CN110442794B - Construction method of analyst portrait system - Google Patents
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- CN110442794B CN110442794B CN201910740963.8A CN201910740963A CN110442794B CN 110442794 B CN110442794 B CN 110442794B CN 201910740963 A CN201910740963 A CN 201910740963A CN 110442794 B CN110442794 B CN 110442794B
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q40/04—Trading; Exchange, e.g. stocks, commodities, derivatives or currency exchange
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06Q40/06—Asset management; Financial planning or analysis
Abstract
The invention discloses a construction method of an analyst portrait system, which comprises the following steps: A. obtaining basic information of an analyst and a user, and generating stock basic information; B. collecting data of analysts and users; C. constructing the image characteristics of an analyst; D. providing an analyst portrait feature acquisition interface and a display interface; the analyst portrait system established by the invention is concise in image and rich in pertinence, can provide visual analyst data display for the platform, and simultaneously provides decision support of a data layer for the system to manage analysts. The platform can be opened for the user with some analyst portrait characteristics, makes the user can be autonomic match analyst's portrait characteristics with self actual conditions, conveniently pays close attention to specific analyst accurately, obtains the newspaper of studying to own value maximize, can effectively promote user's platform content consumption experience.
Description
Technical Field
The invention relates to the technical field of electronic commerce, in particular to a construction method of an analyst portrait system.
Background
With the rapid development of electronic commerce, more and more users choose to search information such as stock information, stock analysis of analysts, industry analysis and the like on a stock investment platform. Various security analysts on the investment platform are various, and users usually select to pay attention to the analysts according to stocks, industry plates, subject matters, investment styles and the like which are interested by the users.
However, there are differences in sex, age, stocks concerned, industry, and even investment style among the concerned analysts of the same analyst, and the concerns of the analysts referred to by the user, the published speech, and the contents of the research and report, the number of comments, or the contents of the comments cannot accurately determine whether the analysts are suitable for the user. In addition, the characteristics of the conventional analysts are usually added manually by the background staff according to subjective factors such as the knowledge and experience of the analysts, or only some main parameter values such as the attention, activity, and securities company of the analysts are listed. The above background addition manner of the analyst characteristics has subjectivity and fixity, and is cumbersome to operate, and the analyst characteristics added in this manner also cannot enable a user to quickly and accurately know whether the analyst is suitable for himself or herself.
In a word, analysts, investment styles and feature information provided by the existing stock investment platform cannot meet the requirement of a user for quickly and accurately selecting proper analysts, and the investment information acquisition experience is easily poor.
Disclosure of Invention
The present invention is directed to a method for constructing an analyst representation system, so as to solve the problems of the background art.
In order to achieve the purpose, the invention provides the following technical scheme:
a construction method of an analyst portrait system comprises the following steps:
A. acquiring basic information of an analyst and a user, and generating stock basic information;
B. collecting data of analysts and users;
C. constructing an analyst portrait characteristic;
D. an analyst portrait feature acquisition interface and a presentation interface are provided.
As a further technical scheme of the invention: the step A is specifically as follows: acquiring basic information of an analyst from a platform database to form basic characteristics of the analyst; and acquiring basic information of a user, constructing related basic information of stocks, and providing basic data for subsequent portrait characteristics.
As a further technical scheme of the invention: the step A comprises the following steps: 1) Obtaining analyst data, such as personal information of the analysts, such as years of employment, security companies, ages, sexes, academic calendars and the like, and forming basic characteristics of the analysts; 2) Acquiring registered user information, such as personal information of user age, gender and the like, and calculating high-dimensional characteristics of an analyst; 3) And constructing stock basic information including stock names, codes, affiliated industries, plates, concepts and the like, and being used for assisting in completing information extraction and characteristic construction of data such as researches, statements, comments and the like of analysts.
As a further technical scheme of the invention: analyst behavior data includes: history of the research and the comments, and user browsing records, comment records and praise records of each research and each comment which are simultaneously obtained.
As a further technical scheme of the invention: user behavior data includes analysts and panels of interest, search, browse, review, and like histories.
As a further technical scheme of the invention: the step C is specifically as follows: and performing user feature extraction and analyst feature extraction by using the acquired analyst and user behavior data and combining stock industry basic information.
