CN111882399A - Service information recommendation method, device, computer system and readable storage medium - Google Patents
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Abstract
The invention discloses a service information recommendation method, a device, a computer system and a readable storage medium, which relate to the technical field of big data, and comprise the steps of receiving a starting signal sent by a user side and collecting user information, wherein the user information comprises basic data, behavior data and equipment data; analyzing the acquired user information to acquire similar data matched with the user information; the method comprises the steps of obtaining target service information based on user information and similar data matched with the user information, pushing the target service information to a user side, searching for similar user perfection associated information through analysis of the user data, and finally obtaining the target service information and pushing the target service information to the user side.
Description
Technical Field
The invention relates to the technical field of big data, in particular to a service information recommendation method, a service information recommendation device, a computer system and a readable storage medium.
Background
The equipment product has the characteristics of high unit product value, long customer transaction period, low repeated purchase frequency and the like, so that the customer group is relatively fixed in the equipment sales scene, and therefore, new customer expansion and old customer maintenance are very important for equipment sales.
The inventor of the invention finds in research that traditional equipment sales are mainly in an offline form, the cost is high, the frequency is low, the timeliness is poor, and due to the fact that customers lack deep knowledge and are difficult to know needed services in detail in time, part of equipment products can be provided with online services at present, but most of current recommended products are mainly based on popularization of service providers, the matching degree of the recommended equipment service products and users is not high, and the accuracy of recommended results is not high enough.
Disclosure of Invention
The invention aims to provide a service information recommendation method, a service information recommendation device, a computer system and a readable storage medium, which are used for solving the problems that in the prior art, the online service of equipment products is mainly promoted based on service information of a service provider end, the matching degree of recommended equipment service products and users is not high, and the precision of recommended results is not enough.
In order to achieve the above object, the present invention provides a service information recommendation method based on big data, including:
receiving a starting signal sent by a user side, and collecting user information, wherein the user information comprises basic data, behavior data and equipment data;
analyzing the acquired user information to acquire similar data matched with the user information;
matching target service information based on the user information and similar data matched with the user information, and pushing the target service information to a user side.
In the above scheme, the analyzing the user information to obtain similar data matched with the user information includes the following steps:
acquiring similar user information matched with the user information from a database according to a preset rule based on the basic data and the behavior data;
acquiring equipment information matched with the equipment data from a database as similar equipment information;
obtaining similar data matched with the user information according to the similar user information and the similar equipment information
In the above scheme, obtaining target service information based on the user information and similar data matched with the user information, and pushing the target service information to a user side includes:
obtaining a first set of service information based on the basic data, the behavior data or and device data weighting;
obtaining a second service information set based on similar data matching with the user information;
and processing the first service information set and the second service information set based on matching weights to obtain the target service information.
In the above scheme, after the target service information is pushed to the user side, the method includes:
monitoring the pushing frequency of the target service information and user click behavior data in real time;
and updating the matching weight based on the pushing frequency of the service information and the user click behavior data.
In the above scheme, after the collecting the user information, the method further includes:
the authenticity of the user information is confirmed;
and acquiring key identity information based on the user information, and verifying the key identity information.
In the above scheme, the method further comprises:
and identifying and matching at least one service provider through a recommendation algorithm based on the user information, and sending the user data to the service provider.
In the foregoing solution, after sending the user data to the service provider, the method further includes:
receiving service information which is sent by each service supply terminal according to the user information in an autonomous matching manner;
and updating the target service information based on the service information sent by the service provider.
In order to achieve the above object, the present invention further provides a service information recommendation apparatus, including:
the acquisition module is used for receiving a starting signal sent by a user side and acquiring user information;
the user information comprises basic data, behavior data and equipment data;
the analysis module is used for analyzing the acquired user information to acquire similar data matched with the user information;
and the processing module is used for matching the target service information based on the similar data and pushing the target service information to the user side.
In order to achieve the above object, the present invention further provides a computer system, which includes a plurality of computer devices, each computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processors of the plurality of computer devices jointly implement the steps of the service information recommendation method when executing the computer program.
In order to achieve the above object, the present invention further provides a computer-readable storage medium including a plurality of storage media, each storage medium having a computer program stored thereon, wherein the computer programs stored in the storage media, when executed by a processor, collectively implement the steps of the service information recommendation method.
