelligent matching module and obtaining the first output information according to the intelligent matching module, step S600 of the embodiment of the present application further includes:
step S610: obtaining real-time consumption data of the multi-complementary commodity;
step S620: the intelligent matching module performs consumption matching according to the real-time consumption data of the multiple complementary commodities and the real-time consumption data of the first favorite commodity to obtain second output information, wherein the second output information is the real-time matching consumption data of the multiple complementary commodities;
step S630: performing data mapping comparison on the first output information and the second output information to obtain a first comparison result;
step S640: obtaining a first recommended commodity according to the first comparison result;
step S650: and generating the first recommendation scheme according to the first recommended commodity.
Specifically, the real-time consumption data of all the complementary commodities are collected according to the real-time consumption data collection period of the first favorite commodity in the data collection process, so that the consumption of the complementary commodity and the favorite commodity is guaranteed to be in the same period, the real-time consumption data of the complementary commodity and the favorite commodity are matched through the intelligent matching module, and therefore second output information output by the intelligent matching module is obtained, wherein the first output information is matched consumption required by a user, the second output information is real-time consumption of the user, and therefore mapping comparison is carried out on the first complementary commodity in the first output information and the first complementary commodity corresponding to the second output information, a first comparison result is obtained, and when the first comparison result is unsatisfied, the complementary commodity corresponding to the first comparison result is used as a recommended commodity, so that intelligent and effective generation of a recommendation scheme is completed, and user experience is improved.
Further, as shown in fig. 4, the comparing the first output information with the second output information to obtain a first comparison result in a data mapping manner, and step S630 of the embodiment of the present application further includes:
step S631: generating first preset comparison data according to the matched consumption data in the first output information;
step S632: generating first real-time comparison data according to the real-time matching consumption data in the second output information;
step S633: obtaining a first compensation commodity by performing compensation matching analysis on the first preset comparison data and the first real-time comparison data;
step S634: and adding the first compensation commodity to the first recommendation scheme as a recommended commodity.
Specifically, the first preset comparison data are generated according to the first output information, N pieces of first preset comparison data are generated according to the second output information, N pieces of first real-time comparison data are generated according to the second output information, the number of the first real-time comparison data is the same as that of the first preset comparison data, the compensating and matching analysis process is that the corresponding M pieces of complementary commodity information are extracted and recorded from M pieces of unsatisfied comparison results in the N pieces of comparison results through generating a first comparison result, a second comparison result and a third comparison result … … nth comparison result, and accordingly the first recommendation scheme is generated according to the M pieces of complementary commodity information compensation. For example, when the consumption index of the paint is 78, the consumption index of the corresponding required consumption board is 11, and the consumption index of the actual user purchasing the board is 2, so the board is required to be used as a compensation commodity, and other complementary commodities are similar. The intelligent data processing based on the intelligent matching module is achieved, intelligent matching is carried out according to the consumption complementarity of the user, and therefore the technical effects of accuracy and intelligence of commodity recommendation are effectively improved.
Further, as shown in fig. 5, the step S500 of the embodiment of the present application further includes:
step S510: a first consumption curve is constructed by analyzing the real-time consumption data of the first favorite commodity;
step S520: obtaining a first demand index of the first favorite commodity by performing consumption growth analysis on the first consumption curve;
step S530: and adjusting the first recommended scheme according to the first demand index to generate a second recommended scheme.
Specifically, by collecting the real-time consumption data of the first favorite commodity and analyzing the increasing trend of the quantity of the real-time consumption data, when the increasing trend of the first user on the first favorite commodity exceeds a certain preset threshold, the requirement degree of the first user on the first favorite commodity becomes high, so that the analysis of the first requirement index is required to be completed through specific increasing trend prediction and judgment of the preset threshold, and then the first recommendation scheme is adjusted according to the first requirement index, so that a second recommendation scheme is generated, wherein the recommendation scheme is adjusted according to the requirement index to optimize the recommendation frequency and recommendation combination of the complementary commodity, and the technical effects of effectively improving the accuracy and the intelligence of commodity recommendation are achieved.
Further, as shown in fig. 6, the method for obtaining multi-complementary merchandise information according to the first complementary merchandise topology structure further includes:
step S410: judging whether the commodities in the multi-complementary commodity have a complementary relationship or not;
step S420: when the commodities in the multiple complementary commodities have a complementary relationship, a first sub-topological structure is generated, wherein the node number of the first sub-topological structure is smaller than that of the first complementary commodity topological structure;
step S430: when the commodities in the multi-complementary commodity have no complementary relationship, judging whether a first coincidence relationship exists or not;
step S440: and when the commodities in the multiple complementary commodities have a first coincidence relation, obtaining a second complementary commodity topological structure.
