CN111159534A - User portrait based aid decision making method and device, equipment and medium - Google Patents
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Abstract
The disclosure relates to a user portrait-based assistant decision-making method and device, electronic equipment and a storage medium, relates to the technical field of data mining, and can be applied to a scene of determining a user decision-making scheme by combining a user portrait generated by multi-party data. The auxiliary decision-making method based on the user portrait comprises the following steps: receiving initial resource data sent by a resource platform, and converting the initial resource data to generate target resource data; sending the target resource data to a user, and receiving feedback information of the user aiming at the target resource data; acquiring platform data of a resource platform, and generating user portrait data of a user according to the platform data and feedback information; decision reference information for the user is determined in conjunction with the user profile data to determine a decision operation for the user based on the decision reference information. The user portrait data is generated by combining the acquired multi-party resource data and the feedback information, so that auxiliary decision making is carried out according to the user portrait data.
Description
Technical Field
The present disclosure relates to the field of data mining technologies, and in particular, to an assistant decision method based on a user portrait, an assistant decision device based on a user portrait, an electronic device, and a computer-readable storage medium.
Background
With the popularization of the internet and the continuous development of communication technology, high-speed mobile communication, smart phones and tablet computers are spread throughout the life of people, and better marketing effect can be achieved by combining the internet technology and marketing activities.
Marketing modes common to the internet today may include: the enterprise serves as a tenant to manage public numbers and pushes marketing content through a marketing platform based on the work number so as to perform marketing activities. The existing marketing mode directly presents marketing graphics, marketing videos and the like generated by a marketing platform to users, and does not collect and analyze user basic information and user behavior information under different marketing contents.
Therefore, the marketing mode fails to establish a corresponding user portrait for a certain type of marketing content, and lacks a scheme for pushing interesting content or providing targeted services for different users according to user interest points; secondary marketing clues aiming at the users cannot be collected in the existing mode, and a good marketing effect is difficult to obtain.
It is to be noted that the information disclosed in the above background section is only for enhancement of understanding of the background of the present disclosure, and thus may include information that does not constitute prior art known to those of ordinary skill in the art.
Disclosure of Invention
The present disclosure is directed to a user portrait-based assistant decision method, a user portrait-based assistant decision device, an electronic device, and a computer-readable storage medium, so as to overcome, at least to a certain extent, the problem in the prior art that a unified user portrait platform is lacked, and effective combination of user behavior data, resource information clues, and user interest points is difficult to achieve.
Additional features and advantages of the disclosure will be set forth in the detailed description which follows, or in part will be obvious from the description, or may be learned by practice of the invention.
According to a first aspect of the present disclosure, there is provided a method for assisting decision making based on a user profile, including: receiving initial resource data sent by a resource platform, and converting the initial resource data to generate target resource data; sending the target resource data to a user, and receiving feedback information of the user aiming at the target resource data; acquiring platform data of a resource platform, and generating user portrait data of a user according to the platform data and feedback information; decision reference information for the user is determined in conjunction with the user profile data to determine a decision operation for the user based on the decision reference information.
Optionally, before receiving the initial resource data sent by the resource platform, the method further includes: receiving a platform access request sent by a resource platform, and determining a platform classification identifier in the platform access request; and determining the platform authority of the resource platform according to the platform classification identifier so as to control the resource platform to finish platform access operation according to the platform authority.
Optionally, the converting the initial resource data to generate the target resource data includes: determining the data content and the data type of the initial resource data; and converting the initial resource data according to the data content and the data type to generate target resource data.
Optionally, the generating user portrait data of the user according to the platform data and the feedback information includes: clustering the platform data to generate corresponding platform information labels; clustering the target resource data to generate a corresponding resource information label; determining an initial user label corresponding to the user according to the feedback information; and performing supplementary processing on the initial user label according to the platform information label and the resource information label to generate user portrait data.
Optionally, determining an initial user tag corresponding to the user according to the feedback information includes: determining user basic information and user behavior information in the feedback information; extracting basic key data from the user basic information to generate corresponding basic feature vectors; extracting behavior key data from the user behavior information to generate corresponding behavior feature vectors; and generating an initial user label according to the basic characteristic vector and the behavior characteristic vector.
Optionally, performing supplementary processing on the initial user tag according to the platform information tag and the resource information tag to generate user portrait data, including: determining the predicted behavior information corresponding to the user according to the platform information label, the resource information label and the user image data; generating a corresponding predicted behavior label according to the predicted behavior information; predictive behavior tags are added to the user tags to generate user portrait data.
Optionally, determining decision reference information for the user in combination with the user profile data includes: respectively determining a platform information label corresponding to the platform data and a resource information label corresponding to the target resource data; inputting the platform information label, the resource information label and the user portrait data into a decision analysis model; and determining decision reference information corresponding to the user by the decision analysis model.