According to the service information recommendation method, the device, the computer system and the readable storage medium, the user data are analyzed, the similar user perfect correlation information is searched, the target service information is finally obtained through multi-dimensional data matching and weighted adjustment, the multi-dimensional data comprise basic data, the behavior data or the correlation information of the equipment with similar equipment of the equipment data, the target service information is finally obtained through the multi-dimensional data weighted adjustment and pushed to the user side, and the problems that in the prior art, service information popularization is mainly carried out on most of on-line service of equipment products based on a service provider side, the matching degree of recommended equipment service products and users is not high, and the recommendation result accuracy is not high are solved.
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Fig. 1 is a schematic application diagram of a first embodiment of a service information recommendation method according to the present invention;
FIG. 2 is a flowchart of a first embodiment of a service information recommendation method according to the present invention;
fig. 3 is a flowchart illustrating a process of verifying user information after acquiring the user information according to a first embodiment of the service information recommendation method of the present invention;
fig. 4 is a flowchart illustrating analyzing the user information to obtain similar data matched with the user information according to a first embodiment of the service information recommendation method of the present invention;
fig. 5 is a flowchart illustrating matching of target service information based on the user-associated data and pushing the target service information to a user side in a first embodiment of the service information recommendation method according to the present invention;
FIG. 6 is a schematic diagram of program modules of a second embodiment of a service information recommendation device according to the present invention;
FIG. 7 is a schematic diagram illustrating program modules of an analysis module according to a second embodiment of the service information recommendation apparatus of the present invention;
FIG. 8 is a schematic diagram illustrating program modules of processing modules in a second embodiment of a service information recommendation apparatus according to the present invention;
fig. 9 is a schematic diagram of a hardware structure of a computer device in the third embodiment of the computer system according to the present invention.
Reference numerals:
A. recommendation server side B, user side C and service supply side
5. Service information recommendation device 51, acquisition module 52 and analysis module
521. First screening unit 522, second screening unit 53 and processing module
531. A first processing unit 532, a second processing unit 533, a third processing unit
54. Monitoring module 55, sending module 56 and adjusting module
6. Computer device 61, memory 62, processor
63. Network interface
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit 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.
The invention provides a service information recommendation method, a device, a computer system and a readable storage medium, which are suitable for the field of data analysis of big data, and aims to provide a service information recommendation method based on an acquisition module, an analysis module and a processing module, wherein the acquisition module is used for receiving an acquisition signal sent by a user terminal and acquiring user information, similar data are acquired based on the acquired user information analysis, target service information is finally acquired through multidimensional matching data and weighting adjustment, the multidimensional data are based on user information (basic data, behavior data or equipment data) and related information of similar equipment, the target service information finally acquired by the multidimensional data weighting adjustment is pushed to the user terminal, and the problems that in the prior art, most of services on the existing equipment product line are mainly promoted based on service information of a service provider terminal, the recommendation device service product is not highly matched with the user, and the recommendation result is not accurate enough, and information interaction and information feedback (for example, monitoring click behavior data of target service information, and service information provided by the service provider and having a high matching degree with the user is used for subsequently adjusting the target service information to improve accuracy) between the recommendation server a and the user side B and between the recommendation server a and the service provider C in the hardware diagram shown in fig. 1 can be used for further improving the accuracy of the recommendation result based on the user similar information.
Example one
Referring to fig. 2, a service information recommendation method of this embodiment is applied to a recommendation server a, and includes:
s10: receiving a starting signal sent by a user side, and collecting user information;
wherein the user information comprises basic data, behavior data and equipment data;
in the present embodiment, the basic information includes, but is not limited to, address information of an enterprise, an industry of the enterprise, a scale of the enterprise, and the like; the behavior data includes but is not limited to historical browsing records, purchasing records and the like of the commodities; the equipment information includes, but is not limited to, equipment information within a main rental contract, start-up data and equipment location data collected through an equipment bracelet, and the like, and further data such as an associated party, historical collaboration, and the like.
Specifically, after the user information is collected, the method further includes checking the user information, referring to fig. 3, and further includes the following steps:
s11: the authenticity of the user information is confirmed;
in this embodiment, as an example and not by way of limitation, the verifying the authenticity of the user information may be performed through a network verification, a multi-level verification, or a pre-stored information table, and the authenticity of the user information may be determined through a comparison between the pre-stored information table and the user information.
S12: and acquiring key identity information based on the user information, and verifying the key identity information.
By way of example, the key identity elements include but are not limited to data such as a certificate type, a certificate number and a mobile phone number, and can be realized through image-text comparison or network verification.
