CN113379545A - Product recommendation method and device, electronic equipment and storage medium - Google Patents
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Abstract
The present disclosure provides a product recommendation method, comprising: acquiring historical transaction information, benchmark risk level and expected income and variance of a product to be recommended of a client; calculating the risk aversion level of the client according to the historical transaction information; calculating the effectiveness of the product to be recommended according to the benchmark risk level, the expected income and variance of the product to be recommended and the risk aversion level; recommending products to the customer based on the effectiveness. The disclosure also provides a product recommendation device, an electronic device and a storage medium.
Description
Technical Field
The disclosure relates to the technical field of product recommendation, in particular to a product recommendation method, a product recommendation device, electronic equipment and a storage medium, which can be used for financial product recommendation.
Background
Banks, which are legal financial institutions that operate monetary credit services, are products of commercial money economy that have progressed to a certain stage. Commercial banking is one of the financial institutions and is the most important financial institution, and the main business scope of commercial banking is to absorb public deposits, issue loans, transact bill posts and the like. Banks have introduced a wide variety of financial products for different businesses.
Traditionally, a bank obtains background information of a user by means of an online questionnaire, such as: income condition, liability condition, investment professional degree, loss willingness, income expectation and the like, and the risk level of the client is obtained according to the information. And the financial manager recommends the financial products matched with the level to the client according to the level of the risk level of the client. Because the client has cognitive deviation to the client, some information in the questionnaire cannot be answered according to objective and unified standards, and further errors are caused to the result of risk level evaluation, and a corresponding calibration mechanism is needed; moreover, when a financial manager recommends a financial product, the financial manager recommends the financial product based on qualitative indexes or information (such as cautious type, robust type, balanced type, aggressive type, and aggressive type), and the accuracy is lacking.
BRIEF SUMMARY OF THE PRESENT DISCLOSURE
In view of the above, the present disclosure provides, in one aspect, a product recommendation method, including: acquiring historical transaction information, benchmark risk level and expected income and variance of a product to be recommended of a client; calculating the risk aversion level of the client according to the historical transaction information; calculating the effectiveness of the product to be recommended according to the benchmark risk level, the expected income and variance of the product to be recommended and the risk aversion level; recommending products to the customer based on the effectiveness.
According to an embodiment of the present disclosure, the historical transaction information includes a transaction type and a transaction amount corresponding to the transaction type; said calculating a risk aversion rating for said customer based on said historical transaction information comprises: and calculating the risk aversion grade according to the transaction type and the transaction amount.
According to an embodiment of the present disclosure, said calculating said risk aversion rating according to said transaction category and said transaction amount comprises: grading the transaction categories to obtain a transaction risk level of each transaction category; and carrying out weighted average on the transaction risk grade and the transaction amount to obtain the risk aversion grade.
According to an embodiment of the present disclosure, the calculating the effectiveness of the product to be recommended according to the benchmark risk level, the expected income and variance of the product to be recommended, and the risk aversion level includes: calibrating the risk aversion grade according to the reference risk grade to obtain a calibrated risk aversion grade; and calculating the effectiveness of the product to be recommended according to the calibrated risk aversion grade and the expected income and variance of the product to be recommended.
According to an embodiment of the present disclosure, the recommending a product to the customer according to the efficiency includes: recommending the products to be recommended with the effectiveness larger than a preset threshold value to the customer.
According to an embodiment of the present disclosure, the recommending a product to the customer according to the efficiency includes:
and recommending the products to be recommended with the maximum efficacy to the customer.
According to an embodiment of the present disclosure, taking a benchmark risk level includes: acquiring basic information of a client; and generating a benchmark risk level of the client according to the basic information.
Another aspect of the present disclosure provides a product recommendation device, including: the acquisition module is used for acquiring historical transaction information, benchmark risk level and expected income and variance of a product to be recommended of a client; a first calculation module for calculating a risk aversion level of the customer based on the historical transaction information; the second calculation module is used for calculating the effectiveness of the product to be recommended according to the benchmark risk level, the expected income and variance of the product to be recommended and the risk aversion level; and the recommending module is used for recommending products to the customer according to the efficiency.
According to an embodiment of the present disclosure, the calculating of the risk aversion level of the customer from the historical transaction information by the first calculation module includes: the historical transaction information comprises transaction types and transaction amounts corresponding to the transaction types; and calculating the risk aversion grade according to the transaction type and the transaction amount.
According to an embodiment of the present disclosure, the first calculation module includes: the rating unit is used for rating the transaction categories to obtain the transaction risk level of each transaction category; and the first calculation unit is used for carrying out weighted average on the transaction risk level and the transaction amount to obtain the risk aversion level.
According to an embodiment of the present disclosure, the second calculation module includes: a calibration unit, configured to calibrate the risk aversion level according to the reference risk level to obtain a calibrated risk aversion level; and the second calculation unit is used for calculating the effectiveness of the product to be recommended according to the calibrated risk aversion grade and the expected income and variance of the product to be recommended.
According to an embodiment of the present disclosure, the recommendation module includes: and the first recommending unit is used for recommending the product to be recommended with the efficiency larger than a preset threshold value to the customer.
According to an embodiment of the present disclosure, the recommendation module includes: and the second recommending unit is used for recommending the product to be recommended with the maximum efficiency to the client.
According to an embodiment of the present disclosure, the obtaining module includes: the acquisition unit is used for acquiring basic information of a client; and the third calculating unit is used for generating a benchmark risk level of the client according to the basic information.
Another aspect of the present disclosure provides an electronic device including: one or more processors; memory for storing one or more programs, wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method as described above.
Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions for implementing the method as described above when executed.
Another aspect of the disclosure provides a computer program comprising computer executable instructions for implementing the method as described above when executed.
Drawings
Fig. 1 schematically shows a system architecture of a product recommendation method and apparatus according to an embodiment of the present disclosure;
FIG. 2 schematically illustrates a flow diagram of a method of product recommendation in accordance with an embodiment of the present disclosure;
FIG. 3 schematically illustrates a block diagram of a product recommendation device according to an embodiment of the present disclosure;
FIG. 4 schematically illustrates a block diagram of a first computing module, according to an embodiment of the present disclosure;
FIG. 5 schematically illustrates a block diagram of a second computing module, according to yet another embodiment of the present disclosure;
FIG. 6 schematically shows a block diagram of a recommendation module according to an embodiment of the disclosure;
FIG. 7 schematically shows a block diagram of an acquisition module according to an embodiment of the disclosure;
fig. 8 schematically shows a block diagram of an electronic device adapted to implement the above described method according to an embodiment of the present disclosure.
[ description of reference ]
100-system architecture; 101-a terminal device; 102-a network; 103-a server;
300-a product recommendation device; 310-an acquisition module; 320-a first calculation module; 330-a second calculation module; 340-a recommendation module;
321-a rating unit; 322-a first calculation unit;
331-a calibration unit; 332-a second calculation unit;
341-first recommendation module; 342-a second recommendation module;
311-an acquisition module; 312-a third calculation module;
800-an electronic device; 801-a processor; an 802-ROM; 803-RAM; 804-a bus; 805-I/O interfaces; 806-an input section; 807-an output section; 808-a storage portion; 809 — a communication section; 810-a driver; 811-removable media.
Detailed Description
Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood that the description is illustrative only and is not intended to limit the scope of the present disclosure. In the following detailed description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the disclosure. It may be evident, however, that one or more embodiments may be practiced without these specific details. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art unless otherwise defined. It is noted that the terms used herein should be interpreted as having a meaning that is consistent with the context of this specification and should not be interpreted in an idealized or overly formal sense.
Where a convention analogous to "at least one of A, B and C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B and C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.). Where a convention analogous to "A, B or at least one of C, etc." is used, in general such a construction is intended in the sense one having skill in the art would understand the convention (e.g., "a system having at least one of A, B or C" would include but not be limited to systems that have a alone, B alone, C alone, a and B together, a and C together, B and C together, and/or A, B, C together, etc.).
Some block diagrams and/or flow diagrams are shown in the figures. It will be understood that some blocks of the block diagrams and/or flowchart illustrations, or combinations thereof, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the instructions, which execute via the processor, create means for implementing the functions/acts specified in the block diagrams and/or flowchart block or blocks. The techniques of this disclosure may be implemented in hardware and/or software (including firmware, microcode, etc.). In addition, the techniques of this disclosure may take the form of a computer program product on a computer-readable storage medium having instructions stored thereon for use by or in connection with an instruction execution system.
An embodiment of the present disclosure provides a product recommendation method, including: acquiring historical transaction information, benchmark risk level and expected income and variance of a product to be recommended of a client; calculating the risk aversion level of the client according to the historical transaction information; calculating the effectiveness of the product to be recommended according to the benchmark risk level, the expected income and variance of the product to be recommended and the risk aversion level; recommending the product to the customer based on the performance.
Fig. 1 schematically illustrates a system architecture 100 of a product recommendation method and apparatus according to an embodiment of the present disclosure. It should be noted that fig. 1 is only an example of a system architecture to which the embodiments of the present disclosure may be applied to help those skilled in the art understand the technical content of the present disclosure, and does not mean that the embodiments of the present disclosure may not be applied to other devices, systems, environments or scenarios.
As shown in fig. 1, the system architecture 100 according to the embodiment may include a terminal device 101, a network 102 and a server 103. Network 102 is used to provide a communication link between terminal device 101 and server 103.
The terminal device 101 may be, for example, a mobile phone, a bank teller machine, a multimedia inquiry machine, or the like. Network 102 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few. The server 103 may be a server capable of making product recommendations for the terminal device. According to the embodiment of the disclosure, in the product recommendation process, the server 103 acquires information of a client using the terminal device 101, such as identity information of the client, through the network 102, and acquires historical transaction information, a benchmark risk level and expected income and variance of a product to be recommended of the client according to the identity information of the client; calculating the risk aversion level of the client according to the historical transaction information; calculating the effectiveness of the product to be recommended according to the benchmark risk level, the expected income and variance of the product to be recommended and the risk aversion level; recommending the product to the customer based on the performance. Then, the product and the information of the product are sent to the terminal device 101 through the network 102, so that a customer using the terminal device 101 obtains an accurate product recommendation service.
It should be noted that the product recommendation method provided by the embodiment of the present disclosure may be executed by the server 103. Accordingly, the product recommendation device provided by the embodiment of the present disclosure may be disposed in the server 103. Alternatively, the product recommendation method provided by the embodiment of the present disclosure may also be executed by a server or a server cluster that is different from the server 103 and is capable of communicating with the terminal device 101 and/or the server 103. Correspondingly, the product recommendation device provided by the embodiment of the present disclosure may also be disposed in a server or a server cluster different from the server 103 and capable of communicating with the terminal device 101 and/or the server 103. Alternatively, the product recommendation method provided by the embodiment of the present disclosure may also be executed by the server 103 in part and the terminal device 101 in part. Correspondingly, the product recommendation device provided by the embodiment of the present disclosure may also be partially disposed in the server 103 and partially disposed in the terminal device 101.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
The product recommendation method provided by the embodiment of the disclosure can be applied to the field of financial services, taking a bank as an example, in a bank branch, a client can handle various query services through a multimedia query machine, meanwhile, the multimedia query machine obtains historical transaction information, a reference risk level and expected income and variance of products to be recommended of the client, calculates a risk aversion level of the client and expected income and variance of the products to be recommended, calculates the efficiency of the products to be recommended according to a calculation result, and recommends the products to the client according to the efficiency. The multimedia inquiry machine can complete the action of accurately recommending financial products to the client, so that the problems of serious deviation of client supervisor and lack of precision in product recommendation are solved, better financial service and financial products more suitable for the requirements of the client are provided for the client, and the satisfaction degree of the client is improved.
However, the conventional method of investigating by questionnaire is susceptible to factors of supervisor, such as the fact that the customer has a great deal of investment in recent times with high risk, such as stock fund, futures, etc., because the customer cannot fully consider his/her situation when answering, and the supervisor expects much return. It is susceptible to being rated aggressive during this customer evaluation. In fact, from the customer's transaction record, the customer primarily purchases lower risk financial products, and the actual risk bearing capacity is moderate. Therefore, the traditional method cannot avoid the subjective cognitive deviation of the client, is easily influenced by various media and propaganda, is not accurate enough in traditional evaluation, cannot quickly acquire the pain point of the client, cannot quickly get the client, cannot recommend the most suitable financial product of the client to the client, loses the benefit of the product and reduces the income of the client. Based on the product recommendation method provided by the embodiment of the disclosure, the technical problems can be at least partially solved.
It should be understood that the product recommendation method provided by the embodiment of the present disclosure is not limited to be applied to the field of financial services, the above description is only exemplary, and for other fields of product recommendation, such as the field related to product sales, the product recommendation method provided by the embodiment of the present disclosure may be applied to perform product recommendation.
FIG. 2 schematically shows a flow diagram of a product recommendation method according to an embodiment of the present disclosure.
As shown in fig. 2, the path planning method may include operations S201 to S204, for example.
In operation S201, historical transaction information of a customer, a benchmark risk level, and expected profits and variances of products to be recommended are acquired.
In operation S202, a risk aversion level of the customer is calculated based on the historical transaction information.
In operation S203, effectiveness of the product to be recommended is calculated according to the benchmark risk level, the expected profit and variance of the product to be recommended, and the risk aversion level.
In operation S204, a product is recommended to a customer according to the performance.
In an embodiment of the disclosure, the effectiveness of the product to be recommended is calculated based on a reference risk level, an expected income and variance of the product to be recommended, and a risk aversion level, wherein the reference wind level takes account of factors in the aspects of customers and the like, the risk aversion level takes account of factors in the aspect of objective transaction records of the customers based on historical transaction records, and also takes account of the expected income and variance of the product to be recommended, so that the product recommendation of the customers is more accurate in a quantitative angle.
The method shown in fig. 2 is further described below in conjunction with embodiments of the present disclosure.
In an embodiment of the present disclosure, the historical transaction information includes transaction types and transaction amounts corresponding to the transaction types; calculating a risk aversion rating for the customer based on the historical transaction information includes: and calculating the risk aversion grade according to the transaction type and the transaction amount. The historical transaction information may be selected as historical transaction records for the customer over 3 years. The historical transaction records within 3 years have high reference value, and the transaction preference of the client is continuously and dynamically changed along with the cognition, financial resources, expected income and the like of the client, so that the transaction preference of the client can be comprehensively considered, and the timeliness of the selection preference of the client can be ensured. Meanwhile, the updating frequency of the historical transaction records of the clients can be selected to be 1d, the latest historical transaction records of the clients are generated day by day, and more accurate information is mastered.
Further, calculating a risk aversion rating based on the transaction category and the transaction amount includes: grading the transaction categories to obtain a transaction risk level of each transaction category; and carrying out weighted average on the transaction risk grade and the transaction amount to obtain the risk aversion grade. The risk aversion grades are divided into 10 grades of 1-10, for the transaction types of currency funds, fixed income types and insurance types, the income is stable, the risk is small, and the corresponding grade is high; for PE fund, FOF fund, HF fund and futures, market receiving and other factors have large influence, belong to financial products with large risk and have low rating. For different transaction analogies, the rating table for the transaction analogies is as follows. The greater the transaction risk level rating, the less risk representing the transaction type.
TABLE 1
Specifically, according to the transaction analogy and the corresponding rating thereof, the ratings of all transaction analogy are weighted and summed according to the proportion of the transaction amount occupied by the transaction amount corresponding to the transaction analogy, and the risk aversion level of the client is obtained. For example, in the investment corresponding to customer a in the last 3 years, customer a bought 20% of the amount of money for company debt and 80% of the amount of money for small stock, and the risk aversion rating was 20% × 7+ 80% × 3 ═ 3.8. The risk aversion grade is analyzed and rated based on specific data in the historical transaction records of the client, the recent risk bearing capacity of the client and the consumption level of the product are comprehensively and objectively evaluated, and the method has important significance for accurately recommending the product.
In a disclosed embodiment, obtaining the benchmark risk level includes: acquiring basic information of a client; and generating a benchmark risk level of the client according to the basic information. The basic information of the client can be selected as reservation information left when the client registers for the first time, and comprises the following steps: name, gender, academic calendar, occupation, etc.; the customer basic information can also include information according to the current economic condition of the customer which can be detected by the bank system, and comprises the following steps: liability conditions, asset conditions, etc. Based on the basic information of the client, evaluating the benchmark risk level of the client, wherein the risk level reflects the type of the risk bearing of the client foundation, and comprises 5 benchmark risk levels which are respectively: cautious, robust, balanced, aggressive, and aggressive. The benchmark risk level represents the basic risk bearing capacity of the client, and does not contain the influence of recent consumption and subjective factors. The scores corresponding to the benchmark risk levels are shown in the table below, and the larger the score is, the smaller the basic risk tolerance is represented.
TABLE 2
In an embodiment of the present disclosure, calculating the effectiveness of the product to be recommended according to the benchmark risk level, the expected income and variance of the product to be recommended, and the risk aversion level includes: calibrating the risk aversion grade according to the reference risk grade to obtain a calibrated risk aversion grade; and calculating the effectiveness of the product to be recommended according to the calibrated risk aversion grade and the expected income and variance of the product to be recommended. The risk aversion level is calibrated according to the reference risk level, the risk aversion level of the client only comprises the recent risk bearing capacity and the consumption level of the product, the risk aversion level of the client is calibrated by using the reference risk level, and the obtained calibrated risk aversion level comprises more information, such as the basic economic level and the basic risk bearing capacity of the client.
Further, a weighted average is performed according to the reference risk level score and the risk aversion level score to obtain a calibrated risk aversion level. The formula for the calibrated risk aversion rating is: a calibrated risk aversion rating ═ reference rating × 2+ aversion rating × b; a. b is a calibration coefficient, a + b is 1, and a and b are real numbers which are not 0, the calibrated risk aversion level is between 0 and 10. For example, one may choose a to b 0.5, for customer a, the base risk rating is 4, robust, the risk aversion rating is 3.8, and then the calibrated risk aversion rating is 4 to 0.5 to 2+3.8 to 0.5 to 5.9.
In one embodiment of the disclosure, for the financial service of the bank, a plurality of products are released to form a financial product pool aiming at different businesses. And acquiring the financial products to be recommended from the financial product pool, and calculating the expected income and variance of each financial product to be recommended. Obtaining the rate of return r(s) of each financial product to be recommended under different market conditions and the probability P(s) of the occurrence of the different market conditions, wherein the time duration condition can be divided into five conditions of big good, common, poor and very poor, the probability corresponding to each condition is determined by the market situation, meanwhile, the different rate of return is determined according to the market conditions, the expected return E (r) ∑ P(s) ×(s), and the variance σ2＝∑P(s)*[r(s)-E(r)]2. The expected yield reflects the average yield over all different time periods, being a weighted average of the expected yield, while the variance reflects the stability of the product and therefore the degree of departure from the mean over time periods.
Then, the effectiveness of the product to be recommended is calculated according to the calibrated risk aversion level and the expected income and variance of the product to be recommended. The efficacy is a function of the calibrated risk aversion rating, the expected benefit, and the variance, i.e., F (expected benefit, variance, calibrated risk aversion rating), which represents efficacy. Where performance is related to the calibrated risk aversion rating and performance is inversely related to expected revenue and variance. Each financial product has its expected income and variance corresponding to the financial product to be recommended, and the expected income and variance are combined with the calibrated risk aversion level of the customer to calculate the efficiency of the customer. The greater the effectiveness, the greater the benefit to the customer on behalf of the product to be recommended, and the more the customer is motivated by the characteristics that are most appropriate for the customer.
In an embodiment of the present disclosure, a product to be recommended with a performance greater than a preset threshold is recommended to the customer. The effectiveness is a function representing the customer effect. Optionally, a preset threshold of effectiveness may be set, where the preset threshold represents a threshold of considerable benefit for the customer, and all products to be recommended that reach the preset threshold are recommended to the customer. If the number of the products to be recommended reaching the preset threshold is large and is not beneficial to the customer to make a selection, the products to be recommended with the maximum efficiency are recommended to the customer, so that the customer can quickly acquire the information of the financial products with the maximum benefit and the characteristics which are most suitable for the customer, and the customer can conveniently make a selection
In summary, the product recommendation method provided by the embodiment of the present disclosure has at least the following advantages: on one hand, through the combination of the basic information of the client and the transaction information, a calibration mechanism for the risk aversion level of the client is completed, so that the calibrated risk aversion level contains more information, such as the basic economic level and the basic risk bearing capacity of the client. (ii) a On the other hand, by combining the risk aversion level after the customer calibration with the information such as the income expectation and the variance of the product to be recommended, the utility maximization product in the optional range is recommended to the customer quantitatively and accurately, the customer can select the most suitable self purchasing characteristics and expected products, the experience and income of the customer are improved, and meanwhile, the self product is popularized.
FIG. 3 schematically shows a block diagram of a product recommendation device according to an embodiment of the disclosure.
As shown in fig. 3, the product recommendation device 300 may include, for example, an obtaining module 310, a first calculating module 320, a second calculating module 330, and a recommending module 340.
An obtaining module 310 is used for obtaining the historical transaction information, the benchmark risk level and the expected income and variance of the product to be recommended of the client.
A first calculation module 320 for calculating a risk aversion rating of the customer based on the historical transaction information.
And the second calculating module 330 is configured to calculate the effectiveness of the product to be recommended according to the benchmark risk level, the expected income and variance of the product to be recommended, and the risk aversion level.
A recommending module 340 for recommending products to the customer according to the efficiency.
FIG. 4 schematically shows a block diagram of a first computing module according to an embodiment of the disclosure.
As shown in fig. 4, the first calculation module 320 may include a rating unit 321 and a first calculation unit 322, for example.
The historical transaction information included in the first calculation module 320 includes transaction types and transaction amounts corresponding to the transaction types, and the first calculation module 320 calculates the risk aversion level according to the transaction types and the transaction amounts.
The rating unit 321 is configured to rate the transaction categories to obtain a transaction risk level of each transaction category.
The first calculating unit 322 is configured to perform weighted average on the transaction risk level and the transaction amount to obtain the risk aversion level.
FIG. 5 schematically shows a block diagram of a second computing module according to yet another embodiment of the present disclosure.
As shown in fig. 5, the second calculating module 330 may further include a calibrating unit 331 and a second calculating unit 332, for example.
The calibration unit 331 is configured to calibrate the risk aversion level according to the reference risk level, and obtain a calibrated risk aversion level.
And a second calculating unit 332 for calculating the effectiveness of the product to be recommended according to the calibrated risk aversion level and the expected income and variance of the product to be recommended.
FIG. 6 schematically shows a block diagram of a recommendation module according to an embodiment of the disclosure.
As shown in fig. 6, the recommending module 340 may include, for example, a first recommending module 341 and a second recommending module 342.
The first recommending module 341 is configured to recommend the product to be recommended, whose effectiveness is greater than a preset threshold, to the customer.
And the second recommending module 342 is used for recommending the products to be recommended with the maximum effectiveness to the customer.
Fig. 7 schematically illustrates a block diagram of an acquisition module according to an embodiment of the disclosure.
As shown in fig. 7, the obtaining module 310 may include, for example, a obtaining unit 311 and a third calculating unit 312.
An obtaining unit 311 is configured to obtain basic information of the client.
And a third calculating unit 312, configured to generate a benchmark risk level of the customer according to the basic information.
Any number of modules, sub-modules, units, sub-units, or at least part of the functionality of any number thereof according to embodiments of the present disclosure may be implemented in one module. Any one or more of the modules, sub-modules, units, and sub-units according to the embodiments of the present disclosure may be implemented by being split into a plurality of modules. Any one or more of the modules, sub-modules, units, sub-units according to embodiments of the present disclosure may be implemented at least in part as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented in any other reasonable manner of hardware or firmware by integrating or packaging a circuit, or in any one of or a suitable combination of software, hardware, and firmware implementations. Alternatively, one or more of the modules, sub-modules, units, sub-units according to embodiments of the disclosure may be at least partially implemented as a computer program module, which when executed may perform the corresponding functions.
For example, any plurality of the obtaining module 310, the first calculating module 320, the second calculating module 330 and the recommending module 340 may be combined and implemented in one module/unit/sub-unit, or any one of the modules/units/sub-units may be split into a plurality of modules/units/sub-units. Alternatively, at least part of the functionality of one or more of these modules/units/sub-units may be combined with at least part of the functionality of other modules/units/sub-units and implemented in one module/unit/sub-unit. According to an embodiment of the disclosure, at least one of the obtaining module 310, the first calculating module 320, the second calculating module 330, and the recommending module 340 may be at least partially implemented as a hardware circuit, such as a Field Programmable Gate Array (FPGA), a Programmable Logic Array (PLA), a system on a chip, a system on a substrate, a system on a package, an Application Specific Integrated Circuit (ASIC), or may be implemented by hardware or firmware in any other reasonable manner of integrating or packaging a circuit, or implemented by any one of three implementations of software, hardware, and firmware, or implemented by a suitable combination of any of them. Alternatively, at least one of the obtaining module 310, the first calculating module 320, the second calculating module 330 and the recommending module 340 may be at least partially implemented as a computer program module, which when executed may perform a corresponding function.
It should be noted that, the product recommendation device portion in the embodiment of the present disclosure corresponds to the product recommendation method portion in the embodiment of the present disclosure, and the specific implementation details and the technical effects thereof are also the same, and are not described herein again.
Fig. 8 schematically shows a block diagram of an electronic device adapted to implement the above described method according to an embodiment of the present disclosure. The electronic device shown in fig. 8 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 8, an electronic device 800 according to an embodiment of the present disclosure includes a processor 801 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage section 808 into a Random Access Memory (RAM) 803. The processor 801 may include, for example, a general purpose microprocessor (e.g., a CPU), an instruction set processor and/or associated chipset, and/or a special purpose microprocessor (e.g., an Application Specific Integrated Circuit (ASIC)), among others. The processor 801 may also include onboard memory for caching purposes. The processor 801 may include a single processing unit or multiple processing units for performing different actions of the method flows according to embodiments of the present disclosure.
In the RAM803, various programs and data necessary for the operation of the electronic apparatus 800 are stored. The processor 801, the ROM802, and the RAM803 are connected to each other by a bus 804. The processor 801 performs various operations of the method flows according to the embodiments of the present disclosure by executing programs in the ROM802 and/or RAM 803. Note that the programs may also be stored in one or more memories other than the ROM802 and RAM 803. The processor 801 may also perform various operations of method flows according to embodiments of the present disclosure by executing programs stored in the one or more memories.
According to embodiments of the present disclosure, method flows according to embodiments of the present disclosure may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable storage medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 809 and/or installed from the removable medium 811. The computer program, when executed by the processor 801, performs the above-described functions defined in the system of the embodiments of the present disclosure. The systems, devices, apparatuses, modules, units, etc. described above may be implemented by computer program modules according to embodiments of the present disclosure.
The present disclosure also provides a computer-readable storage medium, which may be contained in the apparatus/device/system described in the above embodiments; or may exist separately and not be assembled into the device/apparatus/system. The computer-readable storage medium carries one or more programs which, when executed, implement the method according to an embodiment of the disclosure.
According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium. Examples may include, but are not limited to: 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), 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 present disclosure, 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.
For example, according to embodiments of the present disclosure, a computer-readable storage medium may include the ROM802 and/or RAM803 described above and/or one or more memories other than the ROM802 and RAM 803.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. Those skilled in the art will appreciate that various combinations and/or combinations of features recited in the various embodiments and/or claims of the present disclosure can be made, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments and/or claims of the present disclosure may be made without departing from the spirit or teaching of the present disclosure. All such combinations and/or associations are within the scope of the present disclosure.
Claims (16)
1. A method of product recommendation, comprising:
acquiring historical transaction information, benchmark risk level and expected income and variance of a product to be recommended of a client;
calculating the risk aversion level of the client according to the historical transaction information;
calculating the effectiveness of the product to be recommended according to the benchmark risk level, the expected income and variance of the product to be recommended and the risk aversion level;
recommending products to the customer based on the effectiveness.
2. The product recommendation method of claim 1, wherein the historical transaction information includes transaction categories and transaction amounts corresponding to the transaction categories;
said calculating a risk aversion rating for said customer based on said historical transaction information comprises:
and calculating the risk aversion grade according to the transaction type and the transaction amount.
3. The product recommendation method of claim 2, wherein said calculating the risk aversion level based on the transaction category and the transaction amount comprises:
grading the transaction categories to obtain a transaction risk level of each transaction category;
and carrying out weighted average on the transaction risk grade and the transaction amount to obtain the risk aversion grade.
4. The product recommendation method of claim 1, wherein said calculating the effectiveness of the product to be recommended based on the benchmark risk level, the expected profit and variance of the product to be recommended, and the risk aversion level comprises:
calibrating the risk aversion grade according to the reference risk grade to obtain a calibrated risk aversion grade;
and calculating the effectiveness of the product to be recommended according to the calibrated risk aversion grade and the expected income and variance of the product to be recommended.
5. The product recommendation method of claim 1, wherein the recommending products to the customer based on the effectiveness comprises:
recommending the products to be recommended with the effectiveness larger than a preset threshold value to the customer.
6. The product recommendation method of claim 5, wherein the recommending products to the customer based on the effectiveness comprises:
and recommending the products to be recommended with the maximum efficacy to the customer.
7. The product recommendation method of claim 1, wherein obtaining a benchmark risk level comprises:
acquiring basic information of a client;
and generating a benchmark risk level of the client according to the basic information.
8. A product recommendation device comprising:
the acquisition module is used for acquiring historical transaction information, benchmark risk level and expected income and variance of a product to be recommended of a client;
a first calculation module for calculating a risk aversion level of the customer based on the historical transaction information;
the second calculation module is used for calculating the effectiveness of the product to be recommended according to the benchmark risk level, the expected income and variance of the product to be recommended and the risk aversion level;
and the recommending module is used for recommending products to the customer according to the efficiency.
9. The product recommendation device of claim 8, wherein the first calculation module calculating the level of risk aversion of the customer based on the historical transaction information comprises:
the historical transaction information comprises transaction types and transaction amounts corresponding to the transaction types;
and calculating the risk aversion grade according to the transaction type and the transaction amount.
10. The product recommendation method of claim 9, wherein the first computing module comprises:
the rating unit is used for rating the transaction categories to obtain the transaction risk level of each transaction category;
and the first calculation unit is used for carrying out weighted average on the transaction risk level and the transaction amount to obtain the risk aversion level.
11. The product recommendation device of claim 8, wherein the second computing module comprises:
a calibration unit, configured to calibrate the risk aversion level according to the reference risk level to obtain a calibrated risk aversion level;
and the second calculation unit is used for calculating the effectiveness of the product to be recommended according to the calibrated risk aversion grade and the expected income and variance of the product to be recommended.
12. The product recommendation device of claim 8, wherein the recommendation module comprises:
and the first recommending unit is used for recommending the product to be recommended with the efficiency larger than a preset threshold value to the customer.
13. The product recommendation device of claim 12, wherein the recommendation module comprises:
and the second recommending unit is used for recommending the product to be recommended with the maximum efficiency to the client.
14. The product recommendation device of claim 8, wherein the acquisition module comprises:
the acquisition unit is used for acquiring basic information of a client;
and the third calculating unit is used for generating a benchmark risk level of the client according to the basic information.
15. An electronic device, comprising:
one or more processors;
a memory for storing one or more programs,
wherein the one or more programs, when executed by the one or more processors, cause the one or more processors to implement the method of any of claims 1-7.
16. A computer readable storage medium having stored thereon executable instructions which, when executed by a processor, cause the processor to carry out the method of any one of claims 1 to 7.
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