CN109214919A

CN109214919A - A kind of information recommendation method, device, equipment and medium

Info

Publication number: CN109214919A
Application number: CN201810994930.1A
Authority: CN
Inventors: 傅桔选; 尹方亮; 郭锐
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2018-08-29
Filing date: 2018-08-29
Publication date: 2019-01-15

Abstract

The invention discloses a kind of information recommendation method, device, equipment and medium, it is applied to Internet technical field, it is lower that there are accuracy solving the problems, such as information recommendation method in the prior art.Specifically: it calls industry data analysis process the first historical trading data corresponding to various industries to analyze, obtains there are multiple industries of Herd Behavior and alternately industry；Call type data analysis process analyzes each alternative corresponding second historical trading data of industry, obtains the number for the specified type component stock that each alternative industry respectively contains；According to the number for the specified type component stock that each alternative industry respectively contains, chooses popular industry and show.In this way, according to Herd Behavior and comprising the number of specified type component stock choose popular industry so that the popular industry selected had both met current industry Investment Trend, sets requirement is also complied with, to improve the accuracy that popular industry is recommended.

Description

Information recommendation method, device, equipment and medium

Technical Field

The invention relates to the technical field of internet, in particular to an information recommendation method, device, equipment and medium.

Background

With the continuous development of internet technology, personalized recommendation becomes a research hotspot in various fields. In the field of securities, the recommendation of popular industries is generated in order to provide stock investment references for users, realize functions of intelligent stock selection, intelligent stock diagnosis and the like.

At present, most popular industry recommendation methods singly use historical rate of return ranking or historical rotation rules and the like to predict and recommend popular industries to users, and the problem that the accuracy of popular industries recommended to users is low is caused.

Disclosure of Invention

The embodiment of the invention provides an information recommendation method, an information recommendation device, information recommendation equipment and an information recommendation medium, which are used for solving the problem of low accuracy of a popular industry recommendation method in the prior art.

The embodiment of the invention provides the following specific technical scheme:

the embodiment of the invention provides an information recommendation method, which comprises the following steps:

when an information recommendation request is received, first historical transaction data corresponding to each industry are inquired from a historical transaction information database;

calling an industry data analysis process to analyze the first historical transaction data corresponding to each industry to obtain a plurality of industries with a flocked effect and using the industries as alternative industries;

inquiring second historical transaction data corresponding to each alternative industry from a historical transaction information database;

calling a type data analysis process to analyze second historical transaction data corresponding to each alternative industry to obtain the number of specified type component stocks contained in each alternative industry;

and selecting and displaying popular industries from the alternative industries according to the number of the specified type component stocks contained in each alternative industry.

In the information recommendation method provided by the embodiment of the invention, an industry data analysis process is called to analyze the first historical transaction data corresponding to each industry to obtain a plurality of industries with a flocked effect and serve as alternative industries, and the method comprises the following steps of calling the industry data analysis process to execute the following operations:

determining an industry average daily rate of return sequence and an industry rate of return absolute deviation sequence which respectively correspond to each industry in a first time range according to first historical transaction data which respectively correspond to each industry;

analyzing the industry average daily rate of return sequence and the industry rate of return absolute deviation sequence corresponding to each industry to obtain the mapping relation between the industry average daily rate of return and the industry rate of return absolute deviation corresponding to each industry;

and selecting industries of which the corresponding mapping relations are nonlinear mapping relations from various industries as alternative industries.

In the information recommendation method provided by the embodiment of the invention, the determining the industry average daily rate of return sequence and the industry rate of return absolute deviation sequence respectively corresponding to each industry in a first time range according to the first historical transaction data respectively corresponding to each industry comprises the following steps:

analyzing the first historical transaction data corresponding to each industry to obtain a daily yield sequence of each component stock in each industry within a first time range;

determining an industry average daily rate sequence corresponding to each industry in a first time range according to the component stock daily rate sequence of each component stock in the first time range in each industry; and the number of the first and second groups,

and determining the industry yield absolute deviation sequence corresponding to each industry in the first time range according to the component stock daily yield sequence and the industry average daily yield sequence corresponding to each component stock in the first time range.

In the information recommendation method provided in the embodiment of the present invention, the analyzing the industry average daily profitability sequence and the industry profitability absolute deviation sequence corresponding to each industry to obtain the mapping relationship between the industry average daily profitability and the industry profitability absolute deviation corresponding to each industry includes:

and respectively carrying out polynomial regression analysis on the industry average daily rate of return sequence and the industry absolute rate of return deviation sequence which respectively correspond to each industry to obtain a regression expression which respectively corresponds to each industry and represents the mapping relation between the industry average daily rate of return and the industry absolute rate of return deviation.

In the information recommendation method provided in the embodiment of the present invention, an industry whose corresponding mapping relationship is a nonlinear mapping relationship is selected from various industries as an alternative industry, and the method includes:

and selecting industries of which the corresponding regression expressions are quadratic polynomials from various industries as alternative industries.

In the information recommendation method provided in the embodiment of the present invention, an industry whose corresponding mapping relationship is a nonlinear mapping relationship is selected from various industries as an alternative industry, and the method further includes:

selecting industries with negative quadratic term coefficients in corresponding regression expressions from all industries as alternative industries; or,

and respectively carrying out significance analysis on the regression expressions corresponding to the industries to obtain significance values corresponding to the industries, and selecting the industries of which the quadratic coefficient of the corresponding regression expressions is negative and the significance values are greater than a first set threshold value from the industries as alternative industries.