Further, whether the complementary relationship exists in the multiple complementary commodities, namely whether a compensatory relationship exists between the commodities complementary to the first favorite commodity or not is judged, and a sub-topological structure can be generated when the compensatory relationship exists, wherein the node number of the sub-topological structure is smaller than the node number of the first complementary commodity topological structure, so that the centralization and the main position of the first complementary commodity topological structure are ensured, the excessive complementary commodities generated by the sub-topological structure are prevented, the centralization and the accuracy of commodity recommendation are reduced, further, if the compensatory relationship does not exist, whether the usability coincidence exists between all the complementary commodities is judged, and when the usability coincidence exists, the complementary commodities can be further screened through adding a screening mechanism, and the purchasing power of a user is increased. For example, the added screening mechanism can screen the complementary commodities according to the star attorney preference of the user, so that the user experience is improved.
Further, the embodiment S200 of the present application further includes:
step S210: generating first screening color level data by collecting commodity colors of the first favorite commodity;
step S220: generating a first color level map according to the first screening color level data;
step S230: determining a tone scale preference label of the first user according to the first tone scale map;
step S240: and constraining the complementary commodity by taking the tone scale preference label as a second constraint condition.
Specifically, the color of the first favorite commodity is collected to generate a tone-scale card corresponding to the color of each commodity, each commodity is provided with one tone-scale card, so that screening is performed according to the tone-scale cards of all favorite commodities, first screening tone-scale data are generated according to the matching degree of the tone-scale cards and the corresponding tone-scale purchasing degree of a user, a tone-scale map corresponding to the user is constructed, tone-scale label setting of the user is performed by the tone-scale map in the screening or positioning process of the complementary commodity, further refinement and positioning of the complementary commodity are completed, purchasing power of the user is improved, further, optimization of the tone-scale map is performed according to age and gender of the user, or updating of the tone-scale map is performed according to the commodity purchased in real time, interest tide stream color of the webpage browsed by the user is used as an auxiliary reference, and further the second constraint condition is generated, so that the performance of the consumer obtaining the favorite commodity is improved, and the technical effects of the user recommendation requirement and intelligent positioning are met in a targeted manner are achieved.
From the description of the above embodiments, it will be apparent to those skilled in the art that the present application may be implemented by means of software plus necessary general purpose hardware, and of course may also be implemented by means of special purpose hardware, including application specific integrated circuits, special purpose CPUs, special purpose memories, special purpose components, etc. Generally, functions performed by computer programs can be easily implemented by corresponding hardware, and specific hardware structures for implementing the same functions can be varied, such as analog circuits, digital circuits, or dedicated circuits. However, a software program implementation is a preferred embodiment for many more of the cases of the present application. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a readable storage medium, such as a usb disk of a computer, a removable hard disk, a ROM, a RAM, a magnetic disk or an optical disk, etc., comprising several instructions for causing a computer device to perform the method according to the embodiments of the present application.
In summary, the recommendation method and system for improving the consumer to obtain the favorite commodity provided by the embodiment of the application have the following technical effects:
1. The method comprises the steps of obtaining historical consumption commodity information of a user to analyze favorite commodities, further constructing a first complementary commodity topological structure by taking the favorite commodities as a central node, obtaining multi-complementary commodity information according to the first complementary commodity topological structure, further obtaining real-time consumption data of the favorite commodities of the user, inputting the real-time consumption data into an intelligent matching module to perform consumption matching, and further obtaining matching consumption data of the multi-complementary commodities corresponding to the favorite commodities.
2. The method has the advantages that the user is set by adopting the tone scale map, so that the second constraint condition is generated for constraint, the performance of obtaining favorite commodities by consumers is improved, and the technical effects of user recommendation requirements and intelligent positioning are met in a targeted manner.
3. By positioning the preset consumption requirement and the real-time consumption requirement and performing intelligent data processing according to the intelligent matching module, and then performing compensatory analysis on the complementary commodity, the technical effects of accuracy and intelligence of commodity recommendation are effectively improved.
