CN111966911A - Personalized service recommendation method and device and electronic equipment - Google Patents
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Abstract
The invention provides a personalized service recommendation method, a personalized service recommendation device and electronic equipment, which relate to the technical field of data processing and comprise the steps of determining information to be recommended of personalized services to be recommended and specific characteristics corresponding to the information to be recommended; determining specific user information corresponding to the specific characteristics in the pre-acquired temporary user information; and recommending the information to be recommended based on the specific user information. The method determines the information to be recommended of the personalized service to be recommended and the corresponding specific characteristics from the temporary user information, then recommends the information to be recommended according to the specific user information, and can ensure the accurate acquisition of the specific user information because the temporary user information is information obtained by screening from a source database at regular time, thereby achieving the purpose of accurately operating each user and effectively relieving the technical problem of high personalized push abnormal rate of the personalized service recommendation method in the prior art.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a personalized service recommendation method and device and electronic equipment.
Background
With the development of internet technology, leading-edge internet companies leave behind the way of interaction of one of thousands of people, more and more companies can provide some personalized services for users, figures of the users are organized through big data services, and a scheme of operation strategy differentiation is executed according to the characteristics of the users, so that the operation experience of the users is improved, and the satisfactory effect of the users is achieved.
In the existing personalized service recommendation method, a business system reads personalized data of a user in real time, and the method comprises the following steps: the method comprises the steps of obtaining information of regions, sexes, family relations and the like, pushing personalized contents for users according to user personalized data and product strategies, wherein the process of reading the user personalized data is long, the efficiency is low, the response efficiency of big data services is seriously depended on, abnormity easily occurs, once the abnormity occurs, the user personalized service pushing cannot be accurately completed, and the user experience is poor.
In summary, the personalized service recommendation method in the prior art has the technical problem of high personalized push abnormal rate.
Disclosure of Invention
The invention aims to provide a personalized service recommendation method, a personalized service recommendation device and electronic equipment, so as to solve the technical problem that the personalized push abnormal rate is high in the personalized service recommendation method in the prior art.
In a first aspect, an embodiment of the present invention provides a personalized service recommendation method, including: determining information to be recommended of personalized services to be recommended and specific characteristics corresponding to the information to be recommended; determining specific user information corresponding to the specific features in the pre-acquired temporary user information; the temporary user information comprises user information with the specific characteristics screened from a source database at regular time, and the source database is used for storing the pre-acquired user information; recommending the information to be recommended based on the specific user information.
In an optional embodiment, before the step of determining, in the pre-acquired temporary user information, specific user information corresponding to the specific feature, the method further includes: determining a path of a file to be analyzed, wherein the path of the file to be analyzed is used for indicating one or more temporary files in a temporary file system, and the temporary files comprise user information which is screened from a source database at regular time and has the specific characteristics; acquiring the one or more temporary files from the temporary file system based on the path of the file to be analyzed; updating the temporary user information based on the one or more temporary files.
In an optional embodiment, each of the temporary files corresponds to a file number, each of the temporary files includes one or more rows of user data, and each column of each row corresponds to a preset attribute.
In an alternative embodiment, the step of updating the temporary user information based on the one or more temporary files comprises: analyzing one or more temporary files corresponding to the file path to be analyzed line by line, and respectively obtaining user information of one user for each line; and updating the temporary user information based on the user information of one or more users obtained by analysis.
In an alternative embodiment, the specific user information includes: specific characteristics, specific policy characteristics and specific user identities.
In an optional implementation manner, a corresponding relation between the policy characteristics and the recommended policy is predetermined; the step of recommending the information to be recommended based on the specific user information comprises the following steps: determining a target recommendation strategy corresponding to the specific strategy feature included in the specific user information in the corresponding relation between the strategy feature and the recommendation strategy; and recommending the information to be recommended to a user corresponding to the specific user identification included in the specific user information based on the target recommendation strategy.
In a second aspect, an embodiment of the present invention provides a personalized service recommendation apparatus, including: the system comprises a first determining module, a second determining module and a processing module, wherein the first determining module is used for determining information to be recommended of personalized services to be recommended and specific characteristics corresponding to the information to be recommended; the second determining module is used for determining specific user information corresponding to the specific characteristics in the pre-acquired temporary user information; the temporary user information comprises user information with the specific characteristics screened from a source database at regular time, and the source database is used for storing the pre-acquired user information; and the recommending module is used for recommending the information to be recommended based on the specific user information.