In the information recommendation method provided by the embodiment of the invention, if the specified type component stock is a component stock with a leader effect, a type data analysis process is invoked to analyze second historical transaction data corresponding to each alternative industry to obtain the number of the specified type component stocks contained in each alternative industry, and the method comprises the following steps:

analyzing second historical transaction data corresponding to each alternative industry to obtain a component stock closing price sequence of each component stock in a second time range, a first component stock transaction sequence in a third time range and a second component stock transaction sequence in a fourth time range, wherein the component stocks are contained in each alternative industry;

determining the industry average expansion amplitude corresponding to each alternative industry and the component stock expansion amplitude, the component stock average transaction amount, the first component stock total transaction amount and the second component stock total transaction amount corresponding to each component stock contained in each alternative industry according to the component stock closing price sequence, the first component stock transaction amount sequence and the second component stock transaction amount sequence corresponding to each component stock contained in each alternative industry;

selecting the component stock meeting the expansion condition from the component stocks respectively contained in each alternative industry as the specified type component stock respectively contained in each alternative industry according to the industry average expansion amplitude respectively corresponding to each alternative industry and the component stock expansion amplitude, the component stock average transaction amount, the first component stock total transaction amount and the second component stock total transaction amount respectively corresponding to each component stock contained in each alternative industry;

and counting the number of the specified type component stocks contained in each alternative industry.

In the information recommendation method provided in the embodiment of the present invention, the amplitude condition at least includes any one or a combination of the following:

the component strand expansion amplitude is not less than a second set threshold value;

the component stock expansion amplitude is not less than the industry average expansion amplitude, wherein the industry average expansion amplitude is greater than a third set threshold value;

and the ranking of the average contribution amount of the component stocks is not less than a fourth set threshold, and the total contribution amount of the first component stock is greater than that of the second component stock.

In the information recommendation method provided in the embodiment of the present invention, the selecting of hot industries from each candidate industry according to the number of specified type component stocks included in each candidate industry includes:

and selecting the alternative industries containing specified type component stocks of which the number is not less than a fifth set threshold value from the alternative industries as hot industries.

In the information recommendation method provided in the embodiment of the present invention, an alternative industry including designated type component stocks of which the number is not less than a fifth set threshold is selected from among the alternative industries as a popular industry, and the method further includes:

randomly selecting the alternative industries with set number as hot industries from the alternative industries with the number of the contained specified type component stocks not less than a fifth set threshold; or,

and selecting the alternative industries with set number as hot industries from the alternative industries with the number of the contained specified type component stocks not less than a fifth set threshold value according to the sequence from high to low of the number of the contained specified type component stocks.

An embodiment of the present invention provides an information recommendation apparatus, including:

the first query unit is used for querying first historical transaction data corresponding to each industry from the historical transaction information database when receiving the information recommendation request;

the first calling unit is used for calling an industry data analysis process to analyze the first historical transaction data which respectively correspond to the industries and are inquired by the first inquiry unit, so that a plurality of industries with the sheep flock effect are obtained and are used as alternative industries;

the second query unit is used for querying second historical transaction data which are obtained by the first calling unit and respectively correspond to each alternative industry from the historical transaction information database;

the second calling unit is used for calling a type data analysis process to analyze second historical transaction data which respectively correspond to each alternative industry and are inquired by the second inquiry unit so as to obtain the number of specified type component stocks contained in each alternative industry;

the industry selecting unit is used for selecting hot industries from all the alternative industries according to the number of the specified type component stocks contained in all the alternative industries obtained by the second calling unit;

and the recommendation display unit is used for displaying the hot industry selected by the industry selection unit.

In the information recommendation device provided in the embodiment of the present invention, when an industry data analysis process is invoked to analyze first historical transaction data corresponding to each industry to obtain a plurality of industries having a flocked effect and serving as alternative industries, the first invoking unit is specifically configured to invoke the industry data analysis process to perform the following operations:

In the information recommendation device provided in the embodiment of the present invention, when determining the industry average daily rate of return sequence and the industry absolute deviation sequence of the rate of return in the industry, which correspond to each industry in the first time range, according to the first historical transaction data corresponding to each industry, the first invoking unit is specifically configured to:

In the information recommendation device provided in the embodiment of the present invention, when the industry average daily gain sequence and the industry gain absolute deviation sequence corresponding to each industry are analyzed to obtain the mapping relationship between the industry average daily gain and the industry gain absolute deviation corresponding to each industry, the first calling unit is specifically configured to:

In the information recommendation apparatus provided in the embodiment of the present invention, when an industry whose corresponding mapping relationship is a nonlinear mapping relationship is selected from various industries as an alternative industry, the first invoking unit is specifically configured to:

In the information recommendation apparatus provided in the embodiment of the present invention, when an industry whose corresponding mapping relationship is a nonlinear mapping relationship is selected from various industries as an alternative industry, the first invoking unit is further configured to:

In the information recommendation apparatus provided in the embodiment of the present invention, if the specified type component stock determined by the second invocation unit is a component stock having a leader effect, the second invocation unit is specifically configured to invoke the type data analysis process to perform the following operations when the second invocation type data analysis process analyzes the second historical transaction data corresponding to each alternative industry to obtain the number of the specified type component stocks included in each alternative industry:

In the information recommendation apparatus provided in the embodiment of the present invention, the amplitude condition adopted by the second invoking unit when selecting the designated type of component shares at least includes any one or a combination of the following:

In the information recommendation apparatus provided in the embodiment of the present invention, when a popular industry is selected from the various alternative industries according to the number of designated type component shares included in each of the alternative industries obtained by the second invoking unit, the industry selecting unit is specifically configured to:

In the information recommendation apparatus provided in the embodiment of the present invention, when an alternative industry including a number of specified type component stocks not less than a fifth set threshold is selected from among the alternative industries as a popular industry, the industry selection unit is further configured to:

The embodiment of the invention provides a nonvolatile computer readable storage medium, wherein an executable program is stored in the nonvolatile computer readable storage medium, and the executable program is executed by a processor to realize the steps of the information recommendation method provided by the embodiment of the invention.