Example two
Based on the same inventive concept as the recommendation method for promoting consumers to obtain favorite goods in the foregoing embodiment, the present invention further provides a recommendation system for promoting consumers to obtain favorite goods, as shown in fig. 8, the system includes:
a first obtaining unit 11, wherein the first obtaining unit 11 is configured to obtain first historical consumer goods information of a first user;
a second obtaining unit 12, where the second obtaining unit 12 is configured to obtain first favorite commodity information according to the first historical consumer commodity information;
the first construction unit 13 is configured to construct a first complementary commodity topology structure by using the first favorite commodity information as a central node, where the first complementary commodity topology structure is a star topology structure;
a third obtaining unit 14, where the third obtaining unit 14 is configured to obtain multiple complementary commodity information according to the first complementary commodity topology structure;
a fourth obtaining unit 15, where the fourth obtaining unit 15 is configured to obtain real-time consumption data of the first favorite commodity;
the first input unit 16 is configured to input real-time consumption data of the first favorite commodity into an intelligent matching module, and obtain first output information according to the intelligent matching module, where the first output information is matching consumption data of the multiple complementary commodities;
A first generating unit 17, where the first generating unit 17 is configured to generate a first recommendation scheme according to the first output information.
Further, the system further comprises:
a fifth obtaining unit for obtaining a first consumption index by analyzing the price of the first favorite commodity;
the sixth obtaining unit is used for obtaining a comprehensive consumption index by analyzing the price of the first historical consumption commodity;
the first determining unit is used for determining a first recommended consumption level according to the duty ratio information of the first consumption index to the comprehensive consumption index;
the second generation unit is used for generating a first constraint condition according to the first recommended consumption level, wherein the first constraint condition is used for constraining the complementary commodity.
Further, the system further comprises:
a seventh obtaining unit for obtaining real-time consumption data of the multiple complementary commodities;
the eighth obtaining unit is used for performing consumption matching according to the real-time consumption data of the multi-complementary commodities and the real-time consumption data of the first favorite commodities by the intelligent matching module to obtain second output information, wherein the second output information is the real-time matching consumption data of the multi-complementary commodities;
A ninth obtaining unit, configured to perform data mapping comparison on the first output information and the second output information, to obtain a first comparison result;
a tenth obtaining unit, configured to obtain a first recommended commodity according to the first comparison result;
and the third generation unit is used for generating the first recommendation scheme according to the first recommended commodity.
Further, the system further comprises:
the fourth generation unit is used for generating first preset contrast data according to the matched consumption data in the first output information;
the fifth generation unit is used for generating first real-time comparison data according to the real-time matching consumption data in the second output information;
an eleventh obtaining unit, configured to obtain a first compensation commodity by performing compensation matching analysis on the first preset contrast data and the first real-time contrast data;
and the first adding unit is used for adding the first compensation commodity into the first recommendation scheme as a recommended commodity.
Further, the system further comprises:
the second construction unit is used for constructing a first consumption curve by analyzing the real-time consumption data of the first favorite commodity;
a twelfth obtaining unit, configured to obtain a first demand index of the first favorite commodity by performing a consumption growth analysis on the first consumption curve;
and the sixth generation unit is used for adjusting the first recommendation scheme according to the first requirement index to generate a second recommendation scheme.
Further, the system further comprises:
the first judging unit is used for judging whether the commodities in the multiple complementary commodities have a complementary relationship or not;
a seventh generating unit, configured to generate a first sub-topology structure when a complementary relationship exists between the multiple complementary commodities, where the number of nodes of the first sub-topology structure is smaller than the number of nodes of the first complementary commodity topology structure;
the second judging unit is used for judging whether a first coincidence relation exists or not when the complementary relation does not exist in the commodities in the multiple complementary commodities;
And the thirteenth obtaining unit is used for obtaining a second complementary commodity topological structure when the commodities in the multiple complementary commodities have a first coincidence relation.
Further, the system further comprises:
the eighth generation unit is used for generating first screening color level data by collecting commodity colors of the first favorite commodities;
a ninth generation unit, configured to generate a first tone map according to the first screening tone data;
the second determining unit is used for determining the tone scale preference label of the first user according to the first tone scale map;
and the first operation unit is used for restraining the complementary commodity by taking the tone scale preference label as a second constraint condition.
The embodiment of the application can divide the functional modules of the network equipment and the terminal equipment according to the method example, for example, each functional module can be divided corresponding to each function, and two or more functions can be integrated in one receiving module. The integrated modules may be implemented in hardware or in software functional modules. It should be noted that, in the embodiment of the present application, the division of the modules is schematic, which is merely a logic function division, and other division manners may be implemented in actual implementation. From the foregoing detailed description of a recommendation method for promoting consumers to obtain favorite products, those skilled in the art can clearly understand the implementation method of a recommendation system for promoting consumers to obtain favorite products in this embodiment, so the description will not be further described herein for brevity.
