CN113076485A

CN113076485A - Resource recommendation method, device and equipment based on intelligent degradation and storage medium

Info

Publication number: CN113076485A
Application number: CN202110465409.0A
Authority: CN
Inventors: 乐志能
Original assignee: Ping An Technology Shenzhen Co Ltd
Current assignee: Ping An Technology Shenzhen Co Ltd
Priority date: 2021-04-28
Filing date: 2021-04-28
Publication date: 2021-07-06

Abstract

The invention relates to a data analysis technology, and discloses a resource recommendation method based on intelligent degradation, which comprises the following steps: extracting an access identifier of the resource recommendation request; if the access identifier is a first identifier, generating a resource recommendation list according to a first strategy to recommend the user, and storing the resource recommendation list and the user in an associated manner; if the access identifier is a second identifier, acquiring user characteristics and target degradation characteristics; judging whether the user characteristics are in the target degradation characteristics; if not, generating a resource recommendation list according to a first strategy to recommend the user, and storing the resource recommendation list and the user in a correlation manner; and if so, calling the stored resource recommendation list to recommend the user. In addition, the invention also relates to a block chain technology, and the user characteristics can be stored in the nodes of the block chain. The invention also provides a resource recommendation device, equipment and medium based on intelligent degradation. The invention can solve the problem that the computing resources are vacant when the recommendation service of the user is degraded.

Description

Resource recommendation method, device and equipment based on intelligent degradation and storage medium

Technical Field

The invention relates to the technical field of data analysis, in particular to a resource recommendation method and device based on intelligent degradation, electronic equipment and a computer readable storage medium.

Background

When resource recommendation is performed on a user, a unified server often analyzes user data, and generates a recommendation list conforming to the user according to an analysis result, so as to realize intelligent recommendation on the user. However, when the number of concurrent users is too large, or a server has a partial failure, the amount of user data that can be analyzed by the server is partially reduced, and therefore, it is necessary to downgrade the recommendation of the user, for example, to downgrade the accurate recommendation for each user to a uniform template recommendation for a certain class of user groups.

At present, most of solutions for a server to be unable to normally provide a recommendation service are to indiscriminately degrade all users, but the server often only has partial function reduction, and if directly degrading the resource recommendation service of all users, a large amount of computing resources of the server will be left vacant, so how to achieve intelligent degradation of the resource recommendation service becomes an urgent problem to be solved when the server is unable to normally provide the recommendation service.

Disclosure of Invention

The invention provides a resource recommendation method and device based on intelligent degradation and a computer readable storage medium, and mainly aims to solve the problem that computing resources are vacant when the recommendation service of a user is degraded.

In order to achieve the above object, the present invention provides a resource recommendation method based on intelligent degradation, which includes:

acquiring a resource recommendation request of a user, and extracting an access identifier in the resource recommendation request;

if the access identifier is a first identifier, determining that the resource recommendation request is a first request, generating a resource recommendation list according to a first strategy to recommend resources to the user, and performing associated storage on the resource recommendation list and the user;

if the access identifier is a second identifier, determining that the resource recommendation request is a non-primary request, and acquiring pre-stored user characteristics and target degradation characteristics;

judging whether the user characteristics are in target degradation characteristics;

if the user characteristics are not in the target degradation characteristics, a resource recommendation list is generated according to the first strategy to recommend resources to the user, and the resource recommendation list and the user are stored in an associated mode;

and if the user characteristics are in the target degradation characteristics, calling a resource recommendation list stored in association with the user to recommend the resource to the user.

Optionally, the extracting the access identifier in the resource recommendation request includes:

traversing the resource recommendation request to determine a location of a field interval symbol in the resource recommendation request;

dividing the resource recommendation request into a plurality of request fields according to the positions of the field interval symbols, and numbering the request fields from front to back in the resource recommendation request;

and selecting a request field with a preset number, and analyzing the selected request field to obtain a request identifier.

Optionally, the generating a resource recommendation list according to the first policy to perform resource recommendation on the user includes:

acquiring user data of the user, and generating a user portrait of the user according to the user data;

acquiring a plurality of resources to be recommended, and respectively performing matching analysis on the plurality of resources to be recommended and the user portrait to obtain the matching degree of each resource to be recommended and the user portrait;

selecting resources to be recommended with the matching degree larger than a preset matching degree threshold value, and sequencing the selected resources to be recommended according to the sequence of the matching degrees from large to small to generate a resource recommendation list;

and recommending the resources for the user according to the resource recommendation list.

Optionally, the generating a user representation of the user from the user data includes:

performing text conversion on the user data to obtain text data;

performing word segmentation processing on the text data to obtain text word segmentation;

performing word vector conversion on the text word segmentation to obtain a text word vector;

performing feature extraction on the text word vector by using a pre-trained feature extraction algorithm to obtain a feature word vector;

and generating a user portrait of the user according to the feature word vector.

Optionally, the performing word segmentation processing on the text data to obtain text word segmentation includes:

acquiring a pre-constructed standard dictionary, wherein the standard dictionary comprises a plurality of standard participles;

dividing the text data into texts according to a preset first length to obtain search terms;

and searching the search word in the standard dictionary, determining the search word as a text word of the text data when a standard word which is the same as the search word is searched from the standard dictionary, returning to the step of text division, and dividing the text according to a preset second length until the number of times of the text division reaches a preset number of times to obtain the text word corresponding to the text data.

Optionally, the determining whether the user characteristic is in the target degradation characteristic includes:

constructing an index for each of the target degradation features;

retrieving in the target degradation characteristics according to the user characteristics and the index to obtain retrieval content;

detecting the length of the retrieval content, and determining that the user characteristic is not in the target degradation characteristic when the length of the retrieval content is zero;

when the length of the retrieved content is not zero, determining that the user characteristic is within the target degradation characteristic.

Optionally, the invoking a resource recommendation list stored in association with the user to perform resource recommendation on the user includes:

extracting a user ID in the resource recommendation request;

generating a resource recommendation list calling request according to the user ID;

calling a resource recommendation list stored in association with the user by using the resource list calling request;

and recommending the resources for the user by using the resource recommendation list.

In order to solve the above problem, the present invention further provides an apparatus for resource recommendation based on intelligent degradation, the apparatus comprising:

the identification extraction module is used for acquiring a resource recommendation request of a user and extracting an access identification in the resource recommendation request;

the first recommending module is used for determining that the resource recommending request is a first request if the access identifier is a first identifier, generating a resource recommending list according to a first strategy to recommend resources to the user, and storing the resource recommending list and the user in a correlated manner;

the data acquisition module is used for determining that the resource recommendation request is a non-primary request if the access identifier is a second identifier, and acquiring pre-stored user characteristics and target degradation characteristics;

the degradation judging module is used for judging whether the user characteristics are in target degradation characteristics;

the second recommending module is used for generating a resource recommending list according to the first strategy to recommend resources to the user and storing the resource recommending list and the user in a correlation manner if the user characteristics are not in the target degradation characteristics;

and the third recommending module is used for calling a resource recommending list stored in association with the user to recommend the resource to the user if the user characteristic is in the target degradation characteristic.

In order to solve the above problem, the present invention also provides an electronic device, including:

a memory storing at least one instruction; and

and the processor executes the instructions stored in the memory to realize the intelligent degradation-based resource recommendation method.

In order to solve the above problem, the present invention further provides a computer-readable storage medium, which stores at least one instruction, where the at least one instruction is executed by a processor in an electronic device to implement the intelligent degradation-based resource recommendation method described above.

The method comprises the steps of extracting an access identifier in a resource recommendation request, judging the access identifier to determine whether the resource recommendation request is a first request, generating a resource recommendation list of a user according to a first strategy to realize resource recommendation of the user if the resource recommendation request is the first request, and storing the resource recommendation list, so that refined recommendation of the user who requests for the first time is realized; if the request is not the first request, the user characteristics are obtained, the resource recommendation list of the user is generated for the user corresponding to the user characteristics which are not in the target degradation characteristics, refined recommendation is carried out, and degradation recommendation is carried out for the user corresponding to the user characteristics which are in the target degradation characteristics by calling the resource recommendation list generated by history, so that distinctive resource recommendation is carried out for different user groups, indiscriminate recommendation service degradation is avoided for all users, intelligent degradation of the resource recommendation service is further realized when the server cannot normally provide the recommendation service, and the utilization rate of the calculation resources in the server is improved. Therefore, the resource recommendation method, the resource recommendation device, the electronic equipment and the computer-readable storage medium based on intelligent degradation can solve the problem that computing resources are vacant when the recommendation service of the user is degraded.

Drawings

FIG. 1 is a flowchart illustrating a resource recommendation method based on intelligent degradation according to an embodiment of the present invention;

fig. 2 is a schematic flowchart of a process of generating a resource recommendation list and performing resource recommendation according to an embodiment of the present invention;

FIG. 3 is a schematic flow chart illustrating a process for generating a user representation according to an embodiment of the present invention;

FIG. 4 is a functional block diagram of an apparatus for resource recommendation based on intelligent degradation according to an embodiment of the present invention;

fig. 5 is a schematic structural diagram of an electronic device implementing the intelligent degradation-based resource recommendation method according to an embodiment of the present invention.

The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.

Detailed Description

It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

The embodiment of the application provides a resource recommendation method based on intelligent degradation. The executing subject of the resource recommendation method based on intelligent degradation includes, but is not limited to, at least one of electronic devices such as a server and a terminal, which can be configured to execute the method provided by the embodiments of the present application. In other words, the resource recommendation method based on intelligent degradation may be performed by software or hardware installed in the terminal device or the server device, and the software may be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.

Referring to fig. 1, a flowchart of a resource recommendation method based on intelligent degradation according to an embodiment of the present invention is shown. In this embodiment, the resource recommendation method based on intelligent degradation includes:

s1, acquiring a resource recommendation request of a user, and extracting an access identifier in the resource recommendation request.

In the embodiment of the present invention, the resource recommendation request is a request for a user to request to acquire recommended resources, where the recommended resources include, but are not limited to: current politics, news information, entertainment information.

The resource recommendation request can be uploaded by a user through a page for collecting the resource recommendation request in the client, or the resource recommendation request can be generated according to the recommendation requirement input by the user through a pre-installed resource recommendation request generation program.

In one practical application scenario of the present invention, the resource recommendation request includes multiple pieces of information, such as a user ID, a request access identifier, and the like, and the request access identifier can be used to determine whether the resource recommendation request is a first request.

Because the resource recommendation request is often expressed in a fixed field sequence, for example, the resource recommendation request includes three fields, a first field is used for recording the user information sending the request, a second field is used for recording the request identifier, and a third field is used for recording the response mode of the data; therefore, the resource recommendation request can be divided into a plurality of request fields by means of field division, and the request identifier recorded in the resource recommendation request is selected for processing, so as to extract the access identifier in the resource recommendation request.

In the embodiment of the present invention, the extracting the access identifier in the resource recommendation request includes:

In detail, the field interval symbol is used for separating fields in the resource recommendation request, and the field interval symbol may be predefined by a user.

For example, there is a resource recommendation request: xxx < yyyy < zzz, wherein, "<" is a field interval symbol, the resource recommendation request can be divided into three request fields according to the position of the field interval symbol, and the three request fields are numbered in the sequence from front to back to obtain a request field 1 xxx, a request field 2 yyy and a request field 3 zzz; and selecting a preset number 2 request field, and analyzing the number 2 request field to obtain a request identifier.

In other embodiments of the present invention, the access identifier may also be extracted from the resource recommendation request by a computer sentence (such as a java sentence, a python sentence, etc.) having a function of extracting the request identifier.

S2, determining the type of the access identifier, and if the access identifier is the first identifier, performing S3 as described below, or if the access identifier is the second identifier, performing S4 as described below.

S3, determining that the resource recommendation request is a first request, generating a resource recommendation list according to a first strategy to recommend resources to the user, and storing the resource recommendation list and the user in a correlation manner.

In the embodiment of the present invention, the first identifier is used to mark the resource recommendation request as a first request, and if the access expression is the same as the first identifier, the resource recommendation request is determined as the first request, and a resource recommendation list needs to be generated according to a first policy, so as to utilize the resource recommendation list to perform resource recommendation on the user.

In detail, the first policy is an intelligent recommendation policy, that is, the user data is analyzed by acquiring the user data, so that the user is subjected to targeted resource recommendation according to an analysis result.

In the embodiment of the present invention, referring to fig. 2, the generating a resource recommendation list according to a first policy to perform resource recommendation on the user includes:

s21, acquiring user data of the user, and generating a user portrait of the user according to the user data;

s22, acquiring a plurality of resources to be recommended, and respectively performing matching analysis on the plurality of resources to be recommended and the user portrait to obtain the matching degree of each resource to be recommended and the user portrait;

s23, selecting resources to be recommended with the matching degree larger than a preset matching degree threshold value, and sequencing the selected resources to be recommended according to the sequence of the matching degrees from large to small to generate a resource recommendation list;

and S24, recommending the resources to the user according to the resource recommendation list.

In detail, the user data includes, but is not limited to, the age, sex, occupation and hobbies of the user, and the acquired user data may be in various forms (such as video, image and text, etc.) so as to generate a more accurate user profile.

The user data may be analyzed by a pre-trained intelligent model to generate a user representation of the user data, where the intelligent model includes, but is not limited to, an OCR (Optical Character Recognition), an NLP (Natural Language Processing) model, an ASR (Automatic Speech Recognition) model, and the like.

In one embodiment of the present invention, referring to fig. 3, the generating a user representation of the user according to the user data includes:

s31, performing text conversion on the user data to obtain text data;

s32, performing word segmentation processing on the text data to obtain text word segmentation;

s33, performing word vector conversion on the text participles to obtain text word vectors;

s34, extracting the features of the text word vectors by using a pre-trained feature extraction algorithm to obtain feature word vectors;

and S35, generating a user portrait of the user according to the feature word vector.

For example, the user data includes image data and video data, and the image data in the user data may be processed by using an OCR model to convert the image data into text data; and processing the video data in the user data by utilizing the combination of the ASR model and the OCR model so as to convert the video data into text data.

The present embodiment may perform word segmentation processing on the text data by using a pre-constructed standard dictionary, where the standard dictionary includes a plurality of standard words. For example, the text data is divided into different lengths, the division result is retrieved from the standard dictionary, and if the standard participle identical to the division result can be retrieved, the standard participle is determined to be the text participle of the text data.

In one embodiment of the present invention, the performing word segmentation processing on the text data to obtain text words includes:

For example, the text data is divided according to different preset lengths, and the search words obtained by dividing the text each time are searched in the dictionary to obtain text word segmentation until the number of times of text division reaches the preset number of times, so as to implement word segmentation of the text data.

In the embodiment, the word segmentation of the text data is realized in a mode of dividing and searching the text data according to different lengths, the content of the text data does not need to be analyzed, and the efficiency of word segmentation of the text data is improved.

In this embodiment, the text segmentation may be converted into a text word vector by using a preset word2vec model.

Further, the feature extraction algorithm includes, but is not limited to, a bayesian classification algorithm, a logistic regression algorithm, a KNN algorithm, and the like, and the feature extraction algorithm is used for performing feature extraction on the text word vectors to obtain feature word vectors, and the feature word vectors are collected to obtain the user portrait of the user.

In the embodiment of the invention, the plurality of resources to be recommended are selectable resources for recommending to the user, such as current politics, news information, entertainment information and the like.

According to the embodiment of the invention, a plurality of resources to be recommended and the user portrait are respectively subjected to matching analysis by using a preset matching algorithm to obtain the matching degree of each resource to be recommended and the user portrait, wherein the matching algorithm comprises an Euclidean distance algorithm, a cosine distance algorithm and the like.

According to the resource recommendation method and device, the matching value between each resource to be recommended and the user portrait is calculated through a preset matching algorithm, the resource recommendation list is generated according to the sequence of the matching values from large to small, and resource recommendation for the user is achieved according to the resource recommendation list.

For example, there are a resource to be recommended 1, a resource to be recommended 2, a resource to be recommended 3, and a resource to be recommended 4, and it is known through calculation that the matching value of the resource to be recommended 1 and the user image is 80, the matching value of the resource to be recommended 2 and the user image is 94, the matching value of the resource to be recommended 3 and the user image is 75, and the matching value of the resource to be recommended 4 and the user image is 83, then a resource recommendation list is generated in the order of the matching values from large to small: the method comprises the following steps of (1) obtaining resources to be recommended (2), resources to be recommended (4), resources to be recommended (1) and resources to be recommended (3); and recommending the resources for the user according to the sequence of the resources to be recommended in the resource recommendation list.

Further, after the resource recommendation list is generated, the embodiment of the invention stores the resource recommendation list and the user in an associated manner.

For example, the resource recommendation list and the user can be stored in association by generating a link associated with the user ID in the resource recommendation list, and when the user ID is clicked, the resource recommendation list associated with the user ID can be called.

Or, the resource recommendation list can be stored in a pre-constructed database, and the resource recommendation list is named by the user ID, so that the resource recommendation list and the user can be stored in an associated manner.

S4, determining that the resource recommendation request is a non-primary request, and acquiring pre-stored user characteristics and target degradation characteristics.

In the embodiment of the invention, the second identifier is used for marking that the resource recommendation request is a non-primary request, and if the access expression is the same as the second identifier, the resource recommendation request is determined to be the non-primary request and the pre-stored user characteristics and target degradation characteristics need to be acquired.

In detail, the user characteristics are data related to the user, which can classify the user, for example, the user address, the user age, the user gender, and the like.

The target degradation characteristics are user characteristics needing degradation recommendation and can be preset. For example, the preset target degradation characteristics are: the addresses are users with wide and deep addresses not in the north; the recommended degradation is carried out on the users whose addresses contained in the user characteristics are not north, broad and deep.

Specifically, the downgrade recommendation refers to downgrading an accurate resource recommendation to a non-accurate resource recommendation. For example, the resource recommendation is performed on each user according to the user characteristics instead of performing the resource recommendation on all users according to the preset template.

According to the embodiment of the invention, the user characteristics and the target degradation characteristics can be captured from the preset block chain link points through the python statement with the data capture function, and the efficiency of obtaining the user characteristics and the target degradation characteristics from the block chain can be improved by utilizing the high throughput of the block chain to data.

S5, determining whether the user characteristic is in the target degradation characteristic, when the user characteristic is not in the target degradation characteristic, performing S6 described below, or when the user characteristic is in the target degradation characteristic, performing S7 described below.

In the embodiment of the invention, whether the user characteristics are in the target degradation characteristics can be judged in a retrieval mode.

In detail, the determining whether the user characteristic is in a target degradation characteristic includes:

constructing an index for each of the target degradation features;

In detail, the retrieval may be a pointer constructed from any one of the target features, by which the feature can be uniquely retrieved. Thus, by building an index, data retrieval can be performed uniquely and quickly.

Further, the present invention can detect the length of the search content using a java statement having a field detection function. For example, by executing a preset java statement with a field detection function on the search content to acquire a return value corresponding to the statement, when the length of the search content is not zero, the length data of the search content is returned, and when the length of the search content is zero, null is returned.

And judging whether the user characteristics are in the target degradation characteristics according to the length of the obtained retrieval content, and the retrieval content obtained by retrieval does not need to be analyzed in detail, so that the efficiency of judging whether the user characteristics are in the target degradation characteristics is improved.

S6, generating a resource recommendation list according to the first strategy to recommend resources to the user, and storing the resource recommendation list and the user in a correlated manner.

In this embodiment of the present invention, if the user characteristic is not in the target degradation characteristic, a resource recommendation list may be generated according to the same first policy as that in step S3, and a resource recommendation is performed on the user by using the generated resource recommendation list, and at the same time, the resource recommendation list and the user are stored in association in a manner as in step S3, which is not described herein again.

And S7, calling a resource recommendation list stored in association with the user to recommend the resource to the user.

In the embodiment of the invention, if the user characteristics are in the target degradation characteristics, the user is determined to be subjected to resource recommendation once, so that a resource recommendation list stored in association with the user can be called, and the resource recommendation is carried out on the user by calling the used resource recommendation list.

In one embodiment of the present invention, the invoking a resource recommendation list stored in association with the user to perform resource recommendation on the user includes:

extracting a user ID in the resource recommendation request;

In detail, the step of extracting the user ID in the resource recommendation request is consistent with the step of extracting the access identifier in the resource recommendation request in step S1, and is not described herein again.

Specifically, a resource recommendation list calling request can be compiled according to the user ID by using a preset compiler, and the resource recommendation list calling request can realize calling of a resource recommendation list in which the user ID is stored in an associated manner.

Further, the step of recommending the resource to the user by using the resource recommendation list is consistent with the step of recommending the resource to the user in step S3, and is not described herein again.

The method comprises the steps of extracting an access identifier in a resource recommendation request, judging the access identifier to determine whether the resource recommendation request is a first request, generating a resource recommendation list of a user according to a first strategy to realize resource recommendation of the user if the resource recommendation request is the first request, and storing the resource recommendation list, so that refined recommendation of the user who requests for the first time is realized; if the request is not the first request, the user characteristics are obtained, the resource recommendation list of the user is generated for the user corresponding to the user characteristics which are not in the target degradation characteristics, refined recommendation is carried out, and degradation recommendation is carried out for the user corresponding to the user characteristics which are in the target degradation characteristics by calling the resource recommendation list generated by history, so that distinctive resource recommendation is carried out for different user groups, indiscriminate recommendation service degradation is avoided for all users, intelligent degradation of the resource recommendation service is further realized when the server cannot normally provide the recommendation service, and the utilization rate of the calculation resources in the server is improved. Therefore, the resource recommendation method based on intelligent degradation can solve the problem that computing resources are vacant when the recommendation service of the user is degraded.

Fig. 4 is a functional block diagram of a resource recommendation apparatus based on intelligent degradation according to an embodiment of the present invention.

The resource recommendation device 100 based on intelligent degradation can be installed in an electronic device. According to the implemented functions, the resource recommendation device 100 based on intelligent degradation may include an identification extraction module 101, a first recommendation module 102, a data acquisition module 103, a degradation judgment module 104, a second recommendation module 105, and a third recommendation module 106. The module of the present invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.

In the present embodiment, the functions regarding the respective modules/units are as follows:

the identifier extraction module 101 is configured to obtain a resource recommendation request of a user, and extract an access identifier in the resource recommendation request;

the first recommending module 102 is configured to, when it is determined that the access identifier is the first identifier, determine that the resource recommending request is a first request, generate a resource recommending list according to a first policy, recommend a resource to the user, and store the resource recommending list in association with the user;

the data obtaining module 103 is configured to determine that the resource recommendation request is a non-primary request and obtain a pre-stored user characteristic and a target degradation characteristic when it is determined that the access identifier is the second identifier;

the degradation judging module 104 is configured to judge whether the user characteristic is in a target degradation characteristic;

the second recommending module 105 is configured to generate a resource recommending list according to the first policy to recommend a resource to the user when the user characteristic is not in the target degradation characteristic, and store the resource recommending list in association with the user;

the third recommending module 106 is configured to invoke a resource recommending list stored in association with the user to recommend a resource to the user when the user characteristic is in the target degradation characteristic.

In detail, when the resource recommendation device 100 based on intelligent degradation according to the embodiment of the present invention is used, the same technical means as the resource recommendation method based on intelligent degradation described in fig. 1 to 3 is adopted, and the same technical effect can be produced, which is not described herein again.

Fig. 5 is a schematic structural diagram of an electronic device implementing the resource recommendation method based on intelligent degradation according to an embodiment of the present invention.

The electronic device 1 may comprise a processor 10, a memory 11 and a bus, and may further comprise a computer program, such as an intelligent degradation based resource recommendation program, stored in the memory 11 and executable on the processor 10.

The memory 11 includes at least one type of readable storage medium, which includes flash memory, removable hard disk, multimedia card, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as code of a resource recommendation program based on intelligent degradation, etc., but also for temporarily storing data that has been output or is to be output.

The processor 10 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects various components of the whole electronic device by using various interfaces and lines, and executes various functions and processes data of the electronic device 1 by running or executing programs or modules (e.g., resource recommendation programs based on intelligent degradation, etc.) stored in the memory 11 and calling data stored in the memory 11.

The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 11 and at least one processor 10 or the like.

Fig. 5 only shows an electronic device with components, and it will be understood by a person skilled in the art that the structure shown in fig. 5 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or a combination of certain components, or a different arrangement of components.

For example, although not shown, the electronic device 1 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 10 through a power management device, so as to implement functions of charge management, discharge management, power consumption management, and the like through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 1 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.

Further, the electronic device 1 may further include a network interface, and optionally, the network interface may include a wired interface and/or a wireless interface (such as a WI-FI interface, a bluetooth interface, etc.), which are generally used for establishing a communication connection between the electronic device 1 and other electronic devices.

Optionally, the electronic device 1 may further comprise a user interface, which may be a Display (Display), an input unit (such as a Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable for displaying information processed in the electronic device 1 and for displaying a visualized user interface, among other things.

It is to be understood that the described embodiments are for purposes of illustration only and that the scope of the appended claims is not limited to such structures.

The intelligent degradation-based resource recommendation program stored in the memory 11 of the electronic device 1 is a combination of a plurality of instructions, which when executed in the processor 10, can realize:

Specifically, the specific implementation method of the processor 10 for the instruction may refer to the description of the relevant steps in the embodiments corresponding to fig. 1 to 3, which is not repeated herein.

Further, the integrated modules/units of the electronic device 1, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying said computer program code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM).

The present invention also provides a computer-readable storage medium, storing a computer program which, when executed by a processor of an electronic device, may implement:

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method can be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.

The modules described as separate parts may or may not be physically separate, and parts displayed as modules 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 modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.

It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.

The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.

The block chain is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.

Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A resource recommendation method based on intelligent degradation, characterized in that the method comprises:

2. The intelligent degradation-based resource recommendation method according to claim 1, wherein the extracting the access identifier in the resource recommendation request includes:

3. The intelligent degradation-based resource recommendation method of claim 1, wherein the generating a resource recommendation list according to a first policy to make resource recommendations for the user comprises:

4. The intelligent degradation-based resource recommendation method of claim 3, wherein said generating a user representation of the user from the user data comprises:

performing text conversion on the user data to obtain text data;

5. The intelligent degradation-based resource recommendation method of claim 4, wherein the performing word segmentation processing on the text data to obtain text word segmentation comprises:

6. The intelligent degradation based resource recommendation method of any one of claims 1-5, wherein the determining whether the user characteristic is in a target degradation characteristic comprises:

constructing an index for each of the target degradation features;

7. The intelligent degradation-based resource recommendation method of any one of claims 1-5, wherein the invoking of the resource recommendation list stored in association with the user to make a resource recommendation to the user comprises:

extracting a user ID in the resource recommendation request;

8. An apparatus for resource recommendation based on intelligent degradation, the apparatus comprising:

the first recommending module is used for determining that the resource recommending request is a first request when the access identifier is a first identifier, generating a resource recommending list according to a first strategy to recommend resources to the user, and storing the resource recommending list and the user in a correlation manner;

the data acquisition module is used for determining that the resource recommendation request is a non-primary request when the access identifier is a second identifier, and acquiring pre-stored user characteristics and target degradation characteristics;

the second recommending module is used for generating a resource recommending list according to the first strategy to recommend resources to the user when the user characteristics are not in the target degradation characteristics, and storing the resource recommending list and the user in a correlation manner;

and the third recommending module is used for calling a resource recommending list stored in association with the user to recommend the resource to the user when the user characteristic is in the target degradation characteristic.

9. An electronic device, characterized in that the electronic device comprises:

at least one processor; and the number of the first and second groups,

a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,

the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the intelligent degradation based resource recommendation method of any one of claims 1-7.

10. A computer-readable storage medium, storing a computer program, wherein the computer program, when executed by a processor, implements the intelligent degradation-based resource recommendation method of any one of claims 1 to 7.