CN116976970A - Method for retrieving and recommending coupons, electronic equipment and storage medium - Google Patents
Method for retrieving and recommending coupons, electronic equipment and storage medium Download PDFInfo
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- G06Q30/0224—Discounts or incentives, e.g. coupons or rebates based on user history
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0207—Discounts or incentives, e.g. coupons or rebates
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Abstract
The application relates to the technical field of electronic commerce, in particular to a method for retrieving and recommending coupons, which comprises the following steps: acquiring text information of a commodity page browsed by a user, and separating key information, wherein the key information is commodity information, store information and operation information of the user; acquiring shopping behaviors of a user based on the key information and portraying the user, wherein the shopping behaviors of the user comprise the behavior of the user for browsing commodities, the ordering behavior and the preference of the user for browsing the commodities; acquiring store information browsed by a client based on the key information and representing the store portrait; retrieving the preferential information of the commodity of the client intention based on the key information, screening the preferential information conforming to the user portrait according to the commodity class, the user portrait and the shop portrait, and recommending the preferential information to the user; the application can recommend commodity preferential selection meeting the user's intention, and simultaneously filter shops with low credibility, thereby greatly simplifying the shopping flow of the user and reducing the difficulty of the user to search the commodity coupon.
Description
Technical Field
The present application relates to the field of electronic commerce technologies, and in particular, to a method for retrieving and recommending coupons, an electronic device, and a storage medium.
Background
At present, the promotion activity layer of each large e-commerce platform is endless, various preferential information acquisition inlets are fragmented more and more, the use rules are more and more complex, so that a large amount of preferential information is received by a user in a short time, the quantity of the preferential information is huge, the use rules are complex, the user cannot screen out the preferential information of the commodity to be purchased in time, and whether the preferential information which is most suitable for own will is acquired cannot be determined.
For example, in the current e-commerce activities, users open apps, open corresponding activities, open video live broadcast, open commodities and other places, a large amount of preferential information can be popped up, or an e-commerce platform directly sends the preferential information to a user account, so that when the users purchase commodities, the problem that the coupons to be taken cannot be used and the coupons to be used cannot know where to take is often encountered. Accordingly, the present application provides a method, an electronic device, and a storage medium for retrieving and recommending coupons.
Disclosure of Invention
The application aims to provide a method for retrieving and recommending coupons, electronic equipment and a storage medium, which are used for solving the problem that a process of acquiring coupons of goods meeting willingness is difficult in the process of shopping by an electronic commerce.
In order to achieve the above purpose, the present application provides the following technical solutions:
a method of retrieving and recommending coupons, the method comprising the steps of:
acquiring text information of a commodity page browsed by a user, and separating key information, wherein the key information is commodity information, store information and operation information of the user;
acquiring shopping behaviors of a user based on the key information and portraying the user, wherein the shopping behaviors of the user comprise the behavior of the user for browsing commodities, the ordering behavior and the preference of the user for browsing the commodities;
acquiring store information browsed by a client based on the key information and representing the store portrait;
and retrieving the preferential information of the commodity of the client intention based on the key information, screening the preferential information conforming to the user portrait according to the commodity class, the user portrait and the shop portrait, and recommending the preferential information to the user.
Further, the text information is acquired through barrier-free service, when the operation of a user reaches trigger time, the findAccesieboltyNodeInfo object of a component with a specific id is acquired by using findAccesiyinfosByViewId in the getrOotInActiveWindow, and the specific text content of the corresponding component is acquired according to a getText method in the accessibilyNodeInfo object.
Further, the method for portraying the user comprises the following steps:
acquiring equipment information, wherein the equipment information is used as a unique identifier of a user;
binding the obtained key information with a unique identifier of a user, and recording shopping behaviors of the user, wherein the shopping behaviors of the user comprise the behavior of the user for browsing commodities, the ordering behavior and the preference of the user for browsing the commodities;
a tag of the user is generated based on the shopping behavior of the user.
Further, the method for representing the shop image comprises the following steps:
obtaining a scoring reference object related to store reputation;
assigning a score to each scoring reference object based on scoring criteria of the shopping platform scoring reference object;
calculating the product of the score of each score reference object and the corresponding weight, and then obtaining the sum of the products to obtain the store score;
the store is assigned a label based on the store score.