As a further technical scheme of the invention: the step C comprises the following steps: the method comprises the steps of calculating time attributes of analysts and user behavior data by using a statistical method, updating frequency, online time and consulting indexes, calculating popularity of the analysts according to data of articles collected, commented and browsed by the analysts and attention people of the analysts, and extracting stocks, plates, operation suggestions and the like in the research and report of the analysts by using a natural language processing technology to construct plate attributes, long and short line attributes and individual stock preference attributes of the analysts.
As a further technical scheme of the invention: the step D is specifically as follows: and D, storing the analyst portrait characteristics established in the step C into a database, updating the characteristics periodically, and providing support for a display module and a decision module of the platform through an application interface.
As a further technical scheme of the invention: the application interface is specifically an interface for displaying the portrait characteristics to a user, a platform or an internal application interface of a system for obtaining the portrait characteristics by an internal working person of the APP.
Compared with the prior art, the invention has the beneficial effects that: the analyst portrait system established by the invention is concise in image and rich in pertinence, can provide visual analyst data display for the platform, and simultaneously provides decision support of a data layer for the system to manage analysts. The platform can be opened for the user with some analyst portrait characteristics, makes the user can be autonomic match analyst's portrait characteristics with self actual conditions, conveniently pays close attention to specific analyst accurately, obtains the newspaper of studying to own value maximize, can effectively promote user's platform content consumption experience. The omnibearing analyst image system can also provide data support for accurate marketing and personalized recommendation of the system, and operators can screen analysts and research reports by using specific image characteristics and recommend the analysts and the research reports to users by using message reminding or other modes.
Drawings
Fig. 1 is a schematic flow chart of the implementation steps of the present discovery.
Fig. 2 is a schematic diagram of an information acquisition module.
FIG. 3 is a schematic diagram of a feature calculation module.
Fig. 4 is a schematic diagram of an application module.
In the figure: 01-information 'points', 02-positioning target points, 03-direction target points, 04-virtual row numbers of the graphic dot matrixes represented by numbers 1-5, 05-virtual column numbers of the graphic dot matrixes represented by letters A-R, and 06-virtual equidistant positioning network tables.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, 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 invention.
Example 1: referring to fig. 1-4, a method for constructing an analyst representation system includes the following steps:
A. acquiring basic information of an analyst and a user, and generating stock basic information; acquiring basic information of an analyst from a platform database to form basic characteristics of the analyst; acquiring basic information of a user, constructing related basic information of stocks, and providing basic data for subsequent portrait characteristics: the specific process is as follows: 1) Obtaining analyst data, such as personal information of the analyst, such as the years of employment, the security company, the age, the sex, the school calendar and the like, to form basic characteristics of the analyst; 2) Acquiring registered user information, such as personal information of user age, gender and the like, and calculating high-dimensional characteristics of an analyst; 3) Constructing stock basic information including stock names, codes, industries, plates, concepts and the like, and being used for assisting in completing information extraction and feature construction of data such as research and report, speech, comments and the like of analysts;
B. acquiring data of analysts and users; collecting and acquiring behavioral data of an analyst, comprising: historical research and statement, wherein browsing records, comment records, praise records and the like of each research and statement and each user are obtained at the same time, and behavior data of the users, including concerned analysts and plates, and historical records of user behavior data such as searching, browsing, commenting, praise and the like, are collected and obtained;
C. constructing an analyst portrait characteristic; performing user characteristic extraction and analyst characteristic extraction by using the acquired analyst and user behavior data and combining stock industry basic information; the process is as follows: calculating time attributes, updating frequency, online time and consultation indexes of analysts and user behavior data by using a statistical method, calculating popularity of the analysts according to data of articles collected, commented and browsed by the analysts and attention people of the analysts, and constructing plate attributes, long and short line attributes and individual stock preference attributes of the analysts by using a natural language processing technology to extract stocks, plates, operation suggestions and the like in research and report and speech of the analysts;
D. providing an analyst portrait feature acquisition interface and a presentation interface: and D, storing the image characteristics of the analysts established in the step C into a database, updating the image characteristics periodically, and providing support for a display module and a decision module of the platform through an application interface. The application interface is specifically an interface or a platform for showing the portrait characteristics to a user or an internal application interface of a system for obtaining the portrait characteristics by an APP internal worker.
