CN113435948B - E-commerce platform data monitoring method and system - Google Patents
E-commerce platform data monitoring method and system Download PDFInfo
- Publication number
- CN113435948B CN113435948B CN202110979433.6A CN202110979433A CN113435948B CN 113435948 B CN113435948 B CN 113435948B CN 202110979433 A CN202110979433 A CN 202110979433A CN 113435948 B CN113435948 B CN 113435948B
- Authority
- CN
- China
- Prior art keywords
- data
- commerce
- platform
- service
- interest
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2457—Query processing with adaptation to user needs
- G06F16/24575—Query processing with adaptation to user needs using context
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0605—Supply or demand aggregation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0641—Shopping interfaces
- G06Q30/0643—Graphical representation of items or shoppers
Abstract
The invention provides a method and a system for monitoring e-commerce platform data, which determine interest hotspot support of an e-commerce service platform on user behavior data, user interaction data, e-commerce distribution data and e-commerce order data through a business relation sub-network containing interest hotspots, and comprehensively determine the data mining reference of the e-commerce platform data of data mining customized service based on the interest hotspot support and a preset interest strategy attribute value so as to provide a reference basis for subsequent big data mining.
Description
Technical Field
The invention relates to the technical field of electronic commerce data services, in particular to a method and a system for monitoring data of an e-commerce platform.
Background
The electronic commerce platform is an information platform for electronic commerce service providers and electronic commerce users, is a management environment for coordinating and integrating information flow, commodity flow and fund flow in order, relevance and high-efficiency flow by establishing a virtual network space for electronic commerce activities on the Internet and guaranteeing smooth operation of commerce. For the e-commerce platform data stream, the e-commerce platform data stream has a large data mining value and can be used for subsequent business optimization. However, in the solutions in the related art, the data mining reference degree of the e-commerce platform data cannot be effectively evaluated, and thus, an effective reference basis for large data mining cannot be provided.
Disclosure of Invention
In view of this, an object of an embodiment of the present invention is to provide an e-commerce platform data monitoring method applied to an e-commerce platform data monitoring system, where the e-commerce platform data monitoring system is in communication connection with an e-commerce service platform, and the method includes:
the method comprises the steps of obtaining e-commerce platform data streams of each e-commerce service platform in data mining customized service, wherein the e-commerce platform data streams comprise user behavior data, user interaction data, e-commerce distribution data and e-commerce order data of the e-commerce service platform;
acquiring a business relation sub-network of each e-commerce service platform based on a preset business relation network, wherein the business relation sub-network comprises interest hotspots respectively connected with business elements where the user behavior data, the user interaction data, the e-commerce distribution data and the e-commerce order data of the e-commerce service platform are located;
respectively acquiring the user behavior data, the user interaction data, the e-commerce distribution data and the interest hotspot support of the service element where the e-commerce order data is located on the basis of the service relation sub-network;
determining an interest hotspot attribute value of the e-commerce service platform based on the interest hotspot support degree and a preset interest strategy attribute value corresponding to the e-commerce service platform;
and acquiring the data mining reference degree of the E-commerce platform data of the data mining customized service based on the interest hotspot attribute value.
In one possible design, after the step of obtaining the data mining reference of the e-commerce platform data of the customized data mining service based on the interest hotspot attribute value, the method further includes:
acquiring e-commerce platform data of the data mining customized service meeting preset requirements, and screening target e-commerce platform data with effective behaviors from the e-commerce platform data;
acquiring a key intention thermodynamic diagram and a key event thermodynamic diagram of the target e-commerce platform data according to the interest hotspot data of the target e-commerce platform data; the key intention thermodynamic diagram comprises distribution information of key intention labels, the key event thermodynamic diagram comprises distribution information of key event labels, and the target e-commerce platform data comprises e-commerce platform active activity data and e-commerce platform passive activity data;
performing key theme distribution generation on the target e-commerce platform data according to the key intention thermodynamic diagram and the key event thermodynamic diagram of the target e-commerce platform data to obtain key theme distribution data of the target e-commerce platform data;
determining a key theme distribution vector set of the target E-commerce platform data according to the key theme distribution data of the target E-commerce platform data;
and performing frequent theme mining on the target E-commerce platform data according to the key theme distribution vector set of the target E-commerce platform data to determine a frequent theme mining result of the target E-commerce platform data.
In one possible design, the generating of key topic distribution on the target e-commerce platform data according to the key intention thermodynamic diagram and the key event thermodynamic diagram of the target e-commerce platform data to obtain the key topic distribution data of the target e-commerce platform data includes:
extracting the theme track of each thermal unit member contained in the thermodynamic diagram unit data of the key intention thermodynamic diagram and the key event thermodynamic diagram of the target e-commerce platform data to obtain the theme track extraction information of each thermal unit member; the thermodynamic diagram unit data is data information recorded aiming at an application process containing live telecast activity of an E-commerce;
for each thermal unit member, extracting information based on the topic track of the thermal unit member, and determining a plurality of live topic objects related to the thermal unit member in the E-commerce live broadcast activity and related continuous parameters of each related live topic object;
for each thermal unit member, screening the continuous live broadcast subject object of the thermal unit member from a plurality of live broadcast subject objects associated with the thermal unit member based on the associated continuous parameters of the thermal unit member to each associated live broadcast subject object;
determining a live broadcast subject object of which a preset subject promotion tag vector is matched with a continuous live broadcast subject object of a target thermal unit member in a live broadcast subject object library as a selected live broadcast subject object based on a subject generation instruction for the target thermal unit member, and associating the selected live broadcast subject object with the target thermal unit member;
and associating the selected live topic object based on each target thermal unit member to obtain key topic distribution data of the target E-commerce platform data.
For example, in one possible design, the subject trajectory extraction information for each thermal unit member includes:
a plurality of associated nodes of the thermal unit member and associated persistence parameters of the thermal unit member for each associated node associated therewith within the live e-commerce campaign;
the step of determining, for each thermal unit member, a plurality of live topic objects associated with the thermal unit member in the e-commerce live broadcast event and associated persistence parameters of each associated live topic object based on the topic track extraction information of the thermal unit member includes:
and aiming at each thermal unit member, identifying a live broadcast subject object in the E-commerce live broadcast activity, which can be associated by a plurality of associated nodes of the thermal unit member, and determining the associated continuous parameters of each associated node of the thermal unit member as the associated continuous parameters of the live broadcast subject object, which can be associated by the associated node.