Further, the scoring reference object is a shop score, a poor score and a medium score.
Further, the method also comprises the following steps:
judging whether the difference evaluation rate exceeds a set difference evaluation coefficient, and if the difference evaluation rate exceeds the difference evaluation coefficient, the score is 0;
and judging whether the middle evaluation rate exceeds the set middle evaluation coefficient, and if the middle evaluation rate exceeds the middle evaluation coefficient, the score is 0.
Further, the method for retrieving and recommending coupons further comprises the following steps:
and when the user uses the coupon to shop, acquiring a deeplink link in the coupon information, and jumping to a commodity settlement page after the coupon is used.
The application also discloses an electronic device comprising a processor which when executing the computer program stored in the memory, implements the method of retrieving and recommending coupons of any of the above.
The application also discloses a storage medium storing a computer program which, when executed by a processor, causes the processor to implement the method for retrieving and recommending coupons according to any of the above when running the computer program.
In summary, compared with the prior art, the application has the following beneficial effects:
the method for searching and recommending coupons disclosed by the embodiment of the application acquires the intention commodity, shopping preference, store information and the like of the user by acquiring page information of the commodity browsed by the user, acquires all coupons of the commodity after acquiring the intention commodity of the user by the user portrait and the store portrait, screens coupons based on the user portrait and the store portrait, selects and recommends commodity coupons conforming to the intention of the user, and simultaneously can filter stores with low credibility, thereby greatly simplifying the shopping flow of the user and simultaneously reducing the difficulty of searching the intention commodity coupons by the user.
Drawings
Fig. 1 is a flow chart of a method for retrieving and recommending coupons according to an embodiment of the present application.
FIG. 2 is a flow chart of a method for retrieving and recommending representations of users in coupons in accordance with an embodiment of the present application.
FIG. 3 is a flow chart of a method for retrieving and recommending images of a store in coupons in accordance with an embodiment of the present application.
Detailed Description
The technical solutions of the embodiments of the present application will be clearly and completely described below 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, but not all embodiments, and all other embodiments obtained by those skilled in the art without making creative efforts based on the embodiments of the present application are included in the protection scope of the present application.
In order to facilitate an understanding of the application, the following description will briefly explain the nouns appearing in the application.
User portraits, which are a technical means for finely describing and understanding users by using methods such as data analysis and user behavior research, and dividing user groups into different crowd models by combining specific user scenes. The user image classifies the users by collecting and analyzing various data such as behaviors, interests, demands, living habits, psychology, value and the like of the users, and describes a virtual character image describing the characteristics of the users, so that developers are helped to better understand and grasp the demands of the users.
The barrier-free service is a function of the Android framework, and is specifically realized by running in the background through an accessibilitiyservice (namely barrier-free service) and receiving a callback of a specified event through an accessibilitiyevent.
Examples
As shown in fig. 1, a method for retrieving and recommending coupons according to one embodiment of the present application includes the steps of:
step S100, acquiring text information of a commodity page browsed by a user, and separating key information, wherein the key information is commodity information, store information and operation information of the user;
in the step, page information browsed by a user is acquired through barrier-free service;
specifically, self-defining MyAcess business service in the device and inheriting the Acess business service of the system, and registering the defined barrier-free service MyAcess business service in a manifest file AndroiManifest of the system;
in the MyAccess ibilityService, a configuration file is added according to the requirement to limit the time for triggering the self-defined barrier-free service and the application package name to be monitored, for example, an accessibility_service_config.xml file is newly built in an xml folder, and the bottom label of the file is accessibility-service, so that the MyAccess ibilityService service can be started under the condition and the time for limiting the triggering condition and the time of the barrier-free service, such as when a certain electronic commerce APP is started;
when a user normally browses commodities, all text information of a commodity page browsed by the user can be obtained according to a self-defined barrier-free service, and the specific implementation method is that events are processed in an onAcess ibilitement method in an opened MyAcess ibiliteservice;
for example, when a packet name of a Jingdong app is added as a monitoring object in a configuration file accessibility_service_config, if a trigger time is a used page change (typeWindowStateChanged), a user browses a commodity by using the Jingdong app, when the Jingdong app page change is performed, an onAcess ibyingmethod of MyAccess capable service is triggered, when a condition is met, the onAcess ibyingmethod of MyAccess capable service is sent, in the method, whether a commodity page is CHANGED is judged by using a dAcess ibyingmethod in the MyAccess Indonw according to whether an event (the event is a parameter of the onAcess ibyingEvent) is equal to the Acess ibyingmethod, if the commodity page is CHANGED, a corresponding text of a specific Indonepyingcomponent is acquired by using the dAcess ibyingmethod in the MyAccess Indoneperiyservice according to a specific condition;
in some examples, event. Getsource may also be used to obtain accessibilititynodebnfo objects for all components of a commodity page;
in the present embodiment, the commodity information includes a commodity kind, a commodity name, a commodity price; the store information includes store names, information of commodities in the store, and the operation information of the user includes operation information generated during shopping by the user, such as operation of searching for the commodities by the user, operation of joining a shopping cart by the user, operation of browsing by the user, time of the user staying on a commodity page, operation of clicking details of the commodities by the user, and the like.
