CN116542733A - Product recommendation method, device, computer equipment and storage medium - Google Patents
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Abstract
The embodiment of the application belongs to the field of artificial intelligence, and relates to a product recommendation method, which comprises the following steps: loading a target page of a buried point generated in advance based on a resource bit buried point rule; calling a target interface corresponding to a target page; collecting service burial point data corresponding to target resource bits in a target page by a user in a preset time period based on a target interface; generating product conversion data corresponding to the target resource bit based on the service burial point data; and recommending and replacing the products in the target resource bit based on the product conversion data. The application also provides a product recommendation device, computer equipment and a storage medium. In addition, the present application relates to blockchain technology in which product conversion data may be stored. By the method and the device, the recommendation of the proper product to the client at the proper position in the target page is realized, the intelligence and the accuracy of product recommendation are improved, and the use experience of the client is improved.
Description
Technical Field
The application relates to the technical field of artificial intelligence development, in particular to a product recommendation method, a device, computer equipment and a storage medium.
Background
In the business process of promoting the insurance products by business personnel of insurance companies, the existing promoting insurance product channels are mostly in the modes of promoting the insurance products by insurance companies, promoting the business personnel to the ground, selling the insurance products by on-line malls and the like. The method of selling insurance products through an online mall is adopted by most insurance companies because of the advantages of rapidness and high single efficiency.
However, when a promotion page of an insurance product is established in the online mall, various products in the promotion page are arranged randomly by a developer according to personal experience, so that the product information in the promotion page has fixity, and the product cannot be adjusted according to actual customer requirements, so that the accuracy of product recommendation in the promotion page is lower.
Disclosure of Invention
The embodiment of the application aims to provide a product recommendation method, a device, computer equipment and a storage medium, so as to solve the technical problem that when a promotion page of an insurance product is established in an existing online mall, product adjustment cannot be performed according to actual customer requirements, and the accuracy of product recommendation in the promotion page is low.
In order to solve the above technical problems, the embodiments of the present application provide a product recommendation method, which adopts the following technical schemes:
loading a target page of a buried point generated in advance based on a resource bit buried point rule;
invoking a target interface corresponding to the target page;
collecting service burial point data corresponding to a target resource bit in the target page by a user in a preset time period based on the target interface;
generating product conversion data corresponding to the target resource bit based on the service burial point data;
and recommending and replacing the product in the target resource bit based on the product conversion data.
Further, the step of recommending replacement processing for the product in the target resource bit based on the product conversion data specifically includes:
acquiring first product conversion data of various products put in the target resource bit; wherein the category of the first product conversion data includes a plurality of;
based on the first product conversion data, generating product evaluation values respectively corresponding to the products in the target resource position;
determining a target product from all the products based on the product evaluation values;
And recommending and replacing the products in the target resource position based on the target product.
Further, the step of generating product evaluation values corresponding to the products in the target resource bit based on the first product conversion data specifically includes:
acquiring second product conversion data corresponding to the first product; wherein the first product is any one of all the products;
acquiring weights respectively corresponding to the second product conversion data;
generating a product score corresponding to the first product based on the second product conversion data and the weights;
and taking the product score as a product evaluation value of the first product.
Further, the step of determining a target product from all the products based on the product evaluation values specifically includes:
screening out the target product evaluation value with the largest value from all the product evaluation values;
acquiring second products corresponding to the target product evaluation values from all the products;
and taking the second product as the target product.
Further, the step of recommending and replacing the product in the target resource bit based on the target product specifically includes:
Acquiring a preset product set to be recommended;
calling a preset similarity analysis model;
determining a third product matched with the target product from the product set to be recommended based on the similarity analysis model;
and recommending and replacing the product in the target resource position based on the third product.
Further, the step of generating product conversion data corresponding to the target resource bit based on the service burial point data specifically includes:
screening user behavior data corresponding to the data type from the service burial point data based on a preset data type;
acquiring a preset product data statistics rule;
counting the user behavior data based on the product data statistics rules to generate corresponding statistics data;
and taking the statistical data as product conversion data corresponding to the target resource bit.
Further, before the step of loading the target page of the embedded point pre-generated based on the resource bit embedded point rule, the method further comprises:
judging whether a business behavior operation for the target page triggered by the user is received or not;
if yes, determining a jump link page URL corresponding to the target page based on the business behavior operation;
Acquiring position parameters corresponding to the resource bits in the target page based on the resource bit embedding point rule;
and splicing the position parameter on the jump link page URL.
