CN110990446A - Intelligent investment and customer retrieval method and device based on investor portrait - Google Patents
Intelligent investment and customer retrieval method and device based on investor portrait Download PDFInfo
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- CN110990446A CN110990446A CN201911317993.4A CN201911317993A CN110990446A CN 110990446 A CN110990446 A CN 110990446A CN 201911317993 A CN201911317993 A CN 201911317993A CN 110990446 A CN110990446 A CN 110990446A
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2457—Query processing with adaptation to user needs
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
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Abstract
The application provides an intelligent casting and customer retrieval method and device based on an investor portrait, which are applied to an intelligent casting and customer retrieval platform, and the method comprises the following steps: acquiring a retrieval request input by a user, wherein the retrieval request comprises portrait information of the user; extracting investment attribute features from portrait information of a user; searching whether an investment product matched with the investment attribute characteristics exists in a pre-established investment product library; and if so, taking the investment product matched with the investment attribute characteristics as a retrieval result. In the application, the efficiency and the accuracy of investment product retrieval can be improved through the method.
Description
Technical Field
The application relates to the technical field of retrieval, in particular to an intelligent investment and patronizing retrieval method and device based on an investor portrait.
Background
With the improvement of living standard, the demand of users for investment is increasing. Also, users desire to find investment products that fit themselves.
Currently, a professional will typically search for a matching investment product among a plurality of investment products for the user. However, the manual retrieval method is low in efficiency, and the subjectivity of professionals in the retrieval process is strong, so that the accuracy of the retrieval result is affected.
Disclosure of Invention
In order to solve the above technical problems, embodiments of the present application provide an intelligent investment search method and apparatus based on an investor portrait, so as to achieve the purpose of improving search efficiency and accuracy, and the technical solution is as follows:
an intelligent investment and customer retrieval method based on investor figures is applied to an intelligent investment and customer retrieval platform, and comprises the following steps:
acquiring a retrieval request input by a user, wherein the retrieval request comprises portrait information of the user;
extracting investment attribute features from the portrait information of the user;
searching whether an investment product matched with the investment attribute characteristics exists in a pre-established investment product library;
and if so, taking the investment product matched with the investment attribute characteristics as a retrieval result.
Preferably, the method further comprises:
if the investment product library does not have the investment product matched with the investment attribute characteristics, inputting the investment attribute characteristics into a Black-Litterman model to obtain an investment product combination output by the Black-Litterman model;
and taking the investment product combination as a retrieval result.
Preferably, the portrait information of the user includes:
the user inputs portrait information on the intelligent delivery retrieval platform;
or, the user inputs portrait information on the intelligent commissioning search platform and portrait information obtained from an external data source associated with the user.
Preferably, the extracting investment attribute features from the user's portrait information includes:
extracting investment attribute features from the portrait information of the user;
and carrying out normalization processing on the investment attribute characteristics, and replacing the investment attribute characteristics with the normalized investment attribute characteristics.
Preferably, the searching for the investment product matching with the investment attribute feature in the investment product library comprises:
filtering redundant features in the investment attribute features to obtain target investment attribute features;
and searching whether the investment product matched with the target investment attribute characteristic exists in an investment product library.
The utility model provides an intelligence is thrown and is looked after retrieval device based on investor portrays, is applied to intelligence and throws and look after retrieval platform, and the device includes:
the retrieval system comprises an acquisition module, a retrieval module and a display module, wherein the acquisition module is used for acquiring a retrieval request input by a user, and the retrieval request comprises portrait information of the user;
the extraction module is used for extracting investment attribute characteristics from the portrait information of the user;
the searching module is used for searching whether the investment product matched with the investment attribute characteristics exists in a pre-established investment product library;
and the first determining module is used for taking the investment product matched with the investment attribute characteristics as a retrieval result if the investment product matched with the investment attribute characteristics exists.
Preferably, the apparatus further comprises:
a second determining module, configured to, if there is no investment product matching the investment attribute feature in the investment product library, input the investment attribute feature into a Black-Litterman model to obtain an investment product combination output by the Black-Litterman model;
and the third determining module is used for taking the investment product combination as a retrieval result.
