CN116542737A - Big data processing method and system of cross-border e-commerce platform - Google Patents
Big data processing method and system of cross-border e-commerce platform Download PDFInfo
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Abstract
The embodiment of the application discloses a big data processing method and a big data processing system for a cross-border e-commerce platform, which are applied to the cross-border e-commerce platform, wherein the method comprises the following steps: acquiring target character attribute information of a target user; acquiring a browsing record of the target user in a preset time, and extracting N commodities based on the browsing record, wherein each commodity corresponds to commodity attribute information, and N is an integer greater than 1; expanding and recommending the N commodities by utilizing the target character attribute information and the commodity attribute information of the N commodities to obtain M commodities, wherein M is an integer larger than N; screening the M commodities to obtain K commodities, wherein K is an integer greater than or equal to a and less than or equal to M, a is a set value, and a is an integer greater than 1; determining display parameters of the K commodities to obtain K display parameters; pushing the K commodities according to the K display parameters. By adopting the embodiment of the application, the shopping efficiency of the user can be improved.
Description
Technical Field
The application relates to the technical field of cross-border e-commerce, in particular to a big data processing method and a big data processing system of a cross-border e-commerce platform.
Background
The globalization development brings a great deal of demands for overseas shopping, cross-border electronic commerce is generated, global commodity transactions can be carried out on the cross-border electronic commerce platform, and the method is extremely convenient for buyers and sellers. In the big data age, the continuous generation and updating of data causes that valuable data in the whole big data needs to be effectively analyzed, so that at present, a user often needs to browse a large number of commodities to select the commodities needed by the user, and therefore shopping efficiency of the user is reduced.
Disclosure of Invention
The embodiment of the application provides a big data processing method and a big data processing system for a cross-border e-commerce platform, which can improve the shopping efficiency of users.
In a first aspect, an embodiment of the present application provides a big data processing method of a cross-border e-commerce platform, which is applied to the cross-border e-commerce platform, and the method includes:
acquiring target character attribute information of a target user;
acquiring a browsing record of the target user in a preset time, and extracting N commodities based on the browsing record, wherein each commodity corresponds to commodity attribute information, and N is an integer greater than 1;
expanding and recommending the N commodities by utilizing the target character attribute information and the commodity attribute information of the N commodities to obtain M commodities, wherein M is an integer larger than N;
Screening the M commodities to obtain K commodities, wherein K is an integer greater than or equal to a and less than or equal to M, a is a set value, and a is an integer greater than 1;
determining display parameters of the K commodities to obtain K display parameters;
pushing the K commodities according to the K display parameters.
In a second aspect, an embodiment of the present application provides a big data processing system of a cross-border e-commerce platform, applied to the cross-border e-commerce platform, where the system includes: the device comprises an acquisition unit, a recommendation unit, a screening unit, a determination unit and a pushing unit, wherein,
the acquisition unit is used for acquiring target character attribute information of a target user; acquiring a browsing record of the target user in a preset time, and extracting N commodities based on the browsing record, wherein each commodity corresponds to commodity attribute information, and N is an integer greater than 1;
the recommending unit is used for expanding and recommending the N commodities by utilizing the target character attribute information and the commodity attribute information of the N commodities to obtain M commodities, wherein M is an integer larger than N;
the screening unit is used for screening the M commodities to obtain K commodities, wherein K is an integer greater than or equal to a and less than or equal to M, a is a set value, and a is an integer greater than 1;
The determining unit is used for determining the display parameters of the K commodities to obtain K display parameters;
the pushing unit is used for pushing the K commodities according to the K display parameters.
In a third aspect, an embodiment of the present application provides a cross-border e-commerce platform, including a processor, a memory, a human body communication chip, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for performing steps in the first aspect of the embodiments of the present application.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, where the computer program causes a computer to perform some or all of the steps as described in the first aspect of the embodiments of the present application.
In a fifth aspect, embodiments of the present application provide a computer program product, wherein the computer program product comprises a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.
By implementing the embodiment of the application, the following beneficial effects are achieved:
it can be seen that the big data processing method and system of the cross-border e-commerce platform described in the embodiments of the present application are applied to the cross-border e-commerce platform, obtain target character attribute information of a target user, obtain browsing records of the target user within a preset time, extract N commodities based on the browsing records, each commodity corresponds to one commodity attribute information, N is an integer greater than 1, expand and recommend N commodities by using the target character attribute information and the commodity attribute information of the N commodities to obtain M commodities, M is an integer greater than N, screen the M commodities to obtain K commodities, K is an integer greater than or equal to a and less than or equal to M, a is a set value, a is an integer greater than 1, display parameters of K commodities are determined, and obtain K display parameters, the target character attribute information reflects self-purchasing habit of the user, the commodity attribute information reflects the preference of the user on the commodity, not only can identify the shopping habit of the user, but also can realize intelligent commodity selection based on related information of browsing conditions of the user and shopping habit expansion and push commodity, K is an integer greater than N commodity, K is obtained, K is an integer greater than the commodity, K is a commodity can be pushed, and the commodity can be pushed according to the shopping habit of the user, and the shopping habit of the user has certain push efficiency, and the commodity can be pushed by the user has a certain push efficiency, and the commodity has good push performance has the push performance.