In order to solve the above technical problems, the embodiment of the present application further provides a product recommendation device, which adopts the following technical scheme:
the loading module is used for loading a target page of the embedded point which is generated in advance based on the rule of the embedded point of the resource bit;
the calling module is used for calling a target interface corresponding to the target page;
the collection module is used for collecting service burial point data corresponding to the target resource bit in the target page in a preset time period based on the target interface;
the generation module is used for generating product conversion data corresponding to the target resource bit based on the service burial point data;
and the processing module is used for recommending and replacing the products in the target resource bit based on the product conversion data.
In order to solve the above technical problems, the embodiments of the present application further provide a computer device, which adopts the following technical schemes:
loading a target page of a buried point generated in advance based on a resource bit buried point rule;
Invoking a target interface corresponding to the target page;
collecting service burial point data corresponding to a target resource bit in the target page by a user in a preset time period based on the target interface;
generating product conversion data corresponding to the target resource bit based on the service burial point data;
and recommending and replacing the product in the target resource bit based on the product conversion data.
In order to solve the above technical problems, embodiments of the present application further provide a computer readable storage medium, which adopts the following technical solutions:
loading a target page of a buried point generated in advance based on a resource bit buried point rule;
invoking a target interface corresponding to the target page;
collecting service burial point data corresponding to a target resource bit in the target page by a user in a preset time period based on the target interface;
generating product conversion data corresponding to the target resource bit based on the service burial point data;
and recommending and replacing the product in the target resource bit based on the product conversion data.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
firstly, loading a target page of a buried point which is generated in advance based on a resource bit buried point rule; then, a target interface corresponding to the target page is called; collecting service burial point data corresponding to the target resource bit in the target page by a user in a preset time period based on the target interface; generating product conversion data corresponding to the target resource bit based on the service burial point data; and finally, recommending and replacing the products in the target resource bit based on the product conversion data. According to the embodiment of the application, the target page is buried through the use of the resource bit burying rules, so that service burying data corresponding to the target resource bit in the target page can be accurately collected through the corresponding interfaces, product conversion data corresponding to the target resource bit can be intelligently generated based on the service burying data, and further, recommendation replacement processing is performed on products in the target resource bit based on the product conversion data, so that the recommendation of proper products to customers at proper positions in the target page is realized, the intelligence and accuracy of product recommendation are improved, and the use experience of the customers is improved.
Drawings
For a clearer description of the solution in the present application, a brief description will be given below of the drawings that are needed in the description of the embodiments of the present application, it being obvious that the drawings in the following description are some embodiments of the present application, and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow chart of one embodiment of a product recommendation method according to the present application;
FIG. 3 is a schematic structural view of one embodiment of a product recommendation device according to the present application;
FIG. 4 is a schematic structural diagram of one embodiment of a computer device according to the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the applications herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having" and any variations thereof in the description and claims of the present application and in the description of the figures above are intended to cover non-exclusive inclusions. The terms first, second and the like in the description and in the claims or in the above-described figures, are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order.
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 order to better understand the technical solutions of the present application, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings.
As shown in fig. 1, a system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used as a medium to provide communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.
The user may interact with the server 105 via the network 104 using the terminal devices 101, 102, 103 to receive or send messages or the like. Various communication client applications, such as a web browser application, a shopping class application, a search class application, an instant messaging tool, a mailbox client, social platform software, etc., may be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smartphones, tablet computers, electronic book readers, MP3 players (Moving Picture Experts Group Audio Layer III, dynamic video expert compression standard audio plane 3), MP4 (Moving Picture Experts Group Audio Layer IV, dynamic video expert compression standard audio plane 4) players, laptop and desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that, the product recommending method provided in the embodiment of the present application is generally executed by a server/terminal device, and accordingly, the product recommending apparatus is generally disposed in the server/terminal device.
It should be understood that the number of terminal devices, networks and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to FIG. 2, a flow chart of one embodiment of a product recommendation method according to the present application is shown. The product recommendation method comprises the following steps:
Step S201, loading a target page of a buried point generated in advance based on a resource bit buried point rule.
