CN108492124A - Store information recommends method, apparatus and client - Google Patents
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- CN108492124A CN108492124A CN201810058024.0A CN201810058024A CN108492124A CN 108492124 A CN108492124 A CN 108492124A CN 201810058024 A CN201810058024 A CN 201810058024A CN 108492124 A CN108492124 A CN 108492124A
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Abstract
A kind of store information of this specification embodiment offer recommends method, apparatus and client.This method includes：It is determined for compliance with the consumption group corresponding to the consumption preferences of target user；The store information that customer consumption is crossed in the consumption group is obtained, identifies that store information occurrence number is more than or equal to the store information of predetermined threshold value in store information；The store information that the occurrence number is more than or equal to predetermined threshold value recommends the target user.
Description
Technical field
This specification embodiment is related to Internet communication technology field, more particularly to a kind of store information recommends method, dress
It sets and client.
Background technology
Fast development and new retail recently as internet are risen gradually, and internet has accumulated a large amount of user and shop
Spread data, these data are analyzed and are excavated can improve user to shop rate of consumption.Therefore, with big data, artificial
The means such as intelligence are paid attention into marketing recommendation activity on line come more businessmans.
Currently, when carrying out marketing recommendation process, often recommended according to the positive rating in shop or trading volume.Such as
Certain shop positive rating is high, and user just is recommended in the high shop of the positive rating.Alternatively, the trading volume in certain shop is high, just should
Recommend user in the high shop of trading volume.But often exist to improve positive rating or trading volume in above-mentioned existing recommendation method
The single situation of brush is carried out, and there are different customer consumption preference different problems, the shop for recommending user is caused not to be used
The shop that family is liked, recommendation effect are poor.Accordingly, it is desirable to provide more reliable or more effective scheme.
Invention content
The purpose of this specification embodiment is to provide a kind of store information and recommends method, apparatus and client, it is ensured that
The store information of recommendation more meets the hobby of target user, improves responsiveness of the user to the store information of recommendation, and then can be with
User is improved to shop rate of consumption.
This specification embodiment is realized in：
A kind of store information recommendation method, including：
Determine the consumption group where target user, wherein the consumption group is the consumption feature based on characterization user
The consumption feature vector of information carries out clustering determination；
The store information that customer consumption is crossed in the consumption group is obtained, identifies that store information goes out in the store information
Occurrence number is more than or equal to the store information of predetermined threshold value；
The store information that the occurrence number is more than or equal to predetermined threshold value recommends the target user.
A kind of store information recommendation apparatus, including：
Group determination module is consumed, for determining the consumption group where target user, wherein the consumption group is base
Clustering determination is carried out in the consumption feature vector of the consumption feature information of characterization user；
Group's store information acquisition module, for obtaining the store information that customer consumption is crossed in the consumption group；
Store information identification module, go out for identification in the store information store information occurrence number be more than or equal to it is default
The store information of threshold value；
Store information recommending module, the store information for the occurrence number to be more than or equal to predetermined threshold value recommend institute
State target user.
A kind of store information recommendation client, including processor and memory, the memory storage is by the processor
The computer program instructions of execution, the computer program instructions include：
Determine the consumption group where target user, wherein the consumption group is the consumption feature based on characterization user
The consumption feature vector of information carries out clustering determination；
The store information that customer consumption is crossed in the consumption group is obtained, identifies that store information goes out in the store information
Occurrence number is more than or equal to the store information of predetermined threshold value；
The store information that the occurrence number is more than or equal to predetermined threshold value recommends the target user.
As seen from the above, this specification one or more embodiment according to consumption preferences to user by carrying out consumption group
It divides, when carrying out store information recommendation, determines after the consumption group where target user, can directly be used according to target
There is the consumption data for consuming user in group of similar consumption preferences the recommendation of store information is carried out to target user at family, ensure
The store information recommended preferably meets the hobby of target user, improves user general to the response of the store information of recommendation
Rate, and then user can be improved to shop rate of consumption.
Description of the drawings
In order to illustrate more clearly of this specification one or more embodiment or technical solution in the prior art, below will
A brief introduction will be made to the drawings that need to be used in the embodiment or the description of the prior art, it should be apparent that, in being described below
Attached drawing is only some embodiments described in this specification, for those of ordinary skill in the art, is not paying creation
Property labour under the premise of, other drawings may also be obtained based on these drawings.
Fig. 1 is a kind of flow diagram for embodiment that the store information that this specification provides recommends method；
Fig. 2 is that the consumption feature vector for the consumption feature information based on characterization user that this specification provides carries out cluster point
Analysis determines a kind of flow diagram of embodiment of consumption group；
Fig. 3 is a kind of structural schematic diagram of the embodiment for the store information recommendation apparatus that this specification provides；
Fig. 4 is the schematic configuration diagram for recommending client according to the store information of an exemplary embodiment of this specification.
Specific implementation mode
A kind of store information of this specification embodiment offer recommends method, apparatus and client.
In order to make those skilled in the art more fully understand the technical solution in this specification, below in conjunction with this explanation
Attached drawing in book embodiment is clearly and completely described the technical solution in this specification embodiment, it is clear that described
Embodiment be only this specification a part of the embodiment, instead of all the embodiments.The embodiment of base in this manual,
Every other embodiment obtained by those of ordinary skill in the art without making creative efforts, should all belong to
The range of this specification protection.
A kind of store information of this specification introduced below recommends a kind of specific embodiment of method.Fig. 1 is that this specification carries
The store information of confession recommends a kind of flow diagram of embodiment of method, and present description provides such as embodiment or flow chart institutes
The method operating procedure stated, but may include either more or less operating procedure without performing creative labour based on routine.