For example, in one possible design, the live topic object in the live tv campaign that the plurality of association nodes that identify the thermal unit member can associate with includes:
and determining the live broadcast subject objects in the E-commerce live broadcast activities which can be associated with each associated node based on the live broadcast track information of each live broadcast subject object in the page of the live broadcast subject object and the live broadcast track information of each associated node of the thermal unit member.
For example, in one possible design, the screening the persistent live subject objects of the thermal unit member from the plurality of live subject objects associated with the thermal unit member based on the associated persistent parameters of the thermal unit member for each associated live subject object includes: and screening the live broadcast subject objects with the associated continuous parameters meeting the preset continuous parameters from a plurality of live broadcast subject objects associated with the thermal unit members to serve as the continuous live broadcast subject objects of the thermal unit members.
For example, in one possible design, the screening, from among a plurality of live topic objects associated with the thermal unit member, a live topic object whose associated persistent parameter satisfies a preset persistent parameter as a persistent live topic object of the thermal unit member includes: screening the live broadcast subject object with the maximum association continuous parameter from a plurality of live broadcast subject objects associated with the thermal unit members to serve as the continuous live broadcast subject object of the thermal unit members; and/or screening the live topic objects with the associated continuous parameters larger than the preset continuous parameters from a plurality of live topic objects associated with the thermal unit members to serve as the continuous live topic objects of the thermal unit members.
For example, in one possible design, the determining, based on the topic generation instruction for the target thermal unit member, a live topic object whose preset topic promotion tag vector matches with the persistent live topic object of the target thermal unit member in a live topic object library as the selected live topic object includes:
acquiring a tag vector value of a preset subject promotion tag vector of a continuous live broadcast subject object of a target thermal unit member as a target tag vector value based on a subject generation instruction for the target thermal unit member;
and screening the live broadcast subject object of which the tag vector value of the preset subject promotion tag vector and the target tag vector value meet the preset matching requirement in a live broadcast subject object library to serve as the selected live broadcast subject object.
For example, in one possible design, before the screening, in the live topic object library, a live topic object whose tag vector value of the preset topic promotion tag vector and the target tag vector value meet a preset pairing requirement as a selected live topic object, the method further includes:
acquiring historical attention topic information of the target thermal unit member; the historical attention topic information is used for indicating attention attribute information of the target thermal unit member to the live subject object;
in the live topic object library, screening the live topic object whose tag vector value of the preset topic promotion tag vector and the target tag vector value meet preset pairing requirements, as a selected live topic object, including:
and screening the live topic objects of which the label vector values of the preset topic promotion label vectors and the label vector values of the target topic promotion label vectors meet preset matching requirements in a live topic object library, and the live topic objects meeting the attention attribute information indicated by the historical attention topic information to serve as the selected live topic objects.
Another objective of an embodiment of the present invention is to provide an e-commerce platform system, where the e-commerce platform system includes an e-commerce platform data monitoring system and an e-commerce service platform in communication connection with the e-commerce platform data monitoring system, and the e-commerce platform data monitoring system is specifically configured to:
the method comprises the steps of obtaining e-commerce platform data streams of each e-commerce service platform in data mining customized service, wherein the e-commerce platform data streams comprise user behavior data, user interaction data, e-commerce distribution data and e-commerce order data of the e-commerce service platform;
acquiring a business relation sub-network of each e-commerce service platform based on a preset business relation network, wherein the business relation sub-network comprises interest hotspots respectively connected with business elements where the user behavior data, the user interaction data, the e-commerce distribution data and the e-commerce order data of the e-commerce service platform are located;
respectively acquiring the user behavior data, the user interaction data, the e-commerce distribution data and the interest hotspot support of the service element where the e-commerce order data is located on the basis of the service relation sub-network;
determining an interest hotspot attribute value of the e-commerce service platform based on the interest hotspot support degree and a preset interest strategy attribute value corresponding to the e-commerce service platform;
and acquiring the data mining reference degree of the E-commerce platform data of the data mining customized service based on the interest hotspot attribute value.
In the design, an e-commerce platform data stream of each e-commerce service platform in the data mining customized service is acquired, a business relation sub-network of each e-commerce service platform is acquired based on a preset business relation network, interest hotspot support degrees of business elements where user behavior data, user interaction data, e-commerce distribution data and e-commerce order data are located are respectively acquired based on the business relation sub-network, an interest hotspot attribute value of the e-commerce service platform is determined based on the interest hotspot support degrees and preset interest strategy attribute values corresponding to the e-commerce service platform, and a data mining reference degree of the e-commerce platform data of the data mining customized service is acquired based on the interest hotspot attribute values. Therefore, the interest hotspot support degree of the e-commerce service platform on the user behavior data, the user interaction data, the e-commerce distribution data and the e-commerce order data is determined through the business relation sub-network containing the interest hotspots, and the data mining reference degree of the e-commerce platform data of the data mining customized service is comprehensively determined based on the interest hotspot support degree and the preset interest strategy attribute value, so that a reference basis for subsequent big data mining is provided.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are required to be used in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and that for a member of the thermal unit of ordinary skill in the art, other corresponding drawings may also be obtained based on these drawings without inventive effort.
Fig. 1 is a schematic execution flow diagram of a data monitoring method for an e-commerce platform according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of user behavior data of the e-commerce platform data monitoring system according to the embodiment of the present invention.
Detailed Description
The following description is presented to enable one of ordinary skill in the art to make and use the invention and is provided in the context of a particular application and its requirements. It will be apparent to those skilled in the art that various modifications to the disclosed embodiments are possible, and that the general principles defined in this disclosure may be applied to other embodiments and applications without departing from the spirit and scope of the present disclosure. Thus, the present invention is not limited to the described embodiments, but should be accorded the widest scope consistent with the claims.