In this embodiment, the electronic device (e.g., the server/terminal device shown in fig. 1) on which the product recommendation method operates may acquire the target page through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection may include, but is not limited to, 3G/4G/5G connection, wiFi connection, bluetooth connection, wiMAX connection, zigbee connection, UWB (ultra wideband) connection, and other now known or later developed wireless connection. The target page may refer to a product advertisement delivery webpage in an insurance application in the electronic device. The resource bit embedding rule is preset according to actual service requirements. The resource location burying rule can classify the location according to the existing business logic, define the structure body attribute and the code of each channel, platform, page and assembly, form the standard by landing, customize the configurable platform based on the standard to perform unified management, and the location parameter (position_id) standard is: channel (e.g., self-camping, out-cast) -platform (e.g., applet, public number) -page (e.g., arbitrary gate, applet top page) -location (e.g., banner graph) -number (e.g., 1, 2-represent Nth location, location in the same floor, calculation from leftmost) -custom (e.g., vendor code). In addition, the specific implementation process of the embedding processing on the target page based on the above resource bit embedding rule will be described in further detail in the following specific embodiments, which will not be described herein.
Step S202, calling a target interface corresponding to the target page.
In this embodiment, the target interface is a pre-built interface for collecting buried point data from a target page.
Step S203, collecting service burial point data corresponding to the target resource bit in the target page in a preset time period based on the target interface.
In this embodiment, the service burial point data may at least include a product click rate of resource bits in the page, an exposure count of each resource bit in the page, a click count, and a conversion count of each step in the flow. In addition, the target resource bit may refer to any one of the resource bits in the target page, and the resource bit may refer to a location where a component or the like in the target page can place a product advertisement. In addition, the preset time period is not particularly limited, and may be set according to actual service usage requirements, for example, within the previous month from the current time.
And step S204, generating product conversion data corresponding to the target resource bit based on the service burial point data.
In this embodiment, the service burial point data of each resource bit in the target page is subjected to data analysis to generate corresponding product conversion data, so that the operation condition of each resource bit can be clearly and directly known according to the product conversion data, and the type of resource bit delivery and the quality of text content can be clearly compared. The specific implementation process of generating the product conversion data corresponding to the target resource bit based on the service burial point data will be described in further detail in the following specific embodiments, which will not be described herein.
And step S205, recommending and replacing the products in the target resource bit based on the product conversion data.
In this embodiment, the specific implementation process of the recommended replacement processing for the product in the target resource location based on the product conversion data will be described in further detail in the following specific embodiments, which will not be described herein.
Firstly, loading a target page which is pre-generated with embedded points based on a resource bit embedded point rule; then, a target interface corresponding to the target page is called; collecting service burial point data corresponding to the target resource bit in the target page by a user in a preset time period based on the target interface; generating product conversion data corresponding to the target resource bit based on the service burial point data; and finally, recommending and replacing the products in the target resource bit based on the product conversion data. According to the method and the device, the target page is buried through the use of the resource bit burying rules, so that service burying data corresponding to the target resource bit in the target page can be accurately collected through the corresponding interface, product conversion data corresponding to the target resource bit can be intelligently generated based on the service burying data, and further, recommendation replacement processing is carried out on products in the target resource bit based on the product conversion data, so that the appropriate products are recommended to customers at appropriate positions in the target page, the intelligence and the accuracy of product recommendation are improved, and the use experience of the customers is improved.
In some alternative implementations, step S205 includes the steps of:
acquiring first product conversion data of various products put in the target resource bit;
in this embodiment, the category of the first product conversion data includes a plurality of categories. For example, the product conversion data may include conversion order pieces, amounts of the resource bits, and corresponding return data.
And generating product evaluation values respectively corresponding to the products in the target resource bit based on the first product conversion data.
In this embodiment, the specific implementation process of generating the product evaluation values corresponding to the products in the target resource bit based on the first product conversion data will be described in further detail in the following specific embodiments, which will not be described herein.
And determining a target product from all the products based on the product evaluation values.
In this embodiment, the specific implementation process of determining the target product from all the products based on the product evaluation values will be described in further detail in the following specific embodiments, which will not be described herein.
And recommending and replacing the products in the target resource position based on the target product.
In this embodiment, the processing manner of the recommended replacement processing for the product in the target resource location based on the target product is not specifically limited, and for example, the replacement processing for the product in the target resource location may be directly performed by using the target product. Or, a similar product matched with the target product can be determined from a preset product set to be recommended, and then the similar product is used for replacing the product in the target resource position. For example, the home page of the insurance application recommends insurance products, the sports health page recommends medical health products, and the like. The specific implementation process of determining the similar product matching with the target product from the preset product set to be recommended and then replacing the product in the target resource position by using the similar product will be described in further detail in the following specific embodiments, which will not be described herein.
The method comprises the steps of obtaining first product conversion data of various products put in the target resource position; then, based on the first product conversion data, generating product evaluation values respectively corresponding to the products in the target resource position; then determining a target product from all the products based on the product evaluation values; and recommending and replacing the products in the target resource bit based on the target product. According to the method and the device, the product evaluation values corresponding to the products in the target resource position can be generated rapidly based on the first product conversion data of the various products put in the target resource position, and then the target products are accurately determined from all the products based on the product evaluation values, so that the products in the target resource position are recommended and replaced based on the target products, the purpose of recommending the proper products to the clients at proper positions in the target page is achieved, the intelligent of product recommendation is improved, and the use experience of the clients is improved.
In some optional implementations of this embodiment, the generating, based on the first product conversion data, a product evaluation value corresponding to each product in the target resource location includes the following steps:
acquiring second product conversion data corresponding to the first product;
in this embodiment, the first product is any one of all the products.
And obtaining weights respectively corresponding to the second product conversion data.
In this embodiment, the value of the weight corresponding to each second product conversion data is not specifically limited, and may be set according to the actual use requirement, and preferably, the sum value of each weight is ensured to be 1.
A product score corresponding to the first product is generated based on the second product conversion data and the weights.
In this embodiment, a corresponding weighted summation process may be performed on each of the second product conversion data based on each of the weights, to obtain a corresponding sum value, and the sum value is used as a product score corresponding to the first product.
And taking the product score as a product evaluation value of the first product.
The method comprises the steps of obtaining second product conversion data corresponding to a first product; then, obtaining weights respectively corresponding to the second product conversion data; and generating a product score corresponding to the first product based on the second product conversion data and the weight, and taking the product score as a product evaluation value of the first product. According to the method and the device, based on the weights respectively corresponding to the second product conversion data, the product evaluation values respectively corresponding to the products in the target resource position can be rapidly and accurately calculated.
In some optional implementations, the determining a target product from all the products based on the product evaluation values includes:
and screening out the target product evaluation value with the largest value from all the product evaluation values.
And acquiring second products corresponding to the target product evaluation values from all the products.
And taking the second product as the target product.
The method comprises the steps of screening out target product evaluation values with the largest values from all the product evaluation values, then obtaining second products corresponding to the target product evaluation values from all the products, and taking the second products as the target products. Because the target product is the product with the highest product evaluation value in all the corresponding products in the target resource position of the target page, the target product can be ensured to be the highest-quality product, and the accuracy of the generated target product is ensured.
In some optional implementations, the recommended replacement processing for the product in the target resource bit based on the target product includes the following steps:
and acquiring a preset product set to be recommended.
In this embodiment, the set of products to be recommended is a product to be determined in advance according to an actual service requirement, and the product is used for recommending a user. The product to be recommended set comprises a plurality of products to be recommended.
And calling a preset similarity analysis model.
In this embodiment, the first product information of the set of products to be recommended may be obtained, and the second product information of the insurance product purchased by the user in the preset time period may be obtained; vectorizing the first product information to obtain a corresponding first product vector, and vectorizing the second product information to obtain a corresponding second product vector; modeling is conducted through a preset distance calculation method based on the first product vector and the second product vector so as to generate the similarity analysis model. The preset time period is not specifically limited, and may be determined according to actual service requirements. The above-mentioned distance calculating method is not particularly limited, and may be set according to actual service requirements, for example, a cosine distance calculating method may be used.
And determining a third product matched with the target product from the product set to be recommended based on the similarity analysis model.
In this embodiment, the similarity between each product to be recommended and the target product included in the set of products to be recommended may be calculated based on the similarity analysis model, and the product to be recommended with the largest similarity value may be selected as the third product.
And recommending and replacing the product in the target resource position based on the third product.
In this embodiment, performing, based on the third product, recommended replacement processing on the product in the target resource location means: and replacing the product in the target resource bit of the target page with the third product.
