CN109582857A - Based on big data information-pushing method, device, computer equipment and storage medium - Google Patents
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Abstract
The invention discloses one kind to be based on big data information-pushing method, device, computer equipment and storage medium, it include: the first location information and recommendation list for obtaining target terminal, recommendation list includes logging in the set for the recommendation information that the account information mapped historical behavior data statistics of user on target terminal obtains；The recommendation information of second location information belonging to recommendation information and first location information within the scope of pre-determined distance is filtered out in recommendation list as pre- pushed information；Pre- pushed information is pushed in target terminal and is shown.Pass through monitoring objective terminal first location information, second location information belonging to pushed information is obtained from the recommendation list for meeting user interest and demand, recommendation information of the difference of acquisition second location information and first location information within the scope of pre-determined distance, and the corresponding account information of user is pushed in time, in order to which user obtains the relevant information that can may be used in time at once.
Description
Technical field
The present invention relates to information advancing technique fields, are pushed specifically, the present invention relates to one kind based on big data information
Method, apparatus, computer equipment and storage medium.
Background technique
With the development of internet, e-commerce platform becomes the medium of mainstream because of its advantage such as intelligent, convenient, possesses big
The customer resources and information of amount.At present no matter in web page browing or purchase platform, the browsing content according to client is all realized
Hobby recommendation is carried out, selects the time in order to save the product of client.
But the basis recommended is the previous browsing data of client, usually according to the data of a period of time browsing recently
Recommended, and the acquisition of data is usually not to be suitable for multiple application scenarios, the way of recommendation in a certain special scenes and field
The product push being only limitted on line, is applicable in scene and is limited in scope.
Summary of the invention
The purpose of the present invention is intended at least can solve above-mentioned one of technological deficiency, and open one kind is pushed away based on big data information
Delivery method, device, computer equipment and storage medium can be obtained accurately according to the identity attribute of user and behavioral data and be recommended
List, while the location information current according to user, chosen in recommendation list meet the recommendation information of current location information into
Row push, to meet the needs of user is current.
In order to achieve the above object, the present invention discloses a kind of based on big data information-pushing method, comprising:
The first location information and recommendation list of target terminal are obtained, the recommendation list includes logging in use on target terminal
The set for the recommendation information that the account information mapped historical behavior data statistics at family obtains；
Second location information belonging to the recommendation information is filtered out in the recommendation list and the first position is believed
The recommendation information within the scope of pre-determined distance is ceased as pre- pushed information；
The pre- pushed information is pushed in the target terminal and is shown.
Preferably, described push to the pre- pushed information in the target terminal shows further include:
Obtain the behavior property recommendation of the pre- pushed information；The behavior property recommendation is to characterize the pre- push
The numerical value of the fancy grade of information；
Using pre- pushed information of the behavior property recommendation in preset threshold range as target information；
The target information is sent in the target terminal and is shown.
Preferably, the acquisition methods of the recommendation list include:
Obtain account information mapped historical behavior log in a certain preset time period；
It reads the behavior property label of every information in the historical behavior log and counts in same behavior attribute tags
The quantity of information；
The hobby journey of the information in characterization behavior attribute tags is calculated in quantity according to the behavior property label
The behavior property recommendation of degree；
The behavior recommendation mapping of the first preset condition will be met according to the sequence of behavior property recommendation size
Recommendation information is stored in recommendation list.
Preferably, the letter in characterization behavior attribute tags is calculated in the quantity according to the behavior property label
The method of the behavior property recommendation of the fancy grade of breath includes:
Obtain the first weighted value of the behavior property label；
Behavior property recommendation is obtained according to the product of first weighted value and the quantitative value of the behavior property label.
Preferably, the recommendation list further includes the letter for the prediction user preferences enumerated according to individual subscriber attribute information
The set of breath, the set acquisition methods packet of the information of the prediction user preferences enumerated according to individual subscriber attribute information
It includes:
The identity attribute label of user is matched by the account information of user, and is obtained the identity attribute label and mapped
Recommendation information identity attribute recommendation；
The identity attribute label mapped information that acquisition meets the second preset condition is stored in recommendation list.
Preferably, second preset condition includes that the identity attribute recommendation exists according to sequence sequence from high to low
In preset range value.
Preferably, the acquisition methods of the recommendation list further include:
Obtain the second weighted value of characterization behavior property tag set and the third weight of characterization identity attribute collection-label
Value；
The product for calculating second weighted value and behavior property recommendation obtains the first recommendation；The third is calculated simultaneously
The product of weighted value and the identity attribute recommendation obtains the second recommendation；
It is sorted according to the first recommendation with the size of the second recommendation and enumerates corresponding information and form recommendation list.
The application is also disclosed a kind of based on big data information push-delivery apparatus, comprising:
First acquisition module: being configured as executing the first location information and recommendation list for obtaining target terminal, described to push away
Recommending list includes the collection for logging in the recommendation information that the account information mapped historical behavior data statistics on target terminal obtains
It closes；
First processing module: it is configured as executing and is filtered out in the recommendation list second belonging to the recommendation information
Location information and recommendation information of the first location information within the scope of pre-determined distance are as pre- pushed information；
First execution module: it is configured as executing to push to the pre- pushed information in the target terminal showing.
Preferably, further includes:
Second acquisition module: it is configured as executing the behavior property recommendation for obtaining the pre- pushed information；The behavior
Attribute recommendation is the numerical value for characterizing the fancy grade of the pre- pushed information；
Second processing module: it is configured as executing the pre- push by the behavior property recommendation in preset threshold range
Information is as target information；
Second execution module: it is configured as executing for the target information to be sent in the target terminal showing.
Preferably, further includes:
Third obtains module: being configured as execution acquisition account information mapped in a certain preset time period and goes through
History user behaviors log；
Read module: it is configured as executing and reads the behavior property label of every information in the historical behavior log and unite
Count the quantity of information in same behavior attribute tags；
First computing module: it is configured as executing the characterization behavior is calculated according to the quantity of the behavior property label
The behavior property recommendation of the fancy grade of information in attribute tags；
First generation module: the first default item will be met according to the sequence of behavior property recommendation size by being configured as executing
The recommendation information of the behavior recommendation mapping of part is stored in recommendation list.
Preferably, further includes:
4th acquisition module: it is configured as executing the first weighted value for obtaining the behavior property label；
Second computing module: it is configured as executing the quantitative value according to first weighted value and the behavior property label
Product obtain behavior property recommendation.
Preferably, the recommendation list further includes the letter for the prediction user preferences enumerated according to individual subscriber attribute information
The set of breath, further includes:
Matching module: it is configured as executing the identity attribute label for matching the account information by user user, and obtains
Take the identity attribute recommendation of the identity attribute label mapped recommendation information；
By the second generation module: being configured as executing the identity attribute label mapped information for meeting the second preset condition
It is stored in recommendation list.
Preferably, second preset condition includes that the identity attribute recommendation exists according to sequence sequence from high to low
In preset range value.
Preferably, further includes:
5th acquisition module: it is configured as executing the second weighted value for obtaining characterization behavior property tag set and characterization body
The third weighted value of part attribute set label；
Third computing module: it is configured as executing the product for calculating second weighted value and behavior property recommendation and obtains the
One recommendation；The product for calculating the third weighted value and the identity attribute recommendation simultaneously obtains the second recommendation；
Third generation module: it is configured as executing and is sorted and enumerated according to the size of the first recommendation and the second recommendation pair
The information answered forms recommendation list.
A kind of computer equipment, including memory and processor is also disclosed in the application, is stored with calculating in the memory
Machine readable instruction, when the computer-readable instruction is executed by the processor, so that the processor is executed as any of the above-described
The step of described in item based on big data information-pushing method.
A kind of storage medium for being stored with computer-readable instruction is also disclosed in the application, and the computer-readable instruction is by one
When a or multiple processors execute, so that one or more processors execute described in above-mentioned any one based on big data information
The step of method for pushing.
The beneficial effects of the present invention are:
1) it by monitoring objective terminal first location information, obtains and pushes away from the recommendation list for meeting user interest and demand
It delivers letters second location information belonging to breath, obtains the difference of second location information and first location information within the scope of pre-determined distance
Recommendation information, and the corresponding account information of user is pushed in time, can may be used at once in order to which user obtains in time
The relevant information arrived；
2) method in another embodiment, to be sorted using recommendation obtains recommendation information for the journey interested of user
Degree, and user is pushed according to the sequence of this recommendation, in order to ensure that the information pushed is that user is liked.
The additional aspect of the present invention and advantage will be set forth in part in the description, these will become from the following description
Obviously, or practice through the invention is recognized.
Detailed description of the invention
Above-mentioned and/or additional aspect and advantage of the invention will become from the following description of the accompanying drawings of embodiments
Obviously and it is readily appreciated that, in which:
Fig. 1 is that the present invention is based on big data information-pushing method general flow charts；
Fig. 2 is recommendation list acquisition methods flow chart in first embodiment of the invention；
Fig. 3 is that behavior property of the present invention recommends value-acquiring method flow chart；
Fig. 4 is that the present invention is based on the recommendation list method flow diagrams that personal attribute is enumerated；
Fig. 5 is recommendation list acquisition methods flow chart in second embodiment of the invention；
Fig. 6 is the pre- pushed information method for pushing flow chart of the present invention；
Fig. 7 is that the present invention is based on big data information push-delivery apparatus module frame charts；
Fig. 8 is computer equipment basic structure block diagram of the present invention.
Specific embodiment
The embodiment of the present invention is described below in detail, examples of the embodiments are shown in the accompanying drawings, wherein from beginning to end
Same or similar label indicates same or similar element or element with the same or similar functions.Below with reference to attached
The embodiment of figure description is exemplary, and for explaining only the invention, and is not construed as limiting the claims.
Those skilled in the art of the present technique are appreciated that unless expressly stated, singular " one " used herein, " one
It is a ", " described " and "the" may also comprise plural form.It is to be further understood that being arranged used in specification of the invention
Diction " comprising " refer to that there are the feature, integer, step, operation, element and/or component, but it is not excluded that in the presence of or addition
Other one or more features, integer, step, operation, element, component and/or their group.It should be understood that when we claim member
Part is " connected " or when " coupled " to another element, it can be directly connected or coupled to other elements, or there may also be
Intermediary element.In addition, " connection " used herein or " coupling " may include being wirelessly connected or wirelessly coupling.It is used herein to arrange
Diction "and/or" includes one or more associated wholes for listing item or any cell and all combinations.
Those skilled in the art of the present technique are appreciated that unless otherwise defined, all terms used herein (including technology art
Language and scientific term), there is meaning identical with the general understanding of those of ordinary skill in fields of the present invention.Should also
Understand, those terms such as defined in the general dictionary, it should be understood that have in the context of the prior art
The consistent meaning of meaning, and unless idealization or meaning too formal otherwise will not be used by specific definitions as here
To explain.
Those skilled in the art of the present technique are appreciated that " terminal " used herein above, " terminal device " both include wireless communication
The equipment of number receiver, only has the equipment of the wireless signal receiver of non-emissive ability, and including receiving and emitting hardware
Equipment, have on bidirectional communication link, can execute two-way communication reception and emit hardware equipment.This equipment
It may include: honeycomb or other communication equipments, shown with single line display or multi-line display or without multi-line
The honeycomb of device or other communication equipments；PCS (Personal Communications Service, PCS Personal Communications System), can
With combine voice, data processing, fax and/or communication ability；PDA (Personal Digital Assistant, it is personal
Digital assistants), it may include radio frequency receiver, pager, the Internet/intranet access, web browser, notepad, day
It goes through and/or GPS (Global Positioning System, global positioning system) receiver；Conventional laptop and/or palm
Type computer or other equipment, have and/or the conventional laptop including radio frequency receiver and/or palmtop computer or its
His equipment." terminal " used herein above, " terminal device " can be it is portable, can transport, be mounted on the vehicles (aviation,
Sea-freight and/or land) in, or be suitable for and/or be configured in local runtime, and/or with distribution form, operate in the earth
And/or any other position operation in space." terminal " used herein above, " terminal device " can also be communication terminal, on
Network termination, music/video playback terminal, such as can be PDA, MID (Mobile Internet Device, mobile Internet
Equipment) and/or mobile phone with music/video playing function, it is also possible to the equipment such as smart television, set-top box.
The present invention discloses one kind based on big data information-pushing method, specifically, referring to Fig. 1, mainly including following step
It is rapid:
S1000, the first location information and recommendation list for obtaining target terminal, the recommendation list include logging in target end
The set for the recommendation information that the account information mapped historical behavior data statistics of user obtains on end；
Target terminal is the terminal device for logging in the account information of user, is mobile phone, plate, electronics bracelet, computer
Etc. equipment, and preferably a kind of moveable intelligent terminal.Intelligent terminal connects network, can be obtained by IP address current
Location information, obtain target terminal defined herein as first location information, or by the GPS positioning information on intelligent terminal
Current location information.
Historical behavior data are the behavioral data before current time node, can use API (Application
Programming Inerface) interface is from network side acquisition account information mapped behavioral data, specifically, can use
API is got before the browser in terminal device starts to report behavioral data to the network equipment, and user passes through its account information
Execute produced by network access and be stored in the behavioral data of network side.
Recommendation list includes pushing away of logging in that the account information mapped historical behavior data statistics on target terminal obtains
Recommend the set of information.Historical behavior data in this application can for account information execute at the terminal web page browsing, comment,
Purchase object, the related data movement such as collecting, thumb up and recording can be deduced by the number of these operations and operation
The type of the possible interested information of user, and this possible interested same category information is defined as recommendation information, according to certain
This recommendation information is grouped together into recommendation list by a kind of rule.In one embodiment, referring to Fig. 2, recommendation list
Acquisition methods include:
S1100, account account information mapped historical behavior log in a certain preset time period is obtained；
For the ease of statistics, it is effective that the data in some period would generally be specified when choosing historical behavior data
Data, the data acquired in this period are analyzed as target data, this preset time period can be one week, one
Season, half a year, 1 year etc..
In another embodiment, this preset time can also be obtained according to certain rules, such as one
In fixing time, liveness is higher, and the access time interval shorter period is lower for liveness as preset time period
, then longer time section is chosen as preset time period, such as account information A, was logged in 1 year account 2 times, and number is browsed
Amount is no more than 10, then can extend to 2 years to the account information A preset time period for collecting historical data or even longer, if
Account information B can login browsing daily, then can set preset time period to nearest half a year, and obtain within nearly half a year
Historical behavior log.Historical behavior log is to record the set of each data operated of account information, usually according to the time
Axis mode is recorded.
S1200, it reads the behavior property label of every information in the historical behavior log and counts same behavior attribute
The quantity of information in label；
Behavior property label is the categorizing information of category setting belonging to type or behavior for partitive behavior, such as
Browsing, comment, purchase object, collect, thumb up etc. be behavior type, and executing the corresponding content of pages of above-mentioned movement can map
Different classifications, such as daily class, financing insurance class, clothing, toiletries etc..For different content of pages attributes
Multiple subtabs may further be provided in label, such as in financing insurance generic attribute label in the following, may also include fund label, protecting
Dangerous label, stock label etc., and under specific subtab further below settable next stage subtab, such as insurance label
Life insurance, property insurance, sickness insurance etc. can also be arranged in face, moreover, can also continue to further segment, until
Batch etc. below to specific name of product and the product.
According to above-mentioned attribute tags, every a product can be sorted out, consequently facilitating specific data are acquired, according to tool
The behavior property label of body can obtain the quantity for corresponding to information in each behavior property label within a preset period of time, such as
Total browsing information content in class behavior attribute tags is insured in financing, quantity is always collected, always thumbs up quantity etc., and specific
Browsing quantity in corresponding fund label, the browsing quantity in insurance label, browsing quantity in stock label etc..
S1300, the information in characterization behavior attribute tags is calculated according to the quantity of the behavior property label
The behavior property recommendation of fancy grade；
Behavior property recommendation counts to obtain according to historical behavior trace, it will usually according to existing within a preset period of time
The corresponding type of user's operation corresponding to the frequency or account information occurred in historical behavior data is counted, than
Such as, user's operation type is " collection " or " concern ", then it represents that user is more concerned to the corresponding content of the attribute tags,
And only simple browsing then indicates relevant content under user has only slightly understood；User pays close attention to same type of content
Quantity it is more, then it represents that user is interested in the type, according to above-mentioned rule, can determine whether out that user's concern is interested
Where is point, and user is interested or its recommendation of the content of same type of concern is higher in meeting.
For example user logs in its account information and browsed three attribute label of fund classification, stock classification and coverage
Content, and stock classification only has the operation browsed, fund classification collected 5 articles, and bought 2, and insured class receipts
10 hidden have purchased 5 times, then can recognize that user more pays close attention to insurance class product from above-mentioned data, and buy
A possibility that it is bigger, fund class product be after insurance class product after pay close attention to content, at this time recommendation it is highest is insurance class
Product, second it is high be fund class product, and stock type comes below.
In one embodiment, referring to Fig. 3, the quantity according to the behavior property label, which is calculated, characterizes the row
Method for the behavior recommendation of the fancy grade of the information in attribute tags includes:
S1310, the first weighted value for obtaining the behavior property label；
Weighted value indicates the rate of specific gravity of the behavior property label of the category, refers in above-mentioned steps, different user behaviour
Make type its indicate meaning it is different, such as user's operation type be " collection " or " concern ", then it represents that user is to this
The corresponding content of attribute tags is more concerned, and only simple browsing then indicates relevant under user has only slightly understood
Content；Therefore high value can be set by the corresponding content information mapped behavior property label weighted value of user's " collection ",
And " browsing " corresponding content information mapped behavior property label weighted value is set as low value.
Historical behavior log is had read in through the above steps, then can obtain user for a certain information execute classification with
And the behavior property label, therefore therefore obtain the first weighted value of the information.
S1320, behavior property is obtained according to the product of first weighted value and the quantitative value of the behavior property label
Recommendation.
After the first weighted value for obtaining behavior property label according to step S1310 step, also need further to obtain same
A kind of quantitative value of behavior property label, obtains behavior property recommendation with this, in one embodiment state year, and behavior property is recommended
The acquisition methods of value A are obtained by following formula:
A=KX
Wherein, A is behavior property recommendation, and K is the first weighted value, and X is the attribute behavior label letter of the corresponding weighted value
The quantitative value of breath；For example it is 0.9 that the weighted value of collection, which is arranged, the quantity that account information has collected insurance class product is 10, then should
The recommendation for insuring class product is 0.9*10=9.
Further, type corresponding to same behavior attribute tags is not limited to one kind, can also there are many, such as also
Including browsing and paying close attention to, at this point, the acquisition methods of recommendation A are obtained by following formula:
A=(K1X1+K2X2+K3X3 ... KnXn)/n
Wherein, n indicates the quantitative value of the action type of behavior property label.For example, defining the weighted value of " browsing " type
It is 0.2, the weighted value of " concern " type is 0.6, and account information has browsed 30 insurance class products altogether, has paid close attention to 5 insurance classes
Product, then the total recommendation for insuring class product is the recommendation of account information collection and the recommendation of " browsing " and " concern "
The sum of weighted value is divided by the quantity of total action type, as (0.9*10+0.2*30+0.6*5)/3=9.
S1400, it is pushed away according to the sequence of recommendation size by what the behavior recommendation for meeting the first preset condition mapped
Information is recommended to be stored in recommendation list.
After obtaining corresponding behavior property recommendation according to step S1300, behavior property recommendation is bigger, indicates the information
Type be that user is favorite, therefore can be ranked up according to the size of behavior property recommendation.In one embodiment, in order to unite
One management, sets the first preset condition, and the behavior property recommendation mapped recommendation information for meeting the first preset condition is deposited
Storage is in recommendation list.First preset condition can be the sequence of behavior property recommendation in a certain threshold range, for example go
It is stored in preceding 10 information for being pushed to account information in recommendation list for the sequence of attribute recommendation, in this approach,
Ensure that the product pushed to account information is what user liked.
S2000, second location information belonging to the recommendation information and described first are filtered out in the recommendation list
Recommendation information of the location information within the scope of pre-determined distance is as pre- pushed information；
Second location information is location information belonging to the recommendation information, such as when information is the discount coupon about shop
When information, then second location information is the position in the shop.When there are multiple branch in the shop, and multiple branch can use
When this discount coupon, then it will meet the address information of all second location informations of the discount coupon all alternately, with target end
The first location information at end is matched.Selection meets the recommendation information within the scope of pre-determined distance as pre- pushed information.
S3000, it the pre- pushed information is pushed in the target terminal shows.
After obtaining pre- pushed information according to above-mentioned steps, then the pre- pushed information of selection is pushed into target terminal described in location
In shown.
It should be noted that a kind of feasible mode is, according to above-mentioned matched distance, choosing there are many modes of push
The pre- pushed information for taking distance nearest is pushed, or apart from ranking in certain threshold range, such as apart from nearest preceding 3
The pre- pushed information of item is successively pushed.The ranking of the recommendation of pre- pushed information of the another kind for basis within a preset range,
Ranking is pushed near the information of preceding or ranking within a certain range.The side of above only disclosed several push displays
Formula, but it is not limited to above several, it can also be other push choosing methods.
Further for accuracy is increased, also behavior property recommendation can be combined other recommendations obtain one more
Accurately recommend ranking information, for example combines the personal attribute information of account information.Therefore, in one embodiment, recommendation list
It further include the set of the information for the prediction user preferences enumerated according to individual subscriber attribute information, referring to Fig. 4, the basis
The acquisition methods of the set of the information for the prediction user preferences that individual subscriber attribute information is enumerated include:
S1500, the identity attribute label that user is matched by the account information of user, and obtain the identity attribute label
The identity attribute recommendation of mapped recommendation information；
Identity attribute label is the label for characterizing individual subscriber identity, such as the age level of user, the gender of user, family
Front yard situation, investment risk grade etc., the user information that these information can be filled in directly from user when opening account obtain,
It can be and the business personnel of the account information by managing the user is manually entered respective labels and obtain.
In the application, identity attribute label is divided into multiple and different classifications, according to different classifications, mapping is different respectively
Recommendation information, for example, by identity attribute label be divided into age class, gender class, family status class, personal preference and investment wind
The dangerous multiple major class of grade, age class is divided into 30 years old hereinafter, multiple groups such as 31-45 years old, 46-65 years old and over-65s, gender class
It is divided into two male, female groups, family status is divided into married, unmarried, child 2 hereinafter, child 3 and with first-class multiple groups,
Investment risk grade includes multiple groups such as low-risk, average risk and high risk, personal preference include movement class, shopping class,
Multiple groups such as food and drink class, each group are mapped with the recommendation information for meeting the personnel of the group, it should be noted that each
Group mapped recommendation information can be business personnel according to the associated recommendation information of business demand typing, can also be basis
Meet that the historical behavior data statistics of the user of above-mentioned identity attribute label comes out meets user demand or user and may feel
The information of interest.
Further, after getting identity-based attribute tags mapped recommendation information, it can also obtain simultaneously this and push away
Recommend the corresponding identity attribute recommendation of information, the recommendation can be the value that the typing in recommendation information is to confer to, when being based on
When the recommendation information of identity attribute label is obtained from the historical behavior data of the account information of different classifications, identity attribute
The acquisition methods of recommendation can also be the same according to the method for above-mentioned steps S1300.
The identity attribute label mapped information that S1600, acquisition meet the second preset condition is stored in recommendation list.
The acquisition methods with identity attribute label mapped information are disclosed in step S1500, therefore, but and identity
Attribute tags mapped information has very much, needs to be chosen according to certain rules, and second will be met in the application and is preset
The identity attribute label mapped information of condition is stored in recommendation list, and second preset condition includes the identity category
Property recommendation according to from high to low sequence sort in preset range value.For example, existing according to the sequence of identity attribute recommendation
Preceding 10 recommendation information is included in recommendation list, further, can also be the size of identity attribute recommendation in preset range
Etc, for example, identity attribute recommendation be greater than 9 corresponding to recommendation information be included in recommendation list.
It should be noted that when be not provided with identity attribute label, the acquisition methods of the recommendation list of account information by
Step S1100-S1400 is obtained, and still, when there are identity attribute label, is then needed the recommendation of historical behavior data acquisition
List summarized in conjunction with the recommendation list that identity attribute obtains after recommendation list, specifically, referring to Fig. 5, after summarizing
The acquisition methods of recommendation list include:
The of S1700, the second weighted value for obtaining characterization behavior property tag set and characterization identity attribute collection-label
Three weighted values；
It, can be according to certain rules to above two classification when needing two ways to combine acquisition recommendation list
Recommendation information is integrated.In the application, second weighted value is arranged to the recommendation information of attribute tags set respectively, to body
A third weighted value is arranged in the recommendation information of part attribute set, and successively the rate of specific gravity to distinguish the two has in one embodiment
It with the recommendation information in behavior property tag set is obtained by the operation behavior of user within a preset period of time oneself, phase
It can more accurately reflect the hobby of user for, and the recommendation information in identity attribute set is only need according to business
It asks, or the recommendation information of the hobby for presetting the user obtained according to the hobby trend for the crowd for meeting a certain identity properties,
Therefore the second weighted value of behavior property tag set can be assigned to high value, and the third weighted value of identity attribute collection-label is assigned
Lower value is given, successively the data in limited acquisition behavior property label.
The product of S1800, calculating second weighted value and behavior recommendation obtain the first recommendation；Described the is calculated simultaneously
The product of three weighted values and the identity attribute recommendation obtains the second recommendation；
The acquisition methods that behavior property recommends A are disclosed in step S1320, are as follows:
A=(K1X1+K2X2+K3X3 ... KnXn)/n
Wherein, A is behavior property recommendation, and K is the first weighted value, and X is the attribute behavior label letter of the corresponding weighted value
The quantitative value of breath；N indicates the quantitative value of the action type of behavior property label.
Definition identity attribute recommendation is B, defines the second weighted value and T, and definition third weighted value is S, definition behavior category
It is N that first recommendation of the total fancy grade of property, which is the second recommendation that M defines the total fancy grade of identity attribute, then:
M=TA=T [(K1X1+K2X2+K3X3 ... KnXn)/n]
N=SB.
S1900, it is sorted according to the first recommendation with the size of the second recommendation and enumerates corresponding information and formed and recommend column
Table.
It, then can root after the size for obtaining the first recommendation M and the second recommendation N according to the method for above-mentioned steps S1800
Corresponding recommendation information is enumerated to form recommendation list with the sequence of the size of the second recommendation according to the first recommendation.
Further, in another embodiment, based on the recommendation list in above-mentioned a variety of situations, step S2000 is being executed
In, the second location information for needing will acquire is matched with first location information, is met within the scope of pre-determined distance with choosing
Recommendation information is as pre- pushed information, specifically, carrying out matched method is to judge first location information and second location information
Whether within the scope of pre-determined distance, for example, then in recommendation list, choosing second confidence when pre-determined distance range is 500 meters
Breath and recommendation information of the first location information within the scope of 500 meters are pushed as with pre- pushed information.
But when with pre-determined distance within the scope of recommendation information it is more when, it is also necessary to determine according to certain rules
How the pre- pushed information is pushed.Therefore, in the embodiment of the application, finally pre- pushed information is being pushed to
Before in the target terminal, referring to Fig. 6, further comprising the steps of:
S2100, the recommendation for obtaining the pre- pushed information；The recommendation is to characterize the hobby of the pre- pushed information
The numerical value of degree；
The recommendation of every recommendation information has been calculated according to above-mentioned steps S1000, therefore in this step, acquisition pushes away in advance
It delivers letters the recommendation of breath.
S2200, using pre- pushed information of the recommendation in preset threshold range as target information；
Preset threshold is a kind of range of condition chosen about recommendation, and in one embodiment, preset threshold is pre- push
The sequence of the recommendation of information sorts from large to small, and chooses work of the ranking preceding 10
For target information.The target information is that the target terminal for being logged in the account information of user being selected pushes
Information.
In another embodiment, preset threshold can also for recommendation size within some value, for example choose
Preset threshold is 10, then the pre- pushed information using recommendation greater than 10 is as target information.It is above disclosed in the present application several
The choosing method of kind of preset threshold, but the Research on threshold selection of the application be not limited to it is above several, as long as can adopt to pushing away
Recommend the protection scope that the method that value is chosen all is included in the application.
S2300, it the target information is sent in the target terminal shows.
After obtaining target information according to above-mentioned steps, then above-mentioned target information is sent in the target terminal and is carried out
Display.
The application is also disclosed a kind of based on big data information push-delivery apparatus, module frame chart such as Fig. 7, comprising:
First acquisition module 1000: it is configured as executing the first location information and recommendation list for obtaining target terminal, institute
Stating recommendation list includes the recommendation for logging in the account information mapped historical behavior data statistics of user on target terminal and obtaining
The set of information；
Target terminal is the terminal device for logging in account information, is the equipment such as mobile phone, plate, electronics bracelet, computer,
And preferably a kind of moveable intelligent terminal.Intelligent terminal connects network, and current position can be obtained by IP address
Information obtains the current of target terminal defined herein as first location information, or by the GPS positioning information on intelligent terminal
Location information.
Historical behavior data are the behavioral data before current time node, can use API (Application
Programming Inerface) interface from network side acquisition user behavioral data, got specifically, can use API
Before browser in terminal device starts to report behavioral data to the network equipment, user executes produced by network access and stores
In the behavioral data of network side.
Recommendation list includes pushing away of logging in that the account information mapped historical behavior data statistics on target terminal obtains
Recommend the set of information.Historical behavior data in this application can for account information execute at the terminal web page browsing, comment,
Purchase object, the related data movement such as collecting, thumb up and recording can be deduced by the number of these operations and operation
The type of the possible interested information of user, and this possible interested same category information is defined as recommendation information, according to certain
This recommendation information is grouped together into recommendation list by a kind of rule.
First processing module 2000: it is configured as executing and is filtered out in the recommendation list belonging to the recommendation information
Second location information and recommendation information of the first location information within the scope of pre-determined distance are as pre- pushed information；
Second location information is location information belonging to the recommendation information, such as when information is the discount coupon about shop
When information, then second location information is the position in the shop.When there are multiple branch in the shop, and multiple branch can use
When this discount coupon, then it will meet the address information of all second location informations of the discount coupon all alternately, with target end
The first location information at end is matched.Selection meets the recommendation information within the scope of pre-determined distance as pre- pushed information.
First execution module 3000: be configured as execute by the pre- pushed information push in the target terminal show
Show.
After obtaining pre- pushed information according to above-mentioned steps, then the pre- pushed information of selection is pushed into target terminal described in location
In shown.
It should be noted that a kind of feasible mode is, according to above-mentioned matched distance, choosing there are many modes of push
The pre- pushed information for taking distance nearest is pushed, or apart from ranking in certain threshold range, such as apart from nearest preceding 3
The pre- pushed information of item is successively pushed.The ranking of the recommendation of pre- pushed information of the another kind for basis within a preset range,
Ranking is pushed near the information of preceding or ranking within a certain range.The side of above only disclosed several push displays
Formula, but it is not limited to above several, it can also be other push choosing methods.
Further, the application further include: the second acquisition module: be configured as executing the behavior for obtaining the pre- pushed information
Attribute recommendation；The behavior property recommendation is the numerical value for characterizing the fancy grade of the pre- pushed information；Second processing mould
Block: it is configured as executing the pre- pushed information using the behavior property recommendation in preset threshold range as target information；
Second execution module: it is configured as executing for the target information to be sent in the target terminal showing.
Preferably, the application further include: third obtains module: it is configured as executing acquisition account information when a certain default
Between mapped historical behavior log in section；Read module: it is configured as executing and reads every letter in the historical behavior log
The behavior property label of breath and the quantity for counting information in same behavior attribute tags；First computing module: it is configured as executing
The behavior of the fancy grade of the information in characterization behavior attribute tags is calculated in quantity according to the behavior property label
Attribute recommendation；First generation module: first will be met in advance according to the sequence of behavior property recommendation size by being configured as executing
If the recommendation information that the behavior recommendation of condition maps is stored in recommendation list.
Preferably, further includes: the 4th acquisition module: be configured as executing the first weight for obtaining the behavior property label
Value；Second computing module: it is configured as executing multiplying according to first weighted value and the quantitative value of the behavior property label
Product obtains behavior property recommendation.Preferably, the recommendation list further include enumerated according to individual subscriber attribute information it is pre-
It surveys the set of the information of user preferences, further includes: matching module: being configured as executing and the account information matching by user is used
The identity attribute label at family, and obtain the identity attribute recommendation of the identity attribute label mapped recommendation information；By
Two generation modules: it is configured as executing the identity attribute label mapped information for meeting the second preset condition and being stored in recommending column
In table.
Preferably, second preset condition includes that the identity attribute recommendation exists according to sequence sequence from high to low
In preset range value.
Preferably, further includes: the 5th acquisition module: being configured as executing the second of acquisition characterization behavior property tag set
The third weighted value of weighted value and characterization identity attribute collection-label；Third computing module: it is configured as executing and calculates described the
The product of two weighted values and behavior property recommendation obtains the first recommendation；The third weighted value and the identity category are calculated simultaneously
The product of property recommendation obtains the second recommendation；Third generation module: it is configured as executing and be recommended according to the first recommendation with second
The size of value, which sorts and enumerates corresponding information, forms recommendation list.
It is above-mentioned based on big data information push-delivery apparatus be based on module corresponding to big data information-pushing method, each
Functional module executes corresponding big data information-pushing method, and specific function implementation is same, no longer most ripe herein.
Invention additionally discloses a kind of computer equipment, including memory and processor, calculating is stored in the memory
Machine readable instruction, when the computer-readable instruction is executed by the processor, so that processor execution is above-mentioned any one
Big data information-pushing method is based on described in.
The embodiment of the present invention provides computer equipment basic structure block diagram and please refers to Fig. 8.
The computer equipment includes processor, non-volatile memory medium, memory and the net connected by system bus
Network interface.Wherein, the non-volatile memory medium of the computer equipment is stored with operating system, database and computer-readable finger
It enables, control information sequence can be stored in database, when which is executed by processor, may make that processor is real
It is existing a kind of based on big data information-pushing method method.The processor of the computer equipment is calculated for offer and control ability,
Support the operation of entire computer equipment.Computer-readable instruction can be stored in the memory of the computer equipment, the calculating
When machine readable instruction is executed by processor, it is a kind of based on big data information-pushing method to may make that processor executes.The computer
The network interface of equipment is used for and terminal connection communication.It will be understood by those skilled in the art that structure shown in Fig. 8, only
It is the block diagram of part-structure relevant to application scheme, does not constitute the computer being applied thereon to application scheme and set
Standby restriction, specific computer equipment may include than more or fewer components as shown in the figure, or the certain components of combination,
Or with different component layouts.
The status information for prompting behavior that computer equipment is sent by receiving associated client, i.e., whether associated terminal
It opens prompt and whether account information closes the prompt task.By verifying whether above-mentioned task condition is reached, and then to pass
Join terminal and send corresponding preset instructions, so that associated terminal can execute corresponding operation according to the preset instructions, thus real
Effective supervision to associated terminal is showed.Meanwhile when prompt information state and preset status command be not identical, server end
Control associated terminal persistently carries out jingle bell, to prevent what the prompt task of associated terminal from terminating automatically after executing a period of time to ask
Topic.
The present invention also provides a kind of storage mediums for being stored with computer-readable instruction, and the computer-readable instruction is by one
When a or multiple processors execute, so that one or more processors, which execute, is based on big data information described in any of the above-described embodiment
The method of method for pushing.
Those of ordinary skill in the art will appreciate that realizing all or part of the process in above-described embodiment method, being can be with
Relevant hardware is instructed to complete by computer program, which can be stored in a computer-readable storage and be situated between
In matter, the program is when being executed, it may include such as the process of the embodiment of above-mentioned each method.Wherein, storage medium above-mentioned can be
The non-volatile memory mediums such as magnetic disk, CD, read-only memory (Read-Only Memory, ROM) or random storage note
Recall body (Random Access Memory, RAM) etc..
It should be understood that although each step in the flow chart of attached drawing is successively shown according to the instruction of arrow,
These steps are not that the inevitable sequence according to arrow instruction successively executes.Unless expressly stating otherwise herein, these steps
Execution there is no stringent sequences to limit, can execute in the other order.Moreover, at least one in the flow chart of attached drawing
Part steps may include that perhaps these sub-steps of multiple stages or stage are not necessarily in synchronization to multiple sub-steps
Completion is executed, but can be executed at different times, execution sequence, which is also not necessarily, successively to be carried out, but can be with other
At least part of the sub-step or stage of step or other steps executes in turn or alternately.
The above is only some embodiments of the invention, it is noted that for the ordinary skill people of the art
For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also answered
It is considered as protection scope of the present invention.
Claims (10)
1. one kind is based on big data information-pushing method characterized by comprising
The first location information and recommendation list of target terminal are obtained, the recommendation list includes logging in user on target terminal
The set for the recommendation information that account information mapped historical behavior data statistics obtains；
Second location information belonging to the recommendation information is filtered out in the recommendation list and the first location information exists
Recommendation information within the scope of pre-determined distance is as pre- pushed information；
The pre- pushed information is pushed in the target terminal and is shown.
2. according to claim 1 be based on big data information-pushing method, which is characterized in that described to believe the pre- push
Breath, which pushes in the target terminal, to be shown further include:
Obtain the behavior property recommendation of the pre- pushed information；The behavior property recommendation is to characterize the pre- pushed information
Fancy grade numerical value；
Using pre- pushed information of the behavior property recommendation in preset threshold range as target information；
The target information is sent in the target terminal and is shown.
3. according to claim 2 be based on big data information-pushing method, which is characterized in that the acquisition of the recommendation list
Method includes:
Obtain account information mapped historical behavior log in a certain preset time period；
It reads the behavior property label of every information in the historical behavior log and counts information in same behavior attribute tags
Quantity；
The fancy grade of the information in characterization behavior attribute tags is calculated in quantity according to the behavior property label
Behavior property recommendation；
The recommendation of the behavior recommendation mapping of the first preset condition will be met according to the sequence of behavior property recommendation size
Information is stored in recommendation list.
4. according to claim 3 be based on big data information-pushing method, which is characterized in that described according to the behavior category
Property label quantity be calculated characterization behavior attribute tags in information fancy grade behavior property recommendation side
Method includes:
Obtain the first weighted value of the behavior property label；
Behavior property recommendation is obtained according to the product of first weighted value and the quantitative value of the behavior property label.
5. according to claim 2 be based on big data information-pushing method, which is characterized in that the recommendation list further includes
It is described according to individual subscriber attribute information according to the set for the information for predicting user preferences that individual subscriber attribute information is enumerated
The set acquisition methods of the information for the prediction user preferences enumerated include:
The identity attribute label of user is matched by the account information of user, and is obtained the identity attribute label mapped and pushed away
Recommend the identity attribute recommendation of information；
The identity attribute label mapped information that acquisition meets the second preset condition is stored in recommendation list.
6. according to claim 5 be based on big data information-pushing method, which is characterized in that the second preset condition packet
The identity attribute recommendation is included to sort in preset range value according to sequence from high to low.
7. according to claim 5 be based on big data information-pushing method, which is characterized in that the acquisition of the recommendation list
Method further include:
Obtain the second weighted value of characterization behavior property tag set and the third weighted value of characterization identity attribute collection-label；
The product for calculating second weighted value and behavior property recommendation obtains the first recommendation；The third weight is calculated simultaneously
The product of value and the identity attribute recommendation obtains the second recommendation；
It is sorted according to the first recommendation with the size of the second recommendation and enumerates corresponding information and form recommendation list.
8. one kind is based on big data information push-delivery apparatus characterized by comprising
First acquisition module: it is configured as executing the first location information and recommendation list for obtaining target terminal, the recommendation column
Table includes the set for logging in the recommendation information that the account information mapped historical behavior data statistics on target terminal obtains；
First processing module: it is configured as executing and filters out the second position belonging to the recommendation information in the recommendation list
Information and recommendation information of the first location information within the scope of pre-determined distance are as pre- pushed information；
First execution module: it is configured as executing to push to the pre- pushed information in the target terminal showing.
9. a kind of computer equipment, including memory and processor, it is stored with computer-readable instruction in the memory, it is described
When computer-readable instruction is executed by the processor, so that the processor executes such as any one of claims 1 to 7 right
It is required that it is described based on big data information-pushing method the step of.
10. a kind of storage medium for being stored with computer-readable instruction, the computer-readable instruction is handled by one or more
When device executes, so that one or more processors execute counting as described in any one of claims 1 to 7 claim based on big
The step of according to information-pushing method.
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| CN201811198424.8A CN109582857A (en) | 2018-10-15 | 2018-10-15 | Based on big data information-pushing method, device, computer equipment and storage medium |
| PCT/CN2019/070365 WO2020077889A1 (en) | 2018-10-15 | 2019-01-04 | Big data-based information pushing method, apparatus, computer device and storage medium |
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