According to a second aspect of the present disclosure, there is provided a user-portrait-based aid decision device, comprising: the resource data generation module is used for receiving initial resource data sent by the resource platform and converting the initial resource data to generate target resource data; the feedback information receiving module is used for sending the target resource data to the user and receiving feedback information of the user aiming at the target resource data; the portrait data generation module is used for acquiring platform data of the resource platform and generating user portrait data of a user according to the platform data and the feedback information; and the auxiliary decision module is used for determining decision reference information aiming at the user by combining the user portrait data so as to determine decision operation aiming at the user according to the decision reference information.
Optionally, the user-portrait-based aid decision device further includes a platform access module, configured to receive a platform access request sent by the resource platform, and determine a platform classification identifier in the platform access request; and determining the platform authority of the resource platform according to the platform classification identifier so as to control the resource platform to finish platform access operation according to the platform authority.
Optionally, the resource data generating module includes a resource data generating unit, configured to determine data content and a data type of the initial resource data; and converting the initial resource data according to the data content and the data type to generate target resource data.
Optionally, the portrait data generation module includes a portrait data generation unit, configured to perform clustering on the platform data to generate a corresponding platform information tag; clustering the target resource data to generate a corresponding resource information label; determining an initial user label corresponding to the user according to the feedback information; and performing supplementary processing on the initial user label according to the platform information label and the resource information label to generate user portrait data.
Optionally, the portrait data generating unit includes an initial tag determining subunit, configured to determine user basic information and user behavior information in the feedback information; extracting basic key data from the user basic information to generate corresponding basic feature vectors; extracting behavior key data from the user behavior information to generate corresponding behavior feature vectors; and generating an initial user label according to the basic characteristic vector and the behavior characteristic vector.
Optionally, the portrait data generating unit includes a portrait generating subunit, configured to determine, according to the platform information tag, the resource information tag, and the user portrait data, predicted behavior information corresponding to the user; generating a corresponding predicted behavior label according to the predicted behavior information; predictive behavior tags are added to the user tags to generate user portrait data.
Optionally, the assistant decision module includes an assistant decision unit, configured to determine a platform information tag corresponding to the platform data and a resource information tag corresponding to the target resource data, respectively; inputting the platform information label, the resource information label and the user portrait data into a decision analysis model; and determining decision reference information corresponding to the user by the decision analysis model.
According to a third aspect of the present disclosure, there is provided an electronic device comprising: a processor; and a memory having computer readable instructions stored thereon which, when executed by the processor, implement a user profile-based aid decision method according to any of the above.
According to a fourth aspect of the present disclosure, there is provided a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a user representation-based aid decision method according to any one of the above.
The technical scheme provided by the disclosure can comprise the following beneficial effects:
in the auxiliary decision method based on the user portrait in the exemplary embodiment of the present disclosure, initial resource data sent by a resource platform is received, and the initial resource data is converted to generate target resource data; sending the target resource data to a user, and receiving feedback information of the user aiming at the target resource data; acquiring platform data of a resource platform, and generating user portrait data of a user according to the platform data and feedback information; decision reference information for the user is determined in conjunction with the user profile data to determine a decision operation for the user based on the decision reference information. On one hand, the user portrait data corresponding to the user can be generated by combining the multi-party data and the feedback information of the user aiming at the target resource data, and a decision scheme is made for the user by mining the user information and the user interest points generated in the multi-party data and the feedback information. On the other hand, by mining the association relationship between the multi-party data such as the target resource data and the platform data and the user portrait data, the generated user portrait data is more accurate, so that the decision reference information corresponding to the user can be more accurately determined, the execution cost in the decision process can be reduced, and the execution effect of decision operation is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure. It is to be understood that the drawings in the following description are merely exemplary of the disclosure, and that other drawings may be derived from those drawings by one of ordinary skill in the art without the exercise of inventive faculty. In the drawings:
FIG. 1 schematically illustrates a flow chart of a user-representation-based aided decision-making method according to an exemplary embodiment of the present disclosure;
FIG. 2 is a diagram schematically illustrating a prior art system for marketing using an Internet marketing model;
FIG. 3 schematically illustrates a system architecture diagram for marketing based on public number management and user representation data, according to an exemplary embodiment of the present disclosure;
FIG. 4 schematically illustrates a user representation data diagram formed for multi-party data according to an exemplary embodiment of the present disclosure;
FIG. 5 schematically illustrates a block diagram of a user representation-based aid decision-making device, according to an exemplary embodiment of the present disclosure;
FIG. 6 schematically illustrates a block diagram of an electronic device according to an exemplary embodiment of the present disclosure;
fig. 7 schematically illustrates a schematic diagram of a computer-readable storage medium according to an exemplary embodiment of the present disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the disclosure. One skilled in the relevant art will recognize, however, that the subject matter of the present disclosure can be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known structures, methods, devices, implementations, materials, or operations are not shown or described in detail to avoid obscuring aspects of the disclosure.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. That is, these functional entities may be implemented in the form of software, or in one or more software-hardened modules, or in different networks and/or processor devices and/or microcontroller devices.