The method comprises the steps of obtaining a preset product set to be recommended; then calling a preset similarity analysis model; then, based on the similarity analysis model, determining a third product matched with the target product from the product set to be recommended; and recommending and replacing the product in the target resource bit based on the third product. The method and the device can screen the third product matched with the target product from the product set to be recommended quickly and accurately and serve as the final product to be recommended based on the use of the similarity analysis model, and the acquisition efficiency and accuracy of the final product to be recommended are improved. And the recommendation replacement processing is carried out on the products in the target resource position by using the third products, so that the recommendation of the proper products to the clients in the proper positions in the target pages is realized, the intelligence of product recommendation is improved, and the use experience of the clients is improved.
In some alternative implementations of the present embodiment, step S203 includes the steps of:
and screening user behavior data corresponding to the data type from the service burial point data based on a preset data type.
In this embodiment, the data type is a data type preset according to an actual service requirement, and specifically may include a product click rate of a resource bit, an exposure number of each resource bit in a page, a click number, and a conversion number of each step in a flow.
And acquiring a preset product data statistics rule.
In this embodiment, the product data statistics rule is a statistics rule pre-created according to an actual service usage requirement and used for generating corresponding product conversion data according to user behavior data statistics.
And counting the user behavior data based on the product data counting rule to generate corresponding counting data.
In this embodiment, according to the product data statistics rule, corresponding statistics processing may be performed on the user behavior data to generate corresponding statistics data.
And taking the statistical data as product conversion data corresponding to the target resource bit.
According to the method, user behavior data corresponding to the data type are screened out from the service embedded point data based on the preset data type; then obtaining a preset product data statistics rule; and then counting the user behavior data based on the product data statistics rule, generating corresponding statistics data, and taking the statistics data as product conversion data corresponding to the target resource bit. The method and the device can count the user behavior data corresponding to the preset data types in the service embedded point data based on the use of the product data statistics rules, so that the product conversion data corresponding to the target resource bits can be generated rapidly and intelligently.
In some optional implementations of this embodiment, before step S201, the electronic device may further perform the following steps:
and judging whether the business behavior operation triggered by the user to the target page is received or not.
In this embodiment, the business behavior operation may include business behaviors such as entering an order flow, settling a claim, and using a medical care service, which are triggered by the user in the target page.
If yes, determining the jump link page URL corresponding to the target page based on the business behavior operation.
In this embodiment, the jump link page URL (Uniform Resource Locator ) refers to the address of the jump link page after the business action operation is performed on the target page.
And acquiring position parameters corresponding to the resource bits in the target page based on the resource bit embedding point rule.
In this embodiment, the resource bit embedding rule is a embedding rule preset according to an actual service requirement. When a user enters a business process corresponding to the business behavior operation on a target page, the position parameters of the entry position of the user are acquired and stored. The business process may include business orders, customer complaints, service contracts, and the like. The location parameters may include component codes of the target page, data locations, etc. representing parameters of the location. The parameters of the representative positions such as the page where the service definition is located, the component codes, the data positions and the like can be obtained through the front-end resource bit embedded points defined by the service, and the target interfaces corresponding to the target pages are called to obtain the position parameters. For front-end development, a rule specification of position parameter definition is formed, so that a developer can acquire spliced position parameter codes according to the existing logic, and when resource bits change due to service configuration rules (such as a carousel map is provided with a plurality of screens, and a rapid entry configuration item is added), the front-end can autonomously respond to generate new position parameters without iterative publishing. In addition, for business analysis, the existence of the position parameters can enable a business teacher to clearly define conversion details of each resource position, and know the number of exposure people, the number of clicks and the number of conversion people of each step in the process of each page, so that the recommendation optimization of products of the resource position is performed in a targeted manner. In addition, corresponding conversion data of each component, each page and each platform are obtained in an upward summary mode.
And splicing the position parameter on the jump link page URL.
In this embodiment, the position parameter may be spliced to the URL of the jump link page corresponding to the target page as a parameter for the subsequent page to acquire. The position parameters are carried by the full amount of service embedded point data in the using process and spliced on the jump link page URL, and the position parameters fall into a service table so as to facilitate the subsequent corresponding data analysis.
