CN116452275A - Product recommendation method and device based on artificial intelligence and related equipment - Google Patents
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Abstract
The application relates to an artificial intelligence technology, and provides an artificial intelligence-based product recommendation method, an artificial intelligence-based product recommendation device, a computer device and a storage medium, wherein the artificial intelligence-based product recommendation method comprises the following steps: determining an initial user in a preset application system; determining an activity value corresponding to the initial user; selecting a user with the activity value exceeding a preset activity threshold from initial users as a target user; determining basic data and feedback data corresponding to the target user; constructing a target user portrait corresponding to the target user according to the basic data and the feedback data; determining a target product according to the target user portrait; and determining a target recommended conversation corresponding to the target product, and recommending the target product to the target user according to the target recommended conversation. The method and the device can improve accuracy of product recommendation and promote rapid development of smart cities.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to a product recommendation method and device based on artificial intelligence and related equipment.
Background
With the gradual increase of sub-health people and slow patient groups, health managers and hospitals can not meet the daily health management requirements of a plurality of sub-health people and slow patient groups. Lifestyle interventions are critical for chronic disease control and prevention, and more people need more efficient, higher frequency, more real-time, longer term ways of chronic disease management.
For insurance business, disease prevention and control can effectively reduce claim cost, and in addition, agents need to spend a great deal of time and effort to develop relationship with potential clients, create common topics, fully understand users and establish trust before sales conversion is possible. This approach requires high labor costs and the accuracy of product recommendations is low.
Therefore, it is necessary to provide a product recommendation method capable of improving accuracy of product recommendation.
Disclosure of Invention
In view of the foregoing, there is a need for an artificial intelligence-based product recommendation method, product recommendation device, computer device, and storage medium, which can improve accuracy of product recommendation.
An embodiment of the present application provides a product recommendation method based on artificial intelligence, where the product recommendation method based on artificial intelligence includes:
Determining an initial user in a preset application system;
determining an activity value corresponding to the initial user;
selecting a user with the activity value exceeding a preset activity threshold from initial users as a target user;
determining basic data and feedback data corresponding to the target user;
constructing a target user portrait corresponding to the target user according to the basic data and the feedback data;
determining a target product according to the target user portrait;
and determining a target recommended conversation corresponding to the target product, and recommending the target product to the target user according to the target recommended conversation.
Further, in the above method for recommending products based on artificial intelligence provided in the embodiment of the present application, the determining an initial user in a preset application system includes:
acquiring a registration text corresponding to a preset application system;
determining a target position of a preset keyword in the registration text;
taking the text content at the target position as user account information according to a preset data format;
and determining an initial user according to the user account information.
Further, in the above method for recommending products based on artificial intelligence provided in the embodiment of the present application, the determining the activity value corresponding to the initial user includes:
Acquiring a preset time period;
determining single login time of the initial user to login the preset application system within the preset time period;
selecting a login state that the single login time exceeds a preset login time threshold value as effective login;
and determining the number of times of effective login as an activity value corresponding to the initial user.
Further, in the above method for recommending products based on artificial intelligence provided in the embodiment of the present application, the determining the basic data and the feedback data corresponding to the target user includes:
acquiring a preset data source;
respectively acquiring target data from the preset data sources to form basic data corresponding to the target user;
inputting the basic data into a pre-trained risk assessment model to obtain a risk assessment result, and determining health management data corresponding to the target user according to the risk assessment result;
and determining the execution information of the health management data, and preprocessing the execution information to obtain feedback data.
Further, in the product recommendation method based on artificial intelligence provided in the embodiment of the present application, the constructing, according to the basic data and the feedback data, a target user portrait corresponding to the target user includes:
Determining a first label corresponding to the basic data;
determining a second label corresponding to the feedback data;
combining the first label and the second label to obtain a target label;
and constructing a target user portrait corresponding to the target user according to the target label.
Further, in the above product recommendation method based on artificial intelligence provided in the embodiment of the present application, the determining the target product according to the target user portrait includes:
determining a first mapping relation between a preset user portrait and a product to be recommended;
and traversing the first mapping relation according to the target user portrait to obtain a target product.
Further, in the above method for recommending products based on artificial intelligence provided in the embodiment of the present application, the determining a target recommended session corresponding to the target product includes:
determining a second mapping relation between a preset product to be recommended and a recommended conversation;
traversing the second mapping relation according to the target product to obtain an initial recommended conversation;
determining communication preference information corresponding to the target user according to the target user portrait;
and adjusting the initial recommended call according to the communication preference information to obtain a target recommended call.
The second aspect of the embodiments of the present application further provides an artificial intelligence based product recommendation device, where the artificial intelligence based product recommendation device includes:
the initial user determining module is used for determining initial users in a preset application system;
the activity value determining module is used for determining an activity value corresponding to the initial user;
the target user determining module is used for selecting a user with the activity value exceeding a preset activity threshold from initial users as a target user;
the data determining module is used for determining basic data and feedback data corresponding to the target user;
the portrait construction module is used for constructing a target user portrait corresponding to the target user according to the basic data and the feedback data;
the product determining module is used for determining a target product according to the target user portrait;
and the conversation recommending module is used for determining a target conversation recommended by the target product and recommending the target product to the target user according to the target conversation recommended by the target product.