The embodiment of the invention provides information recommendation equipment, which comprises a memory, a processor and a computer program stored on the memory, wherein the processor executes the computer program to realize the steps of the information recommendation method provided by the embodiment of the invention.

The embodiment of the invention has the following beneficial effects:

in the embodiment of the invention, the alternative industries are selected through the flock effect, so that the selected alternative industries can accord with the current industry investment trend, the popular industries selected from the alternative industries can be ensured to accord with the current industry investment trend, the accuracy of the popular industries recommended to users is further improved, in addition, the popular industries are selected from the alternative industries according to the number of the specified type component stocks, the selected popular industries can better accord with the set requirements, and the accuracy of the popular industries recommended to users is further improved.

Drawings

Fig. 1 is a schematic diagram illustrating a principle of generating a herd effect according to an embodiment of the present invention;

fig. 2 is a schematic flowchart of an information recommendation method according to an embodiment of the present invention;

fig. 3A is a schematic diagram of a mapping relationship between an industry average daily profitability and an industry profitability absolute deviation when a flock effect exists according to an embodiment of the present invention;

fig. 3B is a schematic diagram of a mapping relationship between an industry average daily profitability and an industry profitability absolute deviation when no herd effect exists according to the embodiment of the present invention;

FIG. 4 is a schematic interface diagram for displaying a hot plate block of the hot industry according to an embodiment of the present invention;

fig. 5 is a schematic diagram illustrating a recommendation effect of an information recommendation method according to an embodiment of the present invention;

fig. 6 is a schematic functional structure diagram of an information recommendation device according to an embodiment of the present invention;

fig. 7 is a schematic diagram of a hardware structure of an information recommendation device according to an embodiment of the present invention.

Detailed Description

Currently, there are two main recommended methods in the hot industry:

the first method comprises the following steps: a popular industry recommendation method based on historical profitability. The method mainly comprises the steps of determining the expansion degree of each industry according to the historical yield, and selecting a set number of industries as popular industries to recommend to users according to the sequence of the expansion degree from high to low.

The second method comprises the following steps: a popular industry recommendation method based on a historical rotation law. The method mainly comprises the steps of determining a historical rotation rule of the popular industry by using a frequent item set algorithm, and determining the recommendation of the popular industry to a user according to the determined historical rotation rule.

Obviously, the popular industries recommended to the user by adopting the current two popular industry recommendation methods are obtained based on single historical yield or historical rotation law, and have poor predictability and low accuracy. For this reason, the inventor of the present invention thought of selecting, from the respective industries, a plurality of industries in which the herd effect exists as candidate industries, and selecting and displaying popular industries from the respective candidate industries according to the number of designated type component stocks included in the respective candidate industries. Therefore, the alternative industries are selected through the flock effect, so that the selected alternative industries can accord with the current industry investment trend, the popular industries selected from the alternative industries can be guaranteed to accord with the current industry investment trend, the accuracy of the popular industries recommended to the user is improved, in addition, the popular industries are selected from the alternative industries according to the number of the specified type component stocks, the selected popular industries can accord with the set requirements, and the accuracy of the popular industries recommended to the user is further improved.

It is worth mentioning that references to "first," "second," "third," "fourth," and "fifth," etc. herein are used to distinguish similar objects and not necessarily to describe a particular order or sequence, it being understood that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be implemented in other than the order illustrated or described herein, and further that references to "a plurality" herein may refer to two or more.

After briefly introducing the information recommendation method provided by the embodiment of the present invention, the following clearly and completely describes the technical solution in the embodiment of the present invention with reference to the accompanying drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of embodiments of the present invention, and is not a whole embodiment. 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.

In order to facilitate understanding of the present invention, a description will be given first of all to some technical terms involved in the embodiments of the present invention.

The herd effect, which is a phenomenon that investment behaviors converge within a certain time range, is shown in fig. 1, and is a typical example of the herd effect, for example, several sheep with heads jump down a cliff, and the latter sheep jump down the cliff, which is called the "herd effect", also called the "conquer effect".

The first-collar effect is a phenomenon that acts as a collar in the flock effect and encourages other investors to converge with their investment behavior, for example, a collar sheep in the flock effect encourages other sheep to jump down a cliff.

The alternative industries are industries with herd effect in each industry.

And (3) specifying the type component stock as the component stock with the first-collar effect.

The popular industry is the industry which has the sheep flock effect and contains specified type component stocks of which the number is not less than a set threshold value.

Next, an information recommendation method according to an exemplary embodiment of the present invention is described, and specifically, referring to fig. 2, a flow of the information recommendation method according to the exemplary embodiment of the present invention is as follows:

step 200: and when an information recommendation request is received, first historical transaction data corresponding to each industry are inquired from the historical transaction information database.