The current marketing mode of the internet is that tenants manage a single public number or a same subject public number through a marketing platform and complete marketing behaviors on the marketing platform. Referring to fig. 2, fig. 2 schematically shows a system configuration diagram of a related art internet marketing mode. By adopting the marketing system in fig. 2, the management function of the same tenant on multiple public numbers of different subjects is difficult to realize, and an effective manager control mechanism is lacked. In the whole system, a uniform user portrait platform is lacked, corresponding user portraits cannot be established for different users, interested contents cannot be pushed for the users or targeted services cannot be provided for the users according to user interest points, in addition, secondary marketing clues cannot be formed by collecting user behavior data, and marketing schemes meeting the characteristics of the users are difficult to formulate.
Based on this, in the present exemplary embodiment, first, a method for assisting decision based on user portrait is provided, where the method for assisting decision based on user portrait of the present disclosure may be implemented by using a server, and the method of the present disclosure may also be implemented by using a terminal device, where the terminal described in the present disclosure may include a mobile terminal such as a mobile phone, a tablet computer, a notebook computer, a palm computer, a Personal Digital Assistant (PDA), and a fixed terminal such as a desktop computer. FIG. 1 schematically illustrates a schematic diagram of a user profile-based aided decision method flow, according to some embodiments of the present disclosure. Referring to fig. 1, the method for assisting decision based on user profile may include the following steps:
step S110, receiving the initial resource data sent by the resource platform, and performing conversion processing on the initial resource data to generate target resource data.
Step S120, sending the target resource data to the user, and receiving feedback information of the user for the target resource data.
Step S130, platform data of the resource platform is obtained, and user portrait data of the user is generated according to the platform data and the feedback information.
Step S140, determining decision reference information for the user in combination with the user portrait data, so as to determine a decision operation for the user according to the decision reference information.
According to the user profile-based aided decision making method in the present exemplary embodiment, on one hand, in combination with the multi-party data and the feedback information of the user for the target resource data, user profile data corresponding to the user can be generated, and a decision making scheme is made for the user by mining user information and user interest points generated in the multi-party data and the feedback information. On the other hand, by mining the association relationship between the multi-party data such as the target resource data and the platform data and the user portrait data, the generated user portrait data is more accurate, so that the decision reference information corresponding to the user can be more accurately determined, the execution cost in the decision process can be reduced, and the execution effect of decision operation is improved.
In the following, the user-portrait-based aid decision method in the present exemplary embodiment will be further described.
In step S110, initial resource data sent by the resource platform is received, and the initial resource data is transformed to generate target resource data.
In some example embodiments of the present disclosure, the resource platform may be a platform employed to provide the initial resource data. For example, in a scenario of marketing by using various new media platforms, the resource platform may be a platform where a public number is set by an enterprise, may also be a platform corresponding to a microblog, a subscription number, and the like corresponding to the enterprise, and may also be a website platform of an official website of the enterprise, and the like. In a new media scenario, an enterprise or a company using the resource platform may be referred to as a tenant. The initial resource data may be initial resource data sent by tenants such as enterprises, companies, and the like through the resource platform. The target resource data may be resource data obtained by processing the initial resource data through the data processing platform in the present disclosure. The present disclosure will take the resource platform as the platform corresponding to the public account as an example, and will explain the auxiliary decision method based on user portrait in the present disclosure in detail.
Referring to FIG. 3, FIG. 3 schematically illustrates a system architecture for implementing aid decisions based on user profile data. The tenant 310 establishes a connection with the assistant decision platform 330 through the public number platform 320, that is, the tenant 310 can authorize to the assistant decision platform 330 through the public number platform 320, so as to send initial resource data to the assistant decision platform; the public number may include, among other things, the public number of the subject being regulated and the public number of the general user authority. After receiving the initial resource data, the assistant decision platform 330 may process the initial resource data to generate the target resource data 360. In this exemplary embodiment, the target resource data may be data of a marketing campaign type, marketing image-text data, marketing video data, and the like, and the data type of the target resource data is not limited by any feature in the present disclosure, and in an actual usage scenario, the target resource data type may be defined according to specific requirements. Referring to fig. 3, the target resource data in fig. 3 may include a marketing campaign 361, a marketing picture 362, a marketing video 363, and the like.
According to some exemplary embodiments of the present disclosure, a platform access request sent by a resource platform is received, and a platform classification identifier in the platform access request is determined; and determining the platform authority of the resource platform according to the platform classification identifier so as to control the resource platform to finish platform access operation according to the platform authority. The platform access request may be a request generated by a resource platform 320 (e.g., a public platform 320) requesting access to an assistant decision platform 330. The platform class identifier may be an identifier for identifying a type of the platform, for example, in the present embodiment, a public number 321 of a supervised subject and a public number 322 of a general user authority may be included, and thus, corresponding class identifiers may be added to different types of public numbers. The platform permissions may be the corresponding permissions of the resource platform 320 with respect to the initial resource data before publishing the information to the aid decision platform 330. The platform access operation may be an operation of the resource platform 320 to access the assistant decision platform 330.
The resource platform 320 may perform an account registration operation on a registration platform (e.g., a registration website) provided by the assistant decision platform 330 to generate a platform access request, and after receiving the platform access request, the assistant decision platform 330 may perform some qualification certification operations on the resource platform 320, specifically, the assistant decision platform 330 may determine, from the platform access request, a platform classification identifier corresponding to the resource platform 320, that is, which specific rights the public number platform has. And determining the platform authority of the resource platform from the platform classification identifier, and completing the access operation of accessing the auxiliary decision platform according to the platform authority corresponding to the resource platform so as to send initial resource data to the auxiliary decision platform.
For example, taking an endowment business as an example, endowment homes can be set up in different regions of the country, the headquarters are set up in Beijing, the endowment homes in each region can have corresponding endowment home public number platforms, and the public number platform corresponding to the headquarters has the supervision authority for the public number platforms in the subordinate regions. Taking public platform corresponding to four homes of retirement homes such as beijing, shanghai, guangzhou, shenzhen, etc. as an example, before the initial resource data is released to the assistant decision platform 330, the headquarter (beijing) platform may have data viewing and data auditing permissions for the platform in the regions such as beijing, shanghai, guangzhou, shenzhen, etc. And the Shanghai, Guangzhou, Shenzhen and other regions respectively only have data viewing and data auditing authorities of the local region and do not have data viewing and data auditing authorities of other regions. For example, before the public platform corresponding to the Shanghai area issues the initial resource data to the assistant decision platform, the area itself may perform an audit on the content included in the initial resource data, and the Beijing headquarters may also perform a check and an audit on the content of the initial resource data.
Referring to fig. 3, through the management mechanism for the resource platform, the enterprise may authorize, as a tenant, a plurality of different types of public numbers, including a public number of a supervised subject, a public number of a single subject, and the like, to the assistant decision platform, and send initial content that is desired to be marketed to the assistant decision platform, and the assistant decision platform processes the initial content and generates corresponding marketing content, so as to send the generated marketing content to the user side.
According to some exemplary embodiments of the present disclosure, a data content and a data type of the initial resource data are determined; and converting the initial resource data according to the data content and the data type to generate target resource data. The data content may be specific content included in the initial resource data, for example, the initial resource data corresponding to the resource platform for managing health insurance may include contents such as an operational idea of the enterprise, general knowledge related to human physiological health, and health insurance product recommendation. The data type may be the type to which the data content in the initial resource data belongs. For example, the data type may be a text type, an image type, a video type, and the like. The target resource data can be resource data generated after conversion and production processing of the aid decision platform, and the target resource data can be data visually presented to a user for the user to view.
After receiving the initial resource data sent by the resource platform 320, the assistant decision platform 330 may determine data content included in the initial resource data and a data type corresponding to the data content, and perform conversion processing on the initial resource data according to the data content and the data type in the initial resource data to generate target resource data. For example, when the resource platform 320 intends to produce a lottery activity, the resource platform may send the relevant information of the lottery activity, such as the lottery rules and the used text, as the initial resource data to the assistant decision platform 330, and after receiving the relevant initial resource data of the lottery activity, the assistant decision platform 330 may invoke a relevant third-party tool to produce the initial resource data, and generate a lottery activity link (i.e., target resource data) that can be viewed by the user, so that the user may participate in the lottery activity.
In step S120, the target resource data is sent to the user, and feedback information of the user for the target resource data is received.
In some exemplary embodiments of the present disclosure, the feedback information may be information collected by a user according to an operation of the user on the target resource data after the user receives the target resource data. After sending the target resource data to the user side, the user can check the target resource data through the corresponding user side 370, and perform corresponding user operation on the target resource data. For example, when the target resource data is common knowledge about human health, the target resource data may include a text and an image, and a readable public article may be made from the target resource data by using an image-text composition tool, and the common article may be pushed to the user. When the user receives the article, the user can choose to ignore the article, view the article, collect the article, share the article with other friend users, and the like. In addition, when the target resource data is a lottery activity, the lottery activity is pushed to the user side, and the user side may ignore the lottery activity, participate in the lottery activity, share the lottery activity with other friend users, and the like. When the assistant decision platform receives the user operation, the collected user operation can be used as corresponding feedback information.
In step S130, platform data of the resource platform is obtained, and user portrait data of the user is generated according to the platform data and the feedback information.