S20: analyzing the acquired user information to acquire similar data matched with the user information;
it should be noted that the similar data is obtained by analyzing information of all dimensions that the current user may relate to (including, but not limited to, any specific dimension data of the basic data, the behavior data, and the device data is similar), so as to facilitate subsequent search and recommendation of service information based on the associated information.
Specifically, the analyzing the user information to obtain associated data similar to the user information is described with reference to fig. 4, which includes the following steps:
s21: acquiring similar user information matched with the user information from a database according to a preset rule based on the basic data and the behavior data;
in the above embodiment, the obtaining of the similar users may be implemented by calculating and weighting the similarity of multi-dimensional data in the basic data and the behavior data, where the method needs to compare each user in advance, and determine whether a similar user relationship exists between the user sending the signal and another user, for example, determine whether the user a and the user B are similar users, calculate the similarity values of the user a and the user B under the basic data and the behavior data, and perform weighting calculation on the similarity values under each dimension, where the weight corresponding to each dimension may be in a preset manner, or may be adjusted in advance by using a training sample before performing weighting calculation on the similarity values under each dimension, so as to obtain the most accurate weight distribution of the result.
Or whether any devices with consistent related data can be set as similar devices, such as users with consistent locations, consistent historical orders, and the like, can be searched.
S22: and acquiring the equipment information matched with the equipment data from a database as similar equipment information.
For example, the device information with the consistent related data may be searched and set as similar devices, specifically: the device models are consistent, the device location information is within a certain preset range, and the like, the service information corresponding to the similar devices can be obtained according to the similar device information, so that the service information which may become the target service information is matched according to the service information associated with the similar devices in the following S30.
S23: and obtaining similar data matched with the user information according to the similar user information and the similar equipment information.
In the above solution, in order to ensure the comprehensiveness of the subsequently pushed service information, all data that may be associated with similar information is set as similar data, where the associated data of the similar user includes, but is not limited to, a purchase record, a browsing record, historical cooperation data, associated device data, and the like of the similar user. And collecting the similar user associated data and the user information to obtain a user associated data network, obtaining similar data matched with the user information for the network through the user associated data, and in the actual processing process, filtering or screening can be performed to screen out the associated data with low correlation degree so as to improve the user associated data network, so that the accuracy of the recommendation result is improved.
S30: matching target service information based on the similar data, and pushing the target service information to a user side.
Specifically, matching the target service information based on the user association data, and pushing the target service information to the user side, referring to fig. 5, includes:
s31: obtaining a first service information set based on the basic data, the behavior data or the equipment data matching;
for example, a first service information set is obtained according to the basic data, for example, based on the industry attribute and the address information contained in the user basic data, a service provider near the user and consistent with the industry attribute in the user information is obtained according to the address information, and then a map port can be accessed to support a map mode to directly view navigation.
Acquiring a first service information set according to the behavior data or the device data, specifically for example: pushing corresponding spare parts and maintenance services consistent with the equipment model based on the historical purchasing behavior of the user and the monitoring data of the equipment bracelet; the user behavior can also be analyzed based on the user browsing record and the user historical purchase, the user purchase service preference is obtained, and then the service recommendation is carried out on the user.
S32: obtaining a second service information set based on similar data matching with the user information;
in this embodiment, the similar data matched with the user information includes historical service information associated with similar user data and historical service information associated with similar device information, and service information with a purchase rate exceeding a preset range is obtained after statistical analysis is performed according to all similar user data purchase records, browsing records, historical cooperation data and associated device data.
It should be particularly noted that the first service information set or the second service information set may have only one service information, or may have a plurality of service information, and the first service information and the second service information are used as candidate libraries for obtaining the target service information.
And S33, processing the first service information set and the second service information set based on the matching weight to obtain the target service information.
It should be noted that one or more pieces of service information included in the target service information may be included.
Specifically, the first service information and the second service information are integrated, a service information list corresponding to the first service information set and the second service information set is established, an initial weight corresponding to each service information in the service information list is preset, and the initial weight is adjusted according to the frequency of occurrence of each service information in the first service information set and the second service information set, the degree of association with the user equipment and the like, so that the matching weight is obtained.