In practical application, a user may initiate an information recommendation request by clicking an icon such as "industry recommendation", "refresh recommendation", or the like displayed on an information recommendation client, and of course, the information recommendation request may also be initiated automatically when the user opens the information recommendation client, or may be initiated periodically by the information recommendation client, which is not limited specifically herein.

In specific implementation, in order to reduce subsequent calculation amount, when an information recommendation request is received, historical transaction data corresponding to each industry in a first time range can be inquired from a historical transaction information database, wherein the historical transaction data corresponding to each industry includes but is not limited to historical share transaction data of each share included in the industry in the first time range.

Taking an industry as an example, the following describes the acquisition of historical transaction data corresponding to the industry. Assuming that the industry comprises N component stocks, and the first time range is the latest T days, historical component stock transaction data corresponding to the N component stocks in the latest T days can be obtained, and the obtained historical component stock transaction data corresponding to the N component stocks in the latest T days are used as the historical transaction data of the industry in the latest T days.

Step 201: and calling an industry data analysis process to analyze the first historical transaction data corresponding to each industry to obtain a plurality of industries with the herd effect and use the industries as alternative industries.

In particular implementation, the following operations may be performed by invoking an industry data analysis process:

firstly, determining an industry average daily rate of return sequence and an industry rate of return absolute deviation sequence which correspond to each industry in a first time range according to first historical transaction data which correspond to each industry.

Specifically, the first historical transaction data corresponding to each industry in the first time range may be analyzed to obtain a daily component stock yield sequence of each component stock included in each industry in the first time range, where the daily component stock yield sequence in the first time range is a set of daily component stock yields of each day included in the first time range; determining an industry average daily rate of return sequence corresponding to each industry in a first time range according to the component stock daily rate of return sequence of each component stock in the first time range, wherein the industry average daily rate of return sequence corresponding to each industry in the first time range is a set of the industry average daily rates of return of each day in the first time range; and finally, determining the industry yield absolute deviation sequence corresponding to each industry in the first time range according to the component stock daily yield sequence and the industry average daily yield sequence corresponding to each component stock in each industry in the first time range, wherein the industry yield absolute deviation sequence corresponding to each industry in the first time range is a set of the industry yield absolute deviations of each day in the first time range.

Taking an industry as an example, the determination of the industry average daily profitability of any day in the industry average daily profitability sequence corresponding to the industry and the industry profitability absolute deviation of any day in the industry profitability absolute deviation sequence corresponding to the industry is described below. Assuming that the industry contains N component shares, and the first time range is the last T days, the daily yield of the component shares i on the T day can be recorded as R (i, T), according to the daily yield R (i, t) of the N component stocks in the t day, the average daily profitability of the industry on day t may be determined as R (t) sum (R (i, t))/N, wherein sum is a summation function, and further, the absolute deviation of the industry profitability of the industry on the t day can be determined as csad (t) ═ sum (/ R (i, t) -R (t))/N according to the average value of the difference between the component stock daily profitability R (i, t) of the N component stocks on the t day and the industry average daily profitability R (t) ═ sum (R (i, t))/N of the industry on the t day. Thus, the industry average daily profitability sequence of the industry in the last T days can be obtained according to the determined industry average daily profitability R (T) ═ sum (R (i, T))/N of each day, and the industry absolute profitability sequence of the industry in the last T days can be obtained according to the determined industry absolute profitability CSAD (T) (/ R (i, T) -R (T))/N) of each day.

And then, analyzing the industry average daily rate of return sequence and the industry rate of return absolute deviation sequence corresponding to each industry to obtain the mapping relation between the industry average daily rate of return and the industry rate of return absolute deviation corresponding to each industry.

In specific implementation, in order to ensure the accuracy of the obtained mapping relationship, polynomial regression analysis may be performed on the industry average daily rate of return sequence and the industry rate of return absolute deviation sequence corresponding to each industry, respectively, so as to obtain a regression expression representing the mapping relationship between the industry average daily rate of return and the industry rate of return absolute deviation corresponding to each industry.

And finally, selecting industries of which the corresponding mapping relations are nonlinear mapping relations from all industries as alternative industries.

In general, referring to fig. 3A and 3B, the mapping relationship between the industry average daily profitability and the industry profitability absolute deviation corresponding to the industry having the flocked effect is nonlinear, while the mapping relationship between the industry average daily profitability and the industry profitability absolute deviation when the flocked effect does not exist is linear, and based on this, when an alternative industry is selected from each industry, the industry having the nonlinear mapping relationship may be selected as the corresponding mapping relationship. Specifically, if a regression expression representing the mapping relationship between the average daily profitability of the industry and the absolute deviation of the profitability of the industry, which is respectively corresponding to each industry, is obtained through polynomial regression analysis, the industry, in which the corresponding regression expression is a quadratic polynomial, can be selected from each industry as the candidate industry. In order to further improve the accuracy of the selected alternative industries, industries with negative quadratic coefficient of the corresponding regression expression can be selected from all industries to serve as the alternative industries.

Generally, the more significant the flock effect exists in the alternative industry, the greater the credibility of the alternative industry to become the popular industry, and based on this, in order to improve the credibility of the finally obtained popular industry, when the alternative industry is selected from each industry, the significance analysis can be performed on the regression expressions respectively corresponding to each industry, after the significant value respectively corresponding to each industry is obtained, the industry with the secondary term coefficient of the corresponding regression expression being negative and the significant value being greater than the first set threshold value is selected from each industry as the alternative industry, so that the alternative industry with the more significant flock effect can be selected, and the credibility of the finally obtained popular industry is improved.

