CN112184302A - Product recommendation method and device, rule engine and storage medium - Google Patents
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Abstract
The embodiment of the invention discloses a product recommendation method, a product recommendation device, a rule engine and a storage medium. The method comprises the following steps: acquiring user basic information of a target recommendation user, and determining at least one user demand description parameter corresponding to the user basic information; inquiring a product recommendation rule base to obtain a target product recommendation rule matched with the user demand description parameter, wherein the product recommendation rule defines a mapping relation between the user demand description parameter and a financial field index; and obtaining the matched target recommended product according to the target financial field indexes included in the target product recommendation rule and providing the matched target recommended product for the target recommendation user. The method can be used for recommending products through professional financial knowledge according to the basic information of the user without depending on the transaction information or subjective preference of the user, and the product recommendation is more reasonable.
Description
Technical Field
The embodiment of the invention relates to the technical field of finance, in particular to a product recommendation method, a product recommendation device, a rule engine and a storage medium.
Background
When product recommendation is performed, behaviors of users are generally analyzed through a recommendation system, personalized requirements of the users are found, and long-tail commodities are recommended to the users. Prior art recommendation systems rely on user behavioural data, such as a user's history of browsing or purchasing products.
However, in some special application scenarios, such as a scenario of recommendation of financial products, the transaction information of the user is little, and the subjective preference of the user cannot be taken as a recommendation basis. Therefore, the existing recommendation system cannot meet the requirement of reasonably recommending financial products for users.
Disclosure of Invention
The embodiment of the invention provides a product recommendation method, a product recommendation device, a rule engine and a storage medium, which can reasonably recommend products for a user according to the actual situation of the user.
In a first aspect, an embodiment of the present invention provides a product recommendation method, where the method includes:
acquiring user basic information of a target recommendation user, and determining at least one user demand description parameter corresponding to the user basic information;
inquiring a product recommendation rule base to obtain a target product recommendation rule matched with the user demand description parameter, wherein the product recommendation rule defines a mapping relation between the user demand description parameter and a financial field index;
and acquiring the matched target recommended product according to the target financial field index included in the target product recommendation rule and providing the matched target recommended product for a target recommendation user.
In a second aspect, an embodiment of the present invention further provides a product recommendation apparatus, where the apparatus includes:
the system comprises a user demand description parameter determining module, a recommendation module and a recommendation module, wherein the user demand description parameter determining module is used for acquiring user basic information of a target recommendation user and determining at least one user demand description parameter corresponding to the user basic information;
the target product recommendation rule acquisition module is used for inquiring a product recommendation rule base and acquiring a target product recommendation rule matched with the user demand description parameter, and the product recommendation rule defines the mapping relation between the user demand description parameter and the financial field index;
and the target recommended product acquisition module is used for acquiring the matched target recommended product according to the target financial field indexes included in the target product recommendation rule and providing the matched target recommended product for the target recommended user.
In a third aspect, an embodiment of the present invention further provides a rule engine, where the rule engine includes:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement a method of product recommendation as described in any of the embodiments of the invention.
In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a product recommendation method according to any embodiment of the present invention.
According to the technical scheme of the embodiment of the invention, the user basic information of the target recommendation user is obtained, and at least one user requirement description parameter corresponding to the user basic information is determined; inquiring a product recommendation rule base to obtain a target product recommendation rule matched with the user demand description parameter, wherein the product recommendation rule defines a mapping relation between the user demand description parameter and a financial field index; the matched target recommended product is obtained and provided for the target recommended user according to the target financial field indexes included in the target product recommendation rule, the problem of product recommendation is solved, product recommendation is performed through professional financial knowledge according to the basic information of the user without depending on transaction information or subjective preference of the user, and the effect of more reasonable product recommendation is achieved.
Drawings
FIG. 1a is a flow chart of a method for providing product recommendation according to an embodiment of the present invention;
FIG. 1b is a flowchart of a product recommendation method according to an embodiment of the present invention;
FIG. 2a is a flowchart of a product recommendation method according to a second embodiment of the present invention;
FIG. 2b is a flowchart of a product recommendation method according to a second embodiment of the present invention;
FIG. 2c is a flow chart of a rules engine core according to the second embodiment of the present invention;
fig. 2d is a schematic diagram of a data processing flow in a product recommendation method according to the second embodiment;
fig. 3 is a schematic structural diagram of a product recommendation device according to a third embodiment of the present invention;
fig. 4 is a schematic structural diagram of a rule engine according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1a is a flowchart of a product recommendation method according to an embodiment of the present invention, where the embodiment is applicable to a situation where a financial product is recommended to a user in a financial field, the method may be executed by a product recommendation device, the device may be implemented by software, and/or hardware, and the device may be integrated in a rules engine, as shown in fig. 1a, and the method specifically includes:
step 110, obtaining user basic information of the target recommendation user, and determining at least one user requirement description parameter corresponding to the user basic information.
The user basic information can be information data when financing product investment is carried out with the financial field. For example, the user basic information may include information data for performing risk tolerance assessment, such as age, gender, industry, annual pay, disposable amount, and physical health. For example, the user basic information may include information data for performing asset valuations, such as annual salaries, available amounts, and asset spending status. As another example, the user basic information may include requirement information data set by the user when making investment on a financial product, for example, historical product purchase record information, preferred industry information, expected income, maximum acceptable investment period, whether to make recommendation on a financial product according to a certain theme and/or a certain scene, and the like.
The user basic information can be input by a user at an intelligent terminal or a human-computer interaction interface and the like. The rule engine can acquire the basic information of the user by receiving information data input by the user in the intelligent terminal or the human-computer interaction interface. The rule engine can intelligently analyze the basic information of the user to determine the requirement description parameters of the user. Or the intelligent terminal can intelligently analyze the basic information of the user to determine the requirement description parameters of the user and send the requirement description parameters to the rule engine.
Fig. 1b is a flowchart of a product recommendation method according to an embodiment of the present invention, and as shown in fig. 1b, a process of determining a user requirement description parameter may be to perform intelligent analysis on one or more items of user basic information to obtain an evaluation result. Illustratively, the risk tolerance parameter of the user is evaluated by age, gender, industry, annual pay, disposable amount, and physical health, such as grade 3. As yet another example, asset parameters may be assessed by annual pay, disposable amount, and asset spending status, among others. As another example, the special requirement description parameters of the user, such as the profit requirement parameters or the preferred industry parameters, may be determined according to the requirement information data set by the user when the financial product is invested.
And 120, inquiring a product recommendation rule base to obtain a target product recommendation rule matched with the user requirement description parameters.
The product recommendation rule defines a mapping relation between the user requirement description parameter and the financial field index. The product recommendation rule base may be a database storing product recommendation rules. The product recommendation rules may define a mapping relationship between the user demand description parameters and the financial field indicators. For example, the user demand description parameter is a risk tolerance level of 3, the financial field index that can be mapped is a risk level, and the specific risk level may be lower than level 3. One or more target product recommendation rules can be matched according to the user requirement description parameters. For example, target product recommendation rules related to risk levels may be matched according to the risk tolerance parameters; target product recommendation rules and the like related to purchase amount can be matched according to the asset parameters.
In an implementation manner of the embodiment of the present invention, optionally, the financial field index includes at least one of the following: amount of purchases, purchase step size, risk level, product rating, industry affiliated, rate of return, minimum period of possession, commission fees, management companies, asset size, and time to establishment.
Wherein, the purchase starting amount refers to the initial amount of the financial product purchase, such as 1000 yuan. The purchase step size refers to an interval amount of the financial product purchase, for example, 100 yuan. For the financial products with the purchase amount of 1000 yuan and the purchase step length of 100 yuan, only 1000 yuan, 1100 yuan, 1200 yuan and … can be purchased. The risk level refers to a risk coefficient of the financing product, and the risk level can be obtained by comprehensive evaluation according to the investment industry, the investment product, the management enterprise, the historical income condition and the like of the financing product. For example, the risk level for a stock fund is typically higher than a hybrid fund, the risk level for a hybrid fund is typically higher than a monetary fund, and so on. The product rating refers to the quality of the financing product. The product rating can be obtained by comprehensively evaluating historical income conditions, income of management enterprises and products in special time periods, and the like. For example, financial products may be divided into different star classes.
The industry refers to the industry field of financial product investment, such as medical treatment, electronic technology or communication technology. Wherein, when the financing product relates to the multi-industry field, the industry can be the main industry related to the financing product. The profitability is a proportion of profitability obtained by the investment of the financial product, and may be an annual rate, a seven-day annual rate, or the like. The minimum term of possession refers to the minimum time of capital investment, for example, one day, one month, or one year, when financing product investment. The procedure fee is the procedure fee to be paid when investing in the financing product. The management company refers to an enterprise for investment management of financial products. The asset size refers to the total capital invested in the financial product. The establishment time refers to the period of establishment of the financing product.
As shown in FIG. 1b, the product recommendation rules may be configured at the management platform. The product recommendation rules configured by the management platform can be stored in a product recommendation rule base, the product recommendation rule base can be connected with the rule engine, and the rule engine can load the product recommendation rules from the product recommendation rule base according to the user requirement description parameters to determine the target product recommendation rules.
And step 130, acquiring the matched target recommended product according to the target financial field index included in the target product recommendation rule and providing the matched target recommended product for the target recommendation user.
As shown in fig. 1b, when the rule engine loads the target product recommendation rule, the rule engine may screen the financial product according to the target financial field index in the target product recommendation rule to obtain the matched target recommended product. Illustratively, the target product recommendation rule is that the risk level is lower than 3, the included target financial field index is the risk level, the financing products can be screened, and the financing products with the risk level lower than 3 are selected as the target recommended products.
The target financial field indexes in the target product recommendation rules can be the same or different. Multiple target financial field indexes can exist in multiple target product recommendation rules, and financial products can be screened at the same time. Illustratively, a financial product may be screened simultaneously with the rate of return by a risk rating, e.g., screening a financial product with a risk rating below level 3 and a seven-day annual rate in the rate of return above 12%.
As shown in fig. 1b, the target recommended product may be provided to the target recommended user by recommending the user through a display screen, such as a human-computer interaction interface or a display screen in an intelligent terminal.
According to the technical scheme of the embodiment of the invention, the user basic information of the target recommendation user is obtained, and at least one user requirement description parameter corresponding to the user basic information is determined; inquiring a product recommendation rule base to obtain a target product recommendation rule matched with the user requirement description parameters; the matched target recommended products are obtained and provided for the target recommended users according to the target financial field indexes included in the target product recommendation rules, the recommendation problem of financial products is solved, product recommendation is carried out according to the basic information of the users through professional financial knowledge without depending on the transaction information or subjective preference of the users, and the effect of more reasonable product recommendation is achieved.
Example two
Fig. 2a is a flowchart of a product recommendation method provided in a second embodiment of the present invention, where this embodiment is a further refinement of the above technical solution, and the technical solution in this embodiment may be combined with various alternatives in one or more of the above embodiments, as shown in fig. 2a, the method includes:
and 210, acquiring the user basic information of the target recommendation user, and acquiring the investment requirement type of the target recommendation user according to the user basic information.
The rule engine or the intelligent terminal can classify the user basic information, and exemplarily, the unsupervised clustering is adopted to classify the users according to the inherent relation of the user basic information of each user. For example, three categories are identified. Investment requirement types that may have the same theme for the same class. Or, the rule engine or the intelligent terminal can extract key information of the basic information of the user, and the investment requirement type of the user is determined through the extracted key information. For example, when the user inputs the user basic information, the user may obtain whether the user sets a scene and/or a theme, if not, the user may be determined to be of a common investment requirement type, and the investment requirement may be determined according to the user basic information. If so, determining that the user is of a special investment requirement type, and after determining the investment requirement according to other basic information, further determining the investment requirement according to scenes and/or themes; alternatively, the investment requirements may be determined based on the scene and/or topic and then based on other basic information.
In an optional implementation manner of this embodiment, the obtaining the investment requirement type of the target recommendation user according to the user basic information includes: if the user basic information comprises application scene information, determining that the type of the investment requirement of the target recommendation user is a first type of investment requirement corresponding to the application scene information; if the basic user information comprises the theme content information, determining that the investment demand type of the target recommendation user is a second type of investment demand corresponding to the theme content information; if the user basic information comprises application scene information and theme content information, determining that the type of the investment requirement of the target recommendation user is a third type of investment requirement corresponding to the application scene information and the theme content information; and if the application scene information and the theme content information are not included in the user basic information, determining that the type of the investment requirement of the target recommendation user is the fourth type of investment requirement.
The application scene information refers to a scene set by a user when inputting the basic information. Illustratively, the scenario may be determined by data analysis performed by a smart terminal or a rules engine. For example, the popular investment industry in the current time period, such as during a new crown epidemic situation, is medical; in the development period of communication technology, the popular investment industry is electronic technologies such as chips. The first type of investment requirement may be an investment requirement that application scene information needs to be screened, for example, a user sets a scene, the current popular investment industry is medical and electronic technology, and the financial product recommended for the user needs to be related to the medical and electronic technology.
The theme content information means that the user sets a theme when inputting the basic information. Illustratively, the subject matter may be a financial product that is predominantly revenue or that is invested by the same kind of person. Wherein the congeners may be determined based on unsupervised clustering. The second type of investment demand may be an investment demand which needs to be screened according to a theme, for example, a user sets a theme, theme content information is mainly revenue, and revenue is required to be considered preferentially for a financial product recommended by the user.
The third kind of investment requirements are investment requirements which need to be screened according to scenes and themes, for example, scenes and themes are set by users, the current popular investment industry is medical and electronic science and technology, and theme content information is a financing product invested by similar people. The financial products recommended for the users need to further screen the financial products invested by the same people from the financial products related to medical and electronic technologies. Or, the financial products recommended for the user need to be screened from financial products invested by the same people, which are related to medical and electronic technologies.
The first, second and third types of investment requirements are special investment requirements and the fourth type of investment requirements is general investment requirements. Special screening and screening of common investment requirements are required for special investment requirements. The fourth type of investment requirement may be product recommendation based on the user basic information.
And step 220, acquiring at least one user requirement description parameter matched with the investment requirement type.
Wherein each investment requirement type may comprise one or more user requirement description parameters. The user requirement description parameter can be single or several items of comprehensive incarnations of the user basic information. For example, the investment requirement type may include a user requirement description parameter of risk tolerance, and the risk tolerance is determined by comprehensive evaluation of basic information such as age, sex, industry, annual pay, disposable amount, and physical health condition.
In order to specify the relationship between the investment requirement and the user requirement description parameter, in an optional implementation manner of this embodiment, the obtaining at least one user requirement description parameter matching the investment requirement type includes: if the investment demand type is the first type of investment demand, acquiring a real-time industry heat parameter and at least one of the following user demand description parameters: risk tolerance parameters, asset parameters, historical product purchase record parameters, preference industry parameters, investment deadline parameters, and income demand parameters; if the investment demand type is the second type of investment demand, acquiring the income parameter or the investment parameter of the similar recommending user and at least one of the following user demand description parameters: risk tolerance parameters, asset parameters, historical product purchase record parameters, preference industry parameters, investment deadline parameters, and income demand parameters; if the investment demand type is a third type of investment demand, acquiring a real-time industry heat parameter, a profit parameter or an investment parameter of a similar recommending user, and at least one of the following user demand description parameters: risk tolerance parameters, asset parameters, historical product purchase record parameters, preference industry parameters, investment deadline parameters, and income demand parameters; if the investment requirement type is the fourth type of investment requirement, at least one of the following user requirement description parameters is obtained: risk tolerance parameters, asset parameters, historical product purchase record parameters, preferred industry parameters, investment deadline parameters, and revenue demand parameters.
In this embodiment, the requirement description parameters corresponding to the common investment requirement may include one or more of a risk tolerance parameter, an asset parameter, a historical product purchase record parameter, a preferred industry parameter, an investment deadline parameter, and an income requirement parameter. The requirement description parameters corresponding to the special investment requirements can be obtained by adding corresponding requirement description parameters on the basis of the requirement description parameters corresponding to the common investment requirements.
For example, a first type of investment requirement requires the addition of real-time industry heat parameters, which may reflect a hot industry. For example, a user can set a scene, and according to the scene set by the user, a real-time industry popularity parameter can be acquired. Real-time industry heat parameters may be obtained through big data analysis. If the user sets a scene, a real-time industry popularity parameter value is set, and financial products can be screened according to the value set by the user; if the user only sets the scene and does not set the real-time industry popularity parameter value, the financial product screening can be carried out according to the default value of the real-time industry popularity parameter.
The second category of investment requirements requires an increase in the return parameters or investment parameters of the same type of recommending users. If the user sets income parameters when setting the theme, if the income is taken as the main, the rule engine can screen the financial products by taking the income as the main and recommend the user according to the income sequence. If the user sets the investment parameters of the similar recommending users when setting the theme, for example, the investment parameters are recommended according to the investment products of the similar recommending users, the rule engine can acquire the classification of the users and screen the investment products of the similar recommending users to recommend the products to the users.
The third category of investment requirements requires the addition of one or both of real-time industry heat parameters and revenue parameters or investment parameters of the same type of recommending users. The recommended product can be determined by comprehensively screening a plurality of parameters.
And step 230, determining each parameter attribute in the user requirement description parameters.
The user requirement description parameters may correspond to one or more parameter attributes, and the plurality of user requirement description parameters may correspond to one parameter attribute. For example, the parameter attribute of the risk tolerance parameter may be a risk level; the parameter attribute of the asset parameter can be risk level, purchase amount, purchase step length and the like; the parameter attribute of the historical product purchase record parameter can be risk level, earning rate, industry to which the historical product purchase record parameter belongs, management company, investment deadline and the like; the parameter attribute of the preferred industry parameter may be the industry to which it belongs; the parameter attribute of the investment term parameter may be an investment term; the parameter attributes of the revenue demand parameter may be revenue rate and handling fee.
And 240, if the product recommendation rule base comprises the parameter attribute, acquiring a product recommendation rule corresponding to the parameter attribute, and determining a target product recommendation rule according to the attribute value corresponding to the parameter attribute.
The product recommendation rule defines a mapping relation between the user requirement description parameter and the financial field index. The financial domain indicators include at least one of: amount of purchases, purchase step size, risk level, product rating, industry affiliated, rate of return, minimum period of possession, commission fees, management companies, asset size, and time to establishment.
In this embodiment, each product recommendation rule in the product recommendation rule base may be stored by a parameter attribute. One or more product recommendation rules can be obtained according to the parameter attributes, and the target product recommendation rule is determined according to the attribute values corresponding to the parameter attributes. Wherein, the target product recommendation rule can be one or more. Illustratively, the parameter attribute is a risk level, all product recommendation rules related to the risk level in the product recommendation rule base can be obtained, and according to the attribute value of the parameter attribute such as level 3, a target product recommendation rule with the risk level lower than level 3 is determined in the product recommendation rule.
And step 250, if the parameter attribute is not included in the product recommendation rule base, discarding the parameter attribute.
Illustratively, the parameter attribute is a management company, and if the product recommendation rule corresponding to the management company is not set in the product recommendation rule base, the parameter attribute may be discarded, and the financial product is not screened according to the parameter attribute.
And step 260, determining a target attribute value of the target financial field index in the target product recommendation rule, and obtaining a matched target recommended product according to the target attribute value to provide for a target recommended user.
Illustratively, the target product recommendation rule with a risk level lower than 3 is determined in the product recommendation rule according to the attribute value of the parameter attribute, such as 3 levels. The target financial field index in the target product recommendation rule can be a risk level, and the target attribute values are 1 level, 2 levels and 3 levels. Target recommended products with risk levels of 1, 2, and 3 may be provided to the target recommending user.
According to the technical scheme of the embodiment of the invention, the investment requirement type of the target recommendation user is obtained according to the basic information of the user; acquiring at least one user demand description parameter matched with the investment demand type; determining each parameter attribute in the user requirement description parameters; if the product recommendation rule base comprises the parameter attribute, acquiring a product recommendation rule corresponding to the parameter attribute, and determining a target product recommendation rule according to the attribute value corresponding to the parameter attribute; the target attribute value of the target financial field index in the target product recommendation rule is determined, the matched target recommended product is obtained according to the target attribute value and provided for the target recommended user, the recommendation problem of financial products is solved, product recommendation is performed according to the basic information of the user through professional financial knowledge without only depending on the transaction information or subjective preference of the user, and the effect of more reasonable product recommendation is achieved.
Fig. 2b is a flowchart of a product recommendation method according to a second embodiment of the present invention, and as shown in fig. 2b, a manager (a service in fig. 2 b) may configure a rule configuration file through a management platform, where the rule configuration file may include a configuration of a rule corresponding to a scene, a theme, and other basic information. The configured rule profile may be stored in a product recommendation rule base (rule base in fig. 2 b). Information related to financial products, including financial domain indicators, may be stored in a source database. When the calling interface of the rule engine receives a request of recommending products by the intelligent terminal, basic information (parameters in fig. 2 b) of the user can be sent to the adaptation layer. The adaptation layer may determine the investment requirement type of the user according to the user basic information, further match the user requirement description parameters, and determine the parameter attributes of each requirement description parameter (the matching attributes are obtained in fig. 2 b). The adaptation layer sends the parameter attributes to the rule engine core, and the rule engine core acquires the target product recommendation rule from the product recommendation rule base according to the parameter attributes and the corresponding attribute values and carries out rule analysis. The rule engine core sends the analysis result to the execution layer, the execution layer executes the rule, determines a target recommended product from the source database (directly returns the result in fig. 2 b), and returns the target recommended product to the adaptation layer. The adaptation layer provides the target recommended product (the return value in fig. 2 b) to the target recommended user (the intelligent terminal) through the calling interface of the rule engine.
Fig. 2c is a flowchart of a rule engine core according to a second embodiment of the present invention, and as shown in fig. 2c, the rule engine core is composed of three main parts, namely a fact factor, a rule parsing part and a rule executing part. The fact factor includes the fact data (such as user basic information), configuration information (such as rules), and context information (such as additional information) of the client. The Fact factors may be assembled into a Fact object (Fact) for matching with rules. Rule parsing may load a rule set (e.g., rule set 1, rule set 2, rule set n …) through a product recommendation rule base. The rule set can be a rule set, rules corresponding to scenes and themes can be set as the rule set, target recommended products can be conveniently and quickly determined through the rule set, and time and labor are saved. The rule set may include one or more product recommendation rules. For example, the product recommendation rule may be that if condition 1 is satisfied, then action 1 is performed; if condition 2 is satisfied, then the form of action 2 is performed. Rule parsing may determine condition 1, condition 2, …, condition n in a product recommendation rule based on fact objects (e.g., parameter attributes and attribute values). There may be a rule listener in the rule execution, which may obtain the rule engine parameters (attribute values). The rule engine may perform a target recommended product determination operation based on the rule engine parameters and the determined list of product recommendation rules. For example, in this embodiment, operations may be executed by an excute method (a method for providing an application program to implement by itself), an invoke action method (a method for providing a Java reflection), and an ESAction method (a method for providing an elastic search query).
Fig. 2d is a schematic diagram of a data processing flow in a product recommendation method according to the second embodiment, and as shown in fig. 2d, the second embodiment relates to two data processing modes, namely streaming processing and batch processing. In this embodiment, the basic information of the user may be collected through front-end data, such as data of a buried point or a Flume (log collection system); or acquiring the recommended process data and the background log data by the handling engine. The data processing mode when the basic information of the user is obtained can be streaming processing, data can be counted in real time, and the investment requirement of the user can be modified conveniently in the later stage. The big data calculation engine can acquire the basic information of the user through Kafka (subscription message system) to perform instant demand modeling, recommendation feedback correction and the like.
The big data calculation engine can also receive product data, customer data, three-party data and transaction system data provided by other systems, and perform feature processing operations such as real-time data statistics, market data loading, behavior modeling and feature extraction. The data processing mode for acquiring the product data, the customer data, the three-party data and the transaction system data can be batch processing. The features extracted in the feature processing may be stored in a feature center.
The big data computing engine can also receive data of other systems 1, … and n for big data storage, and association mining and heat analysis are carried out by means of association relationship between HBase (database) and elastic search. Wherein, the data acquisition of other systems can be in batch mode.
EXAMPLE III
Fig. 3 is a schematic structural diagram of a product recommendation device according to a third embodiment of the present invention. With reference to fig. 3, the apparatus comprises: a user requirement description parameter determining module 310, a target product recommendation rule obtaining module 320 and a target recommended product obtaining module 330.
The user requirement description parameter determining module 310 is configured to obtain user basic information of a target recommended user, and determine at least one user requirement description parameter corresponding to the user basic information;
a target product recommendation rule obtaining module 320, configured to query a product recommendation rule base, to obtain a target product recommendation rule matching the user requirement description parameter, where a mapping relationship between the user requirement description parameter and a financial field index is defined in the product recommendation rule;
and the target recommended product obtaining module 330 is configured to obtain a matched target recommended product according to the target financial field index included in the target product recommendation rule, and provide the obtained target recommended product to the target recommending user.
Optionally, the financial domain indicator comprises at least one of: amount of purchases, purchase step size, risk level, product rating, industry affiliated, rate of return, minimum period of possession, commission fees, management companies, asset size, and time to establishment.
Optionally, the user requirement description parameter determining module 310 includes:
the investment demand type obtaining unit is used for obtaining the investment demand type of the target recommendation user according to the basic information of the user;
and the user requirement description parameter acquisition unit is used for acquiring at least one user requirement description parameter matched with the investment requirement type.
Optionally, the user requirement description parameter obtaining unit is specifically configured to:
if the user basic information comprises application scene information, determining that the type of the investment requirement of the target recommendation user is a first type of investment requirement corresponding to the application scene information;
if the basic user information comprises the theme content information, determining that the investment demand type of the target recommendation user is a second type of investment demand corresponding to the theme content information;
if the user basic information comprises application scene information and theme content information, determining that the type of the investment requirement of the target recommendation user is a third type of investment requirement corresponding to the application scene information and the theme content information;
and if the application scene information and the theme content information are not included in the user basic information, determining that the type of the investment requirement of the target recommendation user is the fourth type of investment requirement.
Optionally, the user requirement description parameter obtaining unit is specifically configured to:
if the investment demand type is the first type of investment demand, acquiring a real-time industry heat parameter and at least one of the following user demand description parameters: risk tolerance parameters, asset parameters, historical product purchase record parameters, preference industry parameters, investment deadline parameters, and income demand parameters;
if the investment demand type is the second type of investment demand, acquiring the income parameter or the investment parameter of the similar recommending user and at least one of the following user demand description parameters: risk tolerance parameters, asset parameters, historical product purchase record parameters, preference industry parameters, investment deadline parameters, and income demand parameters;
if the investment demand type is a third type of investment demand, acquiring a real-time industry heat parameter, a profit parameter or an investment parameter of a similar recommending user, and at least one of the following user demand description parameters: risk tolerance parameters, asset parameters, historical product purchase record parameters, preference industry parameters, investment deadline parameters, and income demand parameters;
if the investment requirement type is the fourth type of investment requirement, at least one of the following user requirement description parameters is obtained: risk tolerance parameters, asset parameters, historical product purchase record parameters, preferred industry parameters, investment deadline parameters, and revenue demand parameters.
Optionally, the target product recommendation rule obtaining module 320 includes:
the parameter attribute determining unit is used for determining each parameter attribute in the user requirement description parameters;
the target product recommendation rule determining unit is used for acquiring a product recommendation rule corresponding to the parameter attribute if the product recommendation rule base comprises the parameter attribute, and determining the target product recommendation rule according to the attribute value corresponding to the parameter attribute;
and the parameter attribute discarding unit is used for discarding the parameter attribute if the product recommendation rule base does not comprise the parameter attribute.
Optionally, the target recommended product obtaining module 330 includes:
and the target recommended product acquisition unit is used for determining a target attribute value of a target financial field index in the target product recommendation rule, and acquiring a matched target recommended product according to the target attribute value to provide for a target recommended user.
The product recommendation device provided by the embodiment of the invention can execute the product recommendation method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Example four
Fig. 4 is a schematic structural diagram of a rule engine according to a fourth embodiment of the present invention, and as shown in fig. 4, the rule engine includes:
one or more processors 410, one processor 410 being illustrated in FIG. 4;
a memory 420;
the apparatus may further include: an input device 430 and an output device 440.
The processor 410, the memory 420, the input device 430 and the output device 440 of the apparatus may be connected by a bus or other means, for example, in fig. 4.
The memory 420 is a non-transitory computer-readable storage medium, and may be used to store software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to a product recommendation method in the embodiment of the present invention (for example, the user requirement description parameter determining module 310, the target product recommendation rule obtaining module 320, and the target recommended product obtaining module 330 shown in fig. 3). The processor 410 executes various functional applications and data processing of the computer device by executing the software programs, instructions and modules stored in the memory 420, namely, a product recommendation method for implementing the above method embodiments is realized, that is:
acquiring user basic information of a target recommendation user, and determining at least one user demand description parameter corresponding to the user basic information;
inquiring a product recommendation rule base to obtain a target product recommendation rule matched with the user demand description parameter, wherein the product recommendation rule defines a mapping relation between the user demand description parameter and a financial field index;
and acquiring the matched target recommended product according to the target financial field index included in the target product recommendation rule and providing the matched target recommended product for a target recommendation user.
The memory 420 may 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; the storage data area may store data created according to use of the computer device, and the like. Further, the memory 420 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 420 may optionally include memory located remotely from processor 410, which may be connected to the terminal device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The input device 430 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the computer apparatus. The output device 440 may include a display device such as a display screen.
EXAMPLE five
Fifth embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a product recommendation method according to a fifth embodiment of the present invention:
acquiring user basic information of a target recommendation user, and determining at least one user demand description parameter corresponding to the user basic information;
inquiring a product recommendation rule base to obtain a target product recommendation rule matched with the user demand description parameter, wherein the product recommendation rule defines a mapping relation between the user demand description parameter and a financial field index;
and acquiring the matched target recommended product according to the target financial field index included in the target product recommendation rule and providing the matched target recommended product for a target recommendation user.
Any combination of one or more computer-readable media may be employed. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer 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 computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, 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. In the context of this document, a computer 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.
A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer 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.
Computer 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, Smalltalk, 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 computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (16)
1. A method for recommending products, comprising:
acquiring user basic information of a target recommendation user, and determining at least one user demand description parameter corresponding to the user basic information;
inquiring a product recommendation rule base to obtain a target product recommendation rule matched with the user demand description parameter, wherein the product recommendation rule defines a mapping relation between the user demand description parameter and a financial field index;
and acquiring the matched target recommended product according to the target financial field index included in the target product recommendation rule and providing the matched target recommended product for a target recommendation user.
2. The method of claim 1, wherein the financial domain indicators include at least one of: amount of purchases, purchase step size, risk level, product rating, industry affiliated, rate of return, minimum period of possession, commission fees, management companies, asset size, and time to establishment.
3. The method of claim 1, wherein determining at least one user requirement description parameter corresponding to the user basic information comprises:
acquiring the investment demand type of the target recommendation user according to the user basic information;
and acquiring at least one user demand description parameter matched with the investment demand type.
4. The method according to claim 3, wherein obtaining the investment demand type of the target recommendation user according to the user basic information comprises:
if the user basic information comprises application scene information, determining that the investment demand type of the target recommendation user is a first type of investment demand corresponding to the application scene information;
if the user basic information comprises the theme content information, determining that the investment demand type of the target recommendation user is a second type of investment demand corresponding to the theme content information;
if the user basic information comprises application scene information and theme content information, determining that the investment demand type of the target recommendation user is a third type of investment demand corresponding to the application scene information and the theme content information;
and if the user basic information does not comprise application scene information and theme content information, determining that the type of the investment requirement of the target recommendation user is a fourth type of investment requirement.
5. The method of claim 4, wherein obtaining at least one user demand description parameter matching the investment demand type comprises:
if the investment demand type is a first type of investment demand, acquiring a real-time industry heat parameter and at least one of the following user demand description parameters: risk tolerance parameters, asset parameters, historical product purchase record parameters, preference industry parameters, investment deadline parameters, and income demand parameters;
if the investment demand type is a second type of investment demand, acquiring a profit parameter or an investment parameter of a similar recommending user and at least one of the following user demand description parameters: risk tolerance parameters, asset parameters, historical product purchase record parameters, preference industry parameters, investment deadline parameters, and income demand parameters;
if the investment demand type is a third type of investment demand, acquiring a real-time industry heat parameter, a profit parameter or an investment parameter of a similar recommending user, and at least one of the following user demand description parameters: risk tolerance parameters, asset parameters, historical product purchase record parameters, preference industry parameters, investment deadline parameters, and income demand parameters;
if the investment requirement type is a fourth type of investment requirement, acquiring at least one of the following user requirement description parameters: risk tolerance parameters, asset parameters, historical product purchase record parameters, preferred industry parameters, investment deadline parameters, and revenue demand parameters.
6. The method of claim 1, wherein querying a product recommendation rule base to obtain a target product recommendation rule matching the user demand description parameter comprises:
determining each parameter attribute in the user requirement description parameters;
if the product recommendation rule base comprises the parameter attribute, acquiring a product recommendation rule corresponding to the parameter attribute, and determining a target product recommendation rule according to an attribute value corresponding to the parameter attribute;
and if the parameter attribute is not included in the product recommendation rule base, discarding the parameter attribute.
7. The method of claim 6, wherein obtaining the matched target recommended product to provide to the target recommending user according to the target financial field index included in the target product recommending rule comprises:
and determining a target attribute value of a target financial field index in the target product recommendation rule, and obtaining a matched target recommended product according to the target attribute value and providing the matched target recommended product for a target recommendation user.
8. A product recommendation device, comprising:
the system comprises a user demand description parameter determining module, a recommendation module and a recommendation module, wherein the user demand description parameter determining module is used for acquiring user basic information of a target recommendation user and determining at least one user demand description parameter corresponding to the user basic information;
the target product recommendation rule acquisition module is used for inquiring a product recommendation rule base and acquiring a target product recommendation rule matched with the user demand description parameter, and the product recommendation rule defines the mapping relation between the user demand description parameter and the financial field index;
and the target recommended product acquisition module is used for acquiring the matched target recommended product according to the target financial field indexes included in the target product recommendation rule and providing the matched target recommended product for the target recommended user.
9. The apparatus of claim 8, wherein the financial domain indicators comprise at least one of: amount of purchases, purchase step size, risk level, product rating, industry affiliated, rate of return, minimum period of possession, commission fees, management companies, asset size, and time to establishment.
10. The apparatus of claim 8, wherein the user requirement description parameter determining module comprises:
the investment demand type obtaining unit is used for obtaining the investment demand type of the target recommendation user according to the user basic information;
and the user requirement description parameter acquisition unit is used for acquiring at least one user requirement description parameter matched with the investment requirement type.
11. The apparatus according to claim 10, wherein the user requirement description parameter obtaining unit is specifically configured to:
if the user basic information comprises application scene information, determining that the investment demand type of the target recommendation user is a first type of investment demand corresponding to the application scene information;
if the user basic information comprises the theme content information, determining that the investment demand type of the target recommendation user is a second type of investment demand corresponding to the theme content information;
if the user basic information comprises application scene information and theme content information, determining that the investment demand type of the target recommendation user is a third type of investment demand corresponding to the application scene information and the theme content information;
and if the user basic information does not comprise application scene information and theme content information, determining that the type of the investment requirement of the target recommendation user is a fourth type of investment requirement.
12. The apparatus according to claim 11, wherein the user requirement description parameter obtaining unit is specifically configured to:
if the investment demand type is a first type of investment demand, acquiring a real-time industry heat parameter and at least one of the following user demand description parameters: risk tolerance parameters, asset parameters, historical product purchase record parameters, preference industry parameters, investment deadline parameters, and income demand parameters;
if the investment demand type is a second type of investment demand, acquiring a profit parameter or an investment parameter of a similar recommending user and at least one of the following user demand description parameters: risk tolerance parameters, asset parameters, historical product purchase record parameters, preference industry parameters, investment deadline parameters, and income demand parameters;
if the investment demand type is a third type of investment demand, acquiring a real-time industry heat parameter, a profit parameter or an investment parameter of a similar recommending user, and at least one of the following user demand description parameters: risk tolerance parameters, asset parameters, historical product purchase record parameters, preference industry parameters, investment deadline parameters, and income demand parameters;
if the investment requirement type is a fourth type of investment requirement, acquiring at least one of the following user requirement description parameters: risk tolerance parameters, asset parameters, historical product purchase record parameters, preferred industry parameters, investment deadline parameters, and revenue demand parameters.
13. The apparatus of claim 8, wherein the target product recommendation rule obtaining module comprises:
the parameter attribute determining unit is used for determining each parameter attribute in the user requirement description parameters;
a target product recommendation rule determining unit, configured to, if the product recommendation rule base includes the parameter attribute, obtain a product recommendation rule corresponding to the parameter attribute, and determine a target product recommendation rule according to an attribute value corresponding to the parameter attribute;
and the parameter attribute discarding unit is used for discarding the parameter attribute if the parameter attribute is not included in the product recommendation rule base.
14. The apparatus of claim 13, wherein the target recommended product acquisition module comprises:
and the target recommended product acquisition unit is used for determining a target attribute value of a target financial field index in the target product recommendation rule, and acquiring a matched target recommended product according to the target attribute value and providing the matched target recommended product for a target recommended user.
15. A rules engine, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
16. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
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