The terminology used in the description of the invention herein is for the purpose of describing particular example embodiments only and is not intended to limit the scope of the present invention. As used herein, the singular forms "a", "an" and "the" may include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, components, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, components, and/or groups thereof.
These and other features, aspects, and advantages of the present invention, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will become more apparent upon consideration of the following description of the accompanying drawings, all of which form a part of this specification. It is to be understood, however, that the drawings are designed solely for the purposes of illustration and description and are not intended as a definition of the limits of the invention. It should be understood that the drawings are not to scale.
Flow charts are used in the present invention to illustrate operations performed by systems according to some embodiments of the present invention. It should be understood that the operations in the flow diagrams may be performed out of order. Rather, various steps may be processed in reverse order or simultaneously. Further, one or more other operations may be added to the flowchart. One or more operations may also be deleted from the flowchart.
The present invention is described in detail below with reference to the drawings, and the specific operation methods in the method embodiments can also be applied to the apparatus embodiments or the system embodiments.
Fig. 1 is a schematic flow chart of a data monitoring method for an e-commerce platform according to an embodiment of the present invention, and the data monitoring method for the e-commerce platform is described in detail below.
Step S10, obtaining E-commerce platform data flow of each E-commerce service platform in the data mining and customizing service, wherein the E-commerce platform data flow comprises user behavior data, user interaction data, E-commerce distribution data and E-commerce order data of the E-commerce service platform;
in the embodiment, the e-commerce platform data streams of all e-commerce service platforms in the data mining customized service can be automatically acquired from the related cloud database, so that the interest hotspot support degree of the data mining customized service is determined. Or the relevant business users can upload the E-commerce platform data streams of all E-commerce service platforms in the data mining customized service manually.
In an exemplary design mode, data flows of e-commerce platforms of different e-commerce service platforms are different, and the data flows of the e-commerce platforms comprise user behavior data, user interaction data, e-commerce distribution data and e-commerce order data of the e-commerce service platforms. The user behavior data refers to user browsing and operating behavior data, such as sharing behavior data, collecting behavior data, forwarding behavior data, electrode browsing behavior data and the like, the user interaction data refers to bidirectional or multidirectional behavior data generated in the user interaction process, such as barrage interaction data, comment interaction data and the like, the e-commerce distribution data refers to service distribution data in the e-commerce distribution process, such as distribution thermodynamic diagram data of different e-commerce commodity pages and the like, and the e-commerce order data can refer to order placing commodity object data of e-commerce orders and the like.
In an exemplary design, since the e-commerce service platform is used for providing e-commerce services, when determining the interest hotspot support degree of the data mining customized service, the interest hotspot support degree is determined from various types of data of the e-commerce service platform, that is, from user behavior data, user interaction data, e-commerce distribution data and e-commerce order data of the e-commerce service platform.
Step S20, acquiring a business relation sub-network of each e-commerce service platform based on a preset business relation network, wherein the business relation sub-network comprises interest hotspots respectively connected with the user behavior data, the user interaction data, the e-commerce distribution data and business elements where the e-commerce order data are located of the e-commerce service platform;
in an exemplary design, when a business relation sub-network of each e-commerce service platform is obtained in a preset business relation network, for a single e-commerce service platform, associated business relation information is respectively obtained based on user behavior data, user interaction data, e-commerce distribution data and e-commerce order data of the e-commerce service platform, so as to obtain the business relation sub-network of the e-commerce service platform based on the business relation information.
In an exemplary design, a business relationship subnetwork includes a plurality of business relationship members and business relationship attributes of the business relationship members. Because the user behavior data, the user interaction data, the e-commerce distribution data and the e-commerce order data can all have interest hotspots, in the business relationship sub-network, the business elements of the e-commerce service platform, the user behavior data, the user interaction data, the e-commerce distribution data and the e-commerce order data can all be directly related to the interest hotspots. In an exemplary design, since the same interest hotspot may be interested in different interests and a parameter of the interest hotspot support degree is introduced, the service relationship subnetwork may further include a service subscription item and/or a service response location, the interest hotspot may be directly associated with the service subscription item, and the service subscription item may be directly associated with the service response location.
Step S30, respectively acquiring interest hotspot support degrees of service elements where the user behavior data, the user interaction data, the e-commerce distribution data and the e-commerce order data are located based on the service relationship sub-network;
in an exemplary design manner, for a business relation sub-network of a single e-commerce service platform, interest hotspot support degrees of business elements where user behavior data, user interaction data, e-commerce distribution data, and e-commerce order data are located may be respectively obtained based on the business relation sub-network, and the interest hotspot support degree is used to represent the interest hotspot support degree of a corresponding one of the user behavior data, the user interaction data, the e-commerce distribution data, and the e-commerce order data. Because the interest hotspot support degree of the user behavior data, the user interaction data, the e-commerce distribution data and the e-commerce order data can be influenced by the attention data of the interest hotspots, only the interest hotspot support degree in an interval of which the strength of the association relationship is greater than the preset relationship strength is calculated based on the user behavior data, the user interaction data, the e-commerce distribution data and the association relationship between the e-commerce order data and the interest hotspots in the business relationship sub-network when the interest hotspot support degree is calculated.
Step S40, determining an interest hotspot attribute value of the e-commerce service platform based on the interest hotspot support degree and a preset interest strategy attribute value corresponding to the e-commerce service platform;
in an exemplary design mode, the interest hotspot support degree of each e-commerce service platform on user behavior data, user interaction data, e-commerce distribution data and e-commerce order data in the data mining customized service is obtained through calculation, a preset interest policy attribute value corresponding to the corresponding e-commerce service platform is obtained, the interest hotspot attribute value of the e-commerce service platform is determined based on the interest hotspot support degree and the corresponding preset interest policy attribute value, the interest hotspot support degree is higher when the interest hotspot attribute value is higher, wherein the e-commerce platform data stream can include the attribute value of each preset interest mining policy used by the e-commerce service platform, and thus the preset interest policy attribute value corresponding to the e-commerce service platform can be determined.
In an exemplary design mode, when an interest hotspot attribute value of a single e-commerce service platform is determined, the sum of interest hotspot support degrees of the e-commerce service platform on user behavior data, user interaction data, e-commerce distribution data and e-commerce order data is calculated, and the sum of the interest hotspot support degrees is optimized and updated through a preset interest strategy attribute value corresponding to the e-commerce service platform, so that the interest hotspot attribute value of the e-commerce service platform is obtained.