Example 2: on the basis of embodiment 1, the analysts representation system used in the design is divided into three modules: and the information acquisition module, the characteristic calculation module and the application module.
The related information of the stock in this embodiment includes a stock name, a stock alias, a stock code, an industry plate where the stock is located, a concept, and the like, and is used for information extraction and use of a later analyst for researching and reporting and saying.
And 102, acquiring basic information of the user and basic information of the analyst.
In this embodiment, the basic information of the user mainly includes age, sex, occupation, academic calendar, and the like.
Basic information of the analysts, including age, age of the practitioner, company of the analysts, and sex.
And 103, acquiring historical behaviors of the user and historical behaviors of the analyst.
In this embodiment, the user historical behaviors include analysts, which the user focuses on, and history records of searching, browsing, commenting, etc.
The historical behaviors of the analysts comprise historical research reports and statements of the analysts, and the user browsing amount, the comment number and the praise number of each research report and each statement are obtained simultaneously.
Step 201, constructing investment knowledge related to stocks, and designing a stock investment behavior label for learning analyst characteristics from analyst research and statement.
The stock investment behavior label in this embodiment, i.e., the investment operation suggestion of the analyst, is used in combination with the stock instance and time for later computing the analyst's investment-related characteristics.
In step 202, the analyst calculates the statistical features, with a statistical period of three months.
And processing the information acquired in the previous step by using a statistical method, such as counting, mean, mode, median, normalization and the like, to obtain the statistical characteristics of the analyst.
In this embodiment, the statistical features of the analysts include the following dimensions: time attributes, popularity, stock preferences, industry preferences, acceptance, specialty, length of formation, liveness, etc.
The analysts' time attribute, also known as the morning, middle and evening attribute, has four dimensions: morning, noon, afternoon, evening. The ratio of the operation times of the statistical analyst in the four time periods to the total operation times is the value of the corresponding dimension, and the operation times comprise the steps of publishing a study, publishing a speech, and replying comments and messages.
Online interval of analysts: the average of the time intervals between two activities by the statistical analyst, wherein activities within half an hour are considered to be the same activity.
Last time analyst came on: the analyst last online activity was a distance from the current day.
Liveness of analysts: the liveness comprises the liveness of the research and report and the liveness of the speech, and the liveness is calculated in a mode of average interval time of the analyst publishing the research and report or the speech in a statistical period, wherein the research and report takes days as a unit, and the speech takes hours as a unit.
Popularity of analysts: the amount of users who have focused on the analysts is calculated.
Analyst's stock preferences: from the analysts' paper and speech, using the knowledge obtained in step 101, stock information is extracted, the ratio of the number of times each stock is referred to the total stock is counted, top5 is taken, and the ratio is taken.
Panel preferences of analysts: from the analyst's study and speech, the knowledge obtained in step 101 is used to extract plate information, the ratio of the number of times each plate is referred to the number of times all plates are referred to is counted, top5 is taken, and the ratio is taken.
And step 203, calculating to obtain the abstract characteristics of the analyst from the research and report of the analyst and the key information in advance of the language by using a natural language processing technology and a machine learning technology.
In this embodiment, the abstract features of the analysts include the analysts' long and short line preferences, subject preferences, investment age, gold attribute:
the investment preferences of the user, which are used to characterize whether analysts tend to give value or subject investment advice, extract stocks mentioned in the analyst's paper and speech, and calculate the value share ratio recommended by the analysts.
In the present embodiment, the white horse stock is regarded as a value investment, and is not regarded as a subject investment.
Analyzing the preference of teachers to long lines and short lines: the method comprises the steps of constructing a top short line model by using a natural language processing technology, identifying the operation of an analyst on a stock investment suggestion in each study and statement, counting the change frequency, defining the buying and selling operation in one week as a short line, defining the buying and selling operation in more than one month in one week as a middle line, defining the buying and selling operation in more than one month in longer time as a long line, and then counting the proportion of the total number of each type of operation number to obtain the values of three dimensions.