The step of being enumerated in embodiment sequence be only numerous step execution sequences in a kind of mode, do not represent uniquely execute it is suitable
Sequence.When system or client production in practice executes, it can be executed according to embodiment or method shown in the drawings sequence
Either execute (such as environment of parallel processor or multiple threads) parallel.It is specific as shown in Figure 1, the method can be with
Including：
S102：Determine the consumption group where target user, wherein the consumption group is the consumption based on characterization user
The consumption feature vector of characteristic information carries out clustering determination.
In practical applications, on line or offline businesses can by Internet service platform (hereinafter referred to as service platform) into
On line recommend marketing activity come improve user to shop rate of consumption.Specifically, this specification embodiment service platform can be to
User recommends the shop for meeting the consumption preferences of user.In this specification embodiment, service platform is recommending shop mistake to user
Cheng Zhong, it may be determined that the consumption group where target user.Specifically, the consumption group is that the consumption based on characterization user is special
The consumption feature vector of reference breath carries out clustering determination.
In this specification embodiment can first to the user on service platform according to the consumption feature information of user to user
Carry out the division of the consumer group.In a specific embodiment, as shown in Fig. 2, Fig. 2 be this specification provide based on characterization
The consumption feature vector of the consumption feature information of user carries out the flow signal that clustering determines a kind of embodiment of consumption group
Figure.
S1021：The consumption feature information of the user of the first quantity is obtained, the user of first quantity includes the target
User.
Specifically, the user of the first quantity described in this specification embodiment may include the user on service platform.It is excellent
Choosing, carry out solid shop information recommendation processing when, it is contemplated that user can the closer shop of preference chosen distance disappear
Take.Use of the geographical location information in the first predeterminable area of a certain commercial circle position can be chosen in this specification embodiment
Family carries out the division of consumption group, correspondingly, the user of first quantity may include geographical location information on service platform
User in the first predeterminable area of commercial circle position to be recommended.Specifically, commercial circle can described in this specification embodiment
To include the consumption place for including one or more solid shops.
Correspondingly, the target shop may include the user on service platform, can also include geographical on service platform
User of the location information in the first predeterminable area of commercial circle position to be recommended.In addition, in some embodiments, in order to carry
Height recommends conversion ratio, can recommend store information to the higher user of recommendation information feedback rates in conjunction with history recommendation information.
Correspondingly, the target user may include the use that on service platform recommendation information feedback rates are optionally greater than with default feedback rates
Family.Specifically, user may include clicking rate and/or consumption conversion ratio to the feedback rates of recommendation information here.It is described default anti-
Feedback rate can be configured in conjunction with practical application request.
Specifically, the first predeterminable area of commercial circle position to be recommended can be set according to practical situations here
It sets, such as could be provided as in commercial circle position 5km to be recommended.Specifically, the geographical location information of user can here
Think the common fixed geographical location information of user, such as the home address of user, CompanyAddress etc..Specifically, the user
Geographical location information can pass through user set obtain；Furthermore it is also possible to pass through radio communication network (such as GSM nets, CDMA
Net) or external positioning method (such as GPS) acquisition；And it can also be from user's history transaction data (such as online shopping data)
The modes such as extraction address information obtain.
Specifically, in this specification embodiment, the consumption feature information may include that can reflect customer consumption preference
Information.In practical applications, the consumption preferences of user generally with the economic base of user, background experience, consuming capacity and
The correlations such as consumption habit.Correspondingly, consumption feature information described in this specification embodiment can include at least one of the following：
Consumption foundation attribute information, consuming capacity information, consumption habit information.
In one embodiment, the consumption foundation attribute information may include the economic base information and background of user
Undergo information, such as house situation information (whether having room and house class), educational information and occupational information etc..The consumption energy
Force information may include that can reflect the information of customer consumption amount, such as disappear at the user of corresponding proportionate relationship with customer consumption amount
Take grade (in general, customer consumption grade is directly proportional to customer consumption amount).The consumption habit information may include can be anti-
The information of customer consumption data is reflected, such as user buys brand message, personal interest information, trip tool information of commodity etc..
S1023：Consumption feature vector based on the consumption feature information architecture user.
In practical applications, the consumption feature information may not be numerical value, but to a certain degree or the character of trend
Change characterization, in this case, the content that the characterization characterizes can be made to be quantified as a particular value by default rule.Into
And subsequently the value of the quantization can be utilized to characterize corresponding consumption feature information.In a common example, it is possible some
The value of dimension be " in ", then can quantify the character be its ASCII character binary value or hexadecimal value.
In a specific embodiment, the consumption feature vector based on the consumption feature information architecture user can wrap
It includes：
1) the consumption feature information quantization is by the default quantizing rule corresponding to the consumption feature information based on user
Particular value；
2) first eigenvector of user is built based on the particular value after quantization；
3) first eigenvector is standardized, the second feature vector after being standardized will be described
Second feature vector is as the consumption feature vector.
Specifically, in this specification embodiment, the default quantizing rule can in conjunction with corresponding consumption feature information into
Row setting, in a specific embodiment, for example, whether will have room to be quantified as room is 1, no room is 0；Another is specific
In embodiment, for example, customer consumption grade is quantified from 10 to 1 from high to low.
Specifically, in view of that can reflect that the consumption feature information of customer consumption preference may include a variety of different types of
The module of information, the particular value after different types of consumption feature information quantization is different, can be in this specification embodiment
Particular value after quantifying in first eigenvector is standardized.In a specific embodiment, as described above
Whether room and customer consumption grade are had, the former is quantified as 0 or 1；The latter is quantified as 1 to 10, after being standardized, can incite somebody to action
Whether there are room and customer consumption grade to be unified for be characterized with the numerical value between 0 to 1.Specifically, whether have room can with 0 or
1 characterization, customer consumption grade can use 0.1 to 1 characterization.
