CN107194814A - The method and apparatus of recommended user - Google Patents
The method and apparatus of recommended user Download PDFInfo
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- CN107194814A CN107194814A CN201610147837.8A CN201610147837A CN107194814A CN 107194814 A CN107194814 A CN 107194814A CN 201610147837 A CN201610147837 A CN 201610147837A CN 107194814 A CN107194814 A CN 107194814A
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Abstract
The application proposes a kind of method and apparatus of recommended user, and the method for the recommended user includes：After active user logs in financing platform, it is determined that the user to be recommended of the correspondence active user；On the financing platform, the user to be recommended is provided to the active user.This method can provide the user more reference informations on financing platform to user recommendation financing intelligent.
Description
Technical field
The application is related to Internet technical field, more particularly to a kind of method and apparatus of recommended user.
Background technology
With the development of the social economy, people increasingly pay close attention to Investment ＆ Financing.At present in Investment ＆ Financing platform,
Typically recommend related finance product, the higher fund of such as earning rate to user.
But, only recommend finance product to meet user's request, user needs more reference informations.
The content of the invention
The application is intended at least solve one of technical problem in correlation technique to a certain extent.
Therefore, the purpose of the application is to propose a kind of method of recommended user, this method can be with resonable
Recommend financing intelligent to user on wealth platform, provide the user more reference informations.
Further object is to propose a kind of device of recommended user.
To reach above-mentioned purpose, the method for the recommended user that the application first aspect embodiment is proposed, including：
After active user logs in financing platform, it is determined that the user to be recommended of the correspondence active user；
On the financing platform, the user to be recommended is provided to the active user.
The method for the recommended user that the application first aspect embodiment is proposed, by managing money matters on platform to current
User provides user to be recommended, can be provided the user on financing platform to user recommendation financing intelligent
More reference informations.
To reach above-mentioned purpose, the device for the recommended user that the application second aspect embodiment is proposed, including：
Determining module, for after active user logs in financing platform, it is determined that the correspondence active user's treats
The user of recommendation；Module is provided, in the financing platform, providing described to the active user
User to be recommended.
The device for the recommended user that the application second aspect embodiment is proposed, to active user on financing platform
User to be recommended is provided, can be provided the user more on financing platform to user recommendation financing intelligent
Reference information.
The aspect and advantage that the application is added will be set forth in part in the description, and will partly be retouched from following
Become obvious in stating, or recognize by the practice of the application.
Brief description of the drawings
The above-mentioned and/or additional aspect of the application and advantage are from the following description of the accompanying drawings of embodiments
It will be apparent and be readily appreciated that, wherein：
Fig. 1 is the schematic flow sheet of the method for the recommended user that the embodiment of the application one is proposed；
Fig. 2 is the schematic flow sheet of the user to be recommended of determination correspondence active user in the embodiment of the present application；
Fig. 3 is the schematic flow sheet of the method for the recommended user that another embodiment of the application is proposed；
Fig. 4 is the structural representation of the device for the recommended user that another embodiment of the application is proposed；
Fig. 5 is the structural representation of the device for the recommended user that another embodiment of the application is proposed.
Embodiment
Embodiments herein is described below in detail, the example of the embodiment is shown in the drawings, wherein certainly
Beginning to same or similar label eventually represents same or similar module or the mould with same or like function
Block.The embodiments described below with reference to the accompanying drawings are exemplary, is only used for explaining the application, and can not
It is interpreted as the limitation to the application.On the contrary, embodiments herein includes falling into attached claims
All changes, modification and equivalent in the range of spirit and intension.
Fig. 1 is the schematic flow sheet of the method for the recommended user that the embodiment of the application one is proposed.Referring to figure
1, this method includes：
S11：After active user logs in financing platform, it is determined that the correspondence active user's is to be recommended
User.
For example, user can download the application program (APP) for installing Investment ＆ Financing class on intelligent terminal,
User is after APP is opened, and can accordingly be managed money matters platform with registering and logging.
Platform manage money matters it is determined that after active user's login, it may be determined that correspondence active user's is to be recommended
User.