Judging whether the business behavior operation for the target page triggered by the user is received or not; if yes, determining a jump link page URL corresponding to the target page based on the business behavior operation; then, acquiring position parameters corresponding to the resource bits in the target page based on the resource bit embedding point rule; and splicing the position parameters on the jump link page URL to intelligently finish the embedding processing of the target page based on the resource bit embedding rule, and then facilitating the subsequent quick acquisition of service embedding data corresponding to the target resource bit from the target page after embedding. In addition, by automatically acquiring the position parameters corresponding to the resource bits in the target page, even if the resource bits are newly added, front-end development and edition development are not needed, so that the requirement of quick iteration of the service can be responded and met in time, and the intelligence of the embedded point processing of the page is improved.
It should be emphasized that to further ensure the privacy and security of the product transformation data, the product transformation data may also be stored in a blockchain node.
The blockchain referred to in the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by computer readable instructions stored in a computer readable storage medium that, when executed, may comprise the steps of the embodiments of the methods described above. The storage medium may be a nonvolatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a random access Memory (Random Access Memory, RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited in order and may be performed in other orders, unless explicitly stated herein. Moreover, at least some of the steps in the flowcharts of the figures may include a plurality of sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, the order of their execution not necessarily being sequential, but may be performed in turn or alternately with other steps or at least a portion of the other steps or stages.
With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a product recommendation device, where an embodiment of the device corresponds to the embodiment of the method shown in fig. 2, and the device may be specifically applied to various electronic devices.
As shown in fig. 3, the product recommendation device 300 according to the present embodiment includes: a loading module 301, a calling module 302, a collecting module 303, a generating module 304 and a processing module 305. Wherein:
the loading module 301 is configured to load a target page that generates a buried point in advance based on a rule of a buried point of a resource bit;
a calling module 302, configured to call a target interface corresponding to the target page;
the collecting module 303 is configured to collect service burial point data corresponding to a target resource bit in the target page in a preset period of time based on the target interface;
a generating module 304, configured to generate product conversion data corresponding to the target resource bit based on the service burial point data;
and the processing module 305 is used for performing recommended replacement processing on the products in the target resource bit based on the product conversion data.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the product recommendation method in the foregoing embodiment one by one, and are not described herein again.
In some alternative implementations of the present embodiment, the processing module 305 includes:
the first acquisition sub-module is used for acquiring first product conversion data of various products put in the target resource bit; wherein the category of the first product conversion data includes a plurality of;
the generation sub-module is used for generating product evaluation values respectively corresponding to the products in the target resource bit based on the first product conversion data;
a first determination sub-module for determining a target product from all the products based on the product evaluation values;
and the processing sub-module is used for recommending and replacing the products in the target resource bit based on the target product.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the product recommendation method in the foregoing embodiment one by one, and are not described herein again.
In some optional implementations of the present embodiment, generating the sub-module includes:
the first acquisition unit is used for acquiring second product conversion data corresponding to the first product; wherein the first product is any one of all the products;
the second acquisition unit is used for acquiring weights respectively corresponding to the second product conversion data;
A generation unit configured to generate a product score corresponding to the first product based on the second product conversion data and the weight;
a first determining unit configured to take the product score as a product evaluation value of the first product.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the product recommendation method in the foregoing embodiment one by one, and are not described herein again.
In some optional implementations of this embodiment, the first determining submodule includes:
the screening unit is used for screening out target product evaluation values with the largest values from all the product evaluation values;
a third acquisition unit configured to acquire a second product corresponding to the target product evaluation value from among all the products;
and the second determining unit is used for taking the second product as the target product.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the product recommendation method in the foregoing embodiment one by one, and are not described herein again.
In some alternative implementations of the present embodiment, the processing submodule includes:
a fourth obtaining unit, configured to obtain a preset product set to be recommended;
The calling unit is used for calling a preset similarity analysis model;
the third determining unit is used for determining a third product matched with the target product from the product set to be recommended based on the similarity analysis model;
and the processing unit is used for recommending and replacing the products in the target resource bit based on the third product.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the product recommendation method in the foregoing embodiment one by one, and are not described herein again.
In some optional implementations of the present embodiment, the generating module 304 includes:
the screening sub-module is used for screening user behavior data corresponding to the data type from the service burial point data based on a preset data type;
the second acquisition sub-module is used for acquiring preset product data statistics rules;
the statistics sub-module is used for carrying out statistics on the user behavior data based on the product data statistics rules to generate corresponding statistics data;
and the second determining submodule is used for taking the statistical data as product conversion data corresponding to the target resource bit.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the product recommendation method in the foregoing embodiment one by one, and are not described herein again.
