CN116452301A - Commodity recommendation method and system based on big data analysis - Google Patents
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Abstract
The invention belongs to the technical field of information processing, and particularly discloses a commodity recommendation method and a commodity recommendation system based on big data analysis. According to the invention, the big data analysis mode is adopted, the commodity preference of the current consumer can be more accurately mastered from the dimensions of various commodity heat information of each electric commodity platform, the supply capacity of the local supplier library is integrated, and the intelligent commodity recommendation more conforming to the individual condition of each merchant is realized.
Description
Technical Field
The invention belongs to the technical field of information processing, and particularly relates to a commodity recommendation method and system based on big data analysis.
Background
With the continuous development and popularization of electronic commerce and the continuous perfection of payment systems, more and more consumers like and get used to commodity purchase on the internet. In the face of huge online consumer groups, the accurate and effective online commodity recommendation method can improve the income of merchants. For some quick-sales products, the online repurchase rate of the consumer is generally low, and the purchasing behavior is difficult to predict according to the historical data of the consumer, and in the sales process, how to accurately grasp the purchasing requirement of the consumer, and further, recommending the preferred products of the consumer is an important link. Most of traditional commodity recommending modes are judged by sales personnel according to historical sales data, then the sales personnel recommend the hot-sold commodities according to experience judgment, the information dimension according to the judgment is quite single, and the experience mode can lead to low commodity recommending accuracy and cannot accurately grasp the commodity preference of the current consumer because of more uncertain factors when facing the on-line market of fast-sold commodities and new users, and can lead to the problem that supply cannot keep up with marketing because of insufficient supply capacity of a supply chain. Therefore, there is an urgent need for an intelligent commodity recommendation method that better meets the individual situation of each merchant.
Disclosure of Invention
The invention aims to provide a commodity recommendation method and system based on big data analysis, which are used for solving the problems in the prior art.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
in a first aspect, a commodity recommendation method based on big data analysis is provided, including:
acquiring commodity guiding data sets of all electronic commerce platforms and commodity supplying data sets of a local supplier library, wherein the commodity guiding data sets comprise a plurality of platform hot searching commodity information, a plurality of platform hot pushing commodity information and a plurality of platform high-sales commodity information, and a hot searching index corresponding to the platform hot searching commodity information, a hot pushing index corresponding to the platform hot pushing commodity information and a sales index corresponding to the platform high-sales commodity information;
acquiring a plurality of platform hot commodity information according to the hot searching commodity information of each platform, the hot pushing commodity information of each platform and the high sales commodity information of each platform in the commodity guiding data set, acquiring a platform hot index corresponding to the hot commodity information of each platform according to the hot searching index corresponding to the hot searching commodity information of each platform, the hot pushing index corresponding to the hot pushing commodity information of each platform and the sales index corresponding to the high sales commodity information of each platform in the commodity guiding data set, and performing association combination on the hot commodity information of each platform and the corresponding platform hot index to acquire a hot commodity data set of the corresponding electronic commerce platform;
summarizing the popular commodity data sets of all the electronic commerce platforms to obtain summarized commodity data sets, wherein the summarized commodity data sets comprise a plurality of summarized commodity information and summarized popular indexes corresponding to the summarized commodity information;
comparing each piece of supplied commodity information in the supplied commodity data set with each piece of summarized commodity information in the summarized commodity data set, screening out a plurality of pieces of recommended commodity information, and determining a summarized heat index and a supplier performance degree corresponding to each piece of recommended commodity information, wherein the recommended commodity information is corresponding supplied commodity information in the supplied commodity data set and corresponding summarized commodity information in the summarized commodity data set;
substituting the summarized popularity index and the degree of performance of the suppliers corresponding to each piece of recommended commodity information into a preset recommended index calculation model to calculate so as to obtain a recommended index corresponding to each piece of recommended commodity information;
ranking the recommended commodity information according to the recommendation index corresponding to the recommended commodity information, and selecting a plurality of recommended commodity information with highest ranking to form a first commodity recommendation set;
and transmitting the first commodity recommendation set to a document editing end, receiving first commodity recommendation document information corresponding to the first commodity recommendation set and fed back by the document editing end, and pushing the first commodity recommendation document information to a target client.
In one possible design, the calculating according to the commodity information of each platform in the commodity guiding data set, the commodity information of each platform in the hot pushing and the commodity information of each platform in the high sales volume to obtain a plurality of platform hot commodity information includes:
and acquiring a union set of the hot search commodity information of each platform, the hot push commodity information of each platform and the high-sales commodity information of each platform in the commodity guiding data set to obtain a plurality of hot commodity information of each platform, wherein the hot commodity information of each platform corresponds to a plurality of or one of the hot search commodity information of the plurality of platforms, the hot push commodity information of the plurality of platforms and the high-sales commodity information of the plurality of platforms in the commodity guiding data set.