In some embodiments, referring to Fig. 2, it is determined that the flow of the user to be recommended of correspondence active user
It can include：
S21：Obtain the attribute information of the active user.
Wherein, attribute information is, for example, classification information.
Further, each classification can correspond to a kind of investment custom, for example, investment custom can divide
For sane type and radical type, then sane type can be corresponded to respectively and radical type is divided into a kind of classification.
Specifically, financing platform can be analyzed the historical behavior data of user, determine user's
Classification.For example, the finance product that financing platform can analyze user's history purchase, browse, to buy
Exemplified by, it is assumed that the finance product of user's purchase wholly or largely belongs to the finance product of sane type, then
It can determine that user belongs to the corresponding classification of sane type (assuming that category information is referred to as first category).
After the classification of user is determined, user can be identified to (such as user name) and entered with classification information
Row correspondence is preserved, so that after active user logs in, corresponding classification is obtained by the user name of login
Information, obtains the attribute information of active user.
S22：It is determined that having the other users of identical attribute information with the active user.
Assuming that when the attribute information for determining active user is first category, financing platform can be according to preservation
Other users user's mark and the corresponding relation of classification information, it is determined that belonging to other of first category
User.
S23：In the other users, user to be recommended is determined.
For example, financing platform determines user to be recommended in the other users of first category.
Optionally, financing platform can determine user as to be recommended in other users according to earning rate
User.
For example, selection earning rate is used as user to be recommended higher than the user of active user's preset value.
For example, preset value is 20%, then can first obtain the earning rate of active user, then with active user
In generic other users, selection earning rate is made higher than 20% other users of the earning rate of active user
For user to be recommended.
It is understood that the number that can preset user to be recommended is predetermined number, if then above-mentioned
Earning rate is more than predetermined number higher than 20% other users of the earning rate of active user, then can be according to pre-
Imputation method is further screened, to determine user to be recommended.
In addition, if the number of the above-mentioned other users filtered out is equal to predetermined number, then it will directly filter out
Other users be used as user to be recommended.
If the number of the above-mentioned other users filtered out is less than predetermined number, can also be according to other algorithms
Further selection user determines user to be recommended to reach predetermined number.
Particular content may refer to subsequent embodiment.
S12：On the financing platform, the user to be recommended is provided to the active user.Example
Such as, user to be recommended is properly termed as the intelligent that manages money matters, then manages money matters platform after financing intelligent is determined, can be with
The information (such as user name, head portrait) for the intelligent that manages money matters is presented in the homepage or other pages of financing platform
User.
, can be with resonable by providing user to be recommended to active user on financing platform in the present embodiment
Recommend financing intelligent to user on wealth platform, provide the user more reference informations.Further, pass through
The attribute information of active user is determined, and in there are the other users of identical attribute information with active user
User to be recommended is determined, the user more matched with itself can be recommended to user, so as to improve recommended user
Matching efficiency.
Fig. 3 is the schematic flow sheet of the method for the recommended user that another embodiment of the application is proposed.Referring to Fig. 3,
This method includes：
S31：According to default investment target, different users is clustered.
It is understood that the executive agent of the present embodiment can be a kind of financing platform, for example, stock
APP, fund APP etc..
Investment target can include：Operating characteristics index and investment product preference index.
In the present embodiment, exemplified by recommending stock intelligent, then investment product preference index can specifically refer to choosing
Stock preference index.
Specific operating characteristics index and preference index of selecting stocks can be selected according to the actual requirements, the present embodiment
In, operating characteristics index includes：The hand-off frequency, hand-off ratio.Preference of selecting stocks index includes：Scale flow,
Stability bandwidth, average annual dividend, industry concentration ratio.
Wherein, preference of selecting stocks index can according in certain time (one month) click on or share relevent information,
Concern and practical operation are weighted average determination.
Explanation to above-mentioned each index is as shown in table 1.