Drawings
In order to more clearly illustrate the embodiments of the present application 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 below, it being obvious that the drawings in the following description are only some embodiments of the present application, 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 flow chart of a big data processing method of a cross-border e-commerce platform according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of a cross-border e-commerce platform according to an embodiment of the present application;
FIG. 3 is a functional block diagram of a big data processing system of a cross-border e-commerce platform according to an embodiment of the present application.
Detailed Description
In order to make the present application solution better understood by those skilled in the art, the following description will clearly and completely describe the technical solution in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
The terms first, second and the like in the description and in the claims of the present application and in the above-described figures, are used for distinguishing between different objects and not for describing a particular sequential order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed steps or elements but may include other steps or elements not listed or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the present application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.
In the embodiment of the application, the cross-border e-commerce platform may include a server, and the server may include a cloud server.
The embodiments of the present application are described in detail below.
Referring to fig. 1, fig. 1 is a flow chart of a big data processing method of a cross-border e-commerce platform, which is provided in an embodiment of the present application and is applied to the cross-border e-commerce platform, where the big data processing method of the cross-border e-commerce platform may include the following steps:
101. and acquiring target character attribute information of the target user.
In the embodiment of the present application, the target person attribute information may include at least one of the following: the user identity, user location, gender, user income level, user occupation, user preference information, user nationality, user age, shopping purpose, etc., are not limited herein.
Wherein the user preference information may include at least one of: user preference color, user preference style, user preference, and the like, without limitation. Shopping purposes may include at least one of: purchase for oneself, purchase for the user, gift, collection, etc., without limitation.
In the specific implementation, the cross-border e-commerce platform can communicate with the terminal of the target user, and after the target user logs in the cross-border e-commerce platform, the target character attribute information of the target user can be acquired.
102. And acquiring a browsing record of the target user in a preset time, and extracting N commodities based on the browsing record, wherein each commodity corresponds to commodity attribute information, and N is an integer greater than 1.
In this embodiment of the present application, the preset time may be preset or default, for example, the browsing record of the last 1 hour may be obtained.
Wherein the commodity attribute information may include at least one of: the commodity price, the commodity brand, the commodity size, the commodity shape, the commodity inventory condition, the commodity postal fee, the commodity transportation rate, the commodity color, the commodity name, the commodity warranty condition, the commodity insurance (such as whether to package the returned commodity, the false one claim three, etc.), the commodity age limit condition, etc., are not limited herein.
In the embodiment of the application, the cross-border e-commerce platform can acquire browsing records of one or more applications about the target user within a preset time. The one or more applications may be bound with the cross-border e-commerce platform.
In a specific implementation, since the browsing record includes related information of the commodity, the cross-border e-commerce platform can acquire the browsing record of the target user in a preset time, and extract N commodities based on the browsing record, wherein each commodity corresponds to one commodity attribute information, and N is an integer greater than 1.
Optionally, the extracting N commodities based on the browsing record in step 102 may include the following steps:
21. carrying out commodity identification on the browsing records to obtain P commodities, wherein P is an integer greater than or equal to N;
22. determining the browsing duration of each commodity in the P commodities to obtain P total browsing durations;
23. the browsing times of each commodity in the P commodities are truly obtained;
24. determining browsing attention positions of each commodity in the P commodities to obtain P attention position sets;
25. determining the attention degree of each commodity in the P commodities according to the P browsing time lengths, the P browsing times and the P attention position sets to obtain P attention degrees;
26. and selecting N attention degrees larger than a set threshold value from the P attention degrees, and determining commodities corresponding to the N attention degrees.
In this embodiment of the present application, the set threshold may be preset or default.
In a specific implementation of the embodiment of the present application, the browsing record may be used for identifying the commodity to obtain P commodities, where P is an integer greater than or equal to N, and the browsing duration of each commodity in the P commodities may also be determined to obtain P total browsing durations, that is, the total browsing duration may be the total browsing duration of browsing a certain commodity in a preset time, the higher the browsing total duration is, the higher the attention of the user to the commodity is, and otherwise, the shorter the total browsing duration is, the lower the attention of the user to the commodity is. The browsing times of each commodity in the P commodities can be truly obtained, and the higher the browsing times are, the higher the attention of the user to the commodity is, otherwise, the lower the browsing times are, the lower the attention of the user to the commodity is.
In addition, in the embodiment of the present application, the browsing attention position of each commodity in the P commodities may be determined by using an eye tracking technology, so as to obtain P attention position sets, where the browsing page records related information of the commodity, and keywords corresponding to different positions are different, where the keywords may include at least one of the following: characters, chinese characters, pictures and the like are not limited herein, and different keywords have different corresponding attention degrees, so that attention positions of attention of a user can be identified whether the user really is interested in the commodity, further, the commodity pushing efficiency is ensured, and the shopping efficiency is improved.