A third aspect of the embodiments of the present application further provides a computer device, where the computer device includes a processor, where the processor is configured to implement the artificial intelligence based product recommendation method according to any one of the above when executing a computer program stored in a memory.
The fourth aspect of the embodiments of the present application further provides a computer readable storage medium, where a computer program is stored, where the computer program is executed by a processor to implement any one of the above-mentioned product recommendation methods based on artificial intelligence.
According to the product recommendation method based on the artificial intelligence, the product recommendation device based on the artificial intelligence, the computer equipment and the computer readable storage medium, the user with the activity value exceeding the preset activity threshold value in the preset application system is determined to be the target user, and the target user is subjected to product recommendation, and as the number of times that the target user uses the preset application system is more, the preset application system acquires more useful information for product recommendation, and the accuracy of product recommendation can be improved; according to the method and the device for recommending the target product, the target user portraits are built according to the basic data and the feedback data of the target users, the target products are determined according to the target user portraits, the target recommended telephone operation corresponding to the target products is determined, the target products are recommended to the target users according to the target recommended telephone operation, personalized product services can be provided for each user, and therefore the effect of product recommendation is improved. The intelligent city intelligent management system can be applied to various functional modules of intelligent cities such as intelligent government affairs and intelligent traffic, for example, an artificial intelligence based product recommendation module and the like of the intelligent government affairs can promote the rapid development of the intelligent cities.
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Fig. 1 is a flowchart of an artificial intelligence based product recommendation method according to an embodiment of the present application.
Fig. 2 is a flowchart of an activity value determining method according to an embodiment of the present application.
Fig. 3 is a flowchart of a data determining method according to an embodiment of the present application.
Fig. 4 is a flow chart of a method for determining a session according to an embodiment of the present application.
Fig. 5 is a block diagram of an artificial intelligence based product recommendation device according to a second embodiment of the present application.
Fig. 6 is a schematic structural diagram of a computer device according to a third embodiment of the present application.
The following detailed description will further illustrate the application in conjunction with the above-described figures.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. It should be noted that, in the case of no conflict, the embodiments of the present application and the features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, the described embodiments are some, but not all, of the embodiments of the present application.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Among these, artificial intelligence (Artificial Intelligence, AI) is the theory, method, technique and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend and extend human intelligence, sense the environment, acquire knowledge and use knowledge to obtain optimal results.
Artificial intelligence infrastructure technologies generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and other directions.
The product recommending method based on the artificial intelligence provided by the embodiment of the invention is executed by the computer equipment, and correspondingly, the product recommending device based on the artificial intelligence runs in the computer equipment. Fig. 1 is a flowchart of an artificial intelligence based product recommendation method according to an embodiment of the present application. As shown in fig. 1, the product recommendation method may include the following steps, the order of the steps in the flowchart may be changed according to different requirements, and some may be omitted:
S11, determining an initial user in a preset application system.
In at least one embodiment of the present application, the preset application system may refer to an application program associated with a product to be recommended, and in one embodiment, the preset application system may be applied to a medical scene, and the product to be recommended associated with the medical scene may be a medical product. In other embodiments, the preset application system may also be applied to a sales scenario, and the product to be recommended associated with the sales scenario may be an insurance product. The application scenario of the preset application system and the products to be recommended associated with the preset application system can be set according to actual requirements, and the preset application system is not limited. For example, when the product to be recommended is a medical product, the preset application system may be a health management system for managing a health state of the user. The initial users are users registered in a preset application system, and the number of the initial users is at least 1.
Optionally, the determining an initial user in the preset application system includes:
s110, acquiring a registration text corresponding to a preset application system;
s111, determining a target position of a preset keyword in the registration text;
S112, taking the text content at the target position as user account information according to a preset data format;
s113, determining an initial user according to the user account information.
The registration text refers to a text containing user registration information in a preset application system, the registration text is stored in a preset database, and the preset database can be a target node in a blockchain in consideration of reliability and privacy of data storage. The preset keyword is a preset keyword used for identifying user account information, and for example, the preset keyword may be account or ID. And determining the target position of the preset keyword in the registration text to obtain user account information corresponding to the initial user. The preset data format refers to a data format between the preset keyword and the corresponding user account information, and illustratively, the preset data format is { preset keyword: user account information, according to the preset data format, the text content at the target position can be used as the user account information. And the user account information is used for uniquely identifying the identity of the initial user.
S12, determining an activity value corresponding to the initial user.
In at least one embodiment of the present application, the activity value of each initial user is different, and the activity value is used to identify the activity level of the user in the preset application system, where in an embodiment, the activity level may be determined by the time and the number of times the user logs in to the preset application system.
The activity value determining process provided in the embodiment of the present application is described with reference to fig. 2. Optionally, the determining the activity value corresponding to the initial user includes:
s120, acquiring a preset time period;
s121, determining single login time of the initial user to login the preset application system in the preset time period;
s122, selecting a login state that the single login time exceeds a preset login time threshold as effective login;
and S123, determining the number of times of effective login as an activity value corresponding to the initial user.
The preset time period is a preset time period for monitoring a login state of the initial user, and in an embodiment, the preset time period may be a last time period, for example, the preset time period may be a last 3 months. The single login time refers to the time of the initial user for single login of a preset application system, and when the single login time exceeds a preset login time threshold value, the single login state is marked as effective login; and when the single login time does not exceed the preset login time threshold value, marking the login state as invalid login. And counting the number of effective login times in the preset time period as an activity value corresponding to the initial user. The more the number of effective logins in the preset time period is, the higher the corresponding liveness value of the initial user is indicated; the fewer the number of effective logins in the preset time period, the lower the corresponding liveness value of the initial user is indicated.