In an exemplary design manner, a corresponding relation between a preset interest policy attribute value corresponding to a provider service platform and an optimized update value is preset, and when the sum of the interest hotspot support degrees is optimized and updated based on the optimized update value, the larger the preset interest policy attribute value is, the larger the sum of the interest hotspot support degrees after optimization and update is.
Step S50, acquiring the data mining reference degree of the E-commerce platform data of the data mining customized service based on the interest hotspot attribute value.
In an exemplary design manner, after obtaining the attribute value of the interest hotspot of each e-commerce service platform in the data mining customized service through calculation, the data mining reference degree of the e-commerce platform data of the data mining customized service is obtained based on the attribute value of the interest hotspot, for example, the sum of the attribute values of the interest hotspot of each e-commerce service platform in the data mining customized service may be used as the data mining reference degree of the e-commerce platform data of the data mining customized service.
In an exemplary design, since different vendor service platforms are associated with different types of application service tags, therefore, different e-commerce service application label optimization update values can be set in advance for different e-commerce service platforms of the e-commerce service application label, the interest hotspot attribute value of the corresponding e-commerce service platform is optimally updated based on the e-commerce service application label optimization update value, and the sum of the attribute values of the interest hot spots after optimization and update of each e-commerce service platform is used as the data mining reference degree of the e-commerce platform data of the data mining customized service, for example, the e-commerce service application label optimization and update value can be an application label influence index, and acquiring the data mining reference degree of the e-commerce platform data of the data mining customized service based on the application tag influence indexes corresponding to the e-commerce application tags of the e-commerce service platforms in the data mining customized service and the interest hotspot attribute values.
In an exemplary design mode, after the data mining reference degree of the e-commerce platform data of the data mining customized service is obtained, a reference degree range corresponding to the data mining reference degree of the e-commerce platform data can be determined, a data mining priority corresponding to the reference degree range is obtained, and data mining visual suggestion information corresponding to the data mining priority is output to prompt a user of the related interest hotspot support degree condition of the data mining customized service.
Therefore, the interest hotspot support degree of the e-commerce service platform on the user behavior data, the user interaction data, the e-commerce distribution data and the e-commerce order data is determined through the business relation sub-network containing the interest hotspots, and the data mining reference degree of the e-commerce platform data of the data mining customized service is comprehensively determined based on the interest hotspot support degree and the preset interest strategy attribute value, so that a reference basis for subsequent big data mining is provided.
In an exemplary design, step S40 includes:
step S41, obtaining expected attention impact data volume corresponding to each interest hotspot in the business relation sub-network;
in an exemplary design manner, expected attention impact data volume corresponding to each interest hotspot may be obtained based on prior knowledge, for example, an interest hotspot corresponding to the interest hotspot is found in a preset business relationship network, and the number of all the attention impact data having an association relationship with the interest hotspot corresponding to the interest hotspot is obtained as the expected attention impact data volume corresponding to the interest hotspot.
Step S42, determining an attention influence parameter of the e-commerce service platform based on a preset interest strategy attribute value corresponding to the e-commerce service platform and the expected attention influence data volume.
In an exemplary design manner, for a single e-commerce service platform, the sum of expected attention influence data volumes corresponding to all interest hotspots of a business relationship sub-network of the e-commerce service platform is used as the expected attention influence data volume of the e-commerce service platform, a target attention influence data volume corresponding to the e-commerce service platform is calculated based on a corresponding preset interest strategy attribute value of the e-commerce service platform and a weighted value of the expected attention influence data volume, and a ratio of the target attention influence data volume to the expected attention influence data volume of the e-commerce service platform is used as an attention influence parameter.
Step S43, determining the interest hotspot attribute value of the e-commerce service platform based on the interest hotspot support degree and the attention influence parameter.
In an exemplary design mode, for a single e-commerce service platform, the sum of the interest hotspot support degrees of service elements where user behavior data, user interaction data, e-commerce distribution data and e-commerce order data of the e-commerce service platform are located is used as the interest hotspot support degree of the e-commerce service platform, and the product of the interest hotspot support degree of the e-commerce service platform and the concern influence parameter of the e-commerce service platform is used as the interest hotspot attribute value of the e-commerce service platform. In an exemplary design mode, conventional mathematical operations such as addition, subtraction, multiplication, division and the like can be performed by adopting the concerned influence parameters on the basis of the interest hotspot support degree of the e-commerce service platform to obtain the interest hotspot attribute value of the e-commerce service platform.
In an exemplary design, step S20 includes:
step S21, respectively acquiring the user behavior data, the user interaction data, the e-commerce distribution data and the interest objects corresponding to the e-commerce order data of the e-commerce service platform based on the preset service relationship network;
in an exemplary design mode, interest hotspots in relation to user behavior data, user interaction data, e-commerce distribution data and e-commerce order data of each e-commerce service platform can be respectively obtained through a preset service relationship network, that is, interest objects corresponding to the user behavior data, the user interaction data, the e-commerce distribution data and the e-commerce order data are respectively obtained.
Step S22, establishing the interest hotspot based on the user behavior data, the user interaction data, the e-commerce distribution data and the interest object corresponding to the e-commerce order data of the e-commerce service platform;
step S23, respectively associating the business elements of the user behavior data, the user interaction data, the e-commerce distribution data and the e-commerce order data with the corresponding interest hotspots to generate a business relation sub-network of the e-commerce service platform.
In an exemplary design mode, interest hotspots are respectively established by using interest hotspots of user behavior data, user interaction data, e-commerce distribution data and e-commerce order data, and service elements of the user behavior data, the user interaction data, the e-commerce distribution data and the e-commerce order data are respectively associated with the corresponding interest hotspots to generate a service relation sub-network of an e-commerce service platform, for example, when the user behavior data has the interest hotspot 1 and the user interaction data has the interest hotspot 2, the service element of the user behavior data is associated with the service element of the interest hotspot 1, and the service element of the user interaction data is associated with the service element of the interest hotspot 2.