In some exemplary embodiments of the present disclosure, the platform data may be data related to the resource platform, may be data of the resource platform itself, and may also be data of other third parties associated with the resource platform, i.e., owned data 350 in fig. 3. For example, for an Application (APP) developed by an enterprise, the platform data may be data generated from a user operating the APP. The user representation data may be data generated from user representation of the relevant user in question from the captured multi-party data. The third party platform may access the assistant decision platform through an Application Programming Interface (API), so that the assistant decision platform may obtain data obtained from the third party platform related to the resource platform as platform data. And the user portrait platform is connected into the decision auxiliary platform, and corresponding content labels can be attached to marketing content, marketing activities and the like through the user portrait platform so as to create the user portrait by combining the content labels. After the platform data of the resource platform is obtained, the user portrait platform 340 may generate user portrait data of the user by combining the feedback information and the platform data, so as to perform a corresponding decision operation according to the generated user portrait data.
According to some example embodiments of the present disclosure, platform data is clustered to generate corresponding platform information tags; clustering the target resource data to generate a corresponding resource information label; determining an initial user label corresponding to the user according to the feedback information; and performing supplementary processing on the initial user label according to the platform information label and the resource information label to generate user portrait data. The platform information tag may be an information tag extracted from the platform data in order to more accurately classify or locate the platform data. The resource information tag may be an information tag composed of key features extracted from the target resource data in order to more accurately classify the target resource data. The user tag may be a corresponding tag given to the user in order to better grasp the interest point of the user by analyzing the user's own information and the user behavior information. The initial user tag may be a user tag determined according to the feedback information. The supplemental processing may be further refinement and supplemental processing of the user tags in conjunction with other data that enables refinement of the user representation. The user representation data may be data generated by user representation processing of a user in combination with the multi-party data.
After the platform data is obtained, the platform data can be clustered to generate a platform information label, and after the target resource data is clustered, a corresponding resource information label can be generated. And receiving feedback information of the user aiming at the target resource data, and generating a corresponding initial user label according to the feedback information. The initial user tag can be subjected to supplementary processing by combining the obtained platform information tag, resource information tag and the like to generate user portrait data which can describe the characteristics of the user in more detail.
According to some exemplary embodiments of the present disclosure, user basic information and user behavior information in feedback information are determined; extracting basic key data from the user basic information to generate corresponding basic feature vectors; extracting behavior key data from the user behavior information to generate corresponding behavior feature vectors; and generating an initial user label according to the basic characteristic vector and the behavior characteristic vector. The user basic information may be user information determined according to the attributes of the user, for example, the user basic information may include the age of the user, the gender of the user, the marital status of the user, the income status of the user, the occupation of the user, and the like. The user behavior information may be behavior information determined according to an operation of the user on the target resource data. For example, the user behavior information may include an information viewing operation for viewing information content in the resource platform by the user, a sharing operation for sharing the information content by the user, an activity participation operation for participating in a lottery behavior by the user, and the like. The basic key data may be a key field representing basic information of the user. The basic feature vector may be a feature vector generated by vectorizing the basic key data. The behavior critical data may be a key field representing a user operation of the user on the target resource data. The behavior feature vector may be a feature vector generated by vectorizing the behavior key data.
After the user basic information and the user behavior information are determined from the feedback information, basic key data can be extracted from the user basic information, and vectorization processing is performed on the basic key data to generate corresponding basic feature vectors. After the behavior key data are extracted from the behavior basic information, vectorization processing can be performed on the behavior key data to generate corresponding behavior feature vectors. And generating an initial user label according to the acquired basic characteristic vector and the acquired behavior characteristic vector.
According to some exemplary embodiments of the present disclosure, determining predicted behavior information corresponding to a user according to a platform information tag, a resource information tag, and user image data; generating a corresponding predicted behavior label according to the predicted behavior information; predictive behavior tags are added to the user tags to generate user portrait data. The predicted behavior information may be information corresponding to a user operation that is estimated to be possible in the future by the user based on the acquired multi-party data. For example, the number of times that a user views a target product may be counted, and when it is detected that the number of times that a certain user views a certain financial product exceeds a preset threshold, it may be presumed that the user has a high possibility of purchasing the financial product. The predicted behavior tag may be a tag determined according to the predicted behavior information, key information is extracted from the predicted behavior information, and a predicted behavior tag corresponding to the predicted behavior information may be generated.
After the platform information tag, the resource information tag and the user portrait data are obtained, corresponding association relations can be established between the obtained various information tags and the user portrait data, user operation which is possibly performed by a user in the future is predicted through the established association relations, predicted behavior information is generated, key fields in the predicted behavior information are extracted, corresponding predicted behavior tags are generated, the predicted behavior tags are added to the user tags, and the user portrait data corresponding to the user are generated, so that follow-up decisions of the user are determined according to the user portrait data. Referring to FIG. 4, FIG. 4 schematically illustrates a tab diagram of user portrait data corresponding to a user. According to the platform data, the target resource data and other third-party data and feedback information of the user aiming at the target resource data, user portrait data corresponding to the user can be generated, and the characteristics of the user can be represented by adopting a user tag. For example, determining user representation data for the user may include: "90 back", "man", "married", "love sports", "accident user", "health preserving", "lottery fan", "having continuous contract requirement", etc. so as to make the corresponding decision reference information according to the user portrait data.