Step 200, acquiring shopping behaviors of a user based on key information and portraying the user, wherein the shopping behaviors of the user comprise the behavior of the user for browsing commodities, the ordering behavior and the preference of the user for browsing the commodities;
specifically, in this step, as shown in fig. 2, the method for representing the user figure includes the following steps:
step S210, acquiring equipment information, wherein the equipment information is used as a unique identifier of a user;
step S220, binding the obtained key information with a unique identifier of a user, and recording shopping behaviors of the user, wherein the shopping behaviors of the user comprise the behavior of the user for browsing commodities, the ordering behavior and the preference of the user for browsing commodities;
step S230, generating a label and portrait of the user through shopping behavior of the user;
specifically, the number of times that a user browses a certain commodity or purchases a certain commodity is used as a commodity type for judging the preference of the user, and the number of times that the user browses the commodity is used as a basis for judging the preference of the user for browsing the commodity;
taking the example of browsing merchandise, for example: taking a certain time d as an analysis interval (d is the current day of the starting time distance and can be adjusted according to the need), wherein the general next row is a statistical time which is larger than the statistical time of browsing behaviors), in the d time, the total browsing times of a user are a, the browsing times of various products are a1 time, a2 time, a3 time and the like respectively, when the commodity browsing times of the user exceed a recording threshold value, the label of the type of the commodity is judged to be added to the user, if the mobile phone class browsing times of the user in the d time are a10, a10/a is calculated, and if the value is larger than the recording threshold value, the label of the mobile phone is added to the user;
according to the type and price of the commodity purchased by the user, labels such as high quality, young and the like are added for the user.
Step S300, acquiring store information browsed by a client based on key information and representing the store information as a store portrait;
in this embodiment, as shown in fig. 3, the method for representing a user portrait includes the following steps:
step S310, obtaining a scoring reference object related to store reputation;
step S320, scoring each scoring reference object based on scoring criteria of the scoring reference object of the shopping platform;
step S330, calculating the product of the score of each scoring reference object and the corresponding weight, and then obtaining the sum of the products to obtain the score of the store;
step S340, assigning a label to the store based on the store score
Specifically, in this embodiment, based on store information in the key information, all information of the store is searched to obtain score reference objects related to store reputation, including store score, poor score, medium score and the like, and 100 points are taken as an example to obtain the score of information related to store reputation, and the store score is calculated according to the weight of each information;
preferably, in the present embodiment, the score reference object related to the store reputation includes a store score, a difference score, and a middle score, and taking 100 full scores as an example, the score of the store score, the difference score, and the middle score is calculated, and then the store score is calculated according to the weights of the store score, the difference score, and the middle score;
for example, the store score weight is 5 (the full weight is 10, the greater the weight, the higher the proportion of the term in the total score), the store score is 4.5 (the full score is 5), and the score of (4.5/5) ×100×5/10) =45 score is calculated;
the difference evaluation rate is 0.02, the weight is 4, a difference evaluation coefficient is set to be 0.1 (the larger the coefficient is, the wider the difference evaluation rate is, the smaller the coefficient is, the stricter the difference evaluation rate is, if the difference evaluation rate is higher than the difference evaluation coefficient, the score is 0 score), and the score is calculated to be (1-0.02/0.1) (100×4/10) =32 score;
the middle evaluation rate is 0.2, the weight is 1, a difference evaluation coefficient is set to be 0.4 (the larger the coefficient is, the wider the middle evaluation rate is, the smaller the coefficient is, the stricter the middle evaluation rate is, if the middle evaluation rate is higher than the middle evaluation coefficient, the score is 0 score), and the score is calculated to be (1-0.2/0.4) (100×1/10) =5 score;
then the calculation shows that the store score is a=45+32+5=82.