In an alternative embodiment, the apparatus further comprises: a third determining module, configured to determine a path of a file to be parsed, where the path of the file to be parsed is used to indicate one or more temporary files in a temporary file system, and the temporary files include user information with the specific characteristics that is regularly screened from a source database; the acquisition module is used for acquiring the one or more temporary files from the temporary file system based on the path of the file to be analyzed; an update module to update the temporary user information based on the one or more temporary files.
In an optional embodiment, each of the temporary files corresponds to a file number, each of the temporary files includes one or more rows of user data, and each column of each row corresponds to a preset attribute.
In an alternative embodiment, the update module comprises: the analysis unit is used for analyzing one or more temporary files corresponding to the file path to be analyzed line by line and respectively obtaining user information of one user for each line; and the updating unit is used for updating the temporary user information based on the analyzed user information of one or more users.
In an alternative embodiment, the specific user information includes: specific characteristics, specific policy characteristics and specific user identities.
In an optional implementation manner, a corresponding relation between the policy characteristics and the recommended policy is predetermined; the recommendation module is specifically configured to: determining a target recommendation strategy corresponding to the specific strategy feature included in the specific user information in the corresponding relation between the strategy feature and the recommendation strategy; and recommending the information to be recommended to a user corresponding to the specific user identification included in the specific user information based on the target recommendation strategy.
In a third aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, where the memory stores a computer program operable on the processor, and the processor executes the computer program to implement the steps of the method in any one of the foregoing embodiments.
In a fourth aspect, an embodiment of the present invention provides a computer-readable medium having non-volatile program code executable by a processor, the program code causing the processor to perform the method of any one of the foregoing embodiments.
The personalized service recommendation method provided by the invention comprises the following steps: determining information to be recommended of personalized services to be recommended and specific characteristics corresponding to the information to be recommended; determining specific user information corresponding to the specific characteristics in the pre-acquired temporary user information; the temporary user information comprises user information with specific characteristics screened from a source database at regular time, and the source database is used for storing the user information acquired in advance; and recommending the information to be recommended based on the specific user information. The method determines the information to be recommended of the personalized service to be recommended and the corresponding specific characteristics from the temporary user information, then recommends the information to be recommended according to the specific user information, and can ensure the accurate acquisition of the specific user information because the temporary user information is information obtained by screening from a source database at regular time, thereby achieving the purpose of accurately operating each user and effectively relieving the technical problem of high personalized push abnormal rate of the personalized service recommendation method in the prior art.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a personalized service recommendation method according to an embodiment of the present invention;
FIG. 2 is a flowchart of another personalized service recommendation method according to an embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating personalized recommendation of a consuming service according to an embodiment of the present invention;
FIG. 4 is a functional block diagram of a personalized service recommendation apparatus according to an embodiment of the present invention;
fig. 5 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Some embodiments of the invention are described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.
According to the personalized characteristics of the user, the personalized service conforming to the characteristics of the user is provided, the user satisfaction can be greatly improved, however, in the existing personalized service recommendation method, a business system recommends the personalized service for the user according to the user personalized data acquired in real time, the method depends heavily on the response efficiency of big data service, once the user personalized data is abnormal, the user can only receive a default recommendation strategy, the purpose of personalized push cannot be achieved, the user experience is poor, and the personalized push abnormal rate is high. In view of the above, embodiments of the present invention provide a personalized service recommendation method to alleviate the technical problems mentioned above.
Example one
Fig. 1 is a flowchart of a personalized service recommendation method according to an embodiment of the present invention, and as shown in fig. 1, the method specifically includes the following steps:
and step S12, determining information to be recommended of the personalized service to be recommended and specific characteristics corresponding to the information to be recommended.
In step S14, specific user information corresponding to the specific feature is determined from the temporary user information acquired in advance.
And step S16, recommending the information to be recommended based on the specific user information.