It is each in this specification embodiment Playsization treated second feature vector in addition, it is necessary to explanation
A element soldier is not limited only to the numerical value between above-mentioned 0 to 1, can be combined with practical situations be set as 0 to 100 it is equal other
Module.
In addition, in further embodiments, it is contemplated that can reflect in the consumption feature information of customer consumption preference and exist
Some consumption feature information influence customer consumption preference little, correspondingly, in this specification embodiment, the method can be with
Including：
Principal component analysis processing is carried out to the second feature vector, using principal component analysis treated feature vector as
The consumption feature vector.
Specifically, the processing of principal component analysis described in this specification embodiment can include but is not limited to use PCA
(Principal Component Analysis).Here by principal component analysis processing can improve consumption feature vector to
The characterization of family consumption preferences, while the dimensionality reduction to consumption feature vector may be implemented, reduce subsequent calculation amount.
S1025：Similarity between the consumption feature vector of user based on first quantity is to first quantity
User carry out clustering processing, obtain the consumption group of the second quantity.
Specifically, the processing of clustering described in this specification embodiment can include but is not limited to use：DBSCAN
(Density-Based Spatial Clustering of Applications with Noise) clustering algorithm or k-
Means clustering algorithms carry out clustering processing.
In a specific embodiment, disappearing in conjunction with user of the DBSCAN clustering algorithms introduction based on first quantity
The similarity taken between feature vector carries out clustering processing to the user of first quantity, obtains the consumption of the second quantity
Group's specific method：
DBSCAN clustering algorithms need first to determine cluster radius and cluster most parcel in carrying out clustering processing procedure
Containing points.It can be true according to the similarity between the consumption feature vector of the user of first quantity in this specification embodiment
Cluster radius and the cluster for determining third quantity are minimum comprising points.
In a specific embodiment, the consumption feature vector P={ p (i) of the user of the first quantity can be calculated；I=
0,1 ... n } in any user consumption feature vector p (i) and other users consumption feature vector { p (1), p (2) ..., p
(i-1), p (i+1) ..., p (n) between similarity；(p (i) indicates that the consumption feature vector of i-th of user, n indicate user
Total quantity, i.e. the first quantity).Then, according to the size of similarity, sequence from small to large is ranked up D={ di(k)；k
=n-1 }, di(k) the big phase of kth between the consumption feature vector and the consumption feature vector of other users of i-th of user of expression
Like degree；To the size by the big similarity of the kth of the user of the first quantity according to similarity, sequence from small to large is arranged
Sequence；It is fitted similarity change curve after a sequence according to the similarity after sequence, it will be drastically corresponding to changed position
Similarity value, be determined as cluster radius；Cluster minimum corresponding to the cluster radius could be provided as this comprising points and gather
K values corresponding to class radius.
Specifically, the similarity in this specification embodiment between consumption feature vector can include at least one of the following：
Euclidean distance, COS distance, manhatton distance, the outstanding German number of card.
In addition, it is necessary to illustrate, third quantity is less than first quantity in this specification embodiment.
Then, clustering can be carried out respectively comprising points in conjunction with the cluster radius and cluster minimum of the third quantity
Processing.Specifically, may include：
1) any not processed consumption feature vector is chosen from the consumption feature vector of the user of first quantity
As initial consumer feature vector, finds out and be less than or equal to current cluster radius with the similarity of the initial consumer feature vector
Consumption feature vector.
2) it is more than or equal to current cluster most if it is less than the quantity of the consumption feature vector equal to current cluster radius
It is small to include points, then current consumption feature vector and the consumption feature vector formation one less than or equal to current cluster radius
A cluster, and current consumption feature vector is labeled as having accessed.
3) it using any consumption feature vector not processed in current cluster as initial consumer feature vector, repeats above-mentioned
Step 1) and the step of 2) determining cluster, to be extended to cluster.
4) it is less than current cluster most parcel if it is less than the quantity of the consumption feature vector equal to current cluster radius
Containing points, then the point is temporarily labeled is used as noise spot,
If 5) all the points in current cluster are handled, that is, it is marked as having accessed or when noise spot, to described first
Not processed consumption feature vector repeats the above steps to first quantity in the consumption feature vector of the user of quantity
The consumption feature vector of user is all handled, the user after being grouped.
In practical applications, if cluster radius obtains in field DBSCAN clustering algorithms carry out clustering processing procedure
It is worth excessive, most of points (i.e. consumption feature vector) can be caused all to gather in the same cluster, conversely, when cluster radius is too small, meeting
Lead to the division of a cluster.The minimum value acquirement comprising points of cluster is excessive, and the same cluster midpoint can be caused to be marked as peeling off
Point；Conversely, cluster is minimum too small comprising counting, a large amount of core point can be resulted in a finding that.It therefore, can be in this specification embodiment
Above-mentioned k values are configured in conjunction with practical application, meanwhile, in order to ensure preferably to be grouped, multiple k values can be chosen, are obtained more
The different cluster radius of group and cluster are minimum comprising points, correspondingly, utilizing multigroup different cluster radius and cluster most parcel
A variety of different user groupings can be obtained containing points, according to actual packet effect, a kind of user grouping are chosen, by the first quantity
User be divided into the consumption group of the second quantity.
In practical applications, it can be marked by users such as user names during the consumption group where determining target user
Know the consumption group where determining the target user in the consumption group of second quantity.
By the technical solution in above-mentioned this specification embodiment as it can be seen that in this specification embodiment, according to the consumption of user
Preference carries out user the division of consumption group, when carrying out store information recommendation, can be recommended in conjunction with consumption group, be protected
The store information that card is recommended more meets the consumption preferences of target user, and then improves the rate of consumption of user.
