CN107545491A - A kind of data processing method and device of recommendation information - Google Patents
A kind of data processing method and device of recommendation information Download PDFInfo
- Publication number
- CN107545491A CN107545491A CN201710751911.1A CN201710751911A CN107545491A CN 107545491 A CN107545491 A CN 107545491A CN 201710751911 A CN201710751911 A CN 201710751911A CN 107545491 A CN107545491 A CN 107545491A
- Authority
- CN
- China
- Prior art keywords
- recommendation
- information
- recommendation information
- characteristic attribute
- bar
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Abstract
The embodiments of the invention provide a kind of data processing method and device of recommendation information, methods described includes：Obtain user profile；For the m bar recommendation informations of user profile screening predetermined number；Wherein, each bar recommendation information has characteristic attribute；The m bars recommendation information is ranked up according to the characteristic attribute；Show the m bar recommendation informations after the sequence；In the embodiment of the present invention, recommendation information is ranked up according to multiple characteristic attributes, user can be aided in intuitively to carry out decision-making, with it is existing need to check the process of recommendation information repeatedly compared with, improve user's efficiency of decision-making, improve Consumer's Experience.
Description
Technical field
The present invention relates to the technical field of mobile terminal, the data processing method more particularly to a kind of recommendation information and one
The demonstration device of kind recommendation information.
Background technology
In daily life, user can frequently encounter the scene for needing timely feedback requirements, such as select a dining room nearby
Have dinner or a hotel accommodations.User typically can consider the information of multiple dimensions when decision-making, such as price, ring
Border, taste etc..The application program of many operations on mobile terminals can provide the recommendation informations such as cuisines, hotel.
In the prior art, commodity can be compared with sequence using single dimensional information, e.g., user is in some electronics business
It is engaged in specifying all sandals for contrasting A brands and B brands on platform；When e-commerce platform selection is ranked up by the amount of selling,
All sandals of A brands and B brands are shown that still, user can only look into every time successively according to the order of the amount of selling from high to low
The recommendation information of a number of businessmans of a dimension is seen, is unfavorable for improving user's efficiency of decision-making and increases user's viscosity.
The content of the invention
A kind of data processing method of recommendation information of offer of the embodiment of the present invention and accordingly a kind of recommendation information show
Device, a kind of electronic equipment, a kind of computer-readable recording medium, when checking associated recommendation information with solution, user's decision-making effect
The not high above mentioned problem of rate.
In order to solve the above problems, the embodiment of the invention discloses a kind of data processing method of recommendation information, the side
Method includes：
Obtain user profile；
For the m bar recommendation informations of user profile screening predetermined number；Wherein, each bar recommendation information has spy
Levy attribute；
The m bars recommendation information is ranked up according to the characteristic attribute；
Show the m bar recommendation informations after the sequence.
Preferably, it is described show after the sequence m bar recommendation informations the step of include：
M bars recommendation information after being sorted according to the characteristic attribute is organized as recommending sublist accordingly respectively；
Show the recommendation sublist on demand.
Preferably, methods described includes：
Obtain the weight parameter set for the characteristic attribute；
The m bars recommendation information is organized as recommending summary table by the weight parameter according to the characteristic attribute；
Show the recommendation summary table.
Preferably, the recommendation information is the Business Information recommended；The characteristic attribute include vendor location information and/or
Surrounding enviroment information and/or food taste information and/or pre-capita consumption information.
Preferably, the user profile includes user's location information and user personalized information.
Preferably, the characteristic attribute includes characteristic attribute value, and the weight parameter according to the characteristic attribute is by institute
M bar recommendation informations are stated to be organized as including the step of recommending summary table：
Average computation is weighted to the characteristic attribute value according to the weight parameter, obtains weighted results parameter；
The m bars recommendation information is ranked up according to the weighted results parameter, summary table is recommended in generation.