Preferably, the portrait information of the user includes:
the user inputs portrait information on the intelligent delivery retrieval platform;
or, the user inputs portrait information on the intelligent commissioning search platform and portrait information obtained from an external data source associated with the user.
Preferably, the extraction module is specifically configured to:
extracting investment attribute features from the portrait information of the user;
and carrying out normalization processing on the investment attribute characteristics, and replacing the investment attribute characteristics with the normalized investment attribute characteristics.
Preferably, the search module is specifically configured to:
filtering redundant features in the investment attribute features to obtain target investment attribute features;
and searching whether the investment product matched with the target investment attribute characteristic exists in an investment product library.
Compared with the prior art, the beneficial effect of this application is:
in the method, the intelligent delivery retrieval platform acquires the retrieval request input by the user, extracts the investment attribute characteristics from the portrait information of the user contained in the retrieval request, and searches whether the investment product matched with the investment attribute characteristics exists in a pre-established investment product library, so that the automation of investment product retrieval is realized, and the retrieval efficiency is improved.
And moreover, by utilizing the objective investment attribute characteristics and the objective investment product library, whether the investment product with the matched investment attribute characteristics exists or not is searched in the pre-established investment product library, so that the retrieval accuracy can be improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
FIG. 1 is a flow chart of an embodiment 1 of an intelligent investment search method based on investor figures provided by the present application;
FIG. 2 is a flowchart of an embodiment 2 of an intelligent investment search method based on investor figures;
FIG. 3 is a flowchart of an embodiment 3 of an intelligent investment search method based on investor figures according to the present application;
FIG. 4 is a flowchart of an embodiment 4 of an intelligent investment search method based on investor figures;
fig. 5 is a schematic diagram of a logical structure of an intelligent investment search device based on investor figures according to the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The embodiment of the application discloses an intelligent casting and patronizing retrieval method based on an investor portrait, which is applied to an intelligent casting and patronizing retrieval platform and comprises the following steps: acquiring a retrieval request input by a user, wherein the retrieval request comprises portrait information of the user; extracting investment attribute features from the portrait information of the user; searching whether an investment product matched with the investment attribute characteristics exists in a pre-established investment product library; and if so, taking the investment product matched with the investment attribute characteristics as a retrieval result. According to the method and the device, the efficiency and the accuracy of retrieval can be improved.
Next, a description is given to an intelligent investment search method based on an investor figure disclosed in the embodiment of the present application, where the intelligent investment search method based on an investor figure is applied to an intelligent investment search platform, as shown in fig. 1, for the flowchart of embodiment 1 of the intelligent investment search method based on an investor figure provided in the present application, the method may include the following steps:
step S11, a search request input by a user is obtained, where the search request includes the image information of the user.
The portrait information of the user can be understood as: and label information abstracted from each specific information data of the user, such as basic information (such as gender, age and the like), financial conditions, investment knowledge, investment experience, risk preference, policy preference, industry preference and the like.
In this embodiment, the portrait information of the user may include:
the user inputs portrait information on the intelligent delivery retrieval platform;
or, the user inputs portrait information on the intelligent commissioning search platform and portrait information obtained from an external data source associated with the user.
In the case where the portrait information of the user includes portrait information input by the user on the intelligent patronizing search platform and portrait information acquired from an external data source associated with the user, comprehensiveness of user information can be ensured.
The intelligent delivery and review retrieval platform needs to provide a query interface and a query interface, wherein the query interface is used for a user to input a retrieval request, and the query interface is used for acquiring the retrieval request input by the user.
And step S12, extracting investment attribute characteristics from the portrait information of the user.
The investment attribute features may be understood as: characteristics of the investment expectations of the user, such as gender, age, income level, risk preferences, etc., can be reflected.
And step S13, searching whether the investment product matched with the investment attribute characteristics exists in a pre-established investment product library.
In this embodiment, the intelligent delivery and retrieval platform needs to support uploading of investment product information. The uploading means may include, but is not limited to: uploading from a local computer or uploading by inputting an online address.