The analyst's investment age characteristics are calculated as follows:
1. count the number of all users with age records belonging to each age (e.g., 20 x, 21 y, etc.)
2. Statistical attention to the number of aged users belonging to each age group (e.g., p 20 years, q 21 years, etc.) of an analyst
3. Penalizing the purchasing age distribution in b) with 1) the original age distribution (e.g., 20 years p/x people, 21 years q/y people)
4.3 the age corresponding to the maximum value in the results is the age of investment of the analyst
In this embodiment, the process of calculating the gold medal attribute of the analyst is as follows:
1. calculating profit creating capacity: number of times of purchase
2. Calculation accuracy: the percentage of the stock historically recommended by the statistical change analyst to the future recommended period of time, the time horizon, depends on whether the investment advice belongs to the long, medium or short line.
3. And (3) calculating the scarcity: and (4) counting the stock recommended by each analyst, the total number of the analysts recommended recently, and taking the average value of all recommendation times.
The three characteristics (profit creating ability, accuracy and scarcity) are subjected to linear normalization processing and are mapped to a [0 ] interval
A weight (all 1's are currently set) is set for each feature, and new feature values f1, f2, f3 are obtained.
Calculation of gold medal attribute values based on analysts of f1, f2, f3 using TOPSIS method
In this embodiment, the system updates the representation system once a day so that the representation system can accurately capture the change in the analyst's representation dimensions.
In this embodiment, after the analyst completes the calculation, interface support may be provided for other application modules of the system. The system mainly comprises three types of applications, namely a background management interface, an accurate recommendation interface and a platform display interface.
Step 301, the background display and analyst maintenance interface module uses the image characteristics of the analysts to provide decision support for the analysts management and analysis in the background, and can give suggestions of the platform on the shelves of the analysts, and also can provide release suggestions such as pricing and research and report for the analysts.
Step 302, the image characteristics of the analysts are used in an accurate marketing system and a gold medal analyst screening system.
The portrait characteristics established by the embodiment of the invention are concise in image and rich in pertinence, operators can match the portrait characteristics with user data, and can conveniently and accurately select appropriate analysts and reports to be pushed to users, so that the repurchase rate of the users can be obviously improved.
And the gold medal analysts can be selected through the gold medal attributes of the analysts, and the gold medal analysts can be provided for the platform to carry out marketing and recommendation activities for support.
And step 303, screening the analysts by using the image characteristics specific to the analysts, and displaying the screened analysts in a specific area to realize personalized recommendation of the system.
The stock investment platform can use the analyst portrait characteristics established by the embodiment of the invention according to the display strategies set by different modules, and selects an analyst candidate set by combining the preference of the user, and the analyst candidate set is displayed to the user on the platform, so that the individual recommendation of the analyst is realized.
The embodiment is an example of the method aiming at the picture of the analyst of the stock investment platform, and the method is not limited to the picture construction of the analyst and is also suitable for scenes such as the picture construction of the user of the investment platform.
Analyst gold attribute, using three basic characteristics of analysts: creativity, accuracy, scarcity, and setting each feature weight to 1.0, calculated using the TOPSIS method, where more analysts portrait base features can be considered for obtaining gold analyst features that meet the platform policy, where the number of base features and their weight settings do not affect the claims of this patent.
In the application module, each application can select one or more of the analyst portrait features according to the requirements of the application, and for different modules, the selected features are different and may influence the effect of the corresponding module, but do not have a decisive influence.
In the application module, the analyst portrait system can provide data support for an analyst management system, an accurate marketing system, a personalized recommendation system and the like, and can also provide more modules for a platform, the more the modules are applied, the higher the value of the analyst portrait system is, and the application range of the analyst portrait system does not influence the patent claims.
In the stock preference and plate preference characteristics of analysts, we select stock and plates of TOP5 to represent the preferences corresponding to analysts, and the number of selected TOPs is different, which will have a certain influence on the expression of preference characteristics of analysts and subsequent application modules, but has no decisive influence on the two characteristics, so the difference of TOP number selection does not affect the claims of the patent.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Furthermore, it should be understood that although the present specification describes embodiments, not every embodiment includes only a single embodiment, and such description is for clarity purposes only, and it is to be understood that all embodiments may be combined as appropriate by one of ordinary skill in the art to form other embodiments as will be apparent to those of skill in the art from the description herein.