In an exemplary design, step S30 may be implemented as follows.
Step S31, respectively determining the user behavior data, the user interaction data, the e-commerce distribution data and the business relationship object set of the business element where the e-commerce order data is located from the business relationship sub-network;
step S32, obtaining an interest hotspot object set meeting preset service hotspot conditions from a service relation object set of service elements where the user behavior data, the user interaction data, the e-commerce distribution data and the e-commerce order data are located;
step S33, based on the business impact weight of each interest hotspot object in each interest hotspot object set, obtaining the user behavior data, the user interaction data, the e-commerce distribution data, and the interest hotspot support of the business element in which the e-commerce order data is located.
In an exemplary design, the above method may further include the following steps.
Step S60, acquiring E-commerce platform data of the data mining customized service meeting preset requirements, and screening target E-commerce platform data with effective behaviors from the E-commerce platform data;
step S70, obtaining a key intention thermodynamic diagram and a key event thermodynamic diagram of the target e-commerce platform data according to the interest hotspot data of the target e-commerce platform data; the key intention thermodynamic diagram comprises distribution information of key intention labels, the key event thermodynamic diagram comprises distribution information of key event labels, and the target e-commerce platform data comprises e-commerce platform active activity data and e-commerce platform passive activity data;
step S80, performing key theme distribution generation on the target e-commerce platform data according to the key intention thermodynamic diagram and the key event thermodynamic diagram of the target e-commerce platform data to obtain key theme distribution data of the target e-commerce platform data;
step S90, determining a key topic distribution vector set of the target E-commerce platform data according to the key topic distribution data of the target E-commerce platform data;
step S100, performing frequent theme mining on the target E-commerce platform data according to the key theme distribution vector set of the target E-commerce platform data to determine frequent theme mining results of the target E-commerce platform data.
In an exemplary design, step S80 may be implemented as follows.
Step S81, extracting the theme track of each thermal unit member contained in the key intention thermodynamic diagram of the target e-commerce platform data and the thermodynamic diagram unit data of the key event thermodynamic diagram to obtain the theme track extraction information of each thermal unit member; the thermodynamic diagram unit data is data information recorded aiming at an application process containing live telecast activity of an E-commerce;
step S82, aiming at each thermal unit member, extracting information based on the topic track of the thermal unit member, and determining a plurality of live topic objects related to the thermal unit member in the live telecast activity of the power provider and related continuous parameters of each related live topic object;
step S83, aiming at each thermal unit member, based on the association continuous parameter of the thermal unit member to each associated live subject object, screening the continuous live subject object of the thermal unit member from a plurality of live subject objects associated with the thermal unit member;
step S84, based on the theme generating instruction for the target thermal unit member, determining a live broadcast theme object with preset theme promotion label vector matched with the continuous live broadcast theme object of the target thermal unit member in a live broadcast theme object library as a selected live broadcast theme object, and associating the selected live broadcast theme object to the target thermal unit member;
and step S85, obtaining key theme distribution data of the target e-commerce platform data based on the fact that each target thermal unit member is associated with the selected live theme object.
For example, in one exemplary design, the subject trajectory extraction information for each thermal unit member includes: a plurality of associated nodes of the thermal unit member and associated persistence parameters of the thermal unit member for each associated node associated therewith within the live e-commerce campaign;
the step of determining, for each thermal unit member, a plurality of live topic objects associated with the thermal unit member in the e-commerce live broadcast event and associated persistence parameters of each associated live topic object based on the topic track extraction information of the thermal unit member includes: and aiming at each thermal unit member, identifying a live broadcast subject object in the E-commerce live broadcast activity, which can be associated by a plurality of associated nodes of the thermal unit member, and determining the associated continuous parameters of each associated node of the thermal unit member as the associated continuous parameters of the live broadcast subject object, which can be associated by the associated node.
For example, in an exemplary design, the live topic object in the live telecast activity to which the plurality of association nodes identifying the thermal unit member can be associated includes: and determining the live broadcast subject objects in the E-commerce live broadcast activities which can be associated with each associated node based on the live broadcast track information of each live broadcast subject object in the page of the live broadcast subject object and the live broadcast track information of each associated node of the thermal unit member.
For example, in an exemplary design, the screening the persistent live subject object of the thermal unit member from the plurality of live subject objects associated with the thermal unit member based on the associated persistent parameter of the thermal unit member for each associated live subject object includes: and screening the live broadcast subject objects with the associated continuous parameters meeting the preset continuous parameters from a plurality of live broadcast subject objects associated with the thermal unit members to serve as the continuous live broadcast subject objects of the thermal unit members.
For example, in an exemplary design, the screening, from among a plurality of live topic objects associated with the thermal unit member, a live topic object whose associated persistent parameter satisfies a preset persistent parameter as a persistent live topic object of the thermal unit member includes: screening the live broadcast subject object with the maximum association continuous parameter from a plurality of live broadcast subject objects associated with the thermal unit members to serve as the continuous live broadcast subject object of the thermal unit members; and/or screening the live topic objects with the associated continuous parameters larger than the preset continuous parameters from a plurality of live topic objects associated with the thermal unit members to serve as the continuous live topic objects of the thermal unit members.
For example, in an exemplary design, the determining, based on a topic generation instruction for a target thermal unit member, a live topic object whose preset topic promotion tag vector matches a persistent live topic object of the target thermal unit member in a live topic object library as a selected live topic object includes: acquiring a tag vector value of a preset subject promotion tag vector of a continuous live broadcast subject object of a target thermal unit member as a target tag vector value based on a subject generation instruction for the target thermal unit member; and screening the live broadcast subject object of which the tag vector value of the preset subject promotion tag vector and the target tag vector value meet the preset matching requirement in a live broadcast subject object library to serve as the selected live broadcast subject object.