And then, the attention degree of each commodity in the P commodities can be determined according to the P browsing time periods, the P browsing times and the P attention position sets, the P attention degrees are obtained, namely, the browsing time periods and the attention positions of the commodities are considered, namely, the detail information of the commodities are deeply focused by a user, so that the attention degree of the user on the commodities is identified, N attention degrees which are larger than a set threshold value in the P attention degrees can be selected, the commodities corresponding to the N attention degrees are determined, further, the commodity which is focused by the user most can be deeply selected, and on the basis, the commodity pushing is completed, so that the intention of the user can be deeply and accurately analyzed, the shopping efficiency can be improved, the shopping experience of the user can be improved, and the accurate pushing and accurate shopping can be realized.
Further, optionally, the step 25 may determine the attention degree of each commodity in the P commodities according to the P browsing total durations, the P browsing times and the P attention position sets, to obtain P attention degrees, and may include the following steps:
251. determining a keyword set corresponding to each focus position set in the P focus position sets to obtain P keyword sets;
252. determining browsing time length and attention score corresponding to each keyword in the P keyword sets, determining weight corresponding to the browsing time length corresponding to each keyword in the P keyword sets, and carrying out weighted operation on the attention score corresponding to each keyword in the P keyword sets and the corresponding weight to obtain P first reference attention scores;
253. confirming attention scores corresponding to the P browsing total time lengths to obtain P second reference attention scores;
254. according to the P browsing total durations and the P browsing times, P average browsing durations are determined;
255. determining weight pairs corresponding to the P average browsing durations to obtain P weight pairs;
256. and carrying out weighted operation on the P first reference attention scores and the P second reference attention scores according to the P weights to obtain the P attention degrees.
In this embodiment of the present application, each focus position set in the P focus position sets may include a plurality of focus positions, and the cross-border e-commerce platform may determine a keyword set corresponding to each focus position set in the P focus position sets, to obtain P keyword sets, that is, may intercept an image for each focus position in each focus position set, to perform character recognition, to obtain corresponding keywords, and then combine these keywords into the keyword sets.
Then, the eye tracking technology can be used for determining the browsing duration corresponding to each keyword in the P keyword sets, and determining the attention score corresponding to each keyword in the P keyword sets according to the mapping relation between the preset keywords and the attention score. And determining a weight corresponding to the browsing duration of each keyword in the P keyword sets according to a mapping relation between the preset browsing duration of the keywords and the weight, and then carrying out weighted operation on the attention score and the corresponding weight corresponding to each keyword in the P keyword sets to obtain P first reference attention scores, namely, each keyword set corresponds to one first reference attention score.
And then, according to the mapping relation between the preset browsing total duration and the attention scores, the attention scores corresponding to the P browsing total durations can be determined, and P second reference attention scores are obtained. And determining P average browsing time lengths according to the P browsing total time lengths and the P browsing times, namely dividing each browsing total time length by the corresponding browsing times to obtain the corresponding average browsing time lengths. The mapping relation between the preset average browsing duration and the weight pair can be stored in advance, the weight pair can comprise a first weight and a second weight, the sum of the first weight and the second weight is 1, the first weight is a weight corresponding to the first reference attention score, and the second weight is a weight corresponding to the second reference attention score. The method comprises the steps of determining weight pairs corresponding to P average browsing time lengths based on a mapping relation between the preset average browsing time lengths and the weight pairs, obtaining P weight pairs, carrying out weighted operation on P first reference attention scores and P second reference attention scores according to the P weight pairs, obtaining P attention degrees, and directly taking a result of the weighted operation as the attention degree, or presetting a mapping relation between the preset attention score and the attention degree, and converting the attention score into the attention degree based on the mapping relation.
In the example, the relevance between the keyword and the attention score of each attention position is deeply considered in the browsing process of the user, and the relevance degree between the content and the commodity of the attention of the user is deeply mined from browsing details so as to analyze the shopping intention or the desire degree of the user, and the attention degree of the commodity of the user is assisted to be identified by combining the browsing times and the browsing total duration, and then the keyword and the commodity are combined together, so that the attention degree of the commodity of the user, namely the purchasing desire, is deeply analyzed, and especially when the commodity of the user is more in attention, the commodity to be pushed can be ordered based on the attention degree, thereby being beneficial to accurate pushing, avoiding pushing too many commodities to influence the judgment of the user, but reducing the shopping efficiency, pushing the appointed number of commodities, and ensuring the shopping efficiency of the user.
103. And expanding and recommending the N commodities by utilizing the target character attribute information and the commodity attribute information of the N commodities to obtain M commodities, wherein M is an integer larger than N.
In the embodiment of the application, the target character attribute information reflects the self purchasing habit of the user, the commodity attribute information reflects the preference of the user to the commodity, and the target character attribute information and the commodity attribute information are combined to push the commodity which accords with the browsing (user taste) of the user, and the pushed commodity also deeply accords with the self purchasing habit of the user, so that the shopping efficiency is improved.