Preferably, methods described also includes：
Receive for the recommendation summary table or recommend the click commands of recommendation information in sublist；
Respond the click commands and export navigation page corresponding to the recommendation information.
It is described the embodiment of the invention also discloses a kind of data processing equipment of recommendation information in order to solve the above problems
Device includes：
User profile acquisition module, for obtaining user profile；
Recommendation information screening module, for screening the m bar recommendation informations of predetermined number for the user profile；Wherein,
Each bar recommendation information has characteristic attribute；
Order module, for being ranked up according to the characteristic attribute to the m bars recommendation information；
Display module, for showing the m bar recommendation informations after the sequence.
Preferably, the display module includes：
Recommend sublist tissue submodule, for the m bars recommendation information after being sorted according to the characteristic attribute to be organized respectively
Recommend sublist to be corresponding；
Sublist is recommended to show submodule, for showing the recommendation sublist on demand.
Preferably, described device includes：
Weight parameter acquisition module, for obtaining the weight parameter set for the characteristic attribute；
Recommend summary table molded tissue block, for the weight parameter according to the characteristic attribute by the m bars recommendation information tissue
To recommend summary table；
Recommend summary table display module, for showing the recommendation summary table.
Preferably, the recommendation information is the Business Information recommended；The characteristic attribute include vendor location information and/or
Surrounding enviroment information and/or food taste information and/or pre-capita consumption information.
Preferably, the user profile includes user's location information and user personalized information.
Preferably, the characteristic attribute includes characteristic attribute value, and the recommendation summary table molded tissue block includes：
Weighted results gain of parameter submodule is flat for being weighted according to the weight parameter to the characteristic attribute value
Calculate, obtain weighted results parameter；
Recommend summary table generation submodule, for being ranked up according to the weighted results parameter to the m bars recommendation information,
Summary table is recommended in generation.
Preferably, described device also includes：
Click commands receiving module, for receiving for the recommendation summary table or recommending the click of recommendation information in sublist to refer to
Order；
Navigation page output module, navigation page corresponding to the recommendation information is exported for responding the click commands.
In order to solve the above problems, the embodiment of the invention discloses a kind of electronic equipment, including memory, processor and deposit
Storage realizes above-mentioned on a memory and the computer program that can run on a processor, during the computing device described program
The step of method of anticipating.
In order to solve the above problems, the embodiment of the invention discloses a kind of computer-readable recording medium, it is stored thereon with
Computer program, it is characterised in that the program realizes the step of above-mentioned any one method when being executed by processor.
The embodiment of the present invention includes advantages below：
In the embodiment of the present invention, user profile is obtained；For the m bar recommendations of user profile screening predetermined number
Breath；Wherein, each bar recommendation information has characteristic attribute；The m bars recommendation information is arranged according to the characteristic attribute
Sequence；Show the m bar recommendation informations after the sequence；In the embodiment of the present invention, recommendation information is carried out according to multiple characteristic attributes
Sequence, user can be aided in intuitively to carry out decision-making, with it is existing need to check the process of recommendation information repeatedly compared with, improve
User's efficiency of decision-making, improve Consumer's Experience.
Brief description of the drawings
Technical scheme in order to illustrate the embodiments of the present invention more clearly, make required in being described below to embodiment
Accompanying drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the present invention, for
For those of ordinary skill in the art, on the premise of not paying creative work, other can also be obtained according to these accompanying drawings
Accompanying drawing；
Fig. 1 is a kind of step flow chart of the data processing method embodiment one of recommendation information of the embodiment of the present invention；
Fig. 2 is a kind of flow chart of the data processing method of recommendation information in the embodiment of the present invention；
Fig. 3 is a kind of step flow chart of the data processing method embodiment two of recommendation information of the embodiment of the present invention；
Fig. 4 is the flow chart of the data processing method of another recommendation information in the embodiment of the present invention；
Fig. 5 is a kind of structured flowchart of the data processing equipment embodiment three of recommendation information of the embodiment of the present invention.