S104：The store information that customer consumption is crossed in the consumption group is obtained, identifies shop in the store information
Information occurrence number is more than or equal to the store information of predetermined threshold value.
In this specification embodiment, the consumption preferences of the user in same consumption group are similar, correspondingly, can will be same
The store information that user in consumption group often consumes recommends target user.User in the consumption group can be obtained to disappear
The store information taken identifies that store information occurrence number is believed more than or equal to the shop of predetermined threshold value in the store information
Breath.
In this specification embodiment, the predetermined threshold value can be in conjunction with number of users in practical application request and consumption group
It is set, such as is set as the half of total number of users.The general predetermined threshold value is bigger, the store information symbol determined
The probability for closing the consumption preferences of target user is bigger.
In this specification embodiment, the store information may include shop essential information, such as shop title, shop
Location, shop hours etc..In addition, in order to increase user to shop rate of consumption, the store information can also include：Shop is excellent
Favour information.
Furthermore, it is necessary to illustrate, in this specification embodiment, the store information is not limited in above-mentioned shop base
This information and shop favor information can also include in practical applications other information, this specification embodiment is not with above-mentioned
It is limited.
S106：The store information that the occurrence number is more than or equal to predetermined threshold value recommends the target user.
In this specification embodiment, the target is recommended in the store information that occurrence number is more than or equal to predetermined threshold value
It, can be in conjunction with the location information recommended between time and target user and shop, to increase user to shop rate of consumption when user.
In a specific embodiment, it is contemplated that some shops are users can just go to disappear in some fixed periods
Take, such as restaurant.In this specification embodiment, the store information institute that predetermined threshold value can be more than or equal in the occurrence number is right
The store information that the occurrence number is more than or equal to predetermined threshold value in the default consumption time answered recommends the target user.
Specifically, default consumption time here can be configured in conjunction with practical situations, may include one or
Multiple periods.Such as the store information in restaurant can be 10:30 to 13:00 and 16:30 to 20:00 is recommended.
In another specific embodiment, it is contemplated that the shop that user generally likes closer is consumed, this explanation
The target user can be worked as in book embodiment in the occurrence number to be more than or equal to corresponding to the store information of predetermined threshold value
When in the second predeterminable area of geographical location information position, the shop that the occurrence number is more than or equal to predetermined threshold value is believed
Breath recommends the target user.
Specifically, can be according to reality in the second predeterminable area of geographical location information position corresponding to store information
Border applicable cases are configured, such as could be provided as in the 1km apart from the geographical location information position.
In addition, in conjunction with practical application request, the preferred embodiment of above-mentioned two store information can be combined with each other.I.e. in institute
It is right to store information institute more than or equal in the default consumption time corresponding to the store information of predetermined threshold value to state occurrence number
Target user in second predeterminable area of the geographical location information position answered recommends the store information.
Further, the clicking rate and conversion ratio after each store information is recommended can be recorded, to recommend as history
Data, convenient for choosing the recommendation pair to user's store information the most of recommendation information positive feedback based on the history recommendation information
As.
It can be seen that a kind of store information of this specification recommend one or more embodiments of method by user according to
Consumption preferences carry out consumption group division, when carrying out store information recommendation, determine after the consumption group where target user,
Can directly according to target user have similar consumption preferences consumption group in user consumption data to target user into
The recommendation of row store information ensure that the store information of recommendation preferably meets the hobby of target user, improve user to pushing away
The response probability for the store information recommended, and then user can be improved to shop rate of consumption.
On the other hand this specification also provides a kind of store information recommendation apparatus, Fig. 3 is the shop letter that this specification provides
The structural schematic diagram for ceasing a kind of embodiment of recommendation apparatus, as shown in figure 3, described device 300 may include：
Consume group determination module 310, the consumption group being determined for where target user, wherein the consumption
Group is the consumption feature vector progress clustering determination based on the consumption feature information of characterization user；
Group's store information acquisition module 320 can be used for obtaining the shop letter that customer consumption is crossed in the consumption group
Breath；
Store information identification module 330 can be used for identifying that store information occurrence number is more than in the store information
Equal to the store information of predetermined threshold value；
Store information recommending module 340 can be used for the occurrence number being more than or equal to the store information of predetermined threshold value
Recommend the target user.
In another embodiment, the consumption feature vector of the consumption feature information based on characterization user carries out clustering
Determining consumption group may include being determined using following modules：
Consumption feature data obtaining module, can be used for obtaining the consumption feature information of the user of the first quantity, and described the
The user of one quantity includes the target user；
Consumption feature vector build module, can be used for the consumption feature based on the consumption feature information architecture user to
Amount；
Row clustering processing module can be used between the consumption feature vector of the user based on first quantity
Similarity carries out clustering processing to the user of first quantity, obtains the consumption group of the second quantity.
In another embodiment, the consumption feature vector structure module may include：
Quantifying unit can be used for default quantizing rule corresponding to the consumption feature information based on user by the consumption
Characteristic information is quantified as particular value；
First eigenvector construction unit, after can be used for based on quantization particular value structure user fisrt feature to
Amount；
Standardization unit can be used for being standardized the first eigenvector, after obtaining standardization
Second feature vector, using the second feature vector as the consumption feature vector.
In another embodiment, the consumption feature vector structure module can also include：
Principal component analysis processing unit, for carrying out principal component analysis processing to the second feature vector, by principal component
Feature vector after analyzing processing is as the consumption feature vector.
In another embodiment, described device 300 can also include：
User's determining module can be used for before obtaining the consumption feature information of user of the first quantity, determine geographical
User of the location information in the first predeterminable area of commercial circle position to be recommended, by the user in first predeterminable area
User as first quantity.
In another embodiment, the similarity at least may include one of the following：
Euclidean distance, COS distance, manhatton distance, the outstanding German number of card.