For example, in an exemplary design manner, before the screening, in the live topic object library, a live topic object whose tag vector value of the preset topic promotion tag vector and the target tag vector value meet a preset pairing requirement as a selected live topic object, the method further includes: acquiring historical attention topic information of the target thermal unit member; the historical attention topic information is used for indicating attention attribute information of the target thermal unit member to the live subject object;
in the live topic object library, screening the live topic object whose tag vector value of the preset topic promotion tag vector and the target tag vector value meet preset pairing requirements, as a selected live topic object, including: and screening the live topic objects of which the label vector values of the preset topic promotion label vectors and the label vector values of the target topic promotion label vectors meet preset matching requirements in a live topic object library, and the live topic objects meeting the attention attribute information indicated by the historical attention topic information to serve as the selected live topic objects.
Fig. 2 illustrates a hardware structure of the e-commerce platform data monitoring system 100 for implementing the above-described e-commerce platform data monitoring method according to an embodiment of the present invention, and as shown in fig. 2, the e-commerce platform data monitoring system 100 may include a processor 110, a machine-readable storage medium 120, a bus 130, and a communication unit 140.
In one possible design, the e-commerce platform data monitoring system 100 may be a single server or a group of servers. The server group may be centralized or distributed (e.g., the e-commerce platform data monitoring system 100 may be a distributed system). In some embodiments, the e-commerce platform data monitoring system 100 may be local or remote. For example, e-commerce platform data monitoring system 100 may access information and/or data stored in machine-readable storage medium 120 via a network. As another example, e-commerce platform data monitoring system 100 may be directly connected to machine-readable storage medium 120 to access stored information and/or data. In some embodiments, the e-commerce platform data monitoring system 100 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-tiered cloud, and the like, or any combination thereof.
Machine-readable storage medium 120 may store data and/or instructions. In some embodiments, the machine-readable storage medium 120 may store data obtained from an external terminal. In some embodiments, machine-readable storage medium 120 may store data and/or instructions for use by e-commerce platform data monitoring system 100 to perform or use to perform the exemplary methods described in the present disclosure. In some embodiments, the machine-readable storage medium 120 may include mass storage, removable storage, volatile read-write memory, read-only memory (ROM), and the like, or any combination thereof. Exemplary mass storage devices may include magnetic disks, optical disks, solid state disks, and the like. Exemplary removable memory may include flash drives, floppy disks, optical disks, memory cards, compact disks, magnetic tape, and the like. Exemplary volatile read and write memories can include Random Access Memory (RAM). Exemplary RAM may include active random access memory (DRAM), double data rate synchronous active random access memory (DDR SDRAM), passive random access memory (SRAM), thyristor random access memory (T-RAM), and zero capacitance random access memory (Z-RAM), among others. Exemplary read-only memories may include mask read-only memory (MROM), programmable read-only memory (PROM), erasable programmable read-only memory (perrom), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM), digital versatile disc read-only memory, and the like. In some embodiments, the machine-readable storage medium 120 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-tiered cloud, and the like, or any combination thereof.
In a specific implementation process, at least one processor 110 executes computer-executable instructions stored in the machine-readable storage medium 120, so that the processor 110 may execute the e-commerce platform data monitoring method according to the above method embodiment, the processor 110, the machine-readable storage medium 120, and the communication unit 140 are connected through the bus 130, and the processor 110 may be configured to control the transceiving action of the communication unit 140.
For a specific implementation process of the processor 110, reference may be made to the above-mentioned method embodiments executed by the software compatibility testing system 100, which implement principles and technical effects are similar, and this embodiment is not described herein again.
In addition, an embodiment of the present invention further provides a readable storage medium, where the readable storage medium has computer-executable instructions preset therein, and when a processor executes the computer-executable instructions, the method for monitoring data of the e-commerce platform is implemented.
It should be understood that the foregoing description is for purposes of illustration only and is not intended to limit the scope of the present disclosure. Many modifications and variations will be apparent to those of ordinary skill in the art in light of the description of the invention. However, such modifications and variations do not depart from the scope of the present invention.
While the basic concepts have been described above, it will be apparent to those of ordinary skill in the art in view of this disclosure that the above disclosure is intended to be exemplary only and is not intended to limit the invention. Various modifications, improvements and adaptations of the present invention may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed within the present invention and are intended to be within the spirit and scope of the exemplary embodiments of the present invention.
Also, the present invention has been described using specific terms to describe embodiments of the invention. For example, "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the invention. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some of the features, structures, or characteristics of one or more embodiments of the present invention may be combined as suitable.
Moreover, those skilled in the art will recognize that aspects of the present invention may be illustrated and described in terms of several patentable species or situations, including any new and useful process, machine, article, or material combination, or any new and useful modification thereof. Accordingly, aspects of the present invention may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as a "unit", "module", or "system". Furthermore, aspects of the present disclosure may take the form of a computer program product embodied in one or more computer-readable media, with computer-readable program code embodied therein.
A computer readable signal medium may comprise a propagated data signal with computer program code embodied therein, for example, on a baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including electro-magnetic, optical, and the like, or any suitable combination. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code on a computer readable signal medium may be propagated over any suitable medium, including radio, electrical cable, fiber optic cable, RF, or the like, or any combination thereof.
Computer program code required for operation of various portions of the present invention may be written in any one or more of a variety of programming languages, including a persistent activity oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB. NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, an active programming language such as Python, Ruby, and Groovy, or other programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Additionally, the order in which the elements and sequences of the process are described, the use of letters or other designations herein is not intended to limit the order of the processes and methods of the invention unless otherwise indicated by the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it should be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments of the invention. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the invention, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. Similarly, it should be noted that in the preceding description of embodiments of the invention, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments.