Exemplary electronic device
FIG. 9 is a schematic diagram of a computing device of the present application. The computing device 90 shown in fig. 9 may include a memory 91, a processor 92, and an input/output interface 93. The memory 91, the processor 92 and the input/output interface 93 are connected through an internal connection path, the memory 33 is used for storing instructions, and the processor 92 is used for executing the instructions stored in the memory 91, so as to control the input/output interface 93 to receive input data and information, and output data such as operation results.
FIG. 9 is a schematic diagram of a computing device according to another embodiment of the application. The computing device 90 shown in fig. 9 may include a memory 91, a processor 92, and an input/output interface 93. The memory 91, the processor 92 and the input/output interface 93 are connected through an internal connection path, the memory 91 is used for storing instructions, and the processor 92 is used for executing the instructions stored in the memory 92, so as to control the input/output interface 93 to receive input data and information, and output data such as operation results.
In implementation, the steps of the methods described above may be performed by integrated logic circuitry in hardware in processor 92 or by instructions in software. The method for identifying the abnormal message and/or the method for training the abnormal message identification model disclosed by the embodiment of the application can be directly embodied as the execution completion of a hardware processor or the execution completion of the combination execution of hardware and software modules in the processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory 91 and the processor 92 reads the information in the memory 91 and in combination with its hardware performs the steps of the above method. To avoid repetition, a detailed description is not provided herein.
It should be appreciated that in embodiments of the present application, the processor may be a central processing unit (central processing unit, CPU), the processor may also be other general purpose processors, digital signal processors (digital signal processor, DSP), application specific integrated circuits (application specific integrated circuit, ASIC), off-the-shelf programmable gate arrays (field programmable gate array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
It should be appreciated that in embodiments of the present application, the memory may include read only memory and random access memory, and provide instructions and data to the processor. A portion of the processor may also include nonvolatile random access memory. The processor may also store information of the device type, for example.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital subscriber line (Digital Subscriber Line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer readable storage medium may be any available medium that can be read by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., hard Disk, magnetic tape), an optical medium (e.g., digital versatile Disk (Digital Video Disc, DVD)), or a semiconductor medium (e.g., solid State Disk (SSD)), or the like.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (7)
1. A recommendation method for promoting consumers to obtain favorite goods, wherein the method is applied to a recommendation system for promoting consumers to obtain favorite goods, the system comprises an intelligent matching module, and the method comprises the following steps:
obtaining first historical consumption commodity information of a first user;
obtaining first favorite commodity information according to the first historical consumption commodity information;
constructing a first complementary commodity topological structure by taking the first favorite commodity information as a central node, wherein the first complementary commodity topological structure is a star-shaped topological structure;
obtaining information of a plurality of complementary commodities according to the first complementary commodity topological structure;
acquiring real-time consumption data of the first favorite commodity;
inputting the real-time consumption data of the first favorite commodity into the intelligent matching module, and obtaining first output information according to the intelligent matching module, wherein the first output information is the matching consumption data of the plurality of complementary commodities;
Generating a first recommendation scheme according to the first output information;
the method further comprises the steps of inputting the real-time consumption data of the first favorite commodity into the intelligent matching module, and before obtaining the first output information according to the intelligent matching module:
obtaining real-time consumption data of the plurality of complementary commodities;
the intelligent matching module performs consumption matching according to the real-time consumption data of the plurality of complementary commodities and the real-time consumption data of the first favorite commodity to obtain second output information, wherein the second output information is the real-time matching consumption data of the plurality of complementary commodities;
performing data mapping comparison on the first output information and the second output information to obtain a first comparison result;
obtaining a first recommended commodity according to the first comparison result;
generating the first recommendation scheme according to the first recommended commodity;
the step of performing data mapping comparison on the first output information and the second output information to obtain a first comparison result includes:
generating first preset comparison data according to the matched consumption data in the first output information, wherein the number of the first preset comparison data is N;
Generating first real-time comparison data according to the real-time matching consumption data in the second output information, wherein the number of the first real-time comparison data is N as same as the number of the first preset comparison data;
the first preset comparison data and the first real-time comparison data are subjected to compensation matching analysis to obtain first compensation commodities, wherein the compensation matching analysis is that M comparison results, of which the first real-time comparison data do not meet the corresponding first preset comparison data, in N comparison results are obtained through generating a first comparison result, a second comparison result and a third comparison result … … Nth comparison result, and M complementary commodities corresponding to the M comparison results are used as the first compensation commodities;
and adding the first compensation commodity to the first recommendation scheme as a recommended commodity.