The following description will be made by taking an industry as an example, and whether the industry is selected as an alternative industry is provided. Continuing to use the above example, if the polynomial regression analysis is performed on the industry average daily rate of return sequence and the industry rate of return absolute deviation sequence of the industry, the regression expression of the industry is obtained as Then the coefficient gamma of the second order polynomial can be₂And when the number is negative, determining that the sheep flock effect exists in the industry, and further taking the industry as one of alternative industries. Of course, in order to improve the credibility of the finally obtained hot industry, a regression expression of the industry can be used After the significance analysis is carried out to obtain the significance value corresponding to the industry, if the quadratic polynomial coefficient gamma of the regression expression corresponding to the industry₂If the number is negative and the significance value is greater than the first set threshold value, it can be determined that the flock effect existing in the industry is significant, and at this time, the industry can be used as one of the alternative industries.

Step 202: and querying second historical transaction data corresponding to each alternative industry from the historical transaction information database.

In a specific implementation, historical transaction data corresponding to each industry in three time periods of the second time range, the third time range and the fourth time range can be queried from the historical transaction information database, wherein the second historical transaction data corresponding to each industry in three time periods of the second time range, the third time range and the fourth time range includes, but is not limited to, historical component share transaction data of each component share included in the industry in three time periods of the second time range, the third time range and the fourth time range.

Step 203: and calling a type data analysis process to analyze the second historical transaction data corresponding to each alternative industry to obtain the number of the specified type component stocks contained in each alternative industry.

In practical application, the designated type of component stock can be flexibly set according to the recommendation requirement. For example, the component stock preferred by the user is determined according to the historical user investment data, and the component stock preferred by the user is selected from various component stocks contained in the alternative industry and set as the specified type component stock. For another example, the component stock with higher profitability is determined according to the historical profit data, the component stock with higher profitability is selected from the component stocks contained in the alternative industry and is set as the specified type component stock, and the like.

It should be noted that, in the information recommendation method according to the exemplary embodiment of the present invention, the type data analysis process and the industry data analysis process may be two independent data analysis software or may be one data analysis software, and when the type data analysis process and the industry data analysis process are one data analysis software, the data analysis software has a function of analyzing the first historical transaction data corresponding to each industry and a function of analyzing the second historical transaction data corresponding to each alternative industry, which are required in the embodiment of the present invention.

In order to improve the accuracy of the finally obtained popular industry, the embodiment of the invention sets the specified type of component stock as the component stock with the leader effect. Specifically, the following operations may be performed by invoking the type data analysis process:

first, second historical transaction data corresponding to each alternative industry are analyzed, and a component stock closing price sequence of each component stock in a second time range, a first component stock transaction sequence in a third time range and a second component stock transaction sequence in a fourth time range, which are contained in each alternative industry, are obtained.

And then, determining the industry average expansion amplitude corresponding to each alternative industry, and the component stock expansion amplitude, the component stock average transaction amount, the first component stock total transaction amount and the second component stock total transaction amount corresponding to each component stock contained in each alternative industry according to the component stock closing price sequence, the first component stock transaction amount sequence and the second component stock transaction amount sequence corresponding to each component stock contained in each alternative industry.

Specifically, the component stock expansion amplitude of each component stock contained in each alternative industry in the second time range and the industry average expansion amplitude corresponding to each alternative industry in the second time range can be determined according to the component stock closing price sequence of each component stock contained in each alternative industry in the second time range; determining the average contribution amount of each component stock in each alternative industry and the total contribution amount of the first component stock in the third time range according to the first component stock contribution amount sequence of each component stock in the third time range in each alternative industry; and determining the second component share total amount of each component share contained in each candidate industry within the fourth time range according to the second component share amount sequence of each component share contained in each candidate industry within the fourth time range.

And secondly, selecting the component stock meeting the expansion condition from the component stocks respectively contained in each alternative industry as the specified type component stock respectively contained in each alternative industry according to the industry average expansion amplitude respectively corresponding to each alternative industry and the component stock expansion amplitude, the component stock average transaction amount, the first component stock total transaction amount and the second component stock total transaction amount respectively corresponding to each contained component stock.

It is worth mentioning that the amplitude-fluctuation condition may be set to satisfy the following three conditions at the same time:

(1) the component strand expansion amplitude is not less than a second set threshold value.

(2) The component stock expansion amplitude is not less than the industry average expansion amplitude, wherein the industry average expansion amplitude is greater than a third set threshold value.

(3) And the ranking of the average contribution amount of the component stocks is not less than a fourth set threshold, and the total contribution amount of the first component stock is greater than that of the second component stock.

And finally, counting the number of the specified type component stocks contained in each alternative industry.

Whether the component stock included in an industry is a component stock with a captain effect is illustrated below by taking an example of one component stock included in the industry. Assuming that the second time range is the last 12 days, the third time range is the last 5 days, and the fourth time range is the last 10 days, the component stock quotation price sequence of the component stock in the last 12 days, the first component stock quotation sequence of the component stock in the last 5 days, and the second component stock quotation sequence of the component stock in the last 10 days may be determined, then the component stock expansion amplitude of the component stock in the last 12 days and the industry average industry expansion amplitude of the industry in the last 12 days may be determined according to the component stock quotation price sequence of the component stock in the last 12 days, and the component average quotation and the first component stock quotation of the component stock in the last 12 days may be determined according to the first component stock quotation sequence of the component stock in the last 12 days, and the second component stock quotation sequence of the component stock in the last 5 days may be determined according to the second component stock quotation sequence of the component stock in the last 5 days, and finally, determining that the component stock has a head effect when the component stock simultaneously meets three conditions that (1) the component stock expansion amplitude of the component stock in the last 12 days is not less than a second set threshold, (2) the component stock expansion amplitude of the component stock in the last 12 days is not less than the industry average expansion amplitude of the industry in which the component stock is positioned in the last 12 days, wherein the industry average expansion amplitude is greater than a third set threshold, and (3) the ranking of the component stock average transaction amount of the component stock in the last 12 days is not less than a fourth set threshold, and the first component stock total transaction amount of the component stock in the last 12 days is greater than the second component stock total transaction amount of the component stock in the last 5 days, so that the component stock can be used as a specified type component stock contained in the industry and is counted in the number of the specified type component stock contained in the industry.