In step S140, decision reference information for the user is determined in conjunction with the user portrait data, so as to determine a decision operation for the user based on the decision reference information.
In some exemplary embodiments of the present disclosure, the decision reference information may be reference information generated for making a decision scheme for the user after analyzing and processing the user image data and data of other third parties, and the decision scheme may be assisted to be generated based on the decision reference information. The decision-making operation for the user may be a subsequent decision-making scheme made by the user, for example, in a marketing application scenario, the decision-making operation for the user may be to recommend a specific product to the user, may also be to make a subsequent service scheme for the user, and may also be to push articles, videos, activity information, and the like of interest to the user.
After the user portrait data is generated, decision reference information corresponding to the user can be determined by combining the multi-party third party data and the user portrait data, and a subsequent decision operation aiming at the user can be determined according to the decision reference information. For example, in a marketing scenario, from third-party data and user image data, marketing clues corresponding to the user, that is, products in which the user is interested and products which the user may need to purchase, can be mined so as to generate decision reference information for secondary marketing of the user. For example, in an insurance marketing scenario, after the user 1 registers and logs in the insurance platform through the user side, the content in the platform is only browsed a few times, and the user 1 can be considered as a "user needing to promote life", so that some product contents that the user 1 may be interested in can be determined according to the user basic information, and pushed to the user 1, so as to invoke the activity of the user. In addition, after the user 2 consults a large number of health insurance products, the user applies for filling out a health insurance policy, but does not submit the policy and purchase the health insurance products, and the user 2 can be considered as a potential purchaser of the health insurance products, so that detailed descriptions related to some health insurance products can be continuously pushed to the user 2 in the subsequent marketing process, so that the user can know the health insurance products in more detail; in addition, corresponding exclusive customer service can be arranged for the user 2, the real-time dynamic state of the user 2 is tracked, and the purchase desire of the user 2 is mobilized to increase the conversion rate of the insurance policy; wherein the policy conversion rate may be a ratio of the number of people who purchased the insurance product and the number of people who viewed the insurance product.
According to some exemplary embodiments of the present disclosure, a platform information tag corresponding to platform data and a resource information tag corresponding to target resource data are respectively determined; inputting the platform information label, the resource information label and the user portrait data into a decision analysis model; and determining decision reference information corresponding to the user by the decision analysis model. The decision analysis model may be a model employed to generate decision reference information from the platform data, target resource data and user profile data. And inputting the determined platform information label, the resource information label and the user portrait data into a decision analysis model, wherein the decision analysis model can determine decision reference information corresponding to the user, and a decision scheme corresponding to the user can be made according to the decision reference information.
For example, when a system structure for marketing based on public number management and user portrait data in fig. 3 is used for marketing activities, taking a health insurance marketing business process in insurance business as an example, a user portrait platform can be added in the health insurance business process, and the collected user basic information and user behavior information can be analyzed based on the user portrait platform, so that content tags are added to the public number and marketing content in the business process, and user tags are added to users, so that user portrayals are created according to the content tags and the user tags, and health insurance product types which may be interested by different users are obtained.
In addition, by acquiring platform data related to a resource platform, constructed user images can be more detailed, and corresponding decision schemes can be made for different types of users, for example, for a user a who is in a purchase hesitation period for the health insurance product 1, secondary marketing clues for the user a can be collected by combining user image data and user behavior data, for example, other products which are likely to be interested by the user a and are similar to the health insurance product 1 are inferred by combining information such as user age, user type, user interest and the like of the user a, and the selection space of the user a is increased, so that order fulfillment is facilitated. For the sleeping user B, the possible interest points of the user B can be obtained according to the user portrait data, and the corresponding health insurance product related contents such as marketing activities, marketing videos, marketing graphics and the like are pushed to the user B, so that the user B can more comprehensively know the guarantee brought by the health insurance product, and the purpose of activating the user B to pay attention to the health insurance product is achieved.
Furthermore, user portrait data is obtained by establishing the user portrait, user characteristic data of a plurality of users can be extracted from the user portrait data, clustering processing is carried out on the user characteristic data by adopting a clustering method, and similar users can be clustered together to form a similar user group. For example, a user group A ' having the same or similar user characteristics as the user A is determined in a clustering manner, and the information content of products still in a hesitation period is pushed to the user group A ' is increased, so that secondary marketing aiming at the user group A ' is realized, and the policy-preserving conversion rate is improved. In addition, a user group B 'with similar user characteristics to the user B can be found through a clustering method, and corresponding marketing content is pushed to the user group B', so that the attention of the user group B 'to the marketing content is improved, and the activity of the user group B' is effectively improved. By applying the user portrait data and the marketing cue data to the assistant decision platform, secondary marketing guidance can be provided and the purpose of evaluating the marketing effect can be achieved.