After the investment product information is uploaded to the intelligent delivery retrieval platform, the intelligent delivery retrieval platform establishes an investment product library in advance based on the investment product information. The pre-established investment product library includes a plurality of investment products and attribute characteristics of each investment product.
Searching whether an investment product matched with the investment attribute characteristics exists in a pre-established investment product library can be understood as follows: and searching whether the attribute characteristics matched with the investment attribute characteristics exist in a pre-established investment product library.
If so, go to step S14.
And step S14, taking the investment product matched with the investment attribute characteristics as a retrieval result.
In this embodiment, the intelligent delivery and retrieval platform further needs to provide a result display interface and a result display interface. And the result display interface is used for sending the retrieval result to the result display interface for displaying.
In the method, the intelligent delivery retrieval platform acquires the retrieval request input by the user, extracts the investment attribute characteristics from the portrait information of the user contained in the retrieval request, and searches whether the investment product matched with the investment attribute characteristics exists in a pre-established investment product library, so that the automation of investment product retrieval is realized, and the retrieval efficiency is improved.
And moreover, by utilizing the objective investment attribute characteristics and the objective investment product library, whether the investment product with the matched investment attribute characteristics exists or not is searched in the pre-established investment product library, so that the retrieval accuracy can be improved.
As another alternative embodiment of the present application, referring to fig. 2, a schematic flow diagram of an embodiment 2 of an intelligent investment search method based on an investor figure is provided, and this embodiment mainly is an extension of the intelligent investment search method based on an investor figure described in the above embodiment 1, and as shown in fig. 2, the method may include, but is not limited to, the following steps:
step S21, a search request input by a user is obtained, where the search request includes the image information of the user.
And step S22, extracting investment attribute characteristics from the portrait information of the user.
And step S23, searching whether the investment product matched with the investment attribute characteristics exists in a pre-established investment product library.
If yes, go to step S24; if not, go to step S25.
And step S24, taking the investment product matched with the investment attribute characteristics as a retrieval result.
The detailed procedures of steps S21-S24 can be referred to the related descriptions of steps S11-S14 in embodiment 1, and are not described herein again.
And step S25, inputting the investment attribute characteristics into a Black-Litterman model to obtain the investment product combination output by the Black-Litterman model.
The investment product portfolio can be understood as: the combination of different investment products and the asset allocation proportion of different investment products.
The construction process of the Black-Litterman model can be as follows:
the first step is that the risk asset prior income in the investment product is solved through inverse optimization.
If the prior gains are subjected to normal distribution N- (pi, τ Σ), then: λ Σ w _ mkt, where τ is a scalar, λ is a risk aversion coefficient, Σ is a covariance matrix, w _ mkt is a market combining weight, and Π is an equalized yield vector
And secondly, adding the subjective opinion of investors into the market equilibrium profit distribution to form posterior profits.
Suppose that the viewpoint returns are distributed from N to (Q, Ω), N is the number of assets of the market portfolio, k is the number of viewpoints of investors (k ≦ N), p represents a subjective viewpoint matrix (k × N), Q is the viewpoint returns (k × 1), and Ω is a viewpoint error matrix and also confidence (k × k).
The third step: and solving an optimized asset allocation weight coefficient by combining a Markov mean square error model according to the new posterior income formed by the model.
Adding an expected subjective view of the assets, calculating and obtaining posterior income distribution, and obtaining prior data and adding the investor subjective view under the conditions that: n (pi, τ Σ), N (E (R), [ (τ Σ)-1+(P′Ω-1P)]-1) Wherein the new posterior expected yield E (R) is: e (r) ═ Σ [ (τ ∑)-1+(P′Ω-1P)]-1[(τ∑)-1Π+P′Ω-1Q]And finally substituting E (R) into a utility maximization constraint model to obtain an optimal weight w:
w＝(λ∑)-1μ
where e (r) represents the new a posteriori expected rate of return vector and μ represents the desired weight.
And step S26, taking the investment product combination as a retrieval result.
As another alternative embodiment of the present application, referring to fig. 3, a flow diagram of an embodiment 3 of an intelligent investment search method based on investor figures is provided, and this embodiment mainly describes a refinement scheme of the intelligent investment search method based on investor figures described in the above embodiment 1, and as shown in fig. 3, the method may include, but is not limited to, the following steps:
step S31, a search request input by a user is obtained, where the search request includes the image information of the user.
The detailed process of step S31 can be referred to the related description of step S11 in embodiment 1, and is not repeated here.
And step S32, extracting investment attribute characteristics from the portrait information of the user.
And step S33, carrying out normalization processing on the investment attribute characteristics, and replacing the investment attribute characteristics with the normalized investment attribute characteristics.
And the investment attribute characteristics are normalized, so that the dimensional influence of data can be eliminated, and the operation precision can be retrieved.
Steps S32-S33 are specific examples of step S12 of example 1.
And step S34, searching whether the investment product matched with the investment attribute characteristics exists in a pre-established investment product library.
If so, go to step S35.
And step S35, taking the investment product matched with the investment attribute characteristics as a retrieval result.
The detailed procedures of steps S34-S35 can be found in the description related to steps S13-S14 in example 1.
As another alternative embodiment of the present application, referring to fig. 4, a schematic flow chart of an embodiment 4 of an intelligent investment search method based on investor figures provided by the present application is provided, and this embodiment mainly describes a refinement scheme of the intelligent investment search method based on investor figures described in the above embodiment 1, as shown in fig. 4, the method may include, but is not limited to, the following steps:
step S41, a search request input by a user is obtained, where the search request includes the image information of the user.
And step S42, extracting investment attribute characteristics from the portrait information of the user.
And step S43, filtering redundant features in the investment attribute features to obtain target investment attribute features.
The process of filtering redundant ones of the investment attribute features may include:
carrying out missing value cleaning; cleaning format content; performing logic error cleaning; cleaning the non-demand data; and performing relevance verification.
Missing value cleaning can be understood as: after the missing value is positioned, the data supplement missing value is searched again;
format content washing may be understood as: adjusting the storage format of the data;
logical error flushing can be understood as: removing repeated values, unreasonable values and contradictory contents;
non-demand data cleansing can be understood as: removing the data with low relevance;
the relevance verification can be understood as: and verifying whether the data are consistent from different channels.
And step S44, searching whether the investment product matched with the target investment attribute characteristics exists in an investment product library.
If so, go to step S45.
Steps S43-S44 are specific examples of step S13 of example 1.
And step S45, taking the investment product matched with the investment attribute characteristics as a retrieval result.
Next, the investor portrait-based intelligent casting search device according to the present application will be described, and the investor portrait-based intelligent casting search device described below and the investor portrait-based intelligent casting search method described above may be referred to in correspondence with each other.
Referring to fig. 5, the intelligent investment search device based on the investor portrait is applied to an intelligent investment search platform, and comprises: the device comprises an acquisition module 11, an extraction module 12, a search module 13 and a first determination module 14.
An obtaining module 11, configured to obtain a retrieval request input by a user, where the retrieval request includes portrait information of the user;
the portrait information of the user may include:
the user inputs portrait information on the intelligent delivery retrieval platform;
or, the user inputs portrait information on the intelligent commissioning search platform and portrait information obtained from an external data source associated with the user.
An extraction module 12, configured to extract investment attribute features from the portrait information of the user;
a searching module 13, configured to search, in a pre-established investment product library, whether an investment product matching the investment attribute feature exists;
a first determining module 14, configured to, if there is an investment product matching the investment attribute feature, take the investment product matching the investment attribute feature as a search result.
In this embodiment, the intelligent investment search device based on the investor figure may further include:
a second determining module, configured to, if there is no investment product matching the investment attribute feature in the investment product library, input the investment attribute feature into a Black-Litterman model to obtain an investment product combination output by the Black-Litterman model;
and the third determining module is used for taking the investment product combination as a retrieval result.
In this embodiment, the extracting module 12 may be specifically configured to:
extracting investment attribute features from the portrait information of the user;
and carrying out normalization processing on the investment attribute characteristics, and replacing the investment attribute characteristics with the normalized investment attribute characteristics.
In this embodiment, the search module 13 may be specifically configured to:
filtering redundant features in the investment attribute features to obtain target investment attribute features;
and searching whether the investment product matched with the target investment attribute characteristic exists in an investment product library.
It should be noted that each embodiment is mainly described as a difference from the other embodiments, and the same and similar parts between the embodiments may be referred to each other. For the device-like embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the units may be implemented in one or more software and/or hardware when implementing the present application.
From the above description of the embodiments, it is clear to those skilled in the art that the present application can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present application may be essentially or partially implemented in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments of the present application.
The method and the device for intelligent investment search based on the images of investors are introduced in detail, specific examples are applied in the method to explain the principle and the implementation mode of the method, and the description of the embodiments is only used for helping to understand the method and the core idea of the method; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. An intelligent investment and customer retrieval method based on investor figures is characterized by being applied to an intelligent investment and customer retrieval platform and comprising the following steps:
acquiring a retrieval request input by a user, wherein the retrieval request comprises portrait information of the user;
extracting investment attribute features from the portrait information of the user;
searching whether an investment product matched with the investment attribute characteristics exists in a pre-established investment product library;
and if so, taking the investment product matched with the investment attribute characteristics as a retrieval result.
2. The method of claim 1, further comprising:
if the investment product library does not have the investment product matched with the investment attribute characteristics, inputting the investment attribute characteristics into a Black-Litterman model to obtain an investment product combination output by the Black-Litterman model;
and taking the investment product combination as a retrieval result.
3. The method of claim 1, wherein the user's portrait information comprises:
the user inputs portrait information on the intelligent delivery retrieval platform;
or, the user inputs portrait information on the intelligent commissioning search platform and portrait information obtained from an external data source associated with the user.
4. The method of claim 1, wherein said extracting investment attribute features from said user's representation information comprises:
extracting investment attribute features from the portrait information of the user;
and carrying out normalization processing on the investment attribute characteristics, and replacing the investment attribute characteristics with the normalized investment attribute characteristics.
5. The method of claim 1, wherein said searching in a library of investment products for an investment product matching said investment attribute characteristic comprises:
filtering redundant features in the investment attribute features to obtain target investment attribute features;
and searching whether the investment product matched with the target investment attribute characteristic exists in an investment product library.
6. The utility model provides an intelligence is thrown and is looked after retrieval device based on investor portrays, its characterized in that is applied to intelligence and throws and look after retrieval platform, and the device includes:
the retrieval system comprises an acquisition module, a retrieval module and a display module, wherein the acquisition module is used for acquiring a retrieval request input by a user, and the retrieval request comprises portrait information of the user;
the extraction module is used for extracting investment attribute characteristics from the portrait information of the user;
the searching module is used for searching whether the investment product matched with the investment attribute characteristics exists in a pre-established investment product library;
and the first determining module is used for taking the investment product matched with the investment attribute characteristics as a retrieval result if the investment product matched with the investment attribute characteristics exists.
7. The apparatus of claim 6, further comprising:
a second determining module, configured to, if there is no investment product matching the investment attribute feature in the investment product library, input the investment attribute feature into a Black-Litterman model to obtain an investment product combination output by the Black-Litterman model;
and the third determining module is used for taking the investment product combination as a retrieval result.
8. The apparatus of claim 6, wherein the user's portrait information comprises:
the user inputs portrait information on the intelligent delivery retrieval platform;
or, the user inputs portrait information on the intelligent commissioning search platform and portrait information obtained from an external data source associated with the user.
9. The apparatus according to claim 6, wherein the extraction module is specifically configured to:
extracting investment attribute features from the portrait information of the user;
and carrying out normalization processing on the investment attribute characteristics, and replacing the investment attribute characteristics with the normalized investment attribute characteristics.
10. The apparatus of claim 6, wherein the lookup module is specifically configured to:
filtering redundant features in the investment attribute features to obtain target investment attribute features;
and searching whether the investment product matched with the target investment attribute characteristic exists in an investment product library.
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