Optionally, the target person attribute information includes at least one of: user identity, user income level, user preference color, user nationality, user age, shopping purpose;
the commodity attribute information includes at least one of: commodity price, commodity brand, commodity size, commodity shape, commodity inventory condition, commodity postal rate, commodity transportation rate, commodity color and commodity name;
step 103, performing expansion recommendation on the N products by using the target character attribute information and the product attribute information of the N products to obtain M products, may include the following steps:
31. carrying out commodity searching according to commodity attribute information of each commodity in the commodity attribute information of the N commodities and the target character attribute information to obtain N commodity sets;
32. and carrying out de-duplication treatment on the N commodity sets to obtain the M commodities.
According to the embodiment of the application, the cross-border electronic commerce platform can perform commodity searching in the commodity database based on commodity attribute information and target character attribute information of each commodity in the commodity attribute information of N commodities to obtain N commodity sets, namely, each commodity can push a corresponding series of commodities, then the N commodity sets are subjected to duplicate removal processing to obtain M commodities, namely, the same commodities possibly exist, the commodities can be filtered, further, the pushed commodities are more accurate, in addition, the target character attribute information reflects the self-purchasing habit of a user, the commodity attribute information reflects the preference of the user to the commodity, the two commodities are combined, so that the commodity meeting the browsing (user taste) of the user can be pushed, the pushed commodity also deeply meets the self-purchasing habit of the user, and shopping efficiency is improved.
104. And screening the M commodities to obtain K commodities, wherein K is an integer greater than or equal to a and less than or equal to M, a is a set value, and a is an integer greater than 1.
In order to prevent excessive commodities, the M commodities can be screened to obtain K commodities, K is an integer greater than or equal to a and less than or equal to M, a is a set value, the set value can be preset or the system defaults, and a is an integer greater than 1. The screening criteria may include at least one of: the top K products with the highest reserve price, the top K products with the lowest reserve price, the K products with the highest reserve preference, and the like are not limited herein.
105. And determining the display parameters of the K commodities to obtain K display parameters.
In this embodiment of the present application, different commodities may correspond to different display parameters, and the display parameters may include at least one of the following: presentation priority, presentation location, presentation duration, presentation mode, etc., are not limited herein. The display means may comprise at least one of: video presentation, image presentation, location presentation, voice presentation, etc., without limitation.
Optionally, the step 105 of determining the display parameters of the K commodities to obtain K display parameters may include the following steps:
51. Determining the user score of each commodity in the K commodities to obtain K user scores;
52. and determining display priorities based on the K user scores to obtain K display priorities.
In this embodiment of the present invention, different commodities may correspond to different user scores, and further, the user score of each commodity in the K commodities may be determined to obtain K user scores, and then, based on the K user scores, the display priority is determined to obtain K display priorities, for example, a mapping relationship between a preset user score and the display priority may be preset, and based on the mapping relationship, the display priority of each user score in the K user scores may be determined to obtain K display priorities.
106. Pushing the K commodities according to the K display parameters.
In the specific implementation, K commodities can be pushed according to K showing parameters, namely, commodities conforming to browsing (user taste) of a user can be pushed, the pushed commodities also deeply conform to self purchasing habits of the user, and shopping efficiency is improved.
Optionally, the steps 105 to 106 may determine display parameters of the K commodities to obtain K display parameters, and push the K commodities according to the K display parameters, which may include the following steps:
S1, classifying the K commodities to obtain class a commodities, wherein each class of commodities comprises at least one commodity, and a is a positive integer;
s2, extracting key information of each commodity in the class a commodity to obtain a group of key information;
s3, generating a commodity comparison tables from the a group of key information to obtain a commodity comparison tables;
and S4, pushing the K commodities according to the a commodity comparison tables.
In the embodiment of the application, the cross-border e-commerce platform can classify K commodities to obtain class a commodities, each class of commodities comprises at least one commodity, a is a positive integer, and then key information of each commodity in the class a commodities is extracted to obtain a group of key information, wherein the key information can comprise at least one of the following: the commodity name, commodity price, links, commodity material, commodity shelf life, commodity user score, commodity complexity of use, commodity picture, commodity video effect, etc. are not limited herein. And then, the a group of key information can be generated into a commodity comparison table to obtain a commodity comparison table, namely, each commodity comparison table can be an excel table, each row can be each commodity, each column is each key information of the commodity, and K commodities are pushed according to the a commodity comparison table, so that the commodities needing to be pushed can be classified, the key information of each type of commodity can be compared, a user can compare a plurality of different commodities on the same page, the key information of the commodity is clear at a glance, and the shopping efficiency of the user is greatly improved.