For example, when a certain service information appears in the first service information and the second service information at the same time, the weight of the service information is increased, that is, the service information can be preferentially pushed, or the pushing frequency of the service information is increased, and a service information list to be pushed is further obtained according to the statistics of the first service information and the second service information, so as to be pushed to the user side subsequently;
the matching weight can be preset according to the service information type and the relevance with the equipment type, for example, a keyword can be preset, the user equipment is set as a bracelet, the preset weight of the bracelet is 0.8 when the keyword appears in the service information, the weight of the service information related to the bracelet is set to be 0.2 when the keyword appears in the service information, for example, other equipment matched with the bracelet appears in the service information, the service information with the keyword bracelet further performs weight distribution through multiple dimensions, the service information is sorted according to the weight corresponding to each service information, a pushed service information list can be obtained, and the weight of each service information in the first service information set and the second service information set can be adjusted through autonomous learning of a machine through a training sample and a model.
After the target service information is pushed to the user side, referring to fig. 5, the method includes:
s34: monitoring the pushing frequency of the target service information and user click behavior data in real time;
s35: and updating the matching weight based on the pushing frequency of the service information and the user click behavior data.
In the above scheme, the service information pushed by the user can be clicked and viewed independently, and the target service information obtained by matching in step S30 can be adjusted according to the click volume of the user, which is beneficial to further improving the accuracy of the recommended data.
According to the scheme, data extraction and analysis can be performed on industries, areas and past behaviors of potential users, service information can be recommended in a targeted mode, monitoring feedback of each service information of the users can be pushed according to the service information, the popularization scheme can be optimized continuously, and the popularization efficiency and effect are improved.
The scheme further comprises the steps of acquiring the associated data of the similar users from the block chain, and uploading the first service information in the step S31 and the second service information in the step S32 to the block chain, so that the safety of the data and the fair transparency of the data to the users can be guaranteed. The user equipment may download the summary information from the blockchain to verify that the priority list is tampered with. The block chain referred by the application 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.
The method further comprises the following steps:
s40: and identifying and matching at least one service provider through a recommendation algorithm based on the user information, and sending the user data to the service provider.
The recommendation algorithm includes, but is not limited to, a collaborative filtering algorithm, a content recommendation algorithm, a similarity recommendation algorithm, an association rule recommendation, and the like, the service provider matched with the user information is identified through the existing recommendation algorithm, the target service information used for being pushed to the user side can be further adjusted in the following process, so that the target service information is more accurate, therefore, the existing recommendation algorithm can be used for the service provider, the association value between the user and each service provider is identified through the recommendation algorithm based on the user information, and the service provider beyond a certain threshold range is the service provider capable of being matched.
More specifically, after sending the user data to the service provider, referring to fig. 1, the method further includes:
s41: receiving service information which is sent by each service supply terminal according to the user information in an autonomous matching manner;
s42: and updating the target service information based on the service information sent by the service provider.
For example, when the service information sent by the service provider is the target service information, the weight of the service information in the target service information may be increased according to a certain proportion of the association degree with the user equipment, so as to increase the frequency of pushing the service information, and when the service information sent by the service provider is not in the target service information, the service information may be added to the target service information, so as to adjust the matching weight of the service information sent by the service provider when the target service information is obtained by matching the service information sent by the service provider in the S33, thereby further improving the accuracy of the recommendation result.
According to the scheme, the user data is analyzed, the perfect correlation information of similar users is searched, the target service information is finally obtained through multi-dimensional matching data (such as the first service information and the second service information described in S31 and S32) and weighting adjustment, the multi-dimensional data comprises the first service information obtained based on the matching of the user information (basic data, the behavior data or the equipment data) in S31 and the second service information obtained based on the matching of the correlation information of the similar equipment in S32, the target service information obtained finally through the weighting adjustment of the multi-dimensional data is pushed to a user side, the influence of the correlation information of the similar equipment on the information matching process is increased besides the basic information, the behavior data and the equipment data related to the user side when the target service information is obtained, the matching degree of the target recommended service information and the user is further improved, and the accuracy of the recommendation result is further improved, and realizing user personalized recommendation of the equipment service.
According to the scheme, the accuracy of the recommendation result based on the user association information can be improved and the accuracy of the recommendation result can be further improved through information interaction and information feedback between the user side and the service supply side (such as monitoring of clicking behavior data of target service information by S34-S35 and subsequent adjustment of the target service information by the service information which is provided by the service supply side and has high matching degree with the user in S40).
Example two:
referring to fig. 6, a service information recommendation device 5 of the present embodiment includes:
the acquisition module 51 is used for receiving a starting signal sent by a user end and acquiring user information;
wherein the user information comprises basic data, behavior data and equipment data; specifically, the basic information includes, but is not limited to, address information of an enterprise, an industry to which the enterprise belongs, a scale of the enterprise, and the like; the behavior data includes but is not limited to historical browsing records, purchasing records and the like of the commodities; the equipment information includes, but is not limited to, equipment information within a main rental contract, start-up data and equipment location data collected through an equipment bracelet, and the like, and further data such as an associated party, historical collaboration, and the like.