Step 204: and selecting and displaying popular industries from the alternative industries according to the number of the specified type component stocks contained in each alternative industry.

In specific implementation, the candidate industries containing specified type component stocks of which the number is not less than a fifth set threshold value can be selected from the candidate industries to serve as hot industries. For example, from various alternative industries, an alternative industry containing more than 2 component stocks with the leader effect is selected as a hot industry.

In the information recommendation method according to the exemplary embodiment of the present invention, all candidate industries including designated type component stocks of which the number is not less than the fifth set threshold may be regarded as popular industries, a set number of candidate industries may be arbitrarily selected as popular industries from the candidate industries including designated type component stocks of which the number is not less than the fifth set threshold, a set number of candidate industries may be selected as popular industries from the candidate industries including designated type component stocks of which the number is not less than the fifth set threshold in the order from high to low, and a specific selection manner is not specifically limited herein. In practical application, when the popular industry is displayed to a user, the selected popular industry can be configured with equal authority, the selected popular industry is displayed according to a set rotation period, and when the rotation period is overdue, the popular industry can be selected again and displayed by adopting the information recommendation method of the exemplary embodiment of the invention.

For example: assuming that the candidate industries including more than 2 component stocks having the first-lead effect include industries a, B, C, D, E, F, etc., all of these candidate industries can be regarded as hot industries, and the same display weights are configured for the 6 industries of industries a, B, C, D, E, and F, so that the 6 industries can be displayed simultaneously, in practical applications, the 6 hot industries can be displayed simultaneously in the hot plate shown in fig. 4, in addition, the rotation cycle of each hot industry can be set to 5 days, that is, within a time range of 5 days, the hot industries displayed in the hot plate shown in fig. 4 are all the 6 industries of industries a, B, C, D, E, and F, and when the 5-day deadline expires, the information recommendation method of the exemplary embodiment of the present invention is adopted, the hot industry was re-selected and displayed in the hot plate block as shown in fig. 4.

It is worth mentioning that fig. 5 is a schematic diagram of a recommendation effect of the information recommendation method according to the exemplary embodiment of the present invention, and it can be known from the data shown in fig. 5 that the adoption of the information recommendation method according to the exemplary embodiment of the present invention to recommend the popular industry to the user can ensure that the popular industry recommended to the user meets the current industry investment trend, thereby improving the accuracy of the popular industry recommended to the user.

Next, describing the information recommendation apparatus 600 according to an exemplary embodiment of the present invention in detail, referring to fig. 6, the information recommendation apparatus 600 according to an exemplary embodiment of the present invention includes at least:

the first query unit 601 is configured to query, when receiving an information recommendation request, first historical transaction data corresponding to each industry from a historical transaction information database;

a first calling unit 602, configured to call an industry data analysis process to analyze first historical transaction data corresponding to each industry queried by the first querying unit 601, so as to obtain multiple industries with a flocked effect and use the industries as alternative industries;

a second query unit 603, configured to query, from the historical transaction information database, second historical transaction data corresponding to each alternative industry obtained by the first call unit 602;

a second calling unit 604, configured to call a type data analysis process to analyze second historical transaction data corresponding to each alternative industry queried by the second querying unit 603, so as to obtain the number of designated type component stocks included in each alternative industry;

an industry selecting unit 605, configured to select a hot industry from each alternative industry according to the number of specified type component stocks included in each alternative industry obtained by the second invoking unit 604;

and a recommendation display unit 606 for displaying the popular industry selected by the industry selection unit 605.

In the information recommendation device 600, when an industry data analysis process is invoked to analyze first historical transaction data corresponding to each industry, so as to obtain a plurality of industries with a herd effect and use the industries as alternative industries, a first invoking unit 602 is specifically configured to invoke the industry data analysis process to perform the following operations:

In the information recommendation device 600, when determining, according to the first historical transaction data corresponding to each industry, an industry average daily rate of return sequence and an industry absolute deviation sequence of rate of return in each industry within a first time range, a first invoking unit 602 is specifically configured to:

In the information recommendation apparatus 600, when the industry average daily gain sequence and the industry gain absolute deviation sequence corresponding to each industry are analyzed to obtain a mapping relationship between the industry average daily gain and the industry gain absolute deviation corresponding to each industry, the first invoking unit 602 is specifically configured to:

In the information recommendation apparatus 600, when an industry whose corresponding mapping relationship is a non-linear mapping relationship is selected from various industries as an alternative industry, the first invoking unit 602 is specifically configured to:

In the information recommendation apparatus 600, when an industry whose corresponding mapping relationship is a non-linear mapping relationship is selected from the industries as a candidate industry, the first invoking unit 602 is further configured to:

In the information recommendation apparatus 600, if the specified type component stock determined by the second invoking unit 604 is a component stock having a captain effect, when the invoking type data analysis process analyzes the second historical transaction data corresponding to each alternative industry to obtain the number of the specified type component stocks included in each alternative industry, the second invoking unit 604 is specifically configured to invoke the type data analysis process to perform the following operations:

In the information recommendation apparatus 600, the amplitude expansion condition adopted by the second calling unit 604 when selecting the specified type component stock at least includes any one or a combination of the following:

In the information recommendation apparatus 600, when a popular industry is selected from the various candidate industries according to the number of specified type component shares included in each candidate industry obtained by the second invoking unit 604, the industry selecting unit 605 is specifically configured to:

In the information recommendation apparatus 600, when an industry candidate containing the specified type component stock number not less than a fifth set threshold is selected as a popular industry from among the industry candidates, the industry selection unit 605 is further configured to:

It should be noted that, because the principle of the information recommendation apparatus 600 for solving the technical problem is similar to the information recommendation method according to the exemplary embodiment of the present invention, the implementation of the information recommendation apparatus 600 may refer to the implementation of the information recommendation method according to the exemplary embodiment of the present invention, and repeated parts are not described again.

Having described the information recommendation method and apparatus according to the exemplary embodiments of the present invention, a brief description will be given of an information recommendation device according to an exemplary embodiment of the present invention.

Referring to fig. 7, an information recommendation apparatus 700 according to an exemplary embodiment of the present invention may include a processor 71, a memory 72, and a computer program stored on the memory 72, and the processor 71 implements the steps in the information recommendation method according to an exemplary embodiment of the present invention when executing the computer program.

It should be noted that the information recommendation device 700 shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of the embodiments of the present invention.

The Memory 72 may include readable media in the form of volatile Memory, such as Random Access Memory (RAM) 721 and/or cache Memory 722, and may further include Read Only Memory (ROM) 723.

Memory 72 may also include program means 725 having a set (at least one) of program modules 724, program modules 724 including, but not limited to: an operating subsystem, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.

The information recommendation device 700 may also communicate with one or more external devices 74 (e.g., a keyboard, a remote control, etc.), with one or more devices that enable a user to interact with the information recommendation device 700, and/or with any device (e.g., a router, a modem, etc.) that enables the information recommendation device 700 to communicate with one or more other information recommendation devices 700. This communication may be via an Input/Output (I/O) interface 75. Also, the information recommendation device 700 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public Network, such as the internet) via the Network adapter 76. As shown in FIG. 7, the network adapter 76 communicates with the other modules of the information recommendation device 700 via the bus 73. It should be understood that although not shown in FIG. 7, other hardware and/or software modules may be used in conjunction with the information recommendation device 700, including but not limited to: microcode, device drivers, Redundant processors, external disk drive Arrays, disk array (RAID) subsystems, tape drives, and data backup storage subsystems, to name a few.

The following describes a non-volatile computer-readable storage medium that is an exemplary embodiment of the present invention. Embodiments of the present invention provide a non-volatile computer-readable storage medium, which stores computer-executable instructions, where the executable programs are executed by a processor to implement various steps of an information recommendation method according to an exemplary embodiment of the present invention. Specifically, the executable program may be built in the information recommendation apparatus 700, and thus, the information recommendation apparatus 700 may implement the steps of the various information recommendation methods of the exemplary embodiments of the present invention by executing the built-in executable program.

Furthermore, the various information recommendation methods of the exemplary embodiments of the present invention may also be implemented as a program product including program code for causing the information recommendation apparatus 700 to perform the steps of the various information recommendation methods of the exemplary embodiments of the present invention when the program product is executable on the information recommendation apparatus 700.

The program product provided by the embodiment of the present invention may adopt any combination of one or more readable media, wherein the readable media may be readable signal media or readable storage media, and the readable storage media may be but not limited to systems, apparatuses or devices of electric, magnetic, optical, electromagnetic, infrared or semiconductor, or any combination thereof, and specifically, more specific examples (non-exhaustive list) of the readable storage media include: an electrical connection having one or more wires, a portable disk, a hard disk, a RAM, a ROM, an Erasable Programmable Read-Only Memory (EPROM), an optical fiber, a portable Compact disk Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

The program product provided by the embodiment of the invention can adopt a CD-ROM and comprises program codes, and can run on a computing device. However, the program product provided by the embodiments of the present invention is not limited thereto, and in the embodiments of the present invention, the readable storage medium may be any tangible medium that can contain or store the program, which can be used by or in connection with an instruction execution system, apparatus, or device.

A readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, etc., or any suitable combination of the foregoing.

Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device, partly on a remote computing device, or entirely on the remote computing device or server. In situations involving remote computing devices, the remote computing devices may be connected to the user computing device over any kind of network, such as over a LAN or WAN; alternatively, an external computing device may be connected (e.g., through the Internet using an Internet service provider).

It should be noted that although several units or sub-units of the apparatus are mentioned in the above detailed description, such division is merely exemplary and not mandatory. Indeed, the features and functions of two or more of the units described above may be embodied in one unit, according to embodiments of the invention. Conversely, the features and functions of one unit described above may be further divided into embodiments by a plurality of units.

Moreover, while the operations of the method of the invention are depicted in the drawings in a particular order, this does not require or imply that the operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.

As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, 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, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

While preferred embodiments of the present invention have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the invention.

It will be apparent to those skilled in the art that various modifications and variations can be made in the embodiments of the present invention without departing from the spirit or scope of the embodiments of the invention. Thus, if such modifications and variations of the embodiments of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to encompass such modifications and variations.