2. The method of claim 1, wherein the obtaining first historical consumer good information for the first user, the method further comprises:
obtaining a first consumption index by analyzing the price of the first favorite commodity;
obtaining a comprehensive consumption index by analyzing the price of the first historical consumption commodity;
Determining a first recommended consumption level according to the duty ratio information of the first consumption index to the comprehensive consumption index;
and generating a first constraint condition according to the first recommended consumption level, wherein the first constraint condition is used for constraining the complementary commodity.
3. The method of claim 1, wherein the obtaining real-time consumption data for the first favorite commodity further comprises:
a first consumption curve is constructed by analyzing the real-time consumption data of the first favorite commodity;
obtaining a first demand index of the first favorite commodity by performing consumption growth analysis on the first consumption curve;
and adjusting the first recommended scheme according to the first demand index to generate a second recommended scheme.
4. The method of claim 1, wherein the obtaining information for a plurality of complementary merchandise based on the first complementary merchandise topology, the method further comprises:
judging whether the commodities in the plurality of complementary commodities have a complementary relationship or not;
when the commodities in the plurality of complementary commodities have a complementary relationship, a first sub-topological structure is generated, wherein the node number of the first sub-topological structure is smaller than that of the first complementary commodity topological structure;
When the commodities in the plurality of complementary commodities have no complementary relationship, judging whether a first coincidence relationship exists or not;
and when the commodities in the plurality of complementary commodities have a first coincidence relation, obtaining a second complementary commodity topological structure.
5. The method of claim 1, wherein the method further comprises:
generating first screening color level data by collecting commodity colors of the first favorite commodity;
generating a first color level map according to the first screening color level data;
determining a tone scale preference label of the first user according to the first tone scale map;
and constraining the complementary commodity by taking the tone scale preference label as a second constraint condition.
6. A recommendation system for promoting consumer access to favorite goods, wherein the system comprises:
the first obtaining unit is used for obtaining first historical consumption commodity information of a first user;
the second obtaining unit is used for obtaining first favorite commodity information according to the first historical consumption commodity information;
the first construction unit is used for constructing a first complementary commodity topological structure by taking the first favorite commodity information as a central node, wherein the first complementary commodity topological structure is a star-shaped topological structure;
The third obtaining unit is used for obtaining information of a plurality of complementary commodities according to the first complementary commodity topological structure;
a fourth obtaining unit, configured to obtain real-time consumption data of the first favorite commodity;
the first input unit is used for inputting the real-time consumption data of the first favorite commodity into the intelligent matching module, and obtaining first output information according to the intelligent matching module, wherein the first output information is the matching consumption data of the plurality of complementary commodities;
the first generation unit is used for generating a first recommendation scheme according to the first output information;
a seventh obtaining unit for obtaining real-time consumption data of the plurality of complementary commodities;
the eighth obtaining unit is used for performing consumption matching according to the real-time consumption data of the plurality of complementary commodities and the real-time consumption data of the first favorite commodity by the intelligent matching module to obtain second output information, wherein the second output information is the real-time matching consumption data of the plurality of complementary commodities;
A ninth obtaining unit, configured to perform data mapping comparison on the first output information and the second output information, to obtain a first comparison result;
a tenth obtaining unit, configured to obtain a first recommended commodity according to the first comparison result;
the third generation unit is used for generating the first recommendation scheme according to the first recommended commodity;
the fourth generation unit is used for generating first preset comparison data according to the matched consumption data in the first output information, and the number of the first preset comparison data is N;
the fifth generation unit is used for generating first real-time comparison data according to the real-time matching consumption data in the second output information, and the number of the first real-time comparison data is N as same as the number of the first preset comparison data;
the eleventh obtaining unit is configured to obtain a first compensated commodity by performing compensation matching analysis on the first preset comparison data and the first real-time comparison data, where the compensation matching analysis is to obtain M comparison results, in which the first real-time comparison data in the N comparison results does not satisfy the corresponding first preset comparison data, by generating a first comparison result, a second comparison result, and a third comparison result … … nth comparison result, and take M complementary commodities corresponding to the M comparison results as the first compensated commodity;
And the first adding unit is used for adding the first compensation commodity into the first recommendation scheme as a recommended commodity.
7. A recommendation system for enhancing consumer availability of favorite goods comprising at least one processor and a memory, said at least one processor being coupled to said memory for reading and executing instructions in said memory to perform the method of any one of claims 1-5.
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