According to the store score a, stores can be marked with "excellent" (a > =85 points), "good" (85 > a > =70), "medium" (70 > a > =55), "poor" (55 > a > =40), and "not recommended" (a < 40) labels, which are store images;
it should be noted that if there is a need subsequently, other reference systems may be extended to calculate scores, that is, when the shopping platform introduces other evaluation information (score reference objects) related to store reputation, a new score reference object is added to the above-described step of calculating store scores when the store scores, and weights are reset for each information.
Step S400, retrieving the preferential information of the commodity of the client intention based on the key information, screening the preferential information conforming to the user portrait and the shop portrait according to the commodity class, the user portrait and the shop portrait, and recommending the preferential information to the user;
retrieving all the preferential information according to the acquired key information, and screening the preferential information according to the acquired user portrait and store portrait;
when commodity information is screened according to the user portrait, coupons related to the tags are recommended to the user according to the tags of the user, for example, when the user has tags such as 'young', 'high quality', the coupons with high price but high use limit tend to be pushed to the user, and when the user has the tag of 'mobile phone', the mobile phone coupons can be additionally pushed when the commodity coupons are being browsed when the user browses;
when a coupon is recommended according to store portrait, a label of a store is arranged on the coupon, for example, when the store is provided with a 'good' label, a 'good' label can be added to the coupon, when the store is provided with a 'bad' label, a 'bad' label can be added to the coupon, and when the store is provided with a 'not recommended' label, the store coupon is directly screened out.
Examples
As a preferred embodiment of the present application, this embodiment differs from embodiment 1 in that the method further comprises the steps of:
step S500, when a user uses a coupon to shop, acquiring a deeplink link in the coupon information, and jumping to a commodity settlement page after the coupon is used;
specifically, after the user obtains the intended coupon, the user purchases the commodity through the coupon, the current coupon has the function of going to use, and after clicking the use button, the user obtains the deeplink link in the coupon information, and then jumps to the commodity settlement page after the coupon is used, so that the user can use the coupon conveniently.
In summary, the method for retrieving and recommending coupons disclosed in the embodiment of the application obtains the intention commodity, shopping preference, store information and the like of the user by obtaining page information of the commodity browsed by the user, obtains all coupons of the commodity after obtaining the intention commodity of the user by the portrait of the user and the portrait of the store, screens coupons based on the portrait of the user and the portrait of the store, selects and recommends commodity preferential selection conforming to the intention of the user, and can filter stores with low credibility, so that the shopping flow of the user is greatly simplified, and meanwhile, after the user normally browses the commodity to obtain a red package, the user only needs to click to use, and the problem that the commodity does not meet the use condition after the red package is obtained is thoroughly abandoned.
Examples
The application also discloses an electronic device comprising a processor which when executing the computer program stored in the memory implements the method of retrieving and recommending coupons as described in embodiment 1.
Examples
The application also discloses a storage medium storing a computer program which, when executed by a processor, causes the processor to implement the method of retrieving and recommending coupons as described in embodiment 1 when the computer program is run.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in hardware plus software functional units.
In one typical configuration of an embodiment of the application, the electronic device includes one or more processors (CPUs), an input/output interface, a network interface, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash memory (flash-RAM). Memory is an example of computer-readable media.
Storage media, including both permanent and non-permanent, removable and non-removable media, may be implemented in any method or technology for storage of information. The information may be computer readable instructions, data structures, modules of a program, or other data.