In another embodiment, the consumption feature information at least may include one of the following：
Consumption foundation attribute information, consuming capacity information, consumption habit information.
In another embodiment, the store information recommending module 340 may include：
First store information recommendation unit can be used for being more than or equal in the occurrence number store information of predetermined threshold value
The store information that the occurrence number is more than or equal to predetermined threshold value in corresponding default consumption time recommends the target
User.
In another embodiment, the store information recommending module 340 may include：
Second store information recommendation unit can be used for being more than or equal in the occurrence number as the target user default
It is when in the second predeterminable area of the geographical location information position corresponding to the store information of threshold value, the occurrence number is big
In recommending the target user equal to the store information of predetermined threshold value.
The above-mentioned store information that this specification embodiment provides recommends method or apparatus can be in a computer by processor
Corresponding program instruction is executed to realize, such as using the c++ language of windows operating systems the ends PC realize or other for example
It is realized in intelligent terminal using android, iOS system programming language, and the processing logic based on quantum computer is real
Now etc..As shown in figure 4, Fig. 4 is the signal knot for recommending client according to the store information of an exemplary embodiment of this specification
Composition.In hardware view, which may include processor, internal bus, network interface, memory and non-volatile memories
Device is also possible that the required hardware of other business certainly.Processor reads corresponding calculating from nonvolatile memory
It is then run in machine program to memory, forms word string identification device on logic level.Certainly, in addition to software realization mode it
Outside, other realization methods, such as the mode etc. of logical device or software and hardware combining is not precluded in the application, that is to say, that with
The executive agent of lower process flow is not limited to each logic unit, can also be hardware or logical device.
On the other hand this specification embodiment also provides a kind of store information recommendation client, including processor and storage
Device, the memory store the computer program instructions executed by the processor, and the computer program instructions may include：
Determine the consumption group where target user, wherein the consumption group is the consumption feature based on characterization user
The consumption feature vector of information carries out clustering determination；
The store information that customer consumption is crossed in the consumption group is obtained, identifies that store information goes out in the store information
Occurrence number is more than or equal to the store information of predetermined threshold value；
The store information that the occurrence number is more than or equal to predetermined threshold value recommends the target user.
In this specification embodiment, the processor may include central processing unit (CPU) or graphics processor
(GPU), naturally it is also possible to including other microcontroller, logic gates, integrated circuits with logic processing capability etc. or its
It is appropriately combined.Memory described in the embodiment of the present application can be for protecting stored memory device.In digital display circuit, energy
The equipment for preserving binary data can be memory；In integrated circuits, one not physical form have store function
Circuit may be memory, such as RAM, FIFO；In systems, the storage device with physical form can also be named storage
Device etc..When realization, which can also be realized by the way of cloud storage, specific implementation, and this specification is not
Mistake limits.
It can be seen that a kind of store information of this specification recommends the embodiment of method, apparatus or client to pass through to user
Consumption group division is carried out according to consumption preferences, when carrying out store information recommendation, is determined in the consumer group where target user
After group, can directly it be used to target according to the consumption data of user in the consumption group to target user with similar consumption preferences
Family carries out the recommendation of store information, ensure that the store information of recommendation preferably meets the hobby of target user, improves user
To the response probability of the store information of recommendation, and then user can be improved to shop rate of consumption.
It is above-mentioned that this specification specific embodiment is described.Other embodiments are in the scope of the appended claims
It is interior.In some cases, the action recorded in detail in the claims or step can be come according to different from the sequence in embodiment
It executes and desired result still may be implemented.In addition, the process described in the accompanying drawings not necessarily require show it is specific suitable
Sequence or consecutive order could realize desired result.In some embodiments, multitasking and parallel processing be also can
With or it may be advantageous.
In the 1990s, the improvement of a technology can be distinguished clearly be on hardware improvement (for example,
Improvement to circuit structures such as diode, transistor, switches) or software on improvement (improvement for method flow).So
And with the development of technology, the improvement of current many method flows can be considered as directly improving for hardware circuit.
Designer nearly all obtains corresponding hardware circuit by the way that improved method flow to be programmed into hardware circuit.Cause
This, it cannot be said that the improvement of a method flow cannot be realized with hardware entities module.For example, programmable logic device
(Programmable Logic Device, PLD) (such as field programmable gate array (Field Programmable Gate
Array, FPGA)) it is exactly such a integrated circuit, logic function determines device programming by user.By designer
Voluntarily programming comes a digital display circuit " integrated " on a piece of PLD, designs and makes without asking chip maker
Dedicated IC chip.Moreover, nowadays, substitution manually makes IC chip, this programming is also used instead mostly " patrols
Volume compiler (logic compiler) " software realizes that software compiler used is similar when it writes with program development,
And the source code before compiling also write by handy specific programming language, this is referred to as hardware description language
(Hardware Description Language, HDL), and HDL is also not only a kind of, but there are many kind, such as ABEL
(Advanced Boolean Expression Language)、AHDL(Altera Hardware Description
Language)、Confluence、CUPL(Cornell University Programming Language)、HDCal、JHDL
(Java Hardware Description Language)、Lava、Lola、MyHDL、PALASM、RHDL(Ruby
Hardware Description Language) etc., VHDL (Very-High-Speed are most generally used at present
Integrated Circuit Hardware Description Language) and Verilog.Those skilled in the art also answer
This understands, it is only necessary to method flow slightly programming in logic and is programmed into integrated circuit with above-mentioned several hardware description languages,
The hardware circuit for realizing the logical method flow can be readily available.