Claims (7)
1. A data monitoring method of an e-commerce platform is applied to a data monitoring system of the e-commerce platform, the data monitoring system of the e-commerce platform is in communication connection with an e-commerce service platform, and the method is characterized by comprising the following steps:
the method comprises the steps of obtaining e-commerce platform data streams of each e-commerce service platform in data mining customized service, wherein the e-commerce platform data streams comprise user behavior data, user interaction data, e-commerce distribution data and e-commerce order data of the e-commerce service platform;
acquiring a business relation sub-network of each e-commerce service platform based on a preset business relation network, wherein the business relation sub-network comprises interest hotspots respectively connected with business elements where the user behavior data, the user interaction data, the e-commerce distribution data and the e-commerce order data of the e-commerce service platform are located;
respectively acquiring the user behavior data, the user interaction data, the e-commerce distribution data and the interest hotspot support of the service element where the e-commerce order data is located on the basis of the service relation sub-network;
determining an interest hotspot attribute value of the e-commerce service platform based on the interest hotspot support degree and a preset interest strategy attribute value corresponding to the e-commerce service platform;
acquiring a data mining reference degree of the E-commerce platform data of the data mining customized service based on the interest hotspot attribute value;
when the interest hotspot attribute value of a single e-commerce service platform is determined, calculating the sum of the interest hotspot support degrees of the e-commerce service platform on user behavior data, user interaction data, e-commerce distribution data and e-commerce order data, and optimizing and updating the sum of the interest hotspot support degrees through a preset interest strategy attribute value corresponding to the e-commerce service platform to obtain the interest hotspot attribute value of the e-commerce service platform;
in the process of obtaining the data mining reference degree of the e-commerce platform data of the data mining customized service based on the interest hotspot attribute value, different e-commerce service application label optimization update values are set for different e-commerce service platforms of the e-commerce service application labels in advance, the interest hotspot attribute value of the corresponding e-commerce service platform is optimized and updated based on the e-commerce service application label optimization update values, and the sum of the interest hotspot attribute values optimized and updated by each e-commerce service platform is used as the data mining reference degree of the e-commerce platform data of the data mining customized service.
2. The e-commerce platform data monitoring method according to claim 1, wherein the step of obtaining the business relationship sub-network of each e-commerce platform based on the preset business relationship network comprises:
respectively acquiring the user behavior data, the user interaction data, the e-commerce distribution data and interest objects corresponding to the e-commerce order data of the e-commerce service platform based on the preset service relationship network;
establishing the interest hotspot based on the user behavior data, the user interaction data, the e-commerce distribution data and the interest object corresponding to the e-commerce order data of the e-commerce service platform;
and respectively associating the service elements of the user behavior data, the user interaction data, the e-commerce distribution data and the e-commerce order data with the corresponding interest hotspots to generate a service relation sub-network of the e-commerce service platform.
3. The e-commerce platform data monitoring method according to claim 2, wherein the step of associating the service elements of the user behavior data, the user interaction data, the e-commerce distribution data and the e-commerce order data with the corresponding interest hotspots respectively to generate a service relationship sub-network of the e-commerce service platform comprises:
respectively associating business elements where the user behavior data, the user interaction data, the e-commerce distribution data and the e-commerce order data are located with the corresponding interest hotspots;
and associating the interest hotspot with a corresponding service subscription item, and associating the service subscription item with a corresponding service response position to generate a service relation sub-network of the e-commerce service platform.
4. The e-commerce platform data monitoring method according to any one of claims 1 to 3, wherein the step of respectively obtaining the user behavior data, the user interaction data, the e-commerce distribution data and the interest hotspot support of the business element where the e-commerce order data is located based on the business relationship sub-network comprises:
respectively determining a business relation object set of business elements where the user behavior data, the user interaction data, the e-commerce distribution data and the e-commerce order data are located from the business relation sub-network;
obtaining an interest hotspot object set meeting preset service hotspot conditions from a service relation object set of service elements where the user behavior data, the user interaction data, the e-commerce distribution data and the e-commerce order data are located;
and obtaining the user behavior data, the user interaction data, the e-commerce distribution data and the interest hotspot support degree of the service element where the e-commerce order data is located based on the service influence weight of each interest hotspot object in each interest hotspot object set.
5. The e-commerce platform data monitoring method according to any one of claims 1 to 3, wherein after the step of obtaining the data mining reference degree of the e-commerce platform data of the data mining customized service based on the interest hotspot attribute value, the method further comprises:
acquiring e-commerce platform data of the data mining customized service meeting preset requirements, and screening target e-commerce platform data with effective behaviors from the e-commerce platform data;
acquiring a key intention thermodynamic diagram and a key event thermodynamic diagram of the target e-commerce platform data according to the interest hotspot data of the target e-commerce platform data; the key intention thermodynamic diagram comprises distribution information of key intention labels, the key event thermodynamic diagram comprises distribution information of key event labels, and the target e-commerce platform data comprises e-commerce platform active activity data and e-commerce platform passive activity data;
performing key theme distribution generation on the target e-commerce platform data according to the key intention thermodynamic diagram and the key event thermodynamic diagram of the target e-commerce platform data to obtain key theme distribution data of the target e-commerce platform data;
determining a key theme distribution vector set of the target E-commerce platform data according to the key theme distribution data of the target E-commerce platform data;
and performing frequent theme mining on the target E-commerce platform data according to the key theme distribution vector set of the target E-commerce platform data to determine a frequent theme mining result of the target E-commerce platform data.
6. The e-commerce platform data monitoring method of claim 5, wherein the key topic distribution generation of the target e-commerce platform data according to the key intention thermodynamic diagram and the key event thermodynamic diagram of the target e-commerce platform data to obtain the key topic distribution data of the target e-commerce platform data comprises:
extracting the theme track of each thermal unit member contained in the thermodynamic diagram unit data of the key intention thermodynamic diagram and the key event thermodynamic diagram of the target e-commerce platform data to obtain the theme track extraction information of each thermal unit member; the thermodynamic diagram unit data is data information recorded aiming at an application process containing live telecast activity of an E-commerce;
for each thermal unit member, extracting information based on the topic track of the thermal unit member, and determining a plurality of live topic objects related to the thermal unit member in the E-commerce live broadcast activity and related continuous parameters of each related live topic object;
for each thermal unit member, screening the continuous live broadcast subject object of the thermal unit member from a plurality of live broadcast subject objects associated with the thermal unit member based on the associated continuous parameters of the thermal unit member to each associated live broadcast subject object;
determining a live broadcast subject object of which a preset subject promotion tag vector is matched with a continuous live broadcast subject object of a target thermal unit member in a live broadcast subject object library as a selected live broadcast subject object based on a subject generation instruction for the target thermal unit member, and associating the selected live broadcast subject object with the target thermal unit member;
and associating the selected live topic object based on each target thermal unit member to obtain key topic distribution data of the target E-commerce platform data.