The analysis module 52 is configured to analyze the acquired user information to obtain similar data matched with the user information;
preferably, referring to fig. 7, the analysis module 52 further comprises the following:
a first filtering unit 521, configured to obtain similar users and associated data corresponding to the similar users based on the user data;
specifically, the similar data is obtained by analyzing information of all dimensions that the current user may relate to, including but not limited to any specific dimension data similarity among the basic data, the behavior data, and the device data.
A second filtering unit 522, configured to obtain similar data matching the user information based on the similar user and the associated data corresponding to the similar user.
And collecting the similar user associated data and the user information to obtain a user associated data network, obtaining similar data matched with the user information for the network through the user associated data, and filtering or screening the associated data with low correlation degree in the actual processing process to improve the accuracy of the recommendation result.
And the processing module 53 is configured to match the target service information based on the similar data, and push the target service information to the user side.
Preferably, with reference to fig. 8, the processing module 53 further comprises the following:
a first processing unit 531, configured to obtain first service information based on the basic data, the behavior data, or and device data weighting;
a second processing unit 532, configured to obtain second service information based on the associated data of the similar device;
a third processing unit 533, configured to combine the first service information and the second service information based on the matching weights to obtain the target service information.
The service information recommendation apparatus 5 in this embodiment further includes the following:
the monitoring module 54 is configured to monitor the push frequency of the target service information and the user click behavior data in real time; and updating the matching weight based on the pushing frequency of the service information and the user click behavior data.
A sending module 55, configured to identify and match at least one service provider through a recommendation algorithm based on the user information, and send the user data to the service provider.
The recommendation algorithm includes, but is not limited to, a collaborative filtering algorithm, a content recommendation algorithm, a similarity recommendation algorithm, association rule recommendation, and the like, and the target service information pushed to the user side can be adjusted, so that the pushed target service information is more accurate.
And the adjusting module 56 is configured to receive service information sent by each service provider according to the user information in an autonomous matching manner, and adjust the target service information based on the service information sent by the service provider.
According to the technical scheme, data analysis based on big data is performed, an acquisition module is used for receiving an acquisition signal sent by a user side and acquiring user information, similar data is acquired based on the acquired user information analysis, and multi-dimensional data matching is performed, wherein the multi-dimensional data comprises basic data, behavior data or equipment data and related information of similar equipment, and the target service information is pushed to the user side by performing weighted adjustment on the multi-dimensional data.
According to the scheme, the user data is sent to the service supply terminals through the sending module based on the fact that the user information is matched with at least one service supply terminal, then the service information sent by the service supply terminals in an autonomous matching mode is received based on the adjusting module, the target service information is adjusted, information interaction and information feedback are formed among the recommending server terminal, the user terminal and the service supply terminals, the matching degree of the target recommending service information and the user is further improved, the accuracy of the recommending result is further improved, and user personalized recommendation of equipment services is achieved.
The scheme can also be used for extracting and analyzing data of industries, areas, past behaviors and the like of potential users based on the analysis module, the service information can be recommended in a targeted mode, meanwhile, the popularization scheme can be optimized continuously by the monitoring module according to the monitoring effect of each service information, and the popularization efficiency and effect are improved.
Example three:
in order to achieve the above object, the present invention further provides a computer system, which includes a plurality of computer devices 6, and the components of the service information recommendation apparatus 5 according to the second embodiment may be distributed in different computer devices, where the computer devices may be smartphones, tablet computers, notebook computers, desktop computers, rack servers, blade servers, tower servers, or rack servers (including independent servers, or a server cluster formed by a plurality of servers) which execute programs, and the like. The computer device of the embodiment at least includes but is not limited to: a memory 61, a processor 62, a network interface 63, and the service information recommendation device 5, which are communicatively connected to each other through a system bus, as shown in fig. 9. It should be noted that fig. 9 only shows a computer device with components, but it should be understood that not all of the shown components are required to be implemented, and more or fewer components may be implemented instead.
In the present embodiment, the memory 61 (i.e., a readable storage medium) includes a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the memory 61 may be an internal storage unit of the computer device, such as a hard disk or a memory of the computer device. In other embodiments, the memory 61 may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), or the like, provided on the computer device. Of course, the memory 61 may also include both internal and external storage devices of the computer device. In this embodiment, the memory 61 is generally used for storing an operating system and various application software installed on the computer device, such as a program code of the service information recommendation apparatus in the first embodiment. Further, the memory 61 may also be used to temporarily store various types of data that have been output or are to be output.