Table 1
Wherein, Chameleon algorithms can be used during cluster.During Chameleon algorithms are hierarchical clustering
Algorithms most in use.The algorithm, into a K- arest neighbors figure Gk, then passes through drawing for figure first by dataset construction
Algorithm is divided to be divided into substantial amounts of subgraph by Gk is schemed, each subgraph represents an initial submanifold, it is finally solidifying with one
Poly- hierarchical clustering algorithm merges submanifold repeatedly, finds real result cluster.Chameleon algorithms can be with
Relatively low cost is realized in the cluster of aspherical cluster, model by setting threshold value and controlling merging cluster stage
Cluster number and form.
, can be by the user clustering with identical investment custom into same by being clustered according to above-mentioned each index
One classification.
S32：In the classification that cluster is obtained, it is determined that the other users generic with active user, and
In the other users, the user that earning rate is higher than active user's preset value is filtered out.
Wherein, because above-mentioned cluster is carried out according to investment target, it therefore, it can with identical investment
The user of custom gathers for a class, so as to be exactly to have with active user with the generic other users of active user
The other users of identical investment custom.
For example, active user belongs to first category, preset value is 20%, then receipts are filtered out in first category
Beneficial rate is higher than the user of active user 20%.
Furthermore it is possible to pre-set the number of user to be recommended, such as number is 5.
If the number of the user filtered out is more than 5, follow-up S23 can be performed.
If the earning rate filtered out in same category is equal to 5 higher than the number of the user of active user 20%
It is individual, then it can regard this 5 users as user to be recommended.
If the earning rate filtered out in same category is less than 5 higher than the number of the user of active user 20%
When individual, then the user that earning rate is higher than active user 20% can be filtered out in whole classifications.
If the earning rate filtered out in whole classifications is equal to 5 higher than the number of the user of active user 20%
It is individual, then directly it regard the user filtered out as user to be recommended.
If the earning rate filtered out in whole classifications is more than 5 higher than the number of the user of active user 20%
It is individual, then it can perform follow-up S23.
If the number that earning rate is filtered out in whole classifications higher than the user of active user 20% is less than
Or equal to 5, then it regard the actual user filtered out as user to be recommended.
S33：If the number of the user filtered out is more than default recommendation number, according to default focus
The correlation of each user and active user that index calculating sifting goes out, and according to correlation from high to low suitable
Sequence, selection recommends the user of number to be used as user to be recommended.
For example, active user belongs to first category, preset value is 20%, and default recommendation number is 5,
Assuming that earning rate is more than 5 higher than the number of the user of active user 20% in first category, then can be
5 users are further filtered out in these users as user to be recommended.
When further screening, each user that can be gone out according to default focus index calculating sifting with it is current
The correlation of user.Focus index for example including：The investment product of search, investment and concern.
The specific algorithm for calculating correlation can be BM25 algorithms.BM25 algorithms are commonly used to be search for correlation
Mild-natured point.Its main thought is：Morpheme parsing, generation morpheme qi are carried out to Query；Then, for every
Individual search result D, calculates each morpheme qi and D Relevance scores, finally, the correlation by qi relative to D
Property score is weighted summation, so as to obtain Query and D Relevance scores.BM25 algorithms can be realized logical
The behaviors such as search, concern, investment are crossed to match the immediate crowd of focus.
Referring to Fig. 3, in the present embodiment, using the stock of investment and the stock of concern as focus index, with
It is determined that the immediate user of focus with active user.
For example, according to the focus with active user from closely to remote order, being higher than in the earning rate filtered out
In the user of active user 20%, 5 users are selected to be used as user to be recommended successively.
, can be with resonable by providing user to be recommended to active user on financing platform in the present embodiment
Recommend financing intelligent to user on wealth platform, provide the user more reference informations.Further, pass through
The attribute information of active user is determined, and in there are the other users of identical attribute information with active user
User to be recommended is determined, the user more matched with itself can be recommended to user, so as to improve recommended user
Matching efficiency.Further, by with active user have it is identical investment custom other users in,
Select earning rate to be higher than the user of active user's preset value, the use with more high yield can be recommended to user
Family.Further, by further being screened as focus using the stock invested and paid close attention to, it can filter out
The other users more matched with active user's focus.