Controller can be implemented in any suitable manner, for example, controller can take such as microprocessor or processing
The computer for the computer readable program code (such as software or firmware) that device and storage can be executed by (micro-) processor can
Read medium, logic gate, switch, application-specific integrated circuit (Application Specific Integrated Circuit,
ASIC), the form of programmable logic controller (PLC) and embedded microcontroller, the example of controller includes but not limited to following microcontroller
Device：ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicone Labs C8051F320, are deposited
Memory controller is also implemented as a part for the control logic of memory.It is also known in the art that in addition to
Pure computer readable program code mode is realized other than controller, can be made completely by the way that method and step is carried out programming in logic
Controller is obtained in the form of logic gate, switch, application-specific integrated circuit, programmable logic controller (PLC) and embedded microcontroller etc. to come in fact
Existing identical function.Therefore this controller is considered a kind of hardware component, and to including for realizing various in it
The device of function can also be considered as the structure in hardware component.Or even, it can will be regarded for realizing the device of various functions
For either the software module of implementation method can be the structure in hardware component again.
Device, module or the unit that above-described embodiment illustrates can specifically be realized, Huo Zheyou by computer chip or entity
Product with certain function is realized.It is a kind of typically to realize that equipment is computer.Specifically, computer for example can be a
People's computer, laptop computer, cellular phone, camera phone, smart phone, personal digital assistant, media player, navigation
Any equipment in equipment, electronic mail equipment, game console, tablet computer, wearable device or these equipment
Combination.
For convenience of description, it is divided into various units when description apparatus above with function to describe respectively.Certainly, implementing this
The function of each unit is realized can in the same or multiple software and or hardware when specification.
It should be understood by those skilled in the art that, the embodiment of the present invention can be provided as method, apparatus or computer program
Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the present invention
Apply the form of example.Moreover, the present invention can be used in one or more wherein include computer usable program code computer
The computer program production implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.)
The form of product.
The present invention be with reference to according to the method for the embodiment of the present invention, the flow of equipment (device) and computer program product
Figure and/or block diagram describe.It should be understood that can be realized by computer program instructions every first-class in flowchart and/or the block diagram
The combination of flow and/or box in journey and/or box and flowchart and/or the block diagram.These computer programs can be provided
Instruct the processor of all-purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce
A raw machine so that the instruction executed by computer or the processor of other programmable data processing devices is generated for real
The device for the function of being specified in present one flow of flow chart or one box of multiple flows and/or block diagram or multiple boxes.
These computer program instructions, which may also be stored in, can guide computer or other programmable data processing devices with spy
Determine in the computer-readable memory that mode works so that instruction generation stored in the computer readable memory includes referring to
Enable the manufacture of device, the command device realize in one flow of flow chart or multiple flows and/or one box of block diagram or
The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device so that count
Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, in computer or
The instruction executed on other programmable devices is provided for realizing in one flow of flow chart or multiple flows and/or block diagram one
The step of function of being specified in a box or multiple boxes.
In a typical configuration, computing device includes one or more processors (CPU), input/output interface, net
Network interface and memory.
Memory may include computer-readable medium in volatile memory, random access memory (RAM) and/or
The forms such as Nonvolatile memory, such as read-only memory (ROM) or flash memory (flash RAM).Memory is computer-readable medium
Example.
Computer-readable medium includes permanent and non-permanent, removable and non-removable media can be by any method
Or technology realizes information storage.Information can be computer-readable instruction, data structure, the module of program or other data.
The example of the storage medium of computer includes, but are not limited to phase transition internal memory (PRAM), static RAM (SRAM), moves
State random access memory (DRAM), other kinds of random access memory (RAM), read-only memory (ROM), electric erasable
Programmable read only memory (EEPROM), fast flash memory bank or other memory techniques, read-only disc read only memory (CD-ROM) (CD-ROM),
Digital versatile disc (DVD) or other optical storages, magnetic tape cassette, tape magnetic disk storage, graphene stores or other
Magnetic storage apparatus or any other non-transmission medium can be used for storage and can be accessed by a computing device information.According to herein
In define, computer-readable medium does not include temporary computer readable media (transitory media), such as data of modulation
Signal and carrier wave.
It should also be noted that, the terms "include", "comprise" or its any other variant are intended to nonexcludability
Including so that process, method, commodity or equipment including a series of elements include not only those elements, but also wrap
Include other elements that are not explicitly listed, or further include for this process, method, commodity or equipment intrinsic want
Element.In the absence of more restrictions, the element limited by sentence "including a ...", it is not excluded that wanted including described
There is also other identical elements in the process of element, method, commodity or equipment.
It will be understood by those skilled in the art that the embodiment of this specification can be provided as method, apparatus or computer program production
Product.Therefore, complete hardware embodiment, complete software embodiment or implementation combining software and hardware aspects can be used in this specification
The form of example.Moreover, this specification can be used in one or more wherein include computer usable program code computer
The computer program production implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.)
The form of product.
This specification can describe in the general context of computer-executable instructions executed by a computer, such as journey
Sequence module.Usually, program module include routines performing specific tasks or implementing specific abstract data types, program, object,
Component, data structure etc..This specification can also be put into practice in a distributed computing environment, in these distributed computing environment
In, by executing task by the connected remote processing devices of communication network.In a distributed computing environment, program module
It can be located in the local and remote computer storage media including storage device.
Each embodiment in this specification is described in a progressive manner, identical similar portion between each embodiment
Point just to refer each other, and each embodiment focuses on the differences from other embodiments.Especially for device and
For client embodiment, since it is substantially similar to the method embodiment, so description is fairly simple, related place is referring to side
The part of method embodiment illustrates.
The foregoing is merely the embodiments of this specification, are not limited to this specification.For art technology
For personnel, this specification can have various modifications and variations.It is all this specification spirit and principle within made by it is any
Modification, equivalent replacement, improvement etc., should be included within right.