In some optional implementations of the present embodiment, the product recommendation device further includes:
the judging module is used for judging whether the business behavior operation for the target page triggered by the user is received or not;
the determining module is used for determining a jump link page URL corresponding to the target page based on the business behavior operation if the jump link page URL is the same as the target page;
the acquisition module is used for acquiring position parameters corresponding to the resource bits in the target page based on the resource bit embedding point rule;
and the splicing module is used for splicing the position parameter on the jump link page URL.
In this embodiment, the operations performed by the modules or units respectively correspond to the steps of the product recommendation method in the foregoing embodiment one by one, and are not described herein again.
In order to solve the technical problems, the embodiment of the application also provides computer equipment. Referring specifically to fig. 4, fig. 4 is a basic structural block diagram of a computer device according to the present embodiment.
The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It should be noted that only computer device 4 having components 41-43 is shown in the figures, but it should be understood that not all of the illustrated components are required to be implemented and that more or fewer components may be implemented instead. It will be appreciated by those skilled in the art that the computer device herein is a device capable of automatically performing numerical calculations and/or information processing in accordance with predetermined or stored instructions, the hardware of which includes, but is not limited to, microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASICs), programmable gate arrays (fields-Programmable Gate Array, FPGAs), digital processors (Digital Signal Processor, DSPs), embedded devices, etc.
The computer equipment can be a desktop computer, a notebook computer, a palm computer, a cloud server and other computing equipment. The computer equipment can perform man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch pad or voice control equipment and the like.
The memory 41 includes at least one type of readable storage medium including flash memory, hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), random Access Memory (RAM), static Random Access Memory (SRAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), programmable Read Only Memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the storage 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash Card (Flash Card) or the like, which are provided on the computer device 4. Of course, the memory 41 may also comprise both an internal memory unit of the computer device 4 and an external memory device. In this embodiment, the memory 41 is typically used to store an operating system and various application software installed on the computer device 4, such as computer readable instructions of a product recommendation method. Further, the memory 41 may be used to temporarily store various types of data that have been output or are to be output.
The processor 42 may be a central processing unit (Central Processing Unit, CPU), controller, microcontroller, microprocessor, or other data processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or process data, such as computer readable instructions for executing the product recommendation method.
The network interface 43 may comprise a wireless network interface or a wired network interface, which network interface 43 is typically used for establishing a communication connection between the computer device 4 and other electronic devices.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
in the embodiment of the application, firstly, loading a target page which is generated with embedded points in advance based on a resource bit embedded point rule; then, a target interface corresponding to the target page is called; collecting service burial point data corresponding to the target resource bit in the target page by a user in a preset time period based on the target interface; generating product conversion data corresponding to the target resource bit based on the service burial point data; and finally, recommending and replacing the products in the target resource bit based on the product conversion data. According to the embodiment of the application, the target page is buried through the use of the resource bit burying rules, so that service burying data corresponding to the target resource bit in the target page can be accurately collected through the corresponding interfaces, product conversion data corresponding to the target resource bit can be intelligently generated based on the service burying data, and further, recommendation replacement processing is performed on products in the target resource bit based on the product conversion data, so that the recommendation of proper products to customers at proper positions in the target page is realized, the intelligence and accuracy of product recommendation are improved, and the use experience of the customers is improved.
The present application also provides another embodiment, namely, a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the product recommendation method as described above.
Compared with the prior art, the embodiment of the application has the following main beneficial effects:
in the embodiment of the application, firstly, loading a target page which is generated with embedded points in advance based on a resource bit embedded point rule; then, a target interface corresponding to the target page is called; collecting service burial point data corresponding to the target resource bit in the target page by a user in a preset time period based on the target interface; generating product conversion data corresponding to the target resource bit based on the service burial point data; and finally, recommending and replacing the products in the target resource bit based on the product conversion data. According to the embodiment of the application, the target page is buried through the use of the resource bit burying rules, so that service burying data corresponding to the target resource bit in the target page can be accurately collected through the corresponding interfaces, product conversion data corresponding to the target resource bit can be intelligently generated based on the service burying data, and further, recommendation replacement processing is performed on products in the target resource bit based on the product conversion data, so that the recommendation of proper products to customers at proper positions in the target page is realized, the intelligence and accuracy of product recommendation are improved, and the use experience of the customers is improved.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk), comprising several instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method described in the embodiments of the present application.