Fig. 4 is the structural representation of the device for the recommended user that another embodiment of the application is proposed.The device can
With in financing platform.The device is properly termed as investing radar recommended engine.
So that stock is recommended as an example, the device can be embedded in the stock exchange system for being able to record that user's operation trace
Comprising functions such as transaction, concern, information in system, the system, the poly- precious platform of such as ant.
Referring to Fig. 4, the device 40 includes：Determining module 41 and offer module 42.
Determining module 41 is used for after active user logs in financing platform, it is determined that the correspondence active user
User to be recommended.
For example, user can download the application program (APP) for installing Investment ＆ Financing class on intelligent terminal,
User is after APP is opened, and can accordingly be managed money matters platform with registering and logging.
Platform manage money matters it is determined that after active user's login, it may be determined that correspondence active user's is to be recommended
User.
In some embodiments, referring to Fig. 5, determining module 41 includes：
First module 411, for after active user logs in financing platform, obtaining the active user
Attribute information；
Wherein, attribute information is, for example, classification information.
Further, each classification can correspond to a kind of investment custom, for example, investment custom can divide
For sane type and radical type, then sane type can be corresponded to respectively and radical type is divided into a kind of classification.
Specifically, financing platform can be analyzed the historical behavior data of user, determine user's
Classification.For example, the finance product that financing platform can analyze user's history purchase, browse, to buy
Exemplified by, it is assumed that the finance product of user's purchase wholly or largely belongs to the finance product of sane type, then
It can determine that user belongs to the corresponding classification of sane type (assuming that category information is referred to as first category).
After the classification of user is determined, user can be identified to (such as user name) and entered with classification information
Row correspondence is preserved, so that after active user logs in, corresponding classification is obtained by the user name of login
Information, obtains the attribute information of active user.
Second unit 412, has other of identical attribute information for determination and the active user
User；
Assuming that when the attribute information for determining active user is first category, financing platform can be according to preservation
Other users user's mark and the corresponding relation of classification information, it is determined that belonging to other of first category
User.
Third unit 413, in the other users, determining user to be recommended.
For example, financing platform determines user to be recommended in the other users of first category.
Optionally, the third unit 413 specifically for：
In the other users, user is selected to be used as user to be recommended according to earning rate.
For example, selection earning rate is used as user to be recommended higher than the user of active user's preset value.
For example, preset value is 20%, then can first obtain the earning rate of active user, then with active user
In generic other users, selection earning rate is made higher than 20% other users of the earning rate of active user
For user to be recommended.
Further, the third unit 413 specifically for：
In the other users, the user that earning rate is higher than active user's preset value is filtered out；
If the number of the user filtered out is more than default recommendation number, according to default focus index meter
The correlation of each user and active user that filter out；
According to the order of correlation from high to low, selection recommends the user of number to be used as user to be recommended.
There is provided module 42 is used to, in the financing platform, provide described to be recommended to the active user
User.
In addition, third unit is additionally operable to when the number of the above-mentioned other users filtered out is equal to predetermined number,
Directly it regard the other users filtered out as user to be recommended.
Third unit is additionally operable to when the number of the above-mentioned other users filtered out is less than predetermined number, can be with
Further select user to reach predetermined number according to other algorithms, determine user to be recommended.For example,
The user that earning rate is higher than preset value can be filtered out in whole classifications.
If the earning rate filtered out in whole classifications is higher than number of user of active user's preset value etc.
In predetermined number, then directly the user filtered out is regard as user to be recommended.
If the earning rate filtered out in whole classifications is big higher than the number of the user of active user's preset value
In predetermined number, then the phase of each user and active user that are gone out according to default focus index calculating sifting
Guan Xing；According to the order of correlation from high to low, selection recommends the user of number to be used as user to be recommended.
Optionally, the default focus index includes one or more in following item：The investment of search
Product, the investment product of concern, the investment product of investment.
In some embodiments, referring to Fig. 5, the attribute information is classification information, and described device also includes：
Cluster module 43, for according to default investment target, being clustered to different users, to incite somebody to action
Belong to same category of other users as described other use with identical attribute information with active user
Family.