Specifically, when the user personalized service is recommended, firstly, information to be recommended of the personalized service to be recommended and specific characteristics corresponding to the information to be recommended are determined, for example, weather information is recommended to a user in Hebei province, then the information to be recommended of the personalized service to be recommended is the weather information, the specific characteristics corresponding to the information to be recommended is Hebei province, and then, specific user information corresponding to the specific characteristics is determined from temporary user information obtained in advance; the temporary user information comprises user information with specific characteristics screened from a source database at regular time, and the source database is used for storing the user information acquired in advance; that is, the temporary user information is screened from the source database, and is pre-existing user information, and after obtaining the specific feature, the specific user information may be determined from the source database by using the specific feature, where the specific user information corresponds to the user information of the user having the attribute of north-Hebei province in the above example. And finally, after the specific user information is obtained, recommending the information to be recommended according to the personalized service recommendation strategy.
The personalized service recommendation method provided by the invention comprises the following steps: determining information to be recommended of personalized services to be recommended and specific characteristics corresponding to the information to be recommended; determining specific user information corresponding to the specific characteristics in the pre-acquired temporary user information; the temporary user information comprises user information with specific characteristics screened from a source database at regular time, and the source database is used for storing the user information acquired in advance; and recommending the information to be recommended based on the specific user information. The method determines the information to be recommended of the personalized service to be recommended and the corresponding specific characteristics from the temporary user information, then recommends the information to be recommended according to the specific user information, and can ensure the accurate acquisition of the specific user information because the temporary user information is information obtained by screening from a source database at regular time, thereby achieving the purpose of accurately operating each user and effectively relieving the technical problem of high personalized push abnormal rate of the personalized service recommendation method in the prior art.
In an alternative embodiment, before the step S14, the method of the present invention further includes the following steps:
step S131, determining a path of a file to be analyzed, where the path of the file to be analyzed is used to indicate one or more temporary files in the temporary file system, and the temporary files include user information with specific characteristics that is screened from a source database at regular time.
Step S132, one or more temporary files are obtained from the temporary file system based on the file path to be analyzed.
Step S133, updates the temporary user information based on the one or more temporary files.
Generally, after the specific user information is used as the filtering condition, the filtered file is stored in the temporary file system, and therefore, to acquire the temporary user information (including the specific user information), a path of the file to be analyzed needs to be determined first, where the path of the file to be analyzed is used to indicate one or more temporary files in the temporary file system, and the temporary file includes user information with specific characteristics that is periodically filtered from the source database. And then, acquiring at least one temporary file from the temporary file system through the path of the file to be analyzed, and further updating the temporary user information.
In an alternative embodiment, each temporary file corresponds to a file number, each temporary file includes one or more rows of user data, and each column of each row corresponds to a preset attribute.
The personalized service recommendation method provided by the embodiment of the invention mainly relates to a scene which is difficult to process by a mass data service system, divides mass user information into temporary files with small data volume, numbers each temporary file, and stores the numbered temporary files in a temporary file system. Each temporary file includes one or more rows of user data, generally, one row of user data corresponds to one user, and each column of each row corresponds to one preset attribute, such as a first column: user name, second column: the gender of the user, and so on.
In an optional implementation manner, in step S133, the step of updating the temporary user information based on one or more temporary files specifically includes the following steps:
step S1331, analyzing one or more temporary files corresponding to the file path to be analyzed line by line, and obtaining user information of one user for each line.
And step S1332, updating the temporary user information based on the analyzed user information of one or more users.
Specifically, after one or more temporary files are acquired from the temporary file system, since the user information is stored in a row, the temporary files need to be analyzed row by row during analysis, so as to obtain all the user information included in the temporary files, and the user information is used to update the temporary user information.
Optionally, the specific user information includes: a specific feature, a specific policy feature and a specific user identification; the specific characteristics are used for referring to general characteristics of a user group to be recommended; the specific policy features are user features used when policy forking is performed.
The corresponding relation between the strategy characteristics and the recommended strategy is predetermined; in the step S16, recommending information to be recommended based on the specific user information includes the following steps:
step S161, in the correspondence between the policy features and the recommended policies, determines a target recommended policy corresponding to the specific policy features included in the specific user information.
Step S162, recommending information to be recommended to a user corresponding to the specific user identifier included in the specific user information based on the target recommendation policy.
In the embodiment of the present invention, a correspondence between the policy characteristics and the recommendation policies is predetermined, that is, different policy characteristics correspond to different recommendation policies, and specific user information includes specific policy characteristics, so that when performing personalized service recommendation on a user with specific characteristics, a target recommendation policy corresponding to the specific policy characteristics included in the specific user information is first determined, in other words, which target recommendation policies the user with specific characteristics correspond to is determined, and then information to be recommended is recommended to a user corresponding to a specific user identifier included in the specific user information according to the target recommendation policy.
Example two
An embodiment of the present invention provides a personalized service recommendation method, as shown in fig. 2, the method specifically includes the following steps:
and S102, regularly screening user information of the target user from a preset database, and storing the user information into a temporary file system.
In the embodiment of the invention, before personalized service recommendation is performed for a user, user personalized information is collected in real time through a big data service in advance and stored in a preset database, namely, the preset database is used for storing the user personalized information collected in advance, the preset database can select a big data storage medium such as HIVE (data warehouse tool based on Hadoop), the user personalized information refers to information such as regions, gender and family relations, and the refinement can further comprise the mobile phone model of the user, the mobile phone system version, the latest access service time, the once order quantity, the attention state to public numbers and the like. And then, regularly screening user information of a target user from the preset database, and storing the screened user information into a temporary file system, wherein the target user is a user with preset characteristics.
If the preset database is HIVE, the process of screening the user information of the target user can be that at a specific time, a task system (based on a CRON expression) is executed in the HIVE through self-defined SQL to screen out users with preset characteristics, and after the HIVE is executed, the screened data are stored in a temporary file system.
For convenience of understanding, the following description illustrates that the preset database stores user personalized information of a large number of users, and the timing task needs to perform personalized service recommendation on users in beijing city, where the preset characteristic is that the region belongs to beijing city, and the target users are all users whose regions are in beijing.
And step S104, analyzing the user information in the temporary file system to obtain the user characteristics of the target user.
After the user information of the target user is stored in the temporary file system, in order to implement personalized service recommendation of the target user, the user characteristics of each target user also need to be further analyzed, and the user identifier of each target user can also be obtained, wherein the user identifier is information for distinguishing user identities, and the user characteristics include: user name, user gender, etc.
And step S106, executing personalized service recommendation for the target user based on the user characteristics of the target user and the preset recommendation strategy.
Finally, after obtaining the user characteristics of the target users, the personalized service recommendation may be performed for each target user in combination with a preset recommendation policy, for example, if the current time point is at the seventh day before, when the user browses a shopping website, the preset recommendation policy includes an advertisement for recommending different gift purchases for users of different genders, for example, when performing the personalized service recommendation for a user of a female gender among the target users, the recommended advertisement is: "women in love, have purchased the product as a seven-day gift throughout the country, and you will also quickly see the bar! "; when the personalized service recommendation is executed for the user with male gender in the target users, the recommended advertisements are as follows: mr. Hill of love, man had bought the product as a seven-quarter gift nationwide, you also viewed the bar soon! ". The gift purchase advertisement is one of preset recommendation strategies, and the preset recommendation strategies can recommend different advertisements or services for the user according to different user characteristics.
The invention provides a personalized service recommendation method, which comprises the following steps: screening user information of a target user from a preset database at regular time, and storing the user information into a temporary file system, wherein the preset database is used for storing user personalized information which is collected in advance, and the target user is a user with preset characteristics; analyzing the user information in the temporary file system to obtain the user characteristics of the target user; and executing personalized service recommendation for the target user based on the user characteristics of the target user and a preset recommendation strategy.
The existing personalized service recommendation method seriously depends on the response efficiency of big data service, the internet service with higher real-time requirement is far from meeting the requirement, and the user experience is poor due to the fact that the personalized service push of the user cannot be accurately completed easily. Compared with the prior art, the personalized service recommendation method provided by the invention has the advantages that the user personalized data are collected in advance, the user information of the target user is screened out at regular time and stored in the temporary file system, the user information in the temporary file system is analyzed to obtain the user characteristics of the target user, when the user requests related services, the personalized service recommendation can be executed for the target user according to the obtained user characteristics and the preset recommendation strategy, so that the purpose of accurately operating each user is achieved, and the technical problem of high personalized push abnormal rate of the personalized service recommendation method in the prior art is effectively solved.
The implementation of the personalized service recommendation method provided by the embodiment of the present invention is briefly described above, and the data processing procedure involved therein is specifically described below.
In an optional implementation manner, in the step S102, storing the user information into the temporary file system specifically includes the following steps:
step S1021, acquiring the unit processable data volume of the business system and the target data volume contained in the user information.
Step S1022, splitting the user information based on the unit processable data amount and the target data amount to obtain a plurality of data files, and numbering the plurality of data files.
And step S1023, storing the numbered data files into a temporary file system.
Specifically, the personalized service recommendation method provided by the embodiment of the invention mainly relates to a scene which is difficult to process by a mass data service system, and the mass user information is divided into a plurality of data files (hereinafter referred to as small files) with the size of data amount which can be processed by a unit of the service system, the divided data files are numbered, and finally the numbered data files are stored in a temporary file system.
In an optional embodiment, in the step S104, analyzing the user information in the temporary file system to obtain the user characteristics of the target user, specifically including the following steps:
step S1041, reading the numbered data files in the temporary file system in a distributed manner.
As can be seen from the above description, the number of the data files are stored in the temporary file system, and the data amount included in each data file does not exceed the unit processable data amount of the service system, and in order to further increase the data processing speed of the service system, in the embodiment of the present invention, the user information is obtained by using a distributed manner to read the number of the data files in the temporary file system. In the embodiment of the invention, the data files in the temporary file system are stored in rows, and each row of data in the data files corresponds to one user.
Step S1042, analyzing the target data file line by line to obtain all the user characteristics in the target data file.
Step S1043, determining the user characteristic of the target user based on the user characteristics analyzed by all the data files in the temporary file system.
Next, since the user information in the data file is stored in a line-by-line manner, the target data file also needs to be processed in a line-by-line manner during the analysis, so as to obtain all the user characteristics in the target data file, where the target data file is any one of the several numbered data files. After all the data files in the temporary file system are analyzed by using the same processing method, the user characteristics of all the target users can be obtained. The distributed reading data file can combine the calculation of a plurality of systems, and uniformly store or convert the calculation result into a unit required by a product scheme for use.
If the preset database is HIVE, after the HIVE finishes the user information screening of the target user, the screened user information is stored in a temporary file system, meanwhile, the HIVE also informs a service analysis system of the path of the data file in the temporary file system and the number of the data files, after the service analysis system obtains the file path, the file is downloaded to the local server and is analyzed by using a JAVA primary file packet, so that the user characteristics and the user identification are obtained, and the analyzed data is sent to a service processing system through a message queue (MQ/KAFKA), wherein the service analysis system and the service processing system are collectively called as a service system.
In the embodiment of the invention, for massive user information, the service system acquires the user information in a distributed small file reading mode and then performs service operation, so that the aim of accurately reaching a specific user is fulfilled, and personalized operation of the service system is realized. Fig. 3 is a schematic diagram illustrating personalized recommendation of a consumption service according to an embodiment of the present invention.
The method steps involved in the practical execution of the personalized service recommendation method provided by the embodiment of the present invention are described above, and the basic work to be executed before the method is executed is described below.
In an alternative embodiment, the method of the present invention further comprises the steps of:
and step S21, collecting the user personalized information in real time, and storing the user personalized information into a preset database.
In the embodiment of the invention, the user personalized information is firstly interactively collected in real time through the big data related service, and is asynchronously stored in the preset database to provide data support for the issuing of the subsequent timing task.
At least one service policy is determined based on the user personalization information, step S22.
And step S23, determining a preset recommendation strategy based on at least one service strategy.
Next, after obtaining a large amount of user personalized information, the product level may design some service policies according to the user personalized information to perform policy splitting, that is, users with different characteristics execute different recommendation policies, and finally integrate all the recommendation policies to obtain a preset recommendation policy for use in personalized service recommendation.
In summary, according to the personalized service recommendation method provided in the embodiment of the present invention, the user personalized data is collected in the preset database in advance, then the user information of the target user is regularly screened out and stored in the temporary file system, and then the small files in the temporary file system are subjected to distributed processing analysis to obtain the user characteristics of the target user.
EXAMPLE III
The embodiment of the invention also provides a personalized service recommendation device, which is mainly used for executing the personalized service recommendation method provided by the first embodiment of the invention, and the personalized service recommendation device provided by the embodiment of the invention is specifically described below.
Fig. 4 is a functional block diagram of a personalized service recommendation apparatus according to an embodiment of the present invention, and as shown in fig. 4, the apparatus mainly includes: a first determination module 10, a second determination module 20, a recommendation module 30, wherein:
the first determining module 10 is configured to determine information to be recommended of the personalized service to be recommended and a specific feature corresponding to the information to be recommended.
A second determining module 20, configured to determine, in the pre-acquired temporary user information, specific user information corresponding to the specific feature; the temporary user information comprises user information with specific characteristics screened from a source database at regular time, and the source database is used for storing the pre-acquired user information.
And the recommending module 30 is configured to recommend the information to be recommended based on the specific user information.
The invention provides a personalized service recommendation device, which comprises: the first determining module 10 is configured to determine information to be recommended of the personalized service to be recommended and specific features corresponding to the information to be recommended; a second determining module 20, configured to determine, in the pre-acquired temporary user information, specific user information corresponding to the specific feature; the temporary user information comprises user information with specific characteristics screened from a source database at regular time, and the source database is used for storing the user information acquired in advance; and the recommending module 30 is configured to recommend the information to be recommended based on the specific user information. The device determines the specific user information corresponding to the specific characteristics from the temporary user information after determining the information to be recommended and the corresponding specific characteristics of the personalized service to be recommended, and then recommends the information to be recommended according to the specific user information.
Optionally, the apparatus further comprises:
and the third determining module is used for determining a path of the file to be analyzed, wherein the path of the file to be analyzed is used for indicating one or more temporary files in the temporary file system, and the temporary files comprise user information which is screened from the source database at regular time and has specific characteristics.
And the acquisition module is used for acquiring one or more temporary files from the temporary file system based on the path of the file to be analyzed.
And the updating module is used for updating the temporary user information based on one or more temporary files.
Optionally, each temporary file corresponds to a file number, each temporary file includes one or more rows of user data, and each column of each row corresponds to a preset attribute.
Optionally, the update module includes:
and the analysis unit is used for analyzing one or more temporary files corresponding to the path of the file to be analyzed line by line and respectively obtaining user information of one user for each line.
And the updating unit is used for updating the temporary user information based on the user information of one or more users obtained by analysis.
Optionally, the specific user information includes: specific characteristics, specific policy characteristics and specific user identities.
Optionally, a corresponding relationship between the policy features and the recommended policy is predetermined; the recommendation module 30 is specifically configured to:
and determining a target recommendation strategy corresponding to the specific strategy characteristic included in the specific user information in the corresponding relation between the strategy characteristic and the recommendation strategy.
And recommending information to be recommended to a user corresponding to the specific user identification included in the specific user information based on the target recommendation strategy.
Example four
Referring to fig. 5, an embodiment of the present invention provides an electronic device, including: a processor 60, a memory 61, a bus 62 and a communication interface 63, wherein the processor 60, the communication interface 63 and the memory 61 are connected through the bus 62; the processor 60 is arranged to execute executable modules, such as computer programs, stored in the memory 61.
The memory 61 may include a high-speed Random Access Memory (RAM) and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 63 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
The bus 62 may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 5, but this does not indicate only one bus or one type of bus.
The memory 61 is used for storing a program, the processor 60 executes the program after receiving an execution instruction, and the method executed by the apparatus defined by the flow process disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 60, or implemented by the processor 60.
The processor 60 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 60. The Processor 60 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory 61, and the processor 60 reads the information in the memory 61 and, in combination with its hardware, performs the steps of the above method.
The personalized service recommendation method, apparatus, and computer program product of the electronic device provided in the embodiments of the present invention include a computer-readable storage medium storing a processor-executable nonvolatile program code, where instructions included in the program code may be used to execute the method described in the foregoing method embodiments, and specific implementation may refer to the method embodiments, and will not be described herein again.
In addition, functional units 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 functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer-readable storage medium executable by a processor. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings or the orientations or positional relationships that the products of the present invention are conventionally placed in use, and are only used for convenience in describing the present invention and simplifying the description, but do not indicate or imply that the devices or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," "third," and the like are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
Furthermore, the terms "horizontal", "vertical", "overhang" and the like do not imply that the components are required to be absolutely horizontal or overhang, but may be slightly inclined. For example, "horizontal" merely means that the direction is more horizontal than "vertical" and does not mean that the structure must be perfectly horizontal, but may be slightly inclined.
In the description of the present invention, it should also be noted that, unless otherwise explicitly specified or limited, the terms "disposed," "mounted," "connected," and "connected" are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (14)
1. A method for personalized service recommendation, comprising:
determining information to be recommended of personalized services to be recommended and specific characteristics corresponding to the information to be recommended;
determining specific user information corresponding to the specific features in the pre-acquired temporary user information; the temporary user information comprises user information with the specific characteristics screened from a source database at regular time, and the source database is used for storing the pre-acquired user information;
recommending the information to be recommended based on the specific user information.
2. The method according to claim 1, wherein before the step of determining the specific user information corresponding to the specific feature from the pre-acquired temporary user information, the method further comprises:
determining a path of a file to be analyzed, wherein the path of the file to be analyzed is used for indicating one or more temporary files in a temporary file system, and the temporary files comprise user information which is screened from a source database at regular time and has the specific characteristics;
acquiring the one or more temporary files from the temporary file system based on the path of the file to be analyzed;
updating the temporary user information based on the one or more temporary files.
3. The method of claim 2, wherein each of the temporary files corresponds to a file number, each of the temporary files comprises one or more rows of user data, and each column of each row corresponds to a predetermined attribute.
4. The method of claim 3, wherein the step of updating the temporary user information based on the one or more temporary files comprises:
analyzing one or more temporary files corresponding to the file path to be analyzed line by line, and respectively obtaining user information of one user for each line;
and updating the temporary user information based on the user information of one or more users obtained by analysis.
5. The method of claim 1, wherein the specific user information comprises: specific characteristics, specific policy characteristics and specific user identities.
6. The method of claim 5, wherein the correspondence of the policy characteristics to the recommended policy is predetermined; the step of recommending the information to be recommended based on the specific user information comprises the following steps:
determining a target recommendation strategy corresponding to the specific strategy feature included in the specific user information in the corresponding relation between the strategy feature and the recommendation strategy;
and recommending the information to be recommended to a user corresponding to the specific user identification included in the specific user information based on the target recommendation strategy.
7. A personalized service recommendation apparatus, comprising:
the system comprises a first determining module, a second determining module and a processing module, wherein the first determining module is used for determining information to be recommended of personalized services to be recommended and specific characteristics corresponding to the information to be recommended;
the second determining module is used for determining specific user information corresponding to the specific characteristics in the pre-acquired temporary user information; the temporary user information comprises user information with the specific characteristics screened from a source database at regular time, and the source database is used for storing the pre-acquired user information;
and the recommending module is used for recommending the information to be recommended based on the specific user information.
8. The apparatus of claim 7, further comprising:
a third determining module, configured to determine a path of a file to be parsed, where the path of the file to be parsed is used to indicate one or more temporary files in a temporary file system, and the temporary files include user information with the specific characteristics that is regularly screened from a source database;
the acquisition module is used for acquiring the one or more temporary files from the temporary file system based on the path of the file to be analyzed;
an update module to update the temporary user information based on the one or more temporary files.
9. The apparatus of claim 8, wherein each of the temporary files corresponds to a file number, each of the temporary files comprises one or more rows of user data, and each column of each row corresponds to a predetermined attribute.
10. The apparatus of claim 9, wherein the update module comprises:
the analysis unit is used for analyzing one or more temporary files corresponding to the file path to be analyzed line by line and respectively obtaining user information of one user for each line;
and the updating unit is used for updating the temporary user information based on the analyzed user information of one or more users.
11. The apparatus of claim 7, wherein the specific user information comprises: specific characteristics, specific policy characteristics and specific user identities.
12. The apparatus of claim 11, wherein a correspondence of the policy characteristics to the recommended policy is predetermined; the recommendation module is specifically configured to:
determining a target recommendation strategy corresponding to the specific strategy feature included in the specific user information in the corresponding relation between the strategy feature and the recommendation strategy;
and recommending the information to be recommended to a user corresponding to the specific user identification included in the specific user information based on the target recommendation strategy.
13. An electronic device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method of any of claims 1 to 6 when executing the computer program.
14. A computer-readable medium having non-volatile program code executable by a processor, the program code causing the processor to perform the method of any of claims 1 to 6.
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