Embodiment
In order that technical problem, technical scheme and beneficial effect that the embodiment of the present invention solves are more clearly understood, with
Lower combination drawings and Examples, the embodiment of the present invention is further described.It should be appreciated that specific implementation described herein
Example is not intended to limit the present invention only to explain the present invention.
Reference picture 1, the step of showing a kind of data processing method embodiment one of recommendation information of the embodiment of the present invention
Flow chart, specifically it may include steps of：
Step 101, user profile is obtained；
In the specific implementation, the embodiment of the present invention can be applied in the terminal, for example, mobile phone, tablet personal computer, individual
Digital assistants, wearable device (such as glasses, wrist-watch) etc..
In embodiments of the present invention, the operating system of mobile terminal can include Android (Android), IOS, Windows
Phone, Windows etc..
Specific in the embodiment of the present invention, the user profile can include customer position information and user personalized information,
Specifically, can be obtained by GPS (Global Positioning System, global positioning system) chip of mobile terminal
Customer position information, can also by calculating the distance between mobile terminal and the base station position of base station (because be fixed),
Customer position information is got, the embodiment of the present invention is not restricted to this；Further, the user personalized information can include
Age of user information, income information, job information, family information, user's scene information, interest information, habits and customs information, disappear
Take behavioural information etc., the embodiment of the present invention is not limited specifically the species of user personalized information.
Step 102, the m bar recommendation informations of predetermined number are screened for the user profile；Wherein, each bar recommendation
Breath has characteristic attribute；
Apply in the embodiment of the present invention, the m bar recommendation informations of predetermined number are filtered out using the user profile, its
In, each bar recommendation information has characteristic attribute；Specifically, the user profile can be input to preset recommendation information anti-
Present in model, obtain the m bar recommendation informations of the predetermined number by recommendation information feedback model output.
It should be noted that the recommendation information feedback model obtains after data are trained, use can be set first
Family characteristic vector and recommendation information characteristic vector, the user characteristics vector can include characteristic vector corresponding to user profile, should
Recommendation information characteristic vector can include characteristic vector corresponding to raw recommendation information, and the raw recommendation information can include spy
Attribute (such as positional information and/or surrounding enviroment information and/or food taste information and/or pre-capita consumption information) is levied, can be with
The information of businessman in itself, the embodiment of the present invention are not restricted to this including name of firm etc..
, can be with for the recommendation information feedback model after the user characteristics vector and recommendation information characteristic vector are set
It is trained, it is necessary to illustrate, the recommendation information feedback model can include GBDT (Gradient Boosting
Decision Tree, iteration decision tree) model, DNN (Deep Neural Network, deep neural network) model, FM
(Factorization Machine, Factorization machine) model, FFM (Field-aware Factorization Machine,
Field perceptual decomposition machine) model, MLR (Multiple Linear Regression, multiple linear regression) model etc., the present invention
Embodiment is not limited specifically the species of model.
In the step of model training, it is label to set clicking rate (Click Through Rate), the embodiment of the present invention
In, the recommendation information feedback model after training is preset on mobile terminals, believed by calling data-interface to receive user
Breath, the m bar recommendation informations of predetermined number are generated by preset recommendation information feedback model, wherein, the recommendation information can be with
The size of clicking rate is ranked up, and is further presented to user；It should be noted that recommendation information is the certain distance in user
In the range of the raw recommendation information after sequence.
Step 103, the m bars recommendation information is ranked up according to the characteristic attribute；
It is specifically applied in the embodiment of the present invention, m bar recommendation informations is ranked up according to characteristic attribute, for example,
This feature attribute can include vendor location information and/or surrounding enviroment information and/or food taste information and/or pre-capita consumption
The recommendation information can be ranked up by information, mobile terminal for every kind of characteristic attribute, and sublist is recommended in generation.
Step 104, the m bar recommendation informations after the sequence are showed.