Optionally, the investment target includes：
Operator scheme index and investment product preference index.
In the present embodiment, exemplified by recommending stock intelligent, then investment product preference index can specifically refer to choosing
Stock preference index.
Specific operating characteristics index and preference index of selecting stocks can be selected according to the actual requirements, the present embodiment
In, operating characteristics index includes：The hand-off frequency, hand-off ratio.Preference of selecting stocks index includes：Scale flow,
Stability bandwidth, average annual dividend, industry concentration ratio.
Wherein, preference of selecting stocks index can according in certain time (one month) click on or share relevent information,
Concern and practical operation are weighted average determination.
Explanation to above-mentioned each index is as shown in table 1.
There is provided module 42 is used to, in the financing platform, provide described to be recommended to the active user
User.
For example, user to be recommended is properly termed as the intelligent that manages money matters, then platform is managed money matters after financing intelligent is determined,
The information (such as user name, head portrait) for the intelligent that manages money matters can be opened up in the homepage or other pages of financing platform
Now give user.
It is understood that the device of the present embodiment is corresponding with above-mentioned embodiment of the method, particular content can be with
Referring to the associated description in embodiment of the method, no longer describe in detail herein.
, can be with resonable by providing user to be recommended to active user on financing platform in the present embodiment
Recommend financing intelligent to user on wealth platform, provide the user more reference informations.Further, pass through
The attribute information of active user is determined, and in there are the other users of identical attribute information with active user
User to be recommended is determined, the user more matched with itself can be recommended to user, so as to improve recommended user
Matching efficiency.
It should be noted that in the description of the present application, term " first ", " second " etc. are only used for retouching
Purpose is stated, and it is not intended that indicating or implying relative importance.In addition, in the description of the present application, removing
Non- to be otherwise noted, the implication of " multiple " refers at least two.
Any process described otherwise above or method description are construed as in flow chart or herein, table
Show including it is one or more be used for realize specific logical function or process the step of executable instruction generation
Module, fragment or the part of code, and the scope of the preferred embodiment of the application includes other realization,
Wherein can not by order that is shown or discussing, including according to involved function by it is basic and meanwhile in the way of
Or in the opposite order, carrying out perform function, this should be by embodiments herein those of skill in the art
Member is understood.
It should be appreciated that each several part of the application can be realized with hardware, software, firmware or combinations thereof.
In the above-described embodiment, multiple steps or method can in memory and by suitable instruction be held with storage
The software or firmware that row system is performed are realized.If for example, realized with hardware, and in another embodiment party
It is the same in formula, it can be realized with any one of following technology well known in the art or their combination：Have
For the discrete logic for the logic gates that logic function is realized to data-signal, with suitable combination
The application specific integrated circuit of logic gates, programmable gate array (PGA), field programmable gate array (FPGA)
Deng.
Those skilled in the art be appreciated that to realize the whole that above-described embodiment method carries or
Part steps can be by program to instruct the hardware of correlation to complete, and described program can be stored in one kind
In computer-readable recording medium, the program upon execution, including one of the step of embodiment of the method or its group
Close.
In addition, each functional unit in the application each embodiment can be integrated in a processing module,
Can also be that unit is individually physically present, can also two or more units be integrated in a module
In.Above-mentioned integrated module can both be realized in the form of hardware, it would however also be possible to employ software function module
Form is realized.If the integrated module is realized using in the form of software function module and is used as independent product
Sale in use, can also be stored in a computer read/write memory medium.
Storage medium mentioned above can be read-only storage, disk or CD etc..
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ",
The description of " specific example " or " some examples " etc. means to combine that the embodiment or example describe is specific
Feature, structure, material or feature are contained at least one embodiment of the application or example.In this theory
In bright book, identical embodiment or example are not necessarily referring to the schematic representation of above-mentioned term.Moreover,
Specific features, structure, material or the feature of description can be in any one or more embodiments or examples
In combine in an appropriate manner.