Claims (19)
1. a kind of store information recommends method, including：
Determine the consumption group where target user, wherein the consumption group is the consumption feature information based on characterization user
Consumption feature vector carry out clustering determination；
The store information that customer consumption is crossed in the consumption group is obtained, identifies that store information goes out occurrence in the store information
Store information of the number more than or equal to predetermined threshold value；
The store information that the occurrence number is more than or equal to predetermined threshold value recommends the target user.
2. according to the method described in claim 1, wherein, the consumption feature of the consumption feature information based on characterization user to
The consumption group that amount carries out clustering determination includes being determined using following manner：
The consumption feature information of the user of the first quantity is obtained, the user of first quantity includes the target user；
Consumption feature vector based on the consumption feature information architecture user；
Similarity between the consumption feature vector of user based on first quantity carries out the user of first quantity
Clustering processing, obtains the consumption group of the second quantity.
3. according to the method described in claim 2, wherein, the consumption feature based on the consumption feature information architecture user
Vector includes：
The consumption feature information quantization is particular value by default quantizing rule corresponding to the consumption feature information based on user；
The first eigenvector of user is built based on the particular value after quantization；
The first eigenvector is standardized, the second feature vector after being standardized is special by described second
Sign vector is used as the consumption feature vector.
4. according to the method described in claim 3, wherein, the method further includes：
Principal component analysis processing is carried out to the second feature vector, using principal component analysis treated feature vector as described in
Consumption feature vector.
5. according to the method described in claim 2, wherein, before obtaining the consumption feature information of user of the first quantity, institute
The method of stating further includes：
It determines user of the geographical location information in the first predeterminable area of commercial circle position to be recommended, described first is preset
User of the user as first quantity in region.
6. according to the method described in claim 2, wherein, the similarity includes at least one of the following：
Euclidean distance, COS distance, manhatton distance, the outstanding German number of card.
7. method according to any one of claims 1 to 6, wherein the consumption feature information includes at least one of the following：
Consumption foundation attribute information, consuming capacity information, consumption habit information.
8. method according to any one of claims 1 to 6, wherein described that the occurrence number is more than or equal to predetermined threshold value
Store information recommend the target user and include：
It is more than or equal to the appearance in the default consumption time corresponding to the store information of predetermined threshold value in the occurrence number
The store information that number is more than or equal to predetermined threshold value recommends the target user.
9. method according to any one of claims 1 to 6, wherein described that the occurrence number is more than or equal to predetermined threshold value
Store information recommend the target user and include：
Believe when the target user is more than or equal to the geographical location corresponding to the store information of predetermined threshold value in the occurrence number
When ceasing in the second predeterminable area of position, the store information that the occurrence number is more than or equal to predetermined threshold value recommends institute
State target user.
10. a kind of store information recommendation apparatus, including：
Group determination module is consumed, for determining the consumption group where target user, wherein the consumption group is based on table
The consumption feature vector for taking over the consumption feature information at family for use carries out clustering determination；
Group's store information acquisition module, for obtaining the store information that customer consumption is crossed in the consumption group；
Store information identification module goes out store information occurrence number in the store information and is more than or equal to predetermined threshold value for identification
Store information；
Store information recommending module, the store information for the occurrence number to be more than or equal to predetermined threshold value recommend the mesh
Mark user.
11. device according to claim 10, wherein the consumption feature of the consumption feature information based on characterization user
The consumption group that vector carries out clustering determination includes being determined using following modules：
Consumption feature data obtaining module, the consumption feature information of the user for obtaining the first quantity, first quantity
User includes the target user；
Consumption feature vector builds module, is used for the consumption feature vector based on the consumption feature information architecture user；
Row clustering processing module, for the similarity pair between the consumption feature vector of the user based on first quantity
The user of first quantity carries out clustering processing, obtains the consumption group of the second quantity.
12. according to the devices described in claim 11, wherein the consumption feature vector builds module and includes：
Quantifying unit, for the default quantizing rule corresponding to the consumption feature information based on user by the consumption feature information
It is quantified as particular value；
First eigenvector construction unit, the first eigenvector for building user based on the particular value after quantization；
Standardization unit, for being standardized to the first eigenvector, second after being standardized is special
Sign vector, using the second feature vector as the consumption feature vector.
13. device according to claim 12, wherein the consumption feature vector builds module and further includes：
Principal component analysis processing unit, for carrying out principal component analysis processing to the second feature vector, by principal component analysis
Feature vector that treated is as the consumption feature vector.
14. according to the devices described in claim 11, wherein described device further includes：
User's determining module, for before obtaining the consumption feature information of user of the first quantity, determining geographical location information
User in the first predeterminable area of commercial circle position to be recommended, using the user in first predeterminable area as described in
The user of first quantity.
15. according to the devices described in claim 11, wherein the similarity includes at least one of the following：
Euclidean distance, COS distance, manhatton distance, the outstanding German number of card.
16. according to any device of claim 10 to 15, wherein the consumption feature information include at least it is following it
One：
Consumption foundation attribute information, consuming capacity information, consumption habit information.
17. according to any device of claim 10 to 15, wherein the store information recommending module includes：
First store information recommendation unit, for being more than or equal in the occurrence number corresponding to the store information of predetermined threshold value
The store information that the occurrence number is more than or equal to predetermined threshold value in default consumption time recommends the target user.
18. according to any device of claim 10 to 15, wherein the store information recommending module includes：
Second store information recommendation unit, for when the target user is in shop of the occurrence number more than or equal to predetermined threshold value
When spreading in the second predeterminable area of the geographical location information position corresponding to information, the occurrence number is more than or equal to pre-
If the store information of threshold value recommends the target user.
19. a kind of store information recommends client, including processor and memory, the memory storage to be held by the processor
Capable computer program instructions, the computer program instructions include：
Determine the consumption group where target user, wherein the consumption group is the consumption feature information based on characterization user
Consumption feature vector carry out clustering determination；
The store information that customer consumption is crossed in the consumption group is obtained, identifies that store information goes out occurrence in the store information
Store information of the number more than or equal to predetermined threshold value；
The store information that the occurrence number is more than or equal to predetermined threshold value recommends the target user.
Priority Applications (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810058024.0A CN108492124A (en) | 2018-01-22 | 2018-01-22 | Store information recommends method, apparatus and client |
| TW107144285A TW201933232A (en) | 2018-01-22 | 2018-12-10 | Shop information recommendation method, device and client |
| PCT/CN2018/125208 WO2019141072A1 (en) | 2018-01-22 | 2018-12-29 | Method, device, and client for recommending store information |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810058024.0A CN108492124A (en) | 2018-01-22 | 2018-01-22 | Store information recommends method, apparatus and client |
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| CN108492124A true CN108492124A (en) | 2018-09-04 |
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| Application Number | Title | Priority Date | Filing Date |
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| CN201810058024.0A Pending CN108492124A (en) | 2018-01-22 | 2018-01-22 | Store information recommends method, apparatus and client |
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|---|---|
| CN (1) | CN108492124A (en) |
| TW (1) | TW201933232A (en) |
| WO (1) | WO2019141072A1 (en) |
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- 2018-12-10 TW TW107144285A patent/TW201933232A/en unknown
- 2018-12-29 WO PCT/CN2018/125208 patent/WO2019141072A1/en active Application Filing
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| WO2019141072A1 (en) * | 2018-01-22 | 2019-07-25 | 阿里巴巴集团控股有限公司 | Method, device, and client for recommending store information |
| CN109857927A (en) * | 2018-12-24 | 2019-06-07 | 深圳市珍爱捷云信息技术有限公司 | User's recommended method, device, computer equipment and computer readable storage medium |
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| CN111797877A (en) * | 2019-04-09 | 2020-10-20 | Oppo广东移动通信有限公司 | Data processing method, data processing device, storage medium and electronic equipment |
| CN111797877B (en) * | 2019-04-09 | 2024-05-10 | Oppo广东移动通信有限公司 | Data processing method and device, storage medium and electronic equipment |
| CN110110246A (en) * | 2019-05-13 | 2019-08-09 | 北京金和网络股份有限公司 | Shops's recommended method based on geographical information grid density |
| CN110225141A (en) * | 2019-06-28 | 2019-09-10 | 北京金山安全软件有限公司 | Content pushing method and device and electronic equipment |
| CN110225141B (en) * | 2019-06-28 | 2022-04-22 | 北京金山安全软件有限公司 | Content pushing method and device and electronic equipment |
| CN110363583A (en) * | 2019-07-02 | 2019-10-22 | 北京淇瑀信息科技有限公司 | A kind of method, apparatus and electronic equipment based on location information creation consumer consumption behavior label |
| CN110738521A (en) * | 2019-10-10 | 2020-01-31 | 深圳市云积分科技有限公司 | multi-vendor brand customer selling method and device |
| CN110738521B (en) * | 2019-10-10 | 2022-04-15 | 深圳市云积分科技有限公司 | Client selling method and device for multi-merchant brand |
| CN110782278A (en) * | 2019-10-15 | 2020-02-11 | 支付宝(杭州)信息技术有限公司 | Data processing method and device |
| CN110969509A (en) * | 2019-10-25 | 2020-04-07 | 贝壳技术有限公司 | Decoration recommendation method and device, machine-readable storage medium and processor |
| CN110782325A (en) * | 2019-10-31 | 2020-02-11 | 深圳市云积分科技有限公司 | Member information recommendation method and device |
| CN111179011A (en) * | 2019-11-05 | 2020-05-19 | 泰康保险集团股份有限公司 | Insurance product recommendation method and device |
| CN111340565A (en) * | 2020-03-20 | 2020-06-26 | 北京爱笔科技有限公司 | Information recommendation method, device, equipment and storage medium |
| CN111626790A (en) * | 2020-06-01 | 2020-09-04 | 浪潮软件股份有限公司 | Consumer group feature identification method for retail terminal |
| CN111698332A (en) * | 2020-06-23 | 2020-09-22 | 深圳壹账通智能科技有限公司 | Method, device and equipment for distributing business objects and storage medium |
| CN112116403A (en) * | 2020-09-28 | 2020-12-22 | 中国建设银行股份有限公司 | Information recommendation method, device and equipment |
| CN113129053A (en) * | 2021-03-29 | 2021-07-16 | 北京沃东天骏信息技术有限公司 | Information recommendation model training method, information recommendation method and storage medium |
| CN113129053B (en) * | 2021-03-29 | 2024-05-21 | 北京沃东天骏信息技术有限公司 | Information recommendation model training method, information recommendation method and storage medium |
| TWI779887B (en) * | 2021-10-19 | 2022-10-01 | 國立臺灣大學 | Dynamic homestay information recommendation device |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2019141072A1 (en) | 2019-07-25 |
| TW201933232A (en) | 2019-08-16 |
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Effective date of registration: 20200924 Address after: Cayman Enterprise Centre, 27 Hospital Road, George Town, Grand Cayman Islands Applicant after: Innovative advanced technology Co.,Ltd. Address before: Cayman Enterprise Centre, 27 Hospital Road, George Town, Grand Cayman Islands Applicant before: Advanced innovation technology Co.,Ltd. Effective date of registration: 20200924 Address after: Cayman Enterprise Centre, 27 Hospital Road, George Town, Grand Cayman Islands Applicant after: Advanced innovation technology Co.,Ltd. Address before: Greater Cayman, British Cayman Islands Applicant before: Alibaba Group Holding Ltd. |
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