In summary, in the auxiliary decision method based on the user profile in the exemplary embodiment of the present disclosure, initial resource data sent by a resource platform is received, and the initial resource data is transformed to generate target resource data; sending the target resource data to a user, and receiving feedback information of the user aiming at the target resource data; acquiring platform data of a resource platform, and generating user portrait data of a user according to the platform data and feedback information; decision reference information for the user is determined in conjunction with the user profile data to determine a decision operation for the user based on the decision reference information. On one hand, corresponding platform authorities are allocated to the resource platforms according to the platform classification identifications of the resource platforms, and effective management of a plurality of resource platforms of the same tenant can be achieved. On the other hand, data support can be provided for the generated user portrait data by mining multi-party data such as target resource data and platform data, so that the generated user portrait data is more accurate, decision reference information corresponding to a user can be accurately determined, and a decision scheme is made. In another aspect, the multi-party data and the feedback information of the user for the target resource data are combined to generate user portrait data corresponding to the user, and a decision scheme is made for the user by acquiring user information and user interest points generated in multiple marketing activities, so that the execution cost in the decision process can be reduced, and the execution effect of decision operation is improved.
It is noted that although the steps of the methods of the present invention are depicted in the drawings in a particular order, this does not require or imply that the steps must be performed in this particular order, or that all of the depicted steps must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions, etc.
In addition, in the exemplary embodiment, an auxiliary decision device based on user portrait is also provided. Referring to fig. 5, the apparatus 500 for assisting decision based on user profile may include: resource data generation module 510, feedback information receiving module 520, representation data generation module 530, and aid decision module 540.
Specifically, the resource data generation module 510 may be configured to receive initial resource data sent by a resource platform, and perform conversion processing on the initial resource data to generate target resource data; the feedback information receiving module 520 may be configured to send the target resource data to the user, and receive feedback information of the user for the target resource data; the portrait data generation module 530 may be configured to obtain platform data of the resource platform, and generate user portrait data of the user according to the platform data and the feedback information; the aid decision module 540 may be used to determine decision reference information for the user in conjunction with the user profile data to determine a decision operation for the user based on the decision reference information.
The auxiliary decision device 500 based on the user portrait acquires feedback information of a user for target resource data, generates user portrait data corresponding to the user by combining platform data and the feedback information, determines decision reference information corresponding to the user according to the generated user portrait data so as to perform decision operation according to the decision reference information, can acquire user information and user interest points more accurately by establishing a user portrait platform, can complete decision operation according to the determined user information and user interest points, and is an effective auxiliary decision device.
In an exemplary embodiment of the present disclosure, based on the foregoing solution, the user-portrait-based aid decision device further includes a platform access module, and the platform access module is configured to: receiving a platform access request sent by a resource platform, and determining a platform classification identifier in the platform access request; and determining the platform authority of the resource platform according to the platform classification identifier so as to control the resource platform to finish platform access operation according to the platform authority.
In an exemplary embodiment of the present disclosure, based on the foregoing, the resource data generation module includes a resource data generation unit configured to: determining the data content and the data type of the initial resource data; and converting the initial resource data according to the data content and the data type to generate target resource data.
In an exemplary embodiment of the present disclosure, based on the foregoing, the portrait data generation module includes a portrait data generation unit configured to: clustering the platform data to generate corresponding platform information labels; clustering the target resource data to generate a corresponding resource information label; determining an initial user label corresponding to the user according to the feedback information; and performing supplementary processing on the initial user label according to the platform information label and the resource information label to generate user portrait data.
In an exemplary embodiment of the present disclosure, based on the foregoing, the portrait data generation unit includes an initial tag determination subunit configured to: determining user basic information and user behavior information in the feedback information; extracting basic key data from the user basic information to generate corresponding basic feature vectors; extracting behavior key data from the user behavior information to generate corresponding behavior feature vectors; and generating an initial user label according to the basic characteristic vector and the behavior characteristic vector.
In an exemplary embodiment of the present disclosure, based on the foregoing, the portrait data generation unit includes a portrait generation subunit configured to: determining the predicted behavior information corresponding to the user according to the platform information label, the resource information label and the user image data; generating a corresponding predicted behavior label according to the predicted behavior information; predictive behavior tags are added to the user tags to generate user portrait data.
In an exemplary embodiment of the present disclosure, based on the foregoing, the assistant decision module includes an assistant decision unit configured to: respectively determining a platform information label corresponding to the platform data and a resource information label corresponding to the target resource data; inputting the platform information label, the resource information label and the user portrait data into a decision analysis model; and determining decision reference information corresponding to the user by the decision analysis model.
The details of each virtual user-portrait-based aid decision device module are described in detail in the corresponding user-portrait-based aid decision method, and therefore are not described herein again.
It should be noted that although in the above detailed description reference is made to several modules or units of the user profile based aid decision making means, such a division is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit, according to embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into embodiments by a plurality of modules or units.
In addition, in an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
As will be appreciated by one skilled in the art, aspects of the present invention may be embodied as a system, method or program product. Thus, various aspects of the invention may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.