In one possible design, the calculating according to the heat search index corresponding to the heat search commodity information of each platform in the commodity guiding data set, the heat push index corresponding to the heat push commodity information of each platform, and the sales index corresponding to the sales commodity information of each platform to obtain the platform popularity index corresponding to the platform popularity commodity information includes:
when the platform hot commodity information only corresponds to one of a plurality of platform hot searching commodity information, a plurality of platform hot pushing commodity information and a plurality of platform high sales commodity information in the commodity guiding data set, taking a hot searching index corresponding to the platform hot searching commodity information or a hot pushing index corresponding to the platform hot pushing commodity information or a sales index corresponding to the platform high sales commodity information as a platform hot index corresponding to the platform hot commodity information; and when the platform hot commodity information corresponds to a plurality of the plurality of platform hot searching commodity information, the plurality of platform hot pushing commodity information and the plurality of platform high sales commodity information in the commodity guide data set, adding and summing the hot searching indexes corresponding to the corresponding platform hot searching commodity information and/or the hot pushing indexes corresponding to the corresponding platform hot pushing commodity information and/or the sales indexes corresponding to the corresponding platform high sales commodity information to obtain the platform hot indexes corresponding to the platform hot commodity information.
In one possible design, the summarizing the hot commodity data sets of each e-commerce platform to obtain a summarized commodity data set includes:
acquiring a union set of a plurality of platform hot commodity information of each electronic commerce platform hot commodity data set to obtain a plurality of summarized commodity information, wherein each summarized commodity information corresponds to one or more platform hot commodity information respectively;
when summarized commodity information corresponds to only one platform hot commodity information, taking a platform hot index corresponding to the platform hot commodity information as a summarized hot index of the summarized commodity information, adding and summing the platform hot indexes corresponding to the corresponding platform hot commodity information when the summarized commodity information corresponds to the plurality of platform hot commodity information, and obtaining the summarized hot index of the summarized commodity information;
and associating and combining the summarized commodity information with the corresponding summarized heat index to obtain a summarized commodity data set.
In one possible design, the comparing the information of each supplied commodity in the supplied commodity data set with the information of each summarized commodity in the summarized commodity data set, screening out a plurality of recommended commodity information, and determining a summarized popularity index and a supplier performance corresponding to each recommended commodity information includes:
and acquiring intersections of each piece of supplied commodity information in the supplied commodity data set and each piece of summarized commodity information in the summarized commodity data set to obtain a plurality of pieces of recommended commodity information, wherein each piece of recommended commodity information corresponds to one piece of supplied commodity information in the supplied commodity data set and one piece of summarized commodity information in the summarized commodity data set respectively, and taking the summarized popularity index of the corresponding summarized commodity information and the degree of the performance of the supplier corresponding to the supplied commodity information as the summarized popularity index and the degree of the performance of the supplier of the recommended commodity information.
In one possible design, the recommendation index calculation model is:
wherein S represents the recommended index, H represents the summarized heat index, Q represents the degree of performance of the supplier, alpha is a set weight coefficient corresponding to the summarized heat index, beta is a set weight coefficient corresponding to the degree of performance of the supplier, and lambda is a set correction constant.
In one possible design, after screening out the recommended merchandise information, the method further includes:
forming a second commodity recommendation set by using the screened recommended commodity information;
and transmitting the second commodity recommendation set to a document editing end, receiving second commodity recommendation document information corresponding to the second commodity recommendation set fed back by the document editing end, and pushing the second commodity recommendation document information to a target client.
In a second aspect, a commodity recommendation system based on big data analysis is provided, which comprises an acquisition unit, a statistics unit, a summarization unit, a screening unit, a calculation unit, a selection unit and a pushing unit, wherein:
the system comprises an acquisition unit, a storage unit and a storage unit, wherein the acquisition unit is used for acquiring commodity guiding data sets of all electronic commerce platforms and commodity supplying data sets of a local supplier library, the commodity guiding data sets comprise a plurality of platform hot searching commodity information, a plurality of platform hot pushing commodity information and a plurality of platform high sales commodity information, and a hot searching index corresponding to each platform hot searching commodity information, a hot pushing index corresponding to each platform hot pushing commodity information and a sales index corresponding to each platform high sales commodity information, and the commodity supplying data sets comprise a plurality of commodity supplying information and a supplier degree corresponding to each commodity supplying information;
the statistics unit is used for carrying out statistics on the hot search commodity information of each platform, the hot push commodity information of each platform and the high sales commodity information of each platform in the commodity guide data set to obtain a plurality of hot commodity information of each platform, carrying out statistics on the hot search index corresponding to the hot search commodity information of each platform, the hot push index corresponding to the hot push commodity information of each platform and the sales index corresponding to the high sales commodity information of each platform in the commodity guide data set to obtain a hot commodity data set corresponding to the electric commerce platform, and carrying out association and combination on the hot commodity information of each platform and the corresponding hot commodity index of each platform;
the summarizing unit is used for summarizing the popular commodity data sets of all the electronic commerce platforms to obtain summarized commodity data sets, wherein the summarized commodity data sets comprise a plurality of summarized commodity information and summarized popular indexes corresponding to the summarized commodity information;
the screening unit is used for comparing each piece of supplied commodity information in the supplied commodity data set with each piece of summarized commodity information in the summarized commodity data set, screening out a plurality of pieces of recommended commodity information, and determining a summarized popularity index and a supplier performance degree corresponding to each piece of recommended commodity information, wherein the recommended commodity information is not only the corresponding supplied commodity information in the supplied commodity data set but also the corresponding summarized commodity information in the summarized commodity data set;
the computing unit is used for substituting the summarized popularity index and the degree of performance of the suppliers corresponding to each piece of recommended commodity information into a preset recommended index computing model to perform computation so as to obtain a recommended index corresponding to each piece of recommended commodity information;
the selecting unit is used for ranking the recommended commodity information according to the recommendation index corresponding to the recommended commodity information, and selecting a plurality of recommended commodity information with the highest ranking to form a first commodity recommendation set;
the pushing unit is used for transmitting the first commodity recommendation set to the document editing end, receiving the first commodity recommendation document information corresponding to the first commodity recommendation set and fed back by the document editing end, and pushing the first commodity recommendation document information to the target client.
In one possible design, the selecting unit is further configured to compose the selected recommended product information into a second product recommendation set, and the pushing unit is further configured to transmit the second product recommendation set to the document editing end, receive the second product recommendation document information corresponding to the second product recommendation set fed back by the document editing end, and push the second product recommendation document information to the target client.
In a third aspect, there is provided a commodity recommendation system based on big data analysis, comprising:
a memory for storing instructions;
and the processor is used for reading the instructions stored in the memory and executing the commodity recommendation method based on big data analysis according to any one of the first aspects.
In a fourth aspect, there is provided a computer readable storage medium having instructions stored thereon which, when run on a computer, cause the computer to perform the method of any of the first aspects. Also provided is a computer program product comprising instructions which, when run on a computer, cause the computer to perform the big data analysis based commodity recommendation method according to any one of the first aspects.
The beneficial effects are that: according to the invention, large data analysis is performed by acquiring commodity guide data sets of all electronic commerce platforms and commodity supply data sets of a local supplier library, summarized commodity data sets are statistically summarized in multiple dimensions from the directions of a heat search index, a heat push index and a sales volume index according to the commodity guide data sets of all electronic commerce platforms, commodity information of the summarized commodity data sets and commodity information of the commodity supply data sets is compared, intersections of the summarized commodity data sets and commodity information of the commodity supply data sets are extracted to be used as recommended commodity information, a recommended index of the recommended commodity information is calculated according to corresponding summarized heat index and commodity supply performance, and corresponding recommended commodity information is selected according to the recommended index to form a commodity recommended set, so that recommendation document pushing of corresponding commodities is performed according to the commodity recommended set, and efficient and accurate commodity information recommendation processing can be realized. According to the invention, the big data analysis mode is adopted, the commodity preference of the current consumer can be more accurately mastered from the dimensions of various commodity heat information of each electric commodity platform, the supply capacity of the local supplier library is integrated, and the intelligent commodity recommendation more conforming to the individual condition of each merchant is realized.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram showing the steps of the method of example 1 of the present invention;
FIG. 2 is a schematic diagram showing the construction of a system in embodiment 2 of the present invention;
FIG. 3 is a schematic diagram showing the construction of a system in embodiment 3 of the present invention.
Detailed Description
It should be noted that the description of these examples is for aiding in understanding the present invention, but is not intended to limit the present invention. Specific structural and functional details disclosed herein are merely representative of example embodiments of the invention. This invention may, however, be embodied in many alternate forms and should not be construed as limited to the embodiments set forth herein.
It will be appreciated that the term "coupled" is to be interpreted broadly, and may be a fixed connection, a removable connection, or an integral connection, for example, unless explicitly stated and limited otherwise; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the terms in the embodiments can be understood by those of ordinary skill in the art according to the specific circumstances.
In the following description, specific details are provided to provide a thorough understanding of example embodiments. However, it will be understood by those of ordinary skill in the art that the example embodiments may be practiced without these specific details. For example, a system may be shown in block diagrams in order to avoid obscuring the examples with unnecessary detail. In other embodiments, well-known processes, structures, and techniques may be shown without unnecessary detail in order to avoid obscuring the embodiments.
Example 1:
the embodiment provides a commodity recommendation method based on big data analysis, which can be applied to a corresponding big data server, as shown in fig. 1, and comprises the following steps:
s1, acquiring commodity guiding data sets of all electronic commerce platforms and commodity supplying data sets of a local supplier library, wherein the commodity guiding data sets comprise a plurality of platform hot searching commodity information, a plurality of platform hot pushing commodity information and a plurality of platform high sales commodity information, and a hot searching index corresponding to each platform hot searching commodity information, a hot pushing index corresponding to each platform hot pushing commodity information and a sales index corresponding to each platform high sales commodity information, and the commodity supplying data sets comprise a plurality of commodity supplying information and a supplier degree corresponding to each commodity supplying information.
In the implementation, the commodity guiding data set of each e-commerce platform and the commodity supplying data set of the local supplier library can be acquired and acquired in an on-line information capturing or information importing mode. The commodity guiding data set comprises a plurality of platform hot-search commodity information, a plurality of platform hot-push commodity information and a plurality of platform high-sales commodity information, wherein the commodity information can be commodity names or commodity numbers, and a hot-search index corresponding to each platform hot-search commodity information, a hot-push index corresponding to each platform hot-push commodity information and a sales index corresponding to each platform high-sales commodity information. The commodity supply data set comprises a plurality of commodity supply information and commodity supply performance degrees corresponding to the commodity supply information, wherein the commodity supply performance degrees can be determined according to historical commodity supply and performance records of corresponding commodity suppliers.
S2, carrying out statistics on hot search commodity information of each platform, hot pushing commodity information of each platform and high sales commodity information of each platform in commodity guiding data sets to obtain a plurality of hot commodity information of each platform, carrying out statistics on hot search indexes corresponding to the hot search commodity information of each platform, hot pushing indexes corresponding to the hot pushing commodity information of each platform and sales indexes corresponding to the high sales commodity information of each platform in commodity guiding data sets to obtain hot commodity data sets corresponding to the electric commerce platform, and carrying out association and combination on the hot commodity information of each platform and the corresponding hot commodity indexes of the platform.
When the method is implemented, after the commodity guiding data set of each electronic commerce platform is obtained, a plurality of platform hot commodity information can be obtained according to statistics of each platform hot searching commodity information, each platform hot pushing commodity information and each platform high sales commodity information in the commodity guiding data set, wherein the method comprises the step of obtaining a union of each platform hot searching commodity information, each platform hot pushing commodity information and each platform high sales commodity information in the commodity guiding data set to obtain a plurality of platform hot commodity information, and each platform hot commodity information corresponds to a plurality of or one of the plurality of platform hot searching commodity information, the plurality of platform hot pushing commodity information and the plurality of platform high sales commodity information in the commodity guiding data set respectively.
Meanwhile, the platform hot index corresponding to the hot commodity information of each platform can be obtained according to statistics of the hot index corresponding to the hot commodity information of each platform, the hot index corresponding to the hot commodity information of each platform and the sales index corresponding to the sales commodity information of each platform in the commodity guiding data set, and the method comprises the step of taking the hot index corresponding to the hot commodity information of the platform or the sales index corresponding to the sales commodity information of the platform as the platform hot index corresponding to the hot commodity information of the platform when the hot commodity information of the platform only corresponds to one of the hot commodity information of a plurality of platforms, the hot commodity information of the platform and the high sales commodity information of the platform in the commodity guiding data set; and when the platform hot commodity information corresponds to a plurality of the plurality of platform hot searching commodity information, the plurality of platform hot pushing commodity information and the plurality of platform high sales commodity information in the commodity guide data set, adding and summing the hot searching indexes corresponding to the corresponding platform hot searching commodity information and/or the hot pushing indexes corresponding to the corresponding platform hot pushing commodity information and/or the sales indexes corresponding to the corresponding platform high sales commodity information to obtain the platform hot indexes corresponding to the platform hot commodity information.
And finally, associating and combining the hot commodity information of each platform with the corresponding hot commodity index of the platform to obtain a hot commodity data set of the corresponding electronic commerce platform.
And S3, summarizing the popular commodity data sets of all the electronic commerce platforms to obtain summarized commodity data sets, wherein the summarized commodity data sets comprise a plurality of summarized commodity information and summarized popular indexes corresponding to the summarized commodity information.
In the specific implementation, the union sets of the plurality of platform hot commodity information of the platform hot commodity data sets of all electronic commerce can be obtained, a plurality of summarized commodity information is obtained, and each summarized commodity information corresponds to one or a plurality of platform hot commodity information respectively. When summarized commodity information corresponds to only one platform hot commodity information, taking the platform hot index corresponding to the platform hot commodity information as the summarized hot index of the summarized commodity information, and adding and summing the platform hot indexes corresponding to the corresponding platform hot commodity information when the summarized commodity information corresponds to the plurality of platform hot commodity information to obtain the summarized hot index of the summarized commodity information. And finally, associating and combining the summarized commodity information with the corresponding summarized popularity index to obtain a summarized commodity data set.
S4, comparing each piece of supplied commodity information in the supplied commodity data set with each piece of summarized commodity information in the summarized commodity data set, screening out a plurality of pieces of recommended commodity information, and determining a summarized popularity index and a supplier performance degree corresponding to each piece of recommended commodity information, wherein the recommended commodity information is corresponding to each piece of supplied commodity information in the supplied commodity data set and is corresponding to each piece of summarized commodity information in the summarized commodity data set.
In the specific implementation, after the summarized commodity data set is obtained, a supplied commodity data set of a local supplier library is called, intersections are obtained between each supplied commodity information in the supplied commodity data set and each summarized commodity information in the summarized commodity data set, a plurality of recommended commodity information are obtained, each recommended commodity information corresponds to one supplied commodity information in the supplied commodity data set and one summarized commodity information in the summarized commodity data set respectively, and the summarized popularity index of the corresponding summarized commodity information and the supplier performance degree of the corresponding supplied commodity information are used as the summarized popularity index and the supplier performance degree of the recommended commodity information.
S5, substituting the summarized popularity index and the degree of performance of the suppliers corresponding to each piece of recommended commodity information into a preset recommended index calculation model to calculate, and obtaining the recommended index corresponding to each piece of recommended commodity information.
When the method is implemented, after the recommended commodity information is determined, the summarized popularity index and the degree of performance of the suppliers corresponding to the recommended commodity information can be substituted into a preset recommended index calculation model to calculate, and the recommended index corresponding to the recommended commodity information is obtained. The recommendation index calculation model is as follows:
the method comprises the steps of S representing recommended indexes, H representing summarized heat indexes, Q representing supplier performance degrees, alpha being a set weight coefficient corresponding to the summarized heat indexes, beta being a set weight coefficient corresponding to the supplier performance degrees, lambda being a set correction constant, and alpha, beta and lambda being set according to actual calculation requirements.
S6, ranking the recommended commodity information according to the recommendation index corresponding to the recommended commodity information, and selecting a plurality of recommended commodity information with the highest ranking to form a first commodity recommendation set.
In the specific implementation, after the recommendation indexes corresponding to the recommended commodity information are calculated, ranking the recommended commodity information according to the recommendation indexes corresponding to the recommended commodity information, and selecting a plurality of recommended commodity information with the highest ranking to form a first commodity recommendation set.
S7, transmitting the first commodity recommendation set to a document editing end, receiving first commodity recommendation document information corresponding to the first commodity recommendation set and fed back by the document editing end, and pushing the first commodity recommendation document information to a target client.
In the implementation, after the server finally obtains the first commodity recommendation set, the first commodity recommendation set is transmitted to the document editing end, the document editing end carries out pushing document editing of corresponding commodities according to the first commodity recommendation set, the edited first commodity recommendation document information is fed back to the server, and then the server pushes the first commodity recommendation document information to the target client. The server may also skip calculation and ranking selection of the recommendation index after screening out a plurality of pieces of recommended commodity information in step S4, directly use each piece of recommended commodity information screened out to form a second commodity recommendation set, transmit the second commodity recommendation set to the document editing end, make the document editing end perform pushing document editing of the corresponding commodity according to the second commodity recommendation set, feed back the edited second commodity recommendation document information to the server, and then push the second commodity recommendation document information to the target client.
Example 2:
the embodiment provides a commodity recommendation system based on big data analysis, as shown in fig. 2, including an acquisition unit, a statistics unit, a summarization unit, a screening unit, a calculation unit, a selection unit and a pushing unit, wherein:
the system comprises an acquisition unit, a storage unit and a storage unit, wherein the acquisition unit is used for acquiring commodity guiding data sets of all electronic commerce platforms and commodity supplying data sets of a local supplier library, the commodity guiding data sets comprise a plurality of platform hot searching commodity information, a plurality of platform hot pushing commodity information and a plurality of platform high sales commodity information, and a hot searching index corresponding to each platform hot searching commodity information, a hot pushing index corresponding to each platform hot pushing commodity information and a sales index corresponding to each platform high sales commodity information, and the commodity supplying data sets comprise a plurality of commodity supplying information and a supplier degree corresponding to each commodity supplying information;
the statistics unit is used for carrying out statistics on the hot search commodity information of each platform, the hot push commodity information of each platform and the high sales commodity information of each platform in the commodity guide data set to obtain a plurality of hot commodity information of each platform, carrying out statistics on the hot search index corresponding to the hot search commodity information of each platform, the hot push index corresponding to the hot push commodity information of each platform and the sales index corresponding to the high sales commodity information of each platform in the commodity guide data set to obtain a hot commodity data set corresponding to the electric commerce platform, and carrying out association and combination on the hot commodity information of each platform and the corresponding hot commodity index of each platform;
the summarizing unit is used for summarizing the popular commodity data sets of all the electronic commerce platforms to obtain summarized commodity data sets, wherein the summarized commodity data sets comprise a plurality of summarized commodity information and summarized popular indexes corresponding to the summarized commodity information;
the screening unit is used for comparing each piece of supplied commodity information in the supplied commodity data set with each piece of summarized commodity information in the summarized commodity data set, screening out a plurality of pieces of recommended commodity information, and determining a summarized popularity index and a supplier performance degree corresponding to each piece of recommended commodity information, wherein the recommended commodity information is not only the corresponding supplied commodity information in the supplied commodity data set but also the corresponding summarized commodity information in the summarized commodity data set;
the computing unit is used for substituting the summarized popularity index and the degree of performance of the suppliers corresponding to each piece of recommended commodity information into a preset recommended index computing model to perform computation so as to obtain a recommended index corresponding to each piece of recommended commodity information;
the selecting unit is used for ranking the recommended commodity information according to the recommendation index corresponding to the recommended commodity information, and selecting a plurality of recommended commodity information with the highest ranking to form a first commodity recommendation set;
the pushing unit is used for transmitting the first commodity recommendation set to the document editing end, receiving the first commodity recommendation document information corresponding to the first commodity recommendation set and fed back by the document editing end, and pushing the first commodity recommendation document information to the target client.
Optionally, the selecting unit is further configured to compose the selected recommended product information into a second product recommendation set, and the pushing unit is further configured to transmit the second product recommendation set to the document editing end, receive the second product recommendation document information corresponding to the second product recommendation set fed back by the document editing end, and push the second product recommendation document information to the target client.
Example 3:
the present embodiment provides a commodity recommendation system based on big data analysis, as shown in fig. 3, at a hardware level, including:
the data interface is used for establishing data butt joint between the processor and an external data source;
a memory for storing instructions;
and a processor for reading the instructions stored in the memory and executing the commodity recommendation method based on big data analysis in embodiment 1 according to the instructions.
Optionally, the system further comprises an internal bus. The processor and memory and data interfaces may be interconnected by an internal bus, which may be an ISA (Industry Standard Architecture ) bus, a PCI (Peripheral Component Interconnect, peripheral component interconnect standard) bus, or an EISA (Extended Industry Standard Architecture ) bus, among others. The buses may be classified as address buses, data buses, control buses, etc.
The Memory may include, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), flash Memory (Flash Memory), first-in-first-Out Memory (First Input First Output, FIFO), and/or first-in Last Out Memory (FILO), etc. The processor may be a general-purpose processor including a central processing unit (CentralProcessing Unit, CPU), a network processor (Network Processor, NP), etc.; but also digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
Example 4:
the present embodiment provides a computer-readable storage medium having instructions stored thereon, which when executed on a computer, cause the computer to perform the commodity recommendation method based on big data analysis in embodiment 1. The computer readable storage medium refers to a carrier for storing data, and may include, but is not limited to, a floppy disk, an optical disk, a hard disk, a flash Memory, and/or a Memory Stick (Memory Stick), etc., where the computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable system.
The present embodiment also provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the commodity recommendation method based on big data analysis of embodiment 1. Wherein the computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable system.
Finally, it should be noted that: the foregoing description is only of the preferred embodiments of the invention and is not intended to limit the scope of the invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The commodity recommendation method based on big data analysis is characterized by comprising the following steps:
acquiring commodity guiding data sets of all electronic commerce platforms and commodity supplying data sets of a local supplier library, wherein the commodity guiding data sets comprise a plurality of platform hot searching commodity information, a plurality of platform hot pushing commodity information and a plurality of platform high-sales commodity information, and a hot searching index corresponding to the platform hot searching commodity information, a hot pushing index corresponding to the platform hot pushing commodity information and a sales index corresponding to the platform high-sales commodity information;
acquiring a plurality of platform hot commodity information according to the hot searching commodity information of each platform, the hot pushing commodity information of each platform and the high sales commodity information of each platform in the commodity guiding data set, acquiring a platform hot index corresponding to the hot commodity information of each platform according to the hot searching index corresponding to the hot searching commodity information of each platform, the hot pushing index corresponding to the hot pushing commodity information of each platform and the sales index corresponding to the high sales commodity information of each platform in the commodity guiding data set, and performing association combination on the hot commodity information of each platform and the corresponding platform hot index to acquire a hot commodity data set of the corresponding electronic commerce platform;
summarizing the popular commodity data sets of all the electronic commerce platforms to obtain summarized commodity data sets, wherein the summarized commodity data sets comprise a plurality of summarized commodity information and summarized popular indexes corresponding to the summarized commodity information;
comparing each piece of supplied commodity information in the supplied commodity data set with each piece of summarized commodity information in the summarized commodity data set, screening out a plurality of pieces of recommended commodity information, and determining a summarized heat index and a supplier performance degree corresponding to each piece of recommended commodity information, wherein the recommended commodity information is corresponding supplied commodity information in the supplied commodity data set and corresponding summarized commodity information in the summarized commodity data set;
substituting the summarized popularity index and the degree of performance of the suppliers corresponding to each piece of recommended commodity information into a preset recommended index calculation model to calculate so as to obtain a recommended index corresponding to each piece of recommended commodity information;
ranking the recommended commodity information according to the recommendation index corresponding to the recommended commodity information, and selecting a plurality of recommended commodity information with highest ranking to form a first commodity recommendation set;
and transmitting the first commodity recommendation set to a document editing end, receiving first commodity recommendation document information corresponding to the first commodity recommendation set and fed back by the document editing end, and pushing the first commodity recommendation document information to a target client.
2. The commodity recommendation method based on big data analysis according to claim 1, wherein the statistics of the commodity information according to each platform hot search in the commodity guiding dataset, each platform hot push commodity information and each platform high sales commodity information to obtain a plurality of platform hot commodity information comprises:
and acquiring a union set of the hot search commodity information of each platform, the hot push commodity information of each platform and the high-sales commodity information of each platform in the commodity guiding data set to obtain a plurality of hot commodity information of each platform, wherein the hot commodity information of each platform corresponds to a plurality of or one of the hot search commodity information of the plurality of platforms, the hot push commodity information of the plurality of platforms and the high-sales commodity information of the plurality of platforms in the commodity guiding data set.
3. The commodity recommendation method based on big data analysis according to claim 2, wherein the step of obtaining the platform popularity index corresponding to the popular commodity information of each platform according to statistics of the heat search index corresponding to the heat search commodity information of each platform, the heat push index corresponding to the heat push commodity information of each platform and the sales index corresponding to the high sales commodity information of each platform in the commodity guide data set includes:
when the platform hot commodity information only corresponds to one of a plurality of platform hot searching commodity information, a plurality of platform hot pushing commodity information and a plurality of platform high sales commodity information in the commodity guiding data set, taking a hot searching index corresponding to the platform hot searching commodity information or a hot pushing index corresponding to the platform hot pushing commodity information or a sales index corresponding to the platform high sales commodity information as a platform hot index corresponding to the platform hot commodity information; and when the platform hot commodity information corresponds to a plurality of the plurality of platform hot searching commodity information, the plurality of platform hot pushing commodity information and the plurality of platform high sales commodity information in the commodity guide data set, adding and summing the hot searching indexes corresponding to the corresponding platform hot searching commodity information and/or the hot pushing indexes corresponding to the corresponding platform hot pushing commodity information and/or the sales indexes corresponding to the corresponding platform high sales commodity information to obtain the platform hot indexes corresponding to the platform hot commodity information.
4. The commodity recommendation method based on big data analysis according to claim 1, wherein the step of summarizing the hot commodity data sets of each e-commerce platform to obtain a summarized commodity data set comprises the steps of:
acquiring a union set of a plurality of platform hot commodity information of each electronic commerce platform hot commodity data set to obtain a plurality of summarized commodity information, wherein each summarized commodity information corresponds to one or more platform hot commodity information respectively;
when summarized commodity information corresponds to only one platform hot commodity information, taking a platform hot index corresponding to the platform hot commodity information as a summarized hot index of the summarized commodity information, adding and summing the platform hot indexes corresponding to the corresponding platform hot commodity information when the summarized commodity information corresponds to the plurality of platform hot commodity information, and obtaining the summarized hot index of the summarized commodity information;
and associating and combining the summarized commodity information with the corresponding summarized heat index to obtain a summarized commodity data set.
5. The commodity recommending method based on big data analysis according to claim 1, wherein comparing each piece of supplied commodity information in the supplied commodity data set with each piece of summarized commodity information in the summarized commodity data set, screening out a plurality of pieces of recommended commodity information, and determining a summarized popularity index and a supplier performance corresponding to each piece of recommended commodity information, comprises:
and acquiring intersections of each piece of supplied commodity information in the supplied commodity data set and each piece of summarized commodity information in the summarized commodity data set to obtain a plurality of pieces of recommended commodity information, wherein each piece of recommended commodity information corresponds to one piece of supplied commodity information in the supplied commodity data set and one piece of summarized commodity information in the summarized commodity data set respectively, and taking the summarized popularity index of the corresponding summarized commodity information and the degree of the performance of the supplier corresponding to the supplied commodity information as the summarized popularity index and the degree of the performance of the supplier of the recommended commodity information.
6. The commodity recommendation method based on big data analysis according to claim 1, wherein the recommendation index calculation model is:
wherein S represents the recommended index, H represents the summarized heat index, Q represents the degree of performance of the supplier, alpha is a set weight coefficient corresponding to the summarized heat index, beta is a set weight coefficient corresponding to the degree of performance of the supplier, and lambda is a set correction constant.
7. The commodity recommendation method based on big data analysis according to claim 1, wherein after screening out a plurality of recommended commodity information, the method further comprises:
forming a second commodity recommendation set by using the screened recommended commodity information;
and transmitting the second commodity recommendation set to a document editing end, receiving second commodity recommendation document information corresponding to the second commodity recommendation set fed back by the document editing end, and pushing the second commodity recommendation document information to a target client.
8. The commodity recommendation system based on big data analysis is characterized by comprising an acquisition unit, a statistics unit, a summarizing unit, a screening unit, a calculation unit, a selection unit and a pushing unit, wherein:
the system comprises an acquisition unit, a storage unit and a storage unit, wherein the acquisition unit is used for acquiring commodity guiding data sets of all electronic commerce platforms and commodity supplying data sets of a local supplier library, the commodity guiding data sets comprise a plurality of platform hot searching commodity information, a plurality of platform hot pushing commodity information and a plurality of platform high sales commodity information, and a hot searching index corresponding to each platform hot searching commodity information, a hot pushing index corresponding to each platform hot pushing commodity information and a sales index corresponding to each platform high sales commodity information, and the commodity supplying data sets comprise a plurality of commodity supplying information and a supplier degree corresponding to each commodity supplying information;
the statistics unit is used for carrying out statistics on the hot search commodity information of each platform, the hot push commodity information of each platform and the high sales commodity information of each platform in the commodity guide data set to obtain a plurality of hot commodity information of each platform, carrying out statistics on the hot search index corresponding to the hot search commodity information of each platform, the hot push index corresponding to the hot push commodity information of each platform and the sales index corresponding to the high sales commodity information of each platform in the commodity guide data set to obtain a hot commodity data set corresponding to the electric commerce platform, and carrying out association and combination on the hot commodity information of each platform and the corresponding hot commodity index of each platform;
the summarizing unit is used for summarizing the popular commodity data sets of all the electronic commerce platforms to obtain summarized commodity data sets, wherein the summarized commodity data sets comprise a plurality of summarized commodity information and summarized popular indexes corresponding to the summarized commodity information;
the screening unit is used for comparing each piece of supplied commodity information in the supplied commodity data set with each piece of summarized commodity information in the summarized commodity data set, screening out a plurality of pieces of recommended commodity information, and determining a summarized popularity index and a supplier performance degree corresponding to each piece of recommended commodity information, wherein the recommended commodity information is not only the corresponding supplied commodity information in the supplied commodity data set but also the corresponding summarized commodity information in the summarized commodity data set;
the computing unit is used for substituting the summarized popularity index and the degree of performance of the suppliers corresponding to each piece of recommended commodity information into a preset recommended index computing model to perform computation so as to obtain a recommended index corresponding to each piece of recommended commodity information;
the selecting unit is used for ranking the recommended commodity information according to the recommendation index corresponding to the recommended commodity information, and selecting a plurality of recommended commodity information with the highest ranking to form a first commodity recommendation set;
the pushing unit is used for transmitting the first commodity recommendation set to the document editing end, receiving the first commodity recommendation document information corresponding to the first commodity recommendation set and fed back by the document editing end, and pushing the first commodity recommendation document information to the target client.
9. The merchandise recommendation system based on big data analysis according to claim 8, wherein the selecting unit is further configured to compose the selected recommended merchandise information into a second merchandise recommendation set, and the pushing unit is further configured to transmit the second merchandise recommendation set to a document editing end, receive second merchandise recommendation document information corresponding to the second merchandise recommendation set fed back by the document editing end, and push the second merchandise recommendation document information to the target client.
10. The commodity recommendation system based on big data analysis is characterized by comprising:
a memory for storing instructions;
a processor for reading the instructions stored in the memory and executing the commodity recommendation method based on big data analysis according to the instructions as set forth in any one of claims 1 to 7.
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| CN117575747A (en) * | 2024-01-19 | 2024-02-20 | 山东街景智能制造科技股份有限公司 | Personalized recommendation method based on user analysis |
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| CN117575747A (en) * | 2024-01-19 | 2024-02-20 | 山东街景智能制造科技股份有限公司 | Personalized recommendation method based on user analysis |
| CN117575747B (en) * | 2024-01-19 | 2024-04-05 | 山东街景智能制造科技股份有限公司 | Personalized recommendation method based on user analysis |
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