As in a kind of preferred embodiment of the embodiment of the present invention, m bars recommendation information after the sequence can with and form
Form mobile terminal present；Reference picture 2, show a kind of data processing method of recommendation information of the embodiment of the present invention
Flow chart, as shown in Fig. 2 recommendation information feedback model obtains the m bar recommendation informations of predetermined number according to user profile first, will
M bars recommendation information after being sorted according to the characteristic attribute is organized as recommending sublist accordingly respectively, in the recommendation sublist such as Fig. 2
Shown " pre-capita consumption ranking list ", " distance ranking list ", " environment scoring ranking list ", " taste scoring ranking list " etc..
Specific in the embodiment of the present invention, the recommendation sublist can also be showed on demand on the screen of the mobile terminal, this
When, user can direct decision-making, select the recommendation information in above-mentioned recommendation sublist to select a certain shop, such as shop E, directly output should
Navigation page corresponding to the E of shop；Specifically, methods described also includes：Receive for recommending the click of recommendation information in sublist to refer to
Order；Respond the click commands and export navigation page corresponding to the recommendation information.
In the embodiment of the present invention, user profile is obtained；For the m bar recommendations of user profile screening predetermined number
Breath；Wherein, each bar recommendation information has characteristic attribute；The m bars recommendation information is arranged according to the characteristic attribute
Sequence；Show the m bar recommendation informations after the sequence；In the embodiment of the present invention, recommendation information is carried out according to multiple characteristic attributes
Sequence, user can be aided in intuitively to carry out decision-making, with it is existing need to check the process of recommendation information repeatedly compared with, improve
User's efficiency of decision-making, improve Consumer's Experience.
Reference picture 3, the step of showing a kind of data processing method embodiment two of recommendation information of the embodiment of the present invention
Flow chart, specifically it may include steps of：
Step 201, user profile is obtained；
In the embodiment of the present invention, methods described can apply to mobile terminal, and the mobile terminal can gather user profile,
The user profile can include customer position information and user personalized information, and user personalized information can include " user's picture
Personal information in picture ", such as age of user information, income information, job information, family information, user's scene information, interest letter
Breath, habits and customs information, consumer behavior information etc., the embodiment of the present invention is not restricted to this.
Step 202, the m bar recommendation informations of predetermined number are screened for the user profile；Wherein, each bar recommendation
Breath has characteristic attribute；
Further, after mobile terminal collects user profile, the user profile can be input to preset recommendation
Cease in feedback model, filter out the m bars recommendation information of predetermined number, it is necessary to explanation, the preset recommendation information feedback mould
Type is the model after training, after setting user characteristics vector and recommendation information characteristic vector, can feed back mould to the recommendation information
Type is trained, and in the step of model training, it is label to set clicking rate (Click Through Rate), further,
Recommendation information feedback model after training is preset on mobile terminals, by calling data-interface to receive user profile,
M bar recommendation informations are filtered out by preset recommendation information feedback model, wherein, m is positive integer.
Specifically, the recommendation information is the Business Information recommended；The characteristic attribute include vendor location information and/
Or surrounding enviroment information and/or food taste information and/or pre-capita consumption information etc..
Step 203, the m bars recommendation information is ranked up according to the characteristic attribute；
It is specifically applied in the embodiment of the present invention, m bar recommendation informations is ranked up according to characteristic attribute, for example,
This feature attribute can include vendor location information and/or surrounding enviroment information and/or food taste information and/or pre-capita consumption
The recommendation information can be ranked up by information, mobile terminal for every kind of characteristic attribute, i.e. sublist is recommended in generation.
Step 204, the m bar recommendation informations after the sequence are showed；
It is specifically applied in the embodiment of the present invention, this can be showed in the form of multiple lists in the screen of mobile terminal
M bar recommendation informations, i.e., user is presented in the form of the recommendation sublist shown in Fig. 2, it is necessary to illustrate, in the recommendation sublist
Recommendation information can be arranged