Processor 62 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 62 is typically used to control the overall operation of the computer device. In this embodiment, the processor 62 is configured to run the program codes stored in the memory 61 or process data, for example, run the service information recommendation device, so as to implement the service information recommendation method according to the first embodiment.
The network interface 63 may comprise a wireless network interface or a wired network interface, and the network interface 63 is typically used to establish a communication connection between the computer device 6 and other computer devices 6. For example, the network interface 63 is used to connect the computer device 6 to an external terminal via a network, establish a data transmission channel and a communication connection between the computer device 6 and the external terminal, and the like. The network may be a wireless or wired network such as an Intranet (Intranet), the Internet (Internet), a Global System of Mobile communication (GSM), Wideband Code Division Multiple Access (WCDMA), a 4G network, a 5G network, Bluetooth (Bluetooth), Wi-Fi, and the like.
It is noted that fig. 9 only shows the computer device 6 with components 61-63, but it is to be understood that not all shown components are required to be implemented, and that more or less components may be implemented instead.
In this embodiment, the service information recommendation device 5 stored in the memory 61 can also be divided into one or more program modules, and the one or more program modules are stored in the memory 51 and executed by one or more processors (in this embodiment, the processor 62) to complete the present invention.
Example four:
to achieve the above objects, the present invention also provides a computer-readable storage system including a plurality of storage media, such as a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, a server, an App application store, etc., on which a computer program is stored, which when executed by a processor 62, implements corresponding functions. The computer-readable storage medium of this embodiment is used for storing a service information recommendation device, and when being executed by the processor 62, the service information recommendation method of the first embodiment is implemented.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (10)
1. A service information recommendation method is characterized by comprising the following steps:
receiving a starting signal sent by a user side, and collecting user information, wherein the user information comprises basic data, behavior data and equipment data;
analyzing the acquired user information to acquire similar data matched with the user information;
and obtaining target service information based on the user information and similar data matched with the user information, and pushing the target service information to a user side.
2. The method for recommending service information according to claim 1, wherein said analyzing said user information to obtain similar data matching said user information comprises:
acquiring similar user information matched with the user information from a database according to a preset rule based on the basic data and the behavior data;
acquiring equipment information matched with the equipment data from a database as similar equipment information;
and obtaining similar data matched with the user information according to the similar user information and the similar equipment information.
3. The method of claim 1, wherein obtaining target service information based on the user information and similar data matching the user information and pushing the target service information to a user side comprises:
obtaining a first service information set based on the basic data, the behavior data or the equipment data matching;
obtaining a second service information set based on similar data matching with the user information;
and processing the first service information set and the second service information set based on matching weights to obtain the target service information.
4. The method of claim 3, wherein after the pushing the target service information to the user side, the method comprises:
monitoring the pushing frequency of the target service information and user click behavior data in real time;
and updating the matching weight based on the pushing frequency of the service information and the user click behavior data.
5. The method for recommending service information according to claim 1, further comprising, after said collecting user information:
the authenticity of the user information is confirmed;
and acquiring key identity information based on the user information, and verifying the key identity information.
6. The service information recommendation method according to claim 1, further comprising:
and identifying and matching at least one service provider through a recommendation algorithm based on the user information, and sending the user data to the service provider.
7. The service information recommendation method according to claim 6, further comprising, after sending the user data to the service provider:
receiving service information which is sent by each service supply terminal according to the user information in an autonomous matching manner;
and updating the target service information based on the service information sent by the service provider.
8. A service information recommendation apparatus, characterized by comprising:
the acquisition module is used for receiving a starting signal sent by a user side and acquiring user information;
the user information comprises basic data, behavior data and equipment data;
the analysis module is used for analyzing the acquired user information to acquire similar data matched with the user information;
and the processing module is used for matching the target service information based on the similar data and pushing the target service information to the user side.
9. A computer system comprising a plurality of computer devices, each computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processors of the plurality of computer devices when executing the computer program collectively implement the steps of the service information recommendation method of any one of claims 1 to 7.
10. A computer-readable storage medium comprising a plurality of storage media, each storage medium having a computer program stored thereon, wherein the computer programs stored in the storage media, when executed by a processor, collectively implement the steps of the service information recommendation method according to any one of claims 1 to 7.
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