Claims (1)
1. A method for constructing an analysts representation system, comprising the steps of:
A. acquiring basic information of an analyst and a user, and generating stock basic information;
the step A is specifically as follows: acquiring basic information of an analyst from a platform database to form basic characteristics of the analyst; acquiring basic information of a user, constructing stock related basic information, and providing basic data for subsequent portrait characteristics;
the step A comprises the following steps: 1) Obtaining analyst data, such as the years of the analysts, the security companies, the ages, the sexes and the personal information of the academic calendar, and forming the basic characteristics of the analysts; 2) Acquiring registered user information, such as personal information of user age and gender, and calculating high-dimensional characteristics of an analyst; 3) Constructing stock basic information including stock names, codes, industries, plates and concepts, and assisting in completing information extraction and feature construction of data of research, statement and comment of analysts;
B. acquiring data of analysts and users;
analyst behavior data includes: historical research reports and statements, and user browsing records, comment records and praise records of each research report and each statement are obtained simultaneously;
the user behavior data comprises concerned analysts and plates, searching, browsing, commenting and commenting history;
C. constructing the image characteristics of an analyst;
the step C is specifically as follows: performing user feature extraction and analyst feature extraction by using the acquired analyst and user behavior data and combining stock industry basic information;
the step C comprises the following steps: calculating the time attribute, updating frequency, online time and consulting index of an analyst by using a statistical method for the behavioral data of the analyst and the user, calculating the popularity of the analyst according to the collection, comment and browsed data of the analyst and the attention number of the analyst, and extracting the stock, plate and operation suggestion in the research and report of the analyst and the language by using a natural language processing technology to construct the plate attribute, the long-short line attribute and the individual stock preference attribute of the analyst;
D. providing an analyst portrait feature acquisition interface and a display interface;
the step D is specifically as follows: c, storing the image characteristics of the analysts established in the step C into a database, updating the image characteristics periodically, and providing support for a display module and a decision module of the platform through an application interface;
the application interface is specifically an interface for displaying the portrait characteristics to a user, a platform or an internal application interface of a system for obtaining the portrait characteristics by an internal working person of the APP.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2007081519A2 (en) * | 2005-12-30 | 2007-07-19 | Steven Kays | Genius adaptive design |
| CN109785144A (en) * | 2019-01-18 | 2019-05-21 | 国家电网有限公司 | A kind of assets classes method, apparatus, equipment and medium |
Family Cites Families (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7315837B2 (en) * | 1999-11-01 | 2008-01-01 | Accenture Llp | Communication interface for a financial modeling and counseling system |
| CN104867037B (en) * | 2015-05-29 | 2019-03-26 | 北京京东尚科信息技术有限公司 | A kind of data processing method and device of Figure Characteristics |
| CN107515943A (en) * | 2017-09-01 | 2017-12-26 | 安徽教育网络出版有限公司 | User preferences modeling construction method based on collaborative filtering |
| CN110019392B (en) * | 2017-11-07 | 2021-07-23 | 北京大米科技有限公司 | Method for recommending teachers in network teaching system |
| CN109241117A (en) * | 2018-08-02 | 2019-01-18 | 上海慧岳信息科技有限公司 | A kind of matched method and apparatus of painter |
| CN109492029A (en) * | 2018-11-13 | 2019-03-19 | 程在舒 | A kind of stock information displaying method |
-
2019
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2007081519A2 (en) * | 2005-12-30 | 2007-07-19 | Steven Kays | Genius adaptive design |
| CN109785144A (en) * | 2019-01-18 | 2019-05-21 | 国家电网有限公司 | A kind of assets classes method, apparatus, equipment and medium |
Non-Patent Citations (2)
| Title |
|---|
| Masashi Okubo等."Development of avatar generating system by constructing portraits made by friends".《 2013 IEEE RO-MAN》.2013,第1-4页. * |
| 齐艳平等."分析师关注对股价同步性的影响——基于新浪财经网分析师股票推荐数据".《中国林业经济》.2018,第84-92页. * |
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