Although embodiments herein has been shown and described above, it is to be understood that above-described embodiment
It is exemplary, it is impossible to be interpreted as the limitation to the application, one of ordinary skill in the art is the application's
In the range of above-described embodiment can be changed, change, replace and modification.
Claims (12)
1. a kind of method of recommended user, it is characterised in that including：
After active user logs in financing platform, it is determined that the user to be recommended of the correspondence active user；
On the financing platform, the user to be recommended is provided to the active user.
2. according to the method described in claim 1, it is characterised in that described to determine that correspondence is described current
The user to be recommended of user, including：
Obtain the attribute information of the active user；
It is determined that having the other users of identical attribute information with the active user；
In the other users, user to be recommended is determined.
3. method according to claim 2, it is characterised in that described in the other users,
User to be recommended is determined, including：
In the other users, user is selected to be used as user to be recommended according to earning rate.
4. method according to claim 2, it is characterised in that the attribute information is classification information,
Methods described also includes：
According to default investment target, different users is clustered, to belong to same with active user
The other users of one classification are used as the other users with identical attribute information.
5. method according to claim 4, it is characterised in that the investment target includes：
Operator scheme index and investment product preference index.
6. method according to claim 3, it is characterised in that described in the other users,
User is selected as user to be recommended according to earning rate, including：
In the other users, the user that earning rate is higher than active user's preset value is filtered out；
If the number of the user filtered out is more than default recommendation number, according to default focus index meter
The correlation of each user and active user that filter out；
According to the order of correlation from high to low, selection recommends the user of number to be used as user to be recommended.
7. method according to claim 6, it is characterised in that the default focus index bag
Include one or more in following item：The investment product of search, the investment product of concern, the investment production of investment
Product.
8. a kind of device of recommended user, it is characterised in that including：
Determining module, for after active user logs in financing platform, it is determined that corresponding to the active user's
User to be recommended；
Module is provided, in the financing platform, providing described to be recommended to the active user
User.
9. device according to claim 8, it is characterised in that the determining module includes：
First module, for after active user logs in financing platform, obtaining the category of the active user
Property information；
Second unit, for the other users for determining that there is identical attribute information with the active user；
Third unit, in the other users, determining user to be recommended.
10. device according to claim 9, it is characterised in that the third unit is specifically used
In：
In the other users, user is selected to be used as user to be recommended according to earning rate.
11. device according to claim 9, it is characterised in that the attribute information is classification information,
Described device also includes：
Cluster module, for according to default investment target, being clustered to different users, so as to will be with
Active user belongs to same category of other users as the other users with identical attribute information.
12. device according to claim 10, it is characterised in that the third unit specifically for：
In the other users, the user that earning rate is higher than active user's preset value is filtered out；
If the number of the user filtered out is more than default recommendation number, according to default focus index meter
The correlation of each user and active user that filter out；
According to the order of correlation from high to low, selection recommends the user of number to be used as user to be recommended.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610147837.8A CN107194814A (en) | 2016-03-15 | 2016-03-15 | The method and apparatus of recommended user |
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610147837.8A CN107194814A (en) | 2016-03-15 | 2016-03-15 | The method and apparatus of recommended user |
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| CN107194814A true CN107194814A (en) | 2017-09-22 |
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| CN201610147837.8A Pending CN107194814A (en) | 2016-03-15 | 2016-03-15 | The method and apparatus of recommended user |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107784559A (en) * | 2017-11-02 | 2018-03-09 | 深圳金葫芦金融服务有限公司 | One kind is based on big data statistics mobile terminal financial product Intelligent Selection mark implementation |
| CN111612632A (en) * | 2020-05-14 | 2020-09-01 | 中国工商银行股份有限公司 | User investment data processing method and device |
| CN113313597A (en) * | 2020-02-26 | 2021-08-27 | 京东数字科技控股股份有限公司 | Product combination recommendation method, device and system, storage medium and electronic device |
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| CN111612632A (en) * | 2020-05-14 | 2020-09-01 | 中国工商银行股份有限公司 | User investment data processing method and device |
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