It is apparent that the embodiments described above are only some embodiments of the present application, but not all embodiments, the preferred embodiments of the present application are given in the drawings, but not limiting the patent scope of the present application. This application may be embodied in many different forms, but rather, embodiments are provided in order to provide a more thorough understanding of the present disclosure. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described in the foregoing, or equivalents may be substituted for elements thereof. All equivalent structures made by the specification and the drawings of the application are directly or indirectly applied to other related technical fields, and are also within the protection scope of the application.
Claims (10)
1. A method of product recommendation, comprising the steps of:
loading a target page of a buried point generated in advance based on a resource bit buried point rule;
invoking a target interface corresponding to the target page;
collecting service burial point data corresponding to a target resource bit in the target page by a user in a preset time period based on the target interface;
generating product conversion data corresponding to the target resource bit based on the service burial point data;
and recommending and replacing the product in the target resource bit based on the product conversion data.
2. The method for recommending products according to claim 1, wherein the step of recommending replacement processing for the products in the target resource bit based on the product conversion data comprises:
acquiring first product conversion data of various products put in the target resource bit; wherein the category of the first product conversion data includes a plurality of;
based on the first product conversion data, generating product evaluation values respectively corresponding to the products in the target resource position;
determining a target product from all the products based on the product evaluation values;
And recommending and replacing the products in the target resource position based on the target product.
3. The product recommendation method according to claim 2, wherein the step of generating product evaluation values respectively corresponding to the products in the target resource location based on the first product conversion data specifically comprises:
acquiring second product conversion data corresponding to the first product; wherein the first product is any one of all the products;
acquiring weights respectively corresponding to the second product conversion data;
generating a product score corresponding to the first product based on the second product conversion data and the weights;
and taking the product score as a product evaluation value of the first product.
4. The product recommendation method according to claim 2, wherein the step of determining a target product from all the products based on the product evaluation values specifically comprises:
screening out the target product evaluation value with the largest value from all the product evaluation values;
acquiring second products corresponding to the target product evaluation values from all the products;
and taking the second product as the target product.
5. The method for recommending products according to claim 2, wherein the step of recommending replacement processing for the products in the target resource location based on the target product specifically comprises:
acquiring a preset product set to be recommended;
calling a preset similarity analysis model;
determining a third product matched with the target product from the product set to be recommended based on the similarity analysis model;
and recommending and replacing the product in the target resource position based on the third product.
6. The product recommendation method according to claim 1, wherein the step of generating product conversion data corresponding to the target resource bit based on the service burial point data specifically comprises:
screening user behavior data corresponding to the data type from the service burial point data based on a preset data type;
acquiring a preset product data statistics rule;
counting the user behavior data based on the product data statistics rules to generate corresponding statistics data;
and taking the statistical data as product conversion data corresponding to the target resource bit.
7. The product recommendation method according to claim 1, further comprising, before the step of loading a target page in which a buried point is generated in advance based on a resource bit buried point rule:
Judging whether a business behavior operation for the target page triggered by the user is received or not;
if yes, determining a jump link page URL corresponding to the target page based on the business behavior operation;
acquiring position parameters corresponding to the resource bits in the target page based on the resource bit embedding point rule;
and splicing the position parameter on the jump link page URL.
8. A product recommendation device, comprising:
the loading module is used for loading a target page of the embedded point which is generated in advance based on the rule of the embedded point of the resource bit;
the calling module is used for calling a target interface corresponding to the target page;
the collection module is used for collecting service burial point data corresponding to the target resource bit in the target page in a preset time period based on the target interface;
the generation module is used for generating product conversion data corresponding to the target resource bit based on the service burial point data;
and the processing module is used for recommending and replacing the products in the target resource bit based on the product conversion data.
9. A computer device comprising a memory having stored therein computer readable instructions which when executed by a processor implement the steps of the product recommendation method of any of claims 1 to 7.
10. A computer readable storage medium having stored thereon computer readable instructions which when executed by a processor implement the steps of the product recommendation method according to any of claims 1 to 7.
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