Claims

1. An information recommendation method, comprising:

querying second historical transaction data corresponding to each alternative industry from the historical transaction information database;

and selecting hot industries from the various alternative industries and displaying the hot industries according to the number of the specified type component stocks contained in the various alternative industries.

2. The information recommendation method of claim 1, wherein invoking an industry data analysis process to analyze the first historical transaction data corresponding to each industry to obtain a plurality of industries with a flocked effect and to use as alternative industries comprises invoking the industry data analysis process to perform the following operations:

analyzing the industry average daily rate of return sequence and the industry rate of return absolute deviation sequence corresponding to each industry to obtain a mapping relation between the industry average daily rate of return and the industry rate of return absolute deviation corresponding to each industry;

and selecting industries of which the corresponding mapping relations are nonlinear mapping relations from the industries as alternative industries.

3. The information recommendation method according to claim 2, wherein determining the industry average daily rate of return sequence and the industry rate of return absolute deviation sequence corresponding to each industry in a first time range according to the first historical transaction data corresponding to each industry comprises:

analyzing the first historical transaction data corresponding to each industry to obtain a daily yield sequence of each component stock in the first time range, wherein each component stock comprises each industry;

determining an industry average daily rate of return sequence corresponding to each industry in the first time range according to the component stock daily rate of return sequence of each component stock in the first time range; and the number of the first and second groups,

4. The information recommendation method according to claim 2, wherein analyzing the industry average daily profitability sequence and the industry profitability absolute deviation sequence corresponding to each industry to obtain the mapping relationship between the industry average daily profitability and the industry profitability absolute deviation corresponding to each industry comprises:

and respectively carrying out polynomial regression analysis on the industry average daily rate of return sequence and the industry rate of return absolute deviation sequence which respectively correspond to each industry to obtain a regression expression which respectively corresponds to each industry and represents the mapping relation between the industry average daily rate of return and the industry rate of return absolute deviation.

5. The information recommendation method according to claim 4, wherein selecting industries of which the corresponding mapping relationships are non-linear mapping relationships from the industries as alternative industries comprises:

and selecting industries of which the corresponding regression expressions are quadratic polynomials from the industries as the alternative industries.

6. The information recommendation method according to claim 5, wherein an industry whose corresponding mapping relationship is a non-linear mapping relationship is selected from the industries as an alternative industry, further comprising:

selecting industries with negative quadratic term coefficients in corresponding regression expressions from the industries as the alternative industries; or,

and respectively carrying out significance analysis on the regression expressions corresponding to the industries to obtain significance values corresponding to the industries, and selecting the industries of which the quadratic coefficient of the corresponding regression expressions is negative and the significance values are greater than a first set threshold value from the industries as the alternative industries.

7. The information recommendation method according to claim 1, wherein if the specified type component stock is a component stock with a captain effect, invoking a type data analysis process to analyze second historical transaction data corresponding to each of the candidate industries to obtain the number of the specified type component stocks included in each of the candidate industries comprises invoking the type data analysis process to perform the following operations:

selecting the component stock meeting the expansion condition from the component stocks contained in each alternative industry as the specified type component stock contained in each alternative industry according to the industry average expansion amplitude corresponding to each alternative industry and the component stock expansion amplitude, the component stock average transaction amount, the first component stock total transaction amount and the second component stock total transaction amount corresponding to each contained component stock;

8. The information recommendation method of claim 7, wherein the condition of fluctuation includes at least:

9. The information recommendation method according to any one of claims 1 to 8, wherein selecting a popular industry from the respective candidate industries according to the number of specified type component stocks each contained in the respective candidate industries comprises:

and selecting the alternative industries containing specified type component stocks of which the number is not less than a fifth set threshold value from the various alternative industries as the popular industries.

10. The information recommendation method according to claim 9, wherein, as the popular industry, an industry candidate containing a specified type of component stock whose number is not less than a fifth set threshold is selected from the industry candidates, further comprising:

randomly selecting a set number of alternative industries from the alternative industries of which the number of the contained specified type component stocks is not less than a fifth set threshold value as the popular industries; or,

and selecting the alternative industries with set number as the popular industries from the alternative industries with the number not less than a fifth set threshold value according to the sequence from high to low of the number of the contained specified type component stocks.

11. An information recommendation apparatus, comprising:

the first calling unit is used for calling an industry data analysis process to analyze the first historical transaction data which are respectively corresponding to the industries and inquired by the first inquiring unit, so that a plurality of industries with a flocked effect are obtained and are used as alternative industries;

the second query unit is used for querying second historical transaction data which are obtained by the first calling unit and correspond to the alternative industries from the historical transaction information database;

the second calling unit is used for calling a type data analysis process to analyze second historical transaction data which are inquired by the second inquiry unit and respectively correspond to each alternative industry so as to obtain the number of specified type component stocks contained in each alternative industry;

the industry selecting unit is used for selecting hot industries from the various alternative industries according to the number of the specified type component stocks contained in the various alternative industries obtained by the second calling unit;

and the recommendation display unit is used for displaying the popular industry selected by the industry selection unit.

12. A non-transitory computer readable storage medium storing an executable program, the executable program being executed by a processor to implement the steps of the information recommendation method according to any one of claims 1 to 10.

13. An information recommendation device comprising a memory, a processor and a computer program stored on the memory, the processor implementing the steps of the information recommendation method of any one of claims 1-10 when executing the computer program.