Examples of storage media for an electronic device include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include non-transitory computer-readable media (transmission-media), such as modulated data signals and carrier waves.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional modules is illustrated, and in practical application, the above-described functional allocation may be performed by different functional modules according to needs, i.e. the internal structure of the apparatus is divided into different functional modules to perform all or part of the functions described above. The specific working process of the above-described device may refer to the corresponding process in the foregoing method embodiment, which is not described herein again.
Claims (9)
1. A method of retrieving and recommending coupons, said method comprising the steps of:
acquiring text information of a commodity page browsed by a user, and separating key information, wherein the key information is commodity information, store information and operation information of the user;
acquiring shopping behaviors of a user based on the key information and portraying the user, wherein the shopping behaviors of the user comprise the behavior of the user for browsing commodities, the ordering behavior and the preference of the user for browsing the commodities;
acquiring store information browsed by a client based on the key information and representing the store portrait;
and retrieving the preferential information of the commodity of the client intention based on the key information, screening the preferential information conforming to the user portrait according to the commodity class, the user portrait and the shop portrait, and recommending the preferential information to the user.
2. The method for retrieving and recommending coupons according to claim 1, wherein the text information is acquired through a barrier-free service, when the operation of a user reaches a trigger time, an accessibilitiyNodeInfo object of a component with a specific id is acquired by using a findAcccessitiyitiyNodeInfo BViewId in a getrInActiveWindow, and specific text content of the corresponding component is acquired according to a getText method in the accessibiiiyNodeInfo object.
3. The method of retrieving and recommending coupons according to claim 1, wherein the method of portraying the user comprises the steps of:
acquiring equipment information, wherein the equipment information is used as a unique identifier of a user;
binding the obtained key information with a unique identifier of a user, and recording shopping behaviors of the user, wherein the shopping behaviors of the user comprise the behavior of the user for browsing commodities, the ordering behavior and the preference of the user for browsing the commodities;
a tag of the user is generated based on the shopping behavior of the user.
4. The method of retrieving and recommending coupons according to claim 1, wherein the method of portraying a store comprises the steps of:
obtaining a scoring reference object related to store reputation;
assigning a score to each scoring reference object based on scoring criteria of the shopping platform scoring reference object;
calculating the product of the score of each score reference object and the corresponding weight, and then obtaining the sum of the products to obtain the store score;
the store is assigned a label based on the store score.
5. The method of retrieving and recommending coupons of claim 4, wherein said scoring reference object is a store score, a bad score, a medium score.
6. The method for retrieving and recommending coupons according to claim 5, further comprising, when calculating the store score, the steps of:
judging whether the difference evaluation rate exceeds a set difference evaluation coefficient, and if the difference evaluation rate exceeds the difference evaluation coefficient, the score is 0;
and judging whether the middle evaluation rate exceeds the set middle evaluation coefficient, and if the middle evaluation rate exceeds the middle evaluation coefficient, the score is 0.
7. A method of retrieving and recommending coupons according to any of claims 1-6, further comprising the steps of:
and when the user uses the coupon to shop, acquiring a deeplink link in the coupon information, and jumping to a commodity settlement page after the coupon is used.
8. An electronic device comprising a processor, wherein the processor, when executing a computer program stored in a memory, implements a method of retrieving and recommending coupons according to any of claims 1-7.
9. A storage medium storing a computer program which, when executed by a processor, causes the processor to implement a method of retrieving and recommending coupons as in any of claims 1-7 when the computer program is run.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN202311012591.XA CN116976970A (en) | 2023-08-11 | 2023-08-11 | Method for retrieving and recommending coupons, electronic equipment and storage medium |
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| CN202311012591.XA CN116976970A (en) | 2023-08-11 | 2023-08-11 | Method for retrieving and recommending coupons, electronic equipment and storage medium |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117522528A (en) * | 2024-01-04 | 2024-02-06 | 厦门智数联科技有限公司 | Internet data detection and analysis method and system |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117522528A (en) * | 2024-01-04 | 2024-02-06 | 厦门智数联科技有限公司 | Internet data detection and analysis method and system |
| CN117522528B (en) * | 2024-01-04 | 2024-03-12 | 厦门智数联科技有限公司 | Internet data detection and analysis method and system |
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