An electronic device 600 according to such an embodiment of the invention is described below with reference to fig. 6. The electronic device 600 shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 6, the electronic device 600 is embodied in the form of a general purpose computing device. The components of the electronic device 600 may include, but are not limited to: the at least one processing unit 610, the at least one memory unit 620, a bus 630 connecting different system components (including the memory unit 620 and the processing unit 610), and a display unit 640.
Wherein the storage unit stores program code that is executable by the processing unit 610 such that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention as described in the above section "exemplary method" of the present specification.
The storage unit 620 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)621 and/or a cache memory unit 622, and may further include a read only memory unit (ROM) 623.
The storage unit 620 may include a program/utility 624 having a set (at least one) of program modules 625, such program modules 625 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 600 may also communicate with one or more external devices 670 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 600, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 650. Also, the electronic device 600 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 660. As shown, the network adapter 660 communicates with the other modules of the electronic device 600 over the bus 630. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiments of the present disclosure may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, a terminal device, or a network device, etc.) to execute the method according to the embodiments of the present disclosure.
In an exemplary embodiment of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a program product capable of implementing the above-described method of the present specification. In some possible embodiments, aspects of the invention may also be implemented in the form of a program product comprising program code means for causing a terminal device to carry out the steps according to various exemplary embodiments of the invention described in the above-mentioned "exemplary methods" section of the present description, when said program product is run on the terminal device.
Referring to fig. 7, a program product 700 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
A computer readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a 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 embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
Furthermore, the above-described figures are merely schematic illustrations of processes involved in methods according to exemplary embodiments of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is to be limited only by the terms of the appended claims.
Claims (10)
1. A method for assisting decision making based on user portrayal, comprising:
receiving initial resource data sent by a resource platform, and converting the initial resource data to generate target resource data;
sending the target resource data to a user, and receiving feedback information of the user aiming at the target resource data;
acquiring platform data of the resource platform, and generating user portrait data of the user according to the platform data and the feedback information;
determining decision reference information for the user in conjunction with the user profile data to determine a decision operation for the user in accordance with the decision reference information.
2. A user profile-based aid decision making method as claimed in claim 1, wherein prior to receiving initial resource data sent by a resource platform, said method further comprises:
receiving a platform access request sent by the resource platform, and determining a platform classification identifier in the platform access request;
and determining the platform authority of the resource platform according to the platform classification identifier so as to control the resource platform to complete platform access operation according to the platform authority.
3. The user representation-based aid decision-making method according to claim 1, wherein said transforming said initial resource data to generate target resource data comprises:
determining the data content and the data type of the initial resource data;
and converting the initial resource data according to the data content and the data type to generate the target resource data.
4. A user representation-based aid decision-making method as claimed in claim 1 wherein said generating user representation data for said user from said platform data and said feedback information comprises:
clustering the platform data to generate corresponding platform information labels;
clustering the target resource data to generate a corresponding resource information label;
determining an initial user label corresponding to the user according to the feedback information;
and performing supplementary processing on the initial user label according to the platform information label and the resource information label to generate the user portrait data.
5. A user profile-based aid decision making method according to claim 4, wherein said determining an initial user label corresponding to said user based on said feedback information comprises:
determining user basic information and user behavior information in the feedback information;
extracting basic key data from the user basic information to generate corresponding basic feature vectors;
extracting behavior key data from the user behavior information to generate corresponding behavior feature vectors;
and generating the initial user label according to the basic feature vector and the behavior feature vector.
6. A user representation-based aid decision-making method as claimed in claim 4 wherein said complementary processing of said initial user tag according to said platform information tag, resource information tag to generate said user representation data comprises:
determining the predicted behavior information corresponding to the user according to the platform information label, the resource information label and the user image data;
generating a corresponding predicted behavior label according to the predicted behavior information;
adding the predicted behavior tag to the user tag to generate the user representation data.
7. A user representation-based aid decision-making method in accordance with claim 1, wherein said determining decision-making reference information for the user in conjunction with the user representation data comprises:
respectively determining a platform information label corresponding to the platform data and a resource information label corresponding to the target resource data;
inputting the platform information label, the resource information label and the user representation data to a decision analysis model;
determining, by the decision analysis model, decision reference information corresponding to the user.
8. A user profile based aid decision device, comprising:
the resource data generation module is used for receiving initial resource data sent by a resource platform and converting the initial resource data to generate target resource data;
the feedback information receiving module is used for sending the target resource data to a user and receiving feedback information of the user aiming at the target resource data;
the portrait data generation module is used for acquiring platform data of the resource platform and generating user portrait data of the user according to the platform data and the feedback information;
an auxiliary decision module for determining decision reference information for the user in combination with the user representation data, so as to determine a decision operation for the user in accordance with the decision reference information.
9. An electronic device, comprising:
a processor; and
a memory having stored thereon computer readable instructions which, when executed by the processor, implement a user representation-based aid decision method according to any one of claims 1 to 7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out a user representation-based aid decision method according to any one of claims 1 to 7.
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