It can be seen that the big data processing method of the cross-border e-commerce platform described in the embodiment of the application is applied to the cross-border e-commerce platform, the target character attribute information of the target user is obtained, the browsing record of the target user in the preset time is obtained, N commodities are extracted based on the browsing record, each commodity corresponds to one commodity attribute information, N is an integer greater than 1, the N commodities are expanded and recommended by utilizing the target character attribute information and the commodity attribute information of the N commodities to obtain M commodities, M is an integer greater than N, the M commodities are screened to obtain K commodities, K is an integer greater than or equal to a and less than or equal to M, a is a set value, a is an integer greater than 1, the showing parameters of the K commodities are determined, K showing parameters are obtained, the target character attribute information reflects the self-purchasing of the user, the commodity attribute information reflects the preference of the user on the commodities, the intelligent commodity selection is realized, the commodity selection can be realized on the basis of the commodity related information of the browsing condition of the user, the commodity is expanded and the commodity is further realized, the commodity intelligent selection can be quantitatively selected, the commodity can be further quantitatively selected, the commodity can be pushed by the user has a certain shopping habit, and the commodity can not be pushed by the user's own shopping habit, and the commodity has good pushing efficiency.
In accordance with the above embodiments, referring to fig. 2, fig. 2 is a schematic structural diagram of a cross-border e-commerce platform provided in the embodiment of the present application, where the cross-border e-commerce platform includes a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and in the embodiment of the present application, the programs include instructions for performing the following steps:
acquiring target character attribute information of a target user;
acquiring a browsing record of the target user in a preset time, and extracting N commodities based on the browsing record, wherein each commodity corresponds to commodity attribute information, and N is an integer greater than 1;
expanding and recommending the N commodities by utilizing the target character attribute information and the commodity attribute information of the N commodities to obtain M commodities, wherein M is an integer larger than N;
screening the M commodities to obtain K commodities, wherein K is an integer greater than or equal to a and less than or equal to M, a is a set value, and a is an integer greater than 1;
determining display parameters of the K commodities to obtain K display parameters;
pushing the K commodities according to the K display parameters.
Optionally, in the extracting N commodities based on the browsing record, the program includes instructions for performing the following steps:
carrying out commodity identification on the browsing records to obtain P commodities, wherein P is an integer greater than or equal to N;
determining the browsing duration of each commodity in the P commodities to obtain P total browsing durations;
the browsing times of each commodity in the P commodities are truly obtained;
determining browsing attention positions of each commodity in the P commodities to obtain P attention position sets;
determining the attention degree of each commodity in the P commodities according to the P browsing time lengths, the P browsing times and the P attention position sets to obtain P attention degrees;
and selecting N attention degrees larger than a set threshold value from the P attention degrees, and determining commodities corresponding to the N attention degrees.
Further, optionally, in determining the attention degree of each commodity in the P commodities according to the P browsing total durations, the P browsing times and the P attention position sets, obtaining P attention degrees, the program includes instructions for executing the following steps:
determining a keyword set corresponding to each focus position set in the P focus position sets to obtain P keyword sets;
Determining browsing time length and attention score corresponding to each keyword in the P keyword sets, determining weight corresponding to the browsing time length corresponding to each keyword in the P keyword sets, and carrying out weighted operation on the attention score corresponding to each keyword in the P keyword sets and the corresponding weight to obtain P first reference attention scores;
confirming attention scores corresponding to the P browsing total time lengths to obtain P second reference attention scores;
according to the P browsing total durations and the P browsing times, P average browsing durations are determined;
determining weight pairs corresponding to the P average browsing durations to obtain P weight pairs;
and carrying out weighted operation on the P first reference attention scores and the P second reference attention scores according to the P weights to obtain the P attention degrees.
Optionally, the target person attribute information includes at least one of: user identity, user income level, user preference color, user nationality, user age, shopping purpose;
the commodity attribute information includes at least one of: commodity price, commodity brand, commodity size, commodity shape, commodity inventory condition, commodity postal rate, commodity transportation rate, commodity color and commodity name;
In the aspect of expanding and recommending the N commodities by utilizing the target character attribute information and the commodity attribute information of the N commodities to obtain M commodities, the program comprises instructions for executing the following steps:
carrying out commodity searching according to commodity attribute information of each commodity in the commodity attribute information of the N commodities and the target character attribute information to obtain N commodity sets;
and carrying out de-duplication treatment on the N commodity sets to obtain the M commodities.
Optionally, in the determining the display parameters of the K commodities to obtain K display parameters, the program includes instructions for:
determining the user score of each commodity in the K commodities to obtain K user scores;
and determining display priorities based on the K user scores to obtain K display priorities.
Optionally, in the determining the display parameters of the K commodities, obtaining K display parameters, and pushing the K commodities according to the K display parameters, the program includes instructions for executing the following steps:
classifying the K commodities to obtain class a commodities, wherein each class of commodities comprises at least one commodity, and a is a positive integer:
Extracting key information of each commodity in the class a commodity to obtain a group of key information;
generating a commodity comparison tables by the group a key information to obtain a commodity comparison tables;
and pushing the K commodities according to the a commodity comparison tables.
It can be seen that, in the cross-border e-commerce platform of the cross-border e-commerce platform described in the embodiment of the present application, the target character attribute information of the target user is obtained, the browsing record of the target user in the preset time is obtained, N commodities are extracted based on the browsing record, each commodity corresponds to one commodity attribute information, N is an integer greater than 1, the N commodities are expanded and recommended by using the target character attribute information and the commodity attribute information of the N commodities, M is an integer greater than N, the M commodities are screened, K commodities are obtained, K is an integer greater than or equal to a and less than or equal to M, a is a set value, a is an integer greater than 1, the display parameter of the K commodities is determined, the target character attribute information reflects the self purchasing habit of the user, the commodity attribute information reflects the preference of the user on the commodities, not only can identify the shopping habit of the user, but also can realize intelligent commodity selection based on the commodity expansion related information of the browsing condition of the user and the shopping habit of the user, a certain amount of commodities can be left, and the commodity can be pushed according to the shopping habit of the user, and the shopping habit of the user can be pushed by the user, and the commodity can be further pushed according to the shopping efficiency of the user.
Fig. 3 is a functional unit block diagram of a big data processing system 300 of a cross-border e-commerce platform, where the big data processing system 300 of the cross-border e-commerce platform is applied to the cross-border e-commerce platform, and the system 300 includes: an acquisition unit 301, a recommendation unit 302, a screening unit 303, a determination unit 304 and a pushing unit 305, wherein,
the acquiring unit 301 is configured to acquire target character attribute information of a target user; acquiring a browsing record of the target user in a preset time, and extracting N commodities based on the browsing record, wherein each commodity corresponds to commodity attribute information, and N is an integer greater than 1;
the recommending unit 302 is configured to perform expansion recommendation on the N products by using the target character attribute information and the product attribute information of the N products to obtain M products, where M is an integer greater than N;
the screening unit 303 is configured to screen the M products to obtain K products, where K is an integer greater than or equal to a and less than or equal to M, a is a set value, and a is an integer greater than 1;
the determining unit 304 is configured to determine display parameters of the K commodities, to obtain K display parameters;
The pushing unit 305 is configured to push the K commodities according to the K display parameters.
Optionally, in the aspect of extracting N commodities based on the browsing record, the obtaining unit 301 is specifically configured to:
carrying out commodity identification on the browsing records to obtain P commodities, wherein P is an integer greater than or equal to N;
determining the browsing duration of each commodity in the P commodities to obtain P total browsing durations;
the browsing times of each commodity in the P commodities are truly obtained;
determining browsing attention positions of each commodity in the P commodities to obtain P attention position sets;
determining the attention degree of each commodity in the P commodities according to the P browsing time lengths, the P browsing times and the P attention position sets to obtain P attention degrees;
and selecting N attention degrees larger than a set threshold value from the P attention degrees, and determining commodities corresponding to the N attention degrees.
Optionally, in determining the attention degree of each commodity in the P commodities according to the P browsing total durations, the P browsing times and the P attention position sets, to obtain P attention degrees, the obtaining unit 301 is specifically configured to:
Determining a keyword set corresponding to each focus position set in the P focus position sets to obtain P keyword sets;
determining browsing time length and attention score corresponding to each keyword in the P keyword sets, determining weight corresponding to the browsing time length corresponding to each keyword in the P keyword sets, and carrying out weighted operation on the attention score corresponding to each keyword in the P keyword sets and the corresponding weight to obtain P first reference attention scores;
confirming attention scores corresponding to the P browsing total time lengths to obtain P second reference attention scores;
according to the P browsing total durations and the P browsing times, P average browsing durations are determined;
determining weight pairs corresponding to the P average browsing durations to obtain P weight pairs;
and carrying out weighted operation on the P first reference attention scores and the P second reference attention scores according to the P weights to obtain the P attention degrees.
Optionally, the target person attribute information includes at least one of: user identity, user income level, user preference color, user nationality, user age, shopping purpose;
the commodity attribute information includes at least one of: commodity price, commodity brand, commodity size, commodity shape, commodity inventory condition, commodity postal rate, commodity transportation rate, commodity color and commodity name;
In the aspect of expanding and recommending the N products by using the target character attribute information and the product attribute information of the N products to obtain M products, the recommendation unit 302 is specifically configured to:
carrying out commodity searching according to commodity attribute information of each commodity in the commodity attribute information of the N commodities and the target character attribute information to obtain N commodity sets;
and carrying out de-duplication treatment on the N commodity sets to obtain the M commodities.
Optionally, in the determining display parameters of the K commodities, obtaining K display parameters, the determining unit 404 is specifically configured to:
determining the user score of each commodity in the K commodities to obtain K user scores;
and determining display priorities based on the K user scores to obtain K display priorities.
Optionally, in the determining the display parameters of the K commodities, obtaining K display parameters, and pushing the K commodities according to the K display parameters, the system 300 is specifically configured to:
classifying the K commodities to obtain class a commodities, wherein each class of commodities comprises at least one commodity, and a is a positive integer;
extracting key information of each commodity in the class a commodity to obtain a group of key information;
Generating a commodity comparison tables by the group a key information to obtain a commodity comparison tables;
and pushing the K commodities according to the a commodity comparison tables.
It can be seen that the big data processing system of the cross-border e-commerce platform described in the embodiment of the present application is applied to the cross-border e-commerce platform, obtains target character attribute information of a target user, obtains a browsing record of the target user within a preset time, extracts N products based on the browsing record, each product corresponds to one product attribute information, N is an integer greater than 1, expands and recommends N products by using the target character attribute information and the product attribute information of the N products, obtains M products, M is an integer greater than N, screens the M products, obtains K products, K is an integer greater than or equal to a and less than or equal to M, a is a set value, a is an integer greater than 1, determines display parameters of the K products, obtains K display parameters, the target character attribute information reflects self-purchasing of the user, the product attribute information reflects preference of the user on the products, not only can identify shopping habits of the user, but also expands and pushes the products based on related information of the products of the browsing conditions of the user and shopping habits of the user, realizes intelligent selection of the products, K products can be quantitatively screened, and can also quantitatively select the products, and can also push the products according to the shopping habits of the user (the user has a certain shopping habit) and does not have good taste.
It may be understood that the functions of each program module of the big data processing system of the cross-border e-commerce platform of the present embodiment may be specifically implemented according to the method in the foregoing method embodiment, and the specific implementation process may refer to the relevant description of the foregoing method embodiment, which is not repeated herein.
The present application also provides a computer storage medium storing a computer program for electronic data exchange, the computer program causing a computer to execute some or all of the steps of any one of the methods described in the method embodiments above.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any one of the methods described in the method embodiments above. The computer program product may be a software installation package.
It should be noted that, for simplicity of description, the foregoing method embodiments are all expressed as a series of action combinations, but it should be understood by those skilled in the art that the present application is not limited by the order of actions described, as some steps may be performed in other order or simultaneously in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required in the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, such as the above-described division of units, merely a division of logic functions, and there may be additional manners of dividing in actual implementation, such as 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, or may be in electrical or other forms.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over 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 each embodiment 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. The integrated units may be implemented in hardware or in software functional units.
The integrated units described above, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a memory, including several instructions for causing a computer device (which may be a personal computer, a server or a network device, etc.) to perform all or part of the steps of the above-mentioned method of the various embodiments of the present application. And the aforementioned memory includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Those of ordinary skill in the art will appreciate that all or a portion of the steps in the various methods of the above embodiments may be implemented by a program that instructs associated hardware, and the program may be stored in a computer readable memory, which may include: flash disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk.
The foregoing has outlined rather broadly the more detailed description of embodiments of the present application, wherein specific examples are provided herein to illustrate the principles and embodiments of the present application, the above examples being provided solely to assist in the understanding of the methods of the present application and the core ideas thereof; meanwhile, as those skilled in the art will have modifications in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (10)
1. The big data processing method of the cross-border e-commerce platform is characterized by being applied to the cross-border e-commerce platform, and comprises the following steps:
acquiring target character attribute information of a target user;
acquiring a browsing record of the target user in a preset time, and extracting N commodities based on the browsing record, wherein each commodity corresponds to commodity attribute information, and N is an integer greater than 1;
Expanding and recommending the N commodities by utilizing the target character attribute information and the commodity attribute information of the N commodities to obtain M commodities, wherein M is an integer larger than N;
screening the M commodities to obtain K commodities, wherein K is an integer greater than or equal to a and less than or equal to M, a is a set value, and a is an integer greater than 1;
determining display parameters of the K commodities to obtain K display parameters;
pushing the K commodities according to the K display parameters.
2. The method of claim 1, wherein the extracting N items based on the browsing records comprises:
carrying out commodity identification on the browsing records to obtain P commodities, wherein P is an integer greater than or equal to N;
determining the browsing duration of each commodity in the P commodities to obtain P total browsing durations;
the browsing times of each commodity in the P commodities are truly obtained;
determining browsing attention positions of each commodity in the P commodities to obtain P attention position sets;
determining the attention degree of each commodity in the P commodities according to the P browsing time lengths, the P browsing times and the P attention position sets to obtain P attention degrees;
And selecting N attention degrees larger than a set threshold value from the P attention degrees, and determining commodities corresponding to the N attention degrees.
3. The method of claim 2, wherein the determining the attention degree of each of the P commodities according to the P browsing total durations, the P browsing times, and the P attention position sets, to obtain P attention degrees, includes:
determining keyword sets corresponding to each focus position set in the P focus position sets to obtain P keyword sets:
determining browsing time length and attention score corresponding to each keyword in the P keyword sets, determining weight corresponding to the browsing time length corresponding to each keyword in the P keyword sets, and carrying out weighted operation on the attention score corresponding to each keyword in the P keyword sets and the corresponding weight to obtain P first reference attention scores;
confirming attention scores corresponding to the P browsing total time lengths to obtain P second reference attention scores;
according to the P browsing total durations and the P browsing times, P average browsing durations are determined;
determining weight pairs corresponding to the P average browsing durations to obtain P weight pairs;
And carrying out weighted operation on the P first reference attention scores and the P second reference attention scores according to the P weights to obtain the P attention degrees.
4. A method according to any one of claims 1-3, wherein the target persona attribute information includes at least one of: user identity, user income level, user preference color, user nationality, user age, shopping purpose;
the commodity attribute information includes at least one of: commodity price, commodity brand, commodity size, commodity shape, commodity inventory condition, commodity postal rate, commodity transportation rate, commodity color and commodity name;
the expanding recommendation is performed on the N commodities by using the target character attribute information and the commodity attribute information of the N commodities to obtain M commodities, including:
carrying out commodity searching according to commodity attribute information of each commodity in the commodity attribute information of the N commodities and the target character attribute information to obtain N commodity sets;
and carrying out de-duplication treatment on the N commodity sets to obtain the M commodities.
5. A method according to any one of claims 1-3, wherein said determining display parameters of said K articles results in K display parameters, comprising:
Determining the user score of each commodity in the K commodities to obtain K user scores;
and determining display priorities based on the K user scores to obtain K display priorities.
6. A method according to any one of claims 1-3, wherein said determining the display parameters of the K products to obtain K display parameters, pushing the K products according to the K display parameters, comprises:
classifying the K commodities to obtain class a commodities, wherein each class of commodities comprises at least one commodity, and a is a positive integer;
extracting key information of each commodity in the class a commodity to obtain a group of key information;
generating a commodity comparison tables by the group a key information to obtain a commodity comparison tables;
and pushing the K commodities according to the a commodity comparison tables.
7. A big data processing system of a cross-border e-commerce platform, which is applied to the cross-border e-commerce platform, the system comprising: the device comprises an acquisition unit, a recommendation unit, a screening unit, a determination unit and a pushing unit, wherein,
the acquisition unit is used for acquiring target character attribute information of a target user; acquiring a browsing record of the target user in a preset time, and extracting N commodities based on the browsing record, wherein each commodity corresponds to commodity attribute information, and N is an integer greater than 1;
The recommending unit is used for expanding and recommending the N commodities by utilizing the target character attribute information and the commodity attribute information of the N commodities to obtain M commodities, wherein M is an integer larger than N;
the screening unit is used for screening the M commodities to obtain K commodities, wherein K is an integer greater than or equal to a and less than or equal to M, a is a set value, and a is an integer greater than 1;
the determining unit is used for determining the display parameters of the K commodities to obtain K display parameters;
the pushing unit is used for pushing the K commodities according to the K display parameters.
8. The system according to claim 7, wherein in the extracting N commodities based on the browsing records, the obtaining unit is specifically configured to:
carrying out commodity identification on the browsing records to obtain P commodities, wherein P is an integer greater than or equal to N;
determining the browsing duration of each commodity in the P commodities to obtain P total browsing durations;
the browsing times of each commodity in the P commodities are truly obtained;
determining browsing attention positions of each commodity in the P commodities to obtain P attention position sets;
Determining the attention degree of each commodity in the P commodities according to the P browsing time lengths, the P browsing times and the P attention position sets to obtain P attention degrees;
and selecting N attention degrees larger than a set threshold value from the P attention degrees, and determining commodities corresponding to the N attention degrees.
9. The system of claim 8, wherein the obtaining unit is specifically configured to, in determining the attention degree of each of the P commodities according to the P browsing total durations, the P browsing times, and the P attention position sets, obtain P attention degrees:
determining a keyword set corresponding to each focus position set in the P focus position sets to obtain P keyword sets;
determining browsing time length and attention score corresponding to each keyword in the P keyword sets, determining weight corresponding to the browsing time length corresponding to each keyword in the P keyword sets, and carrying out weighted operation on the attention score corresponding to each keyword in the P keyword sets and the corresponding weight to obtain P first reference attention scores;
confirming attention scores corresponding to the P browsing total time lengths to obtain P second reference attention scores;
According to the P browsing total durations and the P browsing times, P average browsing durations are determined;
determining weight pairs corresponding to the P average browsing durations to obtain P weight pairs;
and carrying out weighted operation on the P first reference attention scores and the P second reference attention scores according to the P weights to obtain the P attention degrees.
10. The system of any of claims 7-9, wherein the target persona attribute information includes at least one of: user identity, user income level, user preference color, user nationality, user age, shopping purpose;
the commodity attribute information includes at least one of: commodity price, commodity brand, commodity size, commodity shape, commodity inventory condition, commodity postal rate, commodity transportation rate, commodity color and commodity name;
in the aspect of expanding and recommending the N commodities by utilizing the target character attribute information and the commodity attribute information of the N commodities to obtain M commodities, the recommendation unit is specifically configured to:
carrying out commodity searching according to commodity attribute information of each commodity in the commodity attribute information of the N commodities and the target character attribute information to obtain N commodity sets;
And carrying out de-duplication treatment on the N commodity sets to obtain the M commodities.
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| CN117151747A (en) * | 2023-10-31 | 2023-12-01 | 天津市品茗科技有限公司 | Intelligent service consumption robot recommendation method and system |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117151747A (en) * | 2023-10-31 | 2023-12-01 | 天津市品茗科技有限公司 | Intelligent service consumption robot recommendation method and system |
| CN117151747B (en) * | 2023-10-31 | 2024-01-30 | 天津市品茗科技有限公司 | Intelligent service consumption robot recommendation method and system |
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