S13, selecting the user with the activity value exceeding a preset activity threshold from the initial users as a target user.
In at least one embodiment of the present application, the number of target users is at least 1. The preset liveness threshold is a preset threshold used for evaluating whether to recommend products to users, the users with liveness values exceeding the preset liveness threshold are used as target users, and the target users are recommended to the products, and as the times of using the preset application system by the target users are more, the preset application system collects more useful information for recommending the products, and the accuracy of recommending the products can be improved.
S14, determining basic data and feedback data corresponding to the target user.
In at least one embodiment of the present application, taking the application of the preset application system to a medical scenario as an example, the preset application system may be a health management system. The basic data may refer to health physical examination data, behavioral data, and inquiry data of the target user. The health management system is provided with a risk assessment model, and the risk assessment model is used for obtaining a risk assessment result according to the basic data of the target user and formulating health management data according to the risk assessment result. The health management data may be management data including aspects of diet, exercise, reading, and medication. Illustratively, according to basic data such as age, gender, medical history, illness risk, physical activity, weight management demands, eating habits and the like of a target user, proper total energy of eating intake and corresponding intake marks of each type of nutrient components are formulated, and food materials, quantity and cooking modes meeting the above standards every day are automatically recommended. And (3) formulating proper exercise projects, exercise time and exercise duration according to basic data such as age, gender, medical history, illness risk, physical activity, weight management appeal, exercise habit and the like of the target user. And (3) formulating proper education projects, education forms, education contents and education difficulty according to basic data such as age, gender, medical history, illness risk, physical activity, weight management appeal, reading habit and the like of the target user. The target user uses the execution information based on the health management data as feedback data. Illustratively, the user checks cards to upload health management execution condition data, including diet entry and automatic photographing identification; motion recording or synchronization motion data of the wearable device; reading articles or completing educational content; monitoring and synchronizing index data are completed; finish medication and upload medication data, etc.
The data determination flow provided in the embodiment of the present application is described with reference to fig. 3. Optionally, the determining the basic data and the feedback data corresponding to the target user includes:
s140, acquiring a preset data source;
s141, respectively acquiring target data from the preset data sources to form basic data corresponding to the target user;
s142, inputting the basic data into a pre-trained risk assessment model to obtain a risk assessment result, and determining health management data corresponding to the target user according to the risk assessment result;
s143, determining the execution information of the health management data, and preprocessing the execution information to obtain feedback data.
The preset data sources may include physical examination data sources, behavioral data sources and inquiry data sources, and for each data source, corresponding target data exists, for example, physical examination data is collected from physical examination data sources, behavioral data is collected from behavioral data sources, inquiry data is collected from inquiry data sources, and physical examination data, behavioral data and inquiry data are used as basic data of target users.
In an embodiment, the risk assessment model is a preset model for assessing future risk of the user, and the risk assessment model may be an initial neural network model. The input vector of the risk assessment model may be basic data, and the output vector may be a risk assessment result, for example, the risk assessment result is a future risk level of the user, and the risk level may be a low level, a medium level, or a high level. The model training mode is the prior art and will not be described in detail herein.
In an embodiment, the preset application system collects execution information uploaded by the target user, where the execution information may be a description of an execution condition of each management item in the health management data. In an embodiment, the management items corresponding to the health management data may be a diet management item, a sports management item, a reading management item and a medication management item. For each management item, corresponding execution information exists. In an embodiment, the execution information may be the number of executions in a period of time. The preprocessing of the execution information may calculate an execution frequency according to the number of executions in a period of time, and use the execution frequency as feedback data.
In an embodiment, the preset application system receives the execution frequency of the target user, and adjusts the health management data according to the execution frequency, so as to ensure that the health management data better meets the requirements of the target user. Illustratively, when the execution frequency of the target user is higher, it is indicated that the health management data is easier for the target user to complete, and the standard can be appropriately improved; when the execution frequency of the target user is low, which means that the health management data is difficult for the target user to complete, the standard can be properly lowered. According to the embodiment of the application, the health management data are adjusted according to the feedback data, so that the health management data are ensured to be more in line with the requirements of target users, the target users are ensured to establish enough liveness and viscosity with a preset application system, and the accuracy of product recommendation is improved.
S15, constructing a target user portrait corresponding to the target user according to the basic data and the feedback data.
In at least one embodiment of the present application, the basic data corresponds to a first tag, the number of the first tags is at least 1, the feedback data corresponds to a second tag, and the number of the second tags is at least 1. And combining the first label and the second label to obtain the target label.
Optionally, the constructing the target user portrait corresponding to the target user according to the basic data and the feedback data includes:
s150, determining a first label corresponding to the basic data;
s151, determining a second label corresponding to the feedback data;
s152, combining the first label and the second label to obtain a target label;
s153, constructing a target user portrait corresponding to the target user according to the target label.
The method comprises the steps of establishing a preset database, wherein at least 1 label is stored in the preset database, the labels in the preset database can comprise a first label and a second label, and each label has a corresponding label description. In an embodiment, the determining the first tag corresponding to the base data includes: determining a basic data vector corresponding to the basic data; determining a label description corresponding to a label, and vectorizing the label description to obtain a label description vector; calculating the distance between the basic data vector and the label specification vector; when the distance is larger than a preset distance threshold, taking the label corresponding to the distance as a first label corresponding to the basic data. In an embodiment, the determining the second tag corresponding to the feedback data includes: determining a feedback data vector corresponding to the feedback data; determining a label description corresponding to a label, and vectorizing the label description to obtain a label description vector; calculating the distance between the feedback data vector and the label specification vector; and when the distance is larger than a preset distance threshold, taking the label corresponding to the distance as a second label corresponding to the feedback data vector. The distance between vectors may be euclidean distance, which is not limited herein. The target user portrayal is made up of a plurality of the target tags, which can be understood as a set of a plurality of target tags.
S16, determining a target product according to the target user portrait.
In at least one embodiment of the present application, a first mapping relationship exists between a product to be recommended and a user portrait, and a target product corresponding to the target user portrait can be obtained by querying the first mapping relationship. Illustratively, there are user portraits A, B, and C. For the user portrait A, the corresponding product to be recommended is a product 1; for the user portrait B, the corresponding product to be recommended is a product 2; for user representation C, the corresponding product to be recommended is product 3.
Optionally, the determining a target product according to the target user portrait includes:
s160, determining a first mapping relation between a preset user portrait and a product to be recommended;
and S161, traversing the first mapping relation according to the target user portrait to obtain a target product.
Taking the user portrait a, the user portrait B and the user portrait C as examples, the user portrait a and the product 1 have a mapping relationship, the user portrait B and the product 2 have a mapping relationship, and the user portrait C and the product 3 have a mapping relationship. The target user portrait belongs to one of the user portrait A, the user portrait B and the user portrait C, and when the target user portrait belongs to the user portrait A, the target product is determined to be a product 1; when the target user portrait belongs to the user portrait B, determining that the target product is a product 2; and when the target user portrait belongs to the user portrait C, determining that the target product is a product 3.
S17, determining a target recommended conversation corresponding to the target product, and recommending the target product to the target user according to the target recommended conversation.
In at least one embodiment of the present application, the target recommended session refers to a session that accords with user communication preference information and is used for recommending products to a target user, and by determining that the target recommended session recommends products to the target user, personalized product services are provided for each user, so that the effect of product recommendation is improved. And a second mapping relation exists between the target product and the recommended call operation, and the initial recommended call operation corresponding to the target product can be obtained by inquiring the second mapping relation.
The flow of the session determination provided in the embodiment of the present application is described with reference to fig. 4. Optionally, the determining the target recommended session corresponding to the target product includes:
s170, determining a preset second mapping relation between the product to be recommended and the recommended speaking;
s171, traversing the second mapping relation according to the target product to obtain an initial recommended conversation;
s172, determining communication preference information corresponding to the target user according to the target user portrait;
s173, adjusting the initial recommended telephone according to the communication preference information to obtain a target recommended telephone.
The initial recommendation session refers to session content required for recommending the product to be recommended, the initial recommendation session can be extracted from historical recommendation information of the product to be recommended, and the historical recommendation information can refer to communication content between an agent and a user when the product is recommended in a historical manner. In an embodiment, the content with higher importance degree in the communication content of the agent is extracted from the historical recommendation information to be used as the initial recommendation, the importance degree can be determined by a manual identification mode, and the communication content of the agent with higher importance degree is identified to be used as the initial recommendation. In an embodiment, the speaking content in the initial recommended speaking contains no keywords representing the communication style, and the speaking content in the initial recommended speaking may be divided into a total part and a partial part according to the communication sequence, where the total part is used for recommending the product, and the partial part is used for explaining the reason of recommending the product. The total part and the partial part can be distinguished by adding marks, and the marks can be color marks, letter marks, numerical marks or the like.
The communication preference information refers to a ditch call operation which is preferred by a target user and can enable the conversion rate of the target product to be high, and the conversion rate of the product can be improved by recommending the product by adopting the ditch call operation which is preferred by the target user. In an embodiment, the communication preference information may include a communication style and a communication sequence, and the communication style may include an easy communication style and a serious communication style, and for different communication styles, the communication style may be represented by corresponding keywords. Illustratively, for an relaxed style of communication, keywords such as "you are in the well", "is in the woolen" can be adopted in the initial recommended speaking operation to improve the communication easiness. For serious communication style, keywords such as "hello", "good", "yes" and the like can be adopted in the initial recommended speaking operation to improve the seriousness of communication. The corresponding relation between the communication style and the corresponding keywords is stored in a preset database, and the keywords corresponding to the communication style can be obtained by inquiring the corresponding relation. The communication sequence may include a total score sequence and a total score sequence, for which a product name may be recommended first in a recommendation session, and the reason for recommending the product is explained; for the total order, the reason for recommendation can be set forth in the recommendation technique, and then the related products are recommended.
In an embodiment, the adjusting the initial recommended session according to the communication preference information to obtain the target recommended session includes: and adding keywords representing the communication style into the initial recommended call, and adjusting the communication sequence in the initial recommended call according to the communication sequence to obtain a target recommended call. The communication keyword may be added to a preset position of the initial recommended speaking, and the preset position may be a preset position, which is not limited herein.
According to the product recommendation method based on the artificial intelligence, the user with the activity value exceeding the preset activity threshold value in the preset application system is determined to be the target user, and the target user is recommended, and as the number of times that the target user uses the preset application system is more, the preset application system collects more useful information for product recommendation, and the accuracy of product recommendation can be improved; according to the method and the device for recommending the target product, the target user portraits are built according to the basic data and the feedback data of the target users, the target products are determined according to the target user portraits, the target recommended telephone operation corresponding to the target products is determined, the target products are recommended to the target users according to the target recommended telephone operation, personalized product services can be provided for each user, and therefore the effect of product recommendation is improved.
Referring to fig. 5, fig. 5 is a block diagram of a product recommendation device according to a second embodiment of the present disclosure. In some embodiments, the product recommendation device 20 may include a plurality of functional modules composed of computer program segments. The computer program of the individual program segments in the product recommendation device 20 may be stored in a memory of a computer apparatus and executed by at least one processor to perform (see fig. 1 for details) the product recommendation functions.
In this embodiment, the product recommendation device 20 based on artificial intelligence may be divided into a plurality of functional modules according to the functions performed by the product recommendation device. The functional module may include: an initial user determination module 201, an liveness value determination module 202, a target user determination module 203, a data determination module 204, a portrayal construction module 205, a product determination module 206, and a speech recommendation module 207. A module as referred to in this application refers to a series of computer program segments, stored in a memory, capable of being executed by at least one processor and of performing a fixed function. In the present embodiment, the functions of the respective modules will be described in detail in the following embodiments.
The initial user determination module 201 may be configured to determine an initial user in a preset application system.
In at least one embodiment of the present application, the preset application system may refer to an application program associated with a product to be recommended, and in one embodiment, the preset application system may be applied to a medical scene, and the product to be recommended associated with the medical scene may be a medical product. In other embodiments, the preset application system may also be applied to a sales scenario, and the product to be recommended associated with the sales scenario may be an insurance product. The application scenario of the preset application system and the products to be recommended associated with the preset application system can be set according to actual requirements, and the preset application system is not limited. For example, when the product to be recommended is a medical product, the preset application system may be a health management system for managing a health state of the user. The initial users are users registered in a preset application system, and the number of the initial users is at least 1. Optionally, the determining an initial user in the preset application system includes:
acquiring a registration text corresponding to a preset application system;
determining a target position of a preset keyword in the registration text;
taking the text content at the target position as user account information according to a preset data format;
And determining an initial user according to the user account information.
The registration text refers to a text containing user registration information in a preset application system, the registration text is stored in a preset database, and the preset database can be a target node in a blockchain in consideration of reliability and privacy of data storage. The preset keyword is a preset keyword used for identifying user account information, and for example, the preset keyword may be account or ID. And determining the target position of the preset keyword in the registration text to obtain user account information corresponding to the initial user. The preset data format refers to a data format between the preset keyword and the corresponding user account information, and illustratively, the preset data format is { preset keyword: user account information, according to the preset data format, the text content at the target position can be used as the user account information. And the user account information is used for uniquely identifying the identity of the initial user.
The liveness value determination module 202 may be configured to determine a liveness value corresponding to the initial user.
In at least one embodiment of the present application, the activity value of each initial user is different, and the activity value is used to identify the activity level of the user in the preset application system, where in an embodiment, the activity level may be determined by the time and the number of times the user logs in to the preset application system.
Optionally, the determining the activity value corresponding to the initial user includes:
acquiring a preset time period;
determining single login time of the initial user to login the preset application system within the preset time period;
selecting a login state that the single login time exceeds a preset login time threshold value as effective login;
and determining the number of times of effective login as an activity value corresponding to the initial user.
The preset time period is a preset time period for monitoring a login state of the initial user, and in an embodiment, the preset time period may be a last time period, for example, the preset time period may be a last 3 months. The single login time refers to the time of the initial user for single login of a preset application system, and when the single login time exceeds a preset login time threshold value, the single login state is marked as effective login; and when the single login time does not exceed the preset login time threshold value, marking the login state as invalid login. And counting the number of effective login times in the preset time period as an activity value corresponding to the initial user. The more the number of effective logins in the preset time period is, the higher the corresponding liveness value of the initial user is indicated; the fewer the number of effective logins in the preset time period, the lower the corresponding liveness value of the initial user is indicated.
The target user determining module 203 may be configured to select, from the initial users, a user whose activity value exceeds a preset activity threshold as the target user.
The data determining module 204 may be configured to determine base data and feedback data corresponding to the target user.
In at least one embodiment of the present application, taking the application of the preset application system to a medical scenario as an example, the preset application system may be a health management system. The basic data may refer to health physical examination data, behavioral data, and inquiry data of the target user. The health management system is provided with a risk assessment model, and the risk assessment model is used for obtaining a risk assessment result according to the basic information of the target user and appointing health management data according to the risk assessment result. The health management data may be management data including aspects of diet, exercise, reading, and medication. Illustratively, according to basic data such as age, gender, medical history, illness risk, physical activity, weight management demands, eating habits and the like of a target user, proper total energy of eating intake and corresponding intake marks of each type of nutrient components are formulated, and food materials, quantity and cooking modes meeting the above standards every day are automatically recommended. And (3) formulating proper exercise projects, exercise time and exercise duration according to basic data such as age, gender, medical history, illness risk, physical activity, weight management appeal, exercise habit and the like of the target user. And (3) formulating proper education projects, education forms, education contents and education difficulty according to basic data such as age, gender, medical history, illness risk, physical activity, weight management appeal, reading habit and the like of the target user. The target user uses the execution information based on the health management data as feedback data. Illustratively, the user swipes a card to upload health management performance data. The method comprises diet inputting and automatic photographing identification; motion recording or synchronization motion data of the wearable device; reading articles or completing educational content; monitoring and synchronizing index data are completed; finish medication and upload medication data, etc.
Optionally, the determining the basic data and the feedback data corresponding to the target user includes:
acquiring a preset data source;
respectively acquiring target data from the preset data sources to form basic data corresponding to the target user;
inputting the basic data into a pre-trained risk assessment model to obtain a risk assessment result, and determining health management data corresponding to the target user according to the risk assessment result;
and determining the execution information of the health management data, and preprocessing the execution information to obtain feedback data.
The preset data sources may include physical examination data sources, behavioral data sources and inquiry data sources, and for each data source, corresponding target data exists, for example, physical examination data is collected from physical examination data sources, behavioral data is collected from behavioral data sources, inquiry data is collected from inquiry data sources, and physical examination data, behavioral data and inquiry data are used as basic data of target users.
In an embodiment, the risk assessment model is a preset model for assessing future risk of the user, and the risk assessment model may be an initial neural network model. The input vector of the risk assessment model may be basic data, and the output vector may be a risk assessment result, for example, the risk assessment result is a future risk level of the user, and the risk level may be a low level, a medium level, or a high level. The model training mode is the prior art and will not be described in detail herein.
In an embodiment, the preset application system collects execution information uploaded by the target user, where the execution information may be a description of an execution condition of each management item in the health management data. In an embodiment, the management items corresponding to the health management data may be a diet management item, a sports management item, a reading management item and a medication management item. For each management item, corresponding execution information exists. In an embodiment, the execution information may be the number of executions in a period of time. The preprocessing of the execution information may calculate an execution frequency according to the number of executions in a period of time, and use the execution frequency as feedback data.
In an embodiment, the preset application system receives the execution frequency of the target user, and adjusts the health management data according to the execution frequency, so as to ensure that the health management data better meets the requirements of the target user. Illustratively, when the execution frequency of the target user is higher, it is indicated that the health management data is easier for the target user to complete, and the standard can be appropriately improved; when the execution frequency of the target user is low, which means that the health management data is difficult for the target user to complete, the standard can be properly lowered. According to the embodiment of the application, the health management data are adjusted according to the feedback data, so that the health management data are ensured to be more in line with the requirements of target users, the target users are ensured to establish enough liveness and viscosity with a preset application system, and the accuracy of product recommendation is improved.
The representation construction module 205 may be configured to construct a target user representation corresponding to the target user based on the base data and the feedback data.
In at least one embodiment of the present application, the basic data corresponds to a first tag, the number of the first tags is at least 1, the feedback data corresponds to a second tag, and the number of the second tags is at least 1. And combining the first label and the second label to obtain the target label.
Optionally, the constructing the target user portrait corresponding to the target user according to the basic data and the feedback data includes:
determining a first label corresponding to the basic data;
determining a second label corresponding to the feedback data;
combining the first label and the second label to obtain a target label;
and constructing a target user portrait corresponding to the target user according to the target label.
The method comprises the steps of establishing a preset database, wherein at least 1 label is stored in the preset database, the labels in the preset database can comprise a first label and a second label, and each label has a corresponding label description. In an embodiment, the determining the first tag corresponding to the base data includes: determining a basic data vector corresponding to the basic data; determining a label description corresponding to a label, and vectorizing the label description to obtain a label description vector; calculating the distance between the basic data vector and the label specification vector; when the distance is larger than a preset distance threshold, taking the label corresponding to the distance as a first label corresponding to the basic data. In an embodiment, the determining the second tag corresponding to the feedback data includes: determining a feedback data vector corresponding to the feedback data; determining a label description corresponding to a label, and vectorizing the label description to obtain a label description vector; calculating the distance between the feedback data vector and the label specification vector; and when the distance is larger than a preset distance threshold, taking the label corresponding to the distance as a second label corresponding to the feedback data vector. The distance between vectors may be euclidean distance, which is not limited herein. The target user portrayal is made up of a plurality of the target tags, which can be understood as a set of a plurality of target tags.
The product determination module 206 may be configured to determine a target product based on the target user representation.
In at least one embodiment of the present application, a first mapping relationship exists between a product to be recommended and a user portrait, and a target product corresponding to the target user portrait can be obtained by querying the first mapping relationship. Illustratively, there are user portraits A, B, and C. For the user portrait A, the corresponding product to be recommended is a product 1; for the user portrait B, the corresponding product to be recommended is a product 2; for user representation C, the corresponding product to be recommended is product 3.
Optionally, the determining a target product according to the target user portrait includes:
determining a first mapping relation between a preset user portrait and a product to be recommended;
and traversing the first mapping relation according to the target user portrait to obtain a target product.
Taking the user portrait a, the user portrait B and the user portrait C as examples, the user portrait a and the product 1 have a mapping relationship, the user portrait B and the product 2 have a mapping relationship, and the user portrait C and the product 3 have a mapping relationship. The target user portrait belongs to one of the user portrait A, the user portrait B and the user portrait C, and when the target user portrait belongs to the user portrait A, the target product is determined to be a product 1; when the target user portrait belongs to the user portrait B, determining that the target product is a product 2; and when the target user portrait belongs to the user portrait C, determining that the target product is a product 3.
The speaking recommendation module 207 may be configured to determine a target recommended speaking corresponding to the target product, and recommend the target product to the target user according to the target recommended speaking.
In at least one embodiment of the present application, the target recommended session refers to a session that accords with user communication preference information and is used for recommending products to a target user, and by determining that the target recommended session recommends products to the target user, personalized product services are provided for each user, so that the effect of product recommendation is improved. And a second mapping relation exists between the target product and the recommended call operation, and the initial recommended call operation corresponding to the target product can be obtained by inquiring the second mapping relation.
Optionally, the determining the target recommended session corresponding to the target product includes:
determining a second mapping relation between a preset product to be recommended and a recommended conversation;
traversing the second mapping relation according to the target product to obtain an initial recommended conversation;
determining communication preference information corresponding to the target user according to the target user portrait;
and adjusting the initial recommended call according to the communication preference information to obtain a target recommended call.
The initial recommendation session refers to session content required for recommending the product to be recommended, the initial recommendation session can be extracted from historical recommendation information of the product to be recommended, and the historical recommendation information can refer to communication content between an agent and a user when the product is recommended in a historical manner. In an embodiment, the content with higher importance degree in the communication content of the agent is extracted from the historical recommendation information to be used as the initial recommendation, the importance degree can be determined by a manual identification mode, and the communication content of the agent with higher importance degree is identified to be used as the initial recommendation. In an embodiment, the speaking content in the initial recommended speaking contains no keywords representing the communication style, and the speaking content in the initial recommended speaking may be divided into a total part and a partial part according to the communication sequence, where the total part is used for recommending the product, and the partial part is used for explaining the reason of recommending the product. The total part and the partial part can be distinguished by adding marks, and the marks can be color marks, letter marks, numerical marks or the like.
The communication preference information refers to a ditch call operation which is preferred by a target user and can enable the conversion rate of the target product to be high, and the conversion rate of the product can be improved by recommending the product by adopting the ditch call operation which is preferred by the target user. In an embodiment, the communication preference information may include a communication style and a communication sequence, and the communication style may include an easy communication style and a serious communication style, and for different communication styles, the communication style may be represented by corresponding keywords. Illustratively, for an relaxed style of communication, keywords such as "you are in the well", "is in the woolen" can be adopted in the initial recommended speaking operation to improve the communication easiness. For serious communication style, keywords such as "hello", "good", "yes" and the like can be adopted in the initial recommended speaking operation to improve the seriousness of communication. The corresponding relation between the communication style and the corresponding keywords is stored in a preset database, and the keywords corresponding to the communication style can be obtained by inquiring the corresponding relation. The communication sequence may include a total score sequence and a total score sequence, for which a product name may be recommended first in a recommendation session, and the reason for recommending the product is explained; for the total order, the reason for recommendation can be set forth in the recommendation technique, and then the related products are recommended.
In an embodiment, the adjusting the initial recommended session according to the communication preference information to obtain the target recommended session includes: and adding keywords representing the communication style into the initial recommended call, and adjusting the communication sequence in the initial recommended call according to the communication sequence to obtain a target recommended call. The communication keyword may be added to a preset position of the initial recommended speaking, and the preset position may be a preset position, which is not limited herein.
Fig. 6 is a schematic structural diagram of a computer device according to a third embodiment of the present application. In the preferred embodiment of the present application, the computer device 3 includes a memory 31, at least one processor 32, at least one communication bus 33, and a transceiver 34.
It will be appreciated by those skilled in the art that the configuration of the computer device shown in fig. 6 is not limiting of the embodiments of the present application, and that either a bus type configuration or a star type configuration is possible, and that the computer device 3 may include more or less other hardware or software than illustrated, or a different arrangement of components.
In some embodiments, the computer device 3 is a device capable of automatically performing numerical calculation and/or information processing according to preset or stored instructions, and its hardware includes, but is not limited to, a microprocessor, an application specific integrated circuit, a programmable gate array, a digital processor, an embedded device, and the like. The computer device 3 may also include a client device, which includes, but is not limited to, any electronic product that can interact with a client by way of a keyboard, mouse, remote control, touch pad, or voice control device, such as a personal computer, tablet, smart phone, digital camera, etc.
It should be noted that the computer device 3 is only used as an example, and other electronic products that may be present in the present application or may be present in the future are also included in the scope of the present application and are incorporated herein by reference.
In some embodiments, the memory 31 has stored therein a computer program which, when executed by the at least one processor 32, performs all or part of the steps in the product recommendation method as described. The Memory 31 includes Read-Only Memory (ROM), programmable Read-Only Memory (PROM), erasable programmable Read-Only Memory (EPROM), one-time programmable Read-Only Memory (One-time Programmable Read-Only Memory, OTPROM), electrically erasable rewritable Read-Only Memory (EEPROM), compact disc Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM) or other optical disc Memory, magnetic tape Memory, or any other medium that can be used for computer-readable carrying or storing data.
Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
The blockchain referred to in the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
In some embodiments, the at least one processor 32 is a Control Unit (Control Unit) of the computer device 3, connects the various components of the entire computer device 3 using various interfaces and lines, and performs various functions and processes of the computer device 3 by running or executing programs or modules stored in the memory 31, and invoking data stored in the memory 31. For example, the at least one processor 32, when executing the computer program stored in the memory, implements all or part of the steps of the product recommendation method described in embodiments of the present application; or to implement all or part of the functionality of the product recommendation device. The at least one processor 32 may be comprised of integrated circuits, such as a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functionality, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like.
In some embodiments, the at least one communication bus 33 is arranged to enable connected communication between the memory 31 and the at least one processor 32 or the like.
Although not shown, the computer device 3 may further comprise a power source (such as a battery) for powering the various components, preferably the power source is logically connected to the at least one processor 32 via a power management means, whereby the functions of managing charging, discharging, and power consumption are performed by the power management means. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The computer device 3 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described in detail herein.
The integrated units implemented in the form of software functional modules described above may be stored in a computer readable storage medium. The software functional modules described above are stored in a storage medium and include instructions for causing a computer device (which may be a personal computer, a computer device, or a network device, etc.) or processor (processor) to perform portions of the methods described in various embodiments of the present application.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be other manners of division when actually implemented.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of software functional modules.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it will be obvious that the term "comprising" does not exclude other elements or that the singular does not exclude a plurality. Several of the elements or devices recited in the specification may be embodied by one and the same item of software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
Finally, it should be noted that the above embodiments are merely for illustrating the technical solution of the present application and not for limiting, and although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present application may be modified or substituted without departing from the spirit and scope of the technical solution of the present application.
Claims (10)
1. The product recommendation method based on the artificial intelligence is characterized by comprising the following steps of:
determining an initial user in a preset application system;
determining an activity value corresponding to the initial user;
selecting a user with the activity value exceeding a preset activity threshold from initial users as a target user;
determining basic data and feedback data corresponding to the target user;
constructing a target user portrait corresponding to the target user according to the basic data and the feedback data;
determining a target product according to the target user portrait;
and determining a target recommended conversation corresponding to the target product, and recommending the target product to the target user according to the target recommended conversation.
2. The artificial intelligence based product recommendation method according to claim 1, wherein the determining an initial user in a preset application system comprises:
Acquiring a registration text corresponding to a preset application system;
determining a target position of a preset keyword in the registration text;
taking the text content at the target position as user account information according to a preset data format;
and determining an initial user according to the user account information.
3. The artificial intelligence based product recommendation method of claim 1, wherein the determining the activity value corresponding to the initial user comprises:
acquiring a preset time period;
determining single login time of the initial user to login the preset application system within the preset time period;
selecting a login state that the single login time exceeds a preset login time threshold value as effective login;
and determining the number of times of effective login as an activity value corresponding to the initial user.
4. The method for recommending products based on artificial intelligence according to claim 1, wherein the determining the basic data and the feedback data corresponding to the target user comprises:
acquiring a preset data source;
respectively acquiring target data from the preset data sources to form basic data corresponding to the target user;
inputting the basic data into a pre-trained risk assessment model to obtain a risk assessment result, and determining health management data corresponding to the target user according to the risk assessment result;
And determining the execution information of the health management data, and preprocessing the execution information to obtain feedback data.
5. The method for recommending products based on artificial intelligence according to claim 1, wherein the constructing the target user representation corresponding to the target user based on the basic data and the feedback data comprises:
determining a first label corresponding to the basic data;
determining a second label corresponding to the feedback data;
combining the first label and the second label to obtain a target label;
and constructing a target user portrait corresponding to the target user according to the target label.
6. The artificial intelligence based product recommendation method according to claim 1, wherein said determining a target product from said target user representation comprises:
determining a first mapping relation between a preset user portrait and a product to be recommended;
and traversing the first mapping relation according to the target user portrait to obtain a target product.
7. The method for recommending products based on artificial intelligence according to claim 1, wherein the determining the target recommended session corresponding to the target product comprises:
Determining a second mapping relation between a preset product to be recommended and a recommended conversation;
traversing the second mapping relation according to the target product to obtain an initial recommended conversation;
determining communication preference information corresponding to the target user according to the target user portrait;
and adjusting the initial recommended call according to the communication preference information to obtain a target recommended call.
8. An artificial intelligence based product recommendation device, characterized in that the artificial intelligence based product recommendation device comprises:
the initial user determining module is used for determining initial users in a preset application system;
the activity value determining module is used for determining an activity value corresponding to the initial user;
the target user determining module is used for selecting a user with the activity value exceeding a preset activity threshold from initial users as a target user;
the data determining module is used for determining basic data and feedback data corresponding to the target user;
the portrait construction module is used for constructing a target user portrait corresponding to the target user according to the basic data and the feedback data;
the product determining module is used for determining a target product according to the target user portrait;
And the conversation recommending module is used for determining a target conversation recommended by the target product and recommending the target product to the target user according to the target conversation recommended by the target product.
9. A computer device comprising a processor for implementing the artificial intelligence based product recommendation method according to any one of claims 1 to 7 when executing a computer program stored in a memory.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements the artificial intelligence based product recommendation method according to any of claims 1 to 7.
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