7. The e-commerce platform system is characterized by comprising an e-commerce platform data monitoring system and an e-commerce service platform in communication connection with the e-commerce platform data monitoring system, wherein the e-commerce platform data monitoring system is specifically used for:
the method comprises the steps of obtaining e-commerce platform data streams of each e-commerce service platform in data mining customized service, wherein the e-commerce platform data streams comprise user behavior data, user interaction data, e-commerce distribution data and e-commerce order data of the e-commerce service platform;
acquiring a business relation sub-network of each e-commerce service platform based on a preset business relation network, wherein the business relation sub-network comprises interest hotspots respectively connected with business elements where the user behavior data, the user interaction data, the e-commerce distribution data and the e-commerce order data of the e-commerce service platform are located;
respectively acquiring the user behavior data, the user interaction data, the e-commerce distribution data and the interest hotspot support of the service element where the e-commerce order data is located on the basis of the service relation sub-network;
determining an interest hotspot attribute value of the e-commerce service platform based on the interest hotspot support degree and a preset interest strategy attribute value corresponding to the e-commerce service platform;
acquiring a data mining reference degree of the E-commerce platform data of the data mining customized service based on the interest hotspot attribute value;
when the interest hotspot attribute value of a single e-commerce service platform is determined, calculating the sum of the interest hotspot support degrees of the e-commerce service platform on user behavior data, user interaction data, e-commerce distribution data and e-commerce order data, and optimizing and updating the sum of the interest hotspot support degrees through a preset interest strategy attribute value corresponding to the e-commerce service platform to obtain the interest hotspot attribute value of the e-commerce service platform;
in the process of obtaining the data mining reference degree of the e-commerce platform data of the data mining customized service based on the interest hotspot attribute value, different e-commerce service application label optimization update values are set for different e-commerce service platforms of the e-commerce service application labels in advance, the interest hotspot attribute value of the corresponding e-commerce service platform is optimized and updated based on the e-commerce service application label optimization update values, and the sum of the interest hotspot attribute values optimized and updated by each e-commerce service platform is used as the data mining reference degree of the e-commerce platform data of the data mining customized service.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110979433.6A CN113435948B (en) | 2021-08-25 | 2021-08-25 | E-commerce platform data monitoring method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110979433.6A CN113435948B (en) | 2021-08-25 | 2021-08-25 | E-commerce platform data monitoring method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113435948A CN113435948A (en) | 2021-09-24 |
| CN113435948B true CN113435948B (en) | 2021-12-28 |
Family
ID=77797830
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110979433.6A Active CN113435948B (en) | 2021-08-25 | 2021-08-25 | E-commerce platform data monitoring method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113435948B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114840764B (en) * | 2022-05-25 | 2022-11-25 | 皓量科技(深圳)有限公司 | Big data mining method serving user interest analysis and cloud AI deployment system |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8504488B2 (en) * | 2008-04-30 | 2013-08-06 | Cisco Technology, Inc. | Network data mining to determine user interest |
| CN101770520A (en) * | 2010-03-05 | 2010-07-07 | 南京邮电大学 | User interest modeling method based on user browsing behavior |
| US20160203523A1 (en) * | 2014-02-21 | 2016-07-14 | Lithium Technologies, Inc. | Domain generic large scale topic expertise and interest mining across multiple online social networks |
| CN105872054B (en) * | 2016-03-30 | 2019-01-08 | 江苏大学 | A kind of mobile social networking resource discovery method based on interest hot spot |
| CN106202430A (en) * | 2016-07-13 | 2016-12-07 | 武汉斗鱼网络科技有限公司 | Live platform user interest-degree digging system based on correlation rule and method for digging |
| CN112199580B (en) * | 2020-09-11 | 2021-05-28 | 智广海联大数据技术有限公司 | Service processing method and artificial intelligence platform for cloud computing and big data positioning |
| CN112749851B (en) * | 2021-01-20 | 2021-09-07 | 国网江苏省电力有限公司信息通信分公司 | Big data demand prediction method based on artificial intelligence and big data cloud service center |
-
2021
- 2021-08-25 CN CN202110979433.6A patent/CN113435948B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN113435948A (en) | 2021-09-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113392330B (en) | Big data processing method and system based on internet behaviors | |
| WO2019061991A1 (en) | Multi-element universal model platform modeling method, electronic device, and computer readable storage medium | |
| CN107807967B (en) | Real-time recommendation method, electronic device and computer-readable storage medium | |
| CN112463991B (en) | Historical behavior data processing method and device, computer equipment and storage medium | |
| US9535978B2 (en) | Semantic mapping of topic map meta-models identifying assets and events to include weights | |
| US10042915B2 (en) | Semantic mapping of topic map meta-models identifying assets and events to include directionality | |
| US20170083815A1 (en) | Current behavior evaluation with multiple process models | |
| US20200372398A1 (en) | Model quality and related models using provenance data | |
| CN109408522A (en) | A kind of update method and device of user characteristic data | |
| CN113435948B (en) | E-commerce platform data monitoring method and system | |
| CN113285960B (en) | Data encryption method and system for service data sharing cloud platform | |
| CN112800197A (en) | Method and device for determining target fault information | |
| CN110569218A (en) | offline modification method and device for EXT file system and storage medium | |
| CN115454781B (en) | Data visualization display method and system based on enterprise architecture system | |
| CN113868544A (en) | Intelligent service file processing method and service server | |
| CN110719325A (en) | Method and device for responding to user request | |
| CN114328632A (en) | User data analysis method and device based on bitmap and computer equipment | |
| CN113870965A (en) | Data sharing method and system based on health big data archive | |
| CN112181407B (en) | Service realization processing method, device, system, electronic equipment and storage medium | |
| CN109213541A (en) | APP application shows the method, apparatus and electronic equipment at interface | |
| CN109885710B (en) | User image depicting method based on differential evolution algorithm and server | |
| CN113596061A (en) | Network security vulnerability response method and system based on block chain technology | |
| CN113705683A (en) | Recommendation model training method and device, electronic equipment and storage medium | |
| CN111158873A (en) | System state machine implementation method and device, storage medium and processor | |
| CN112417259A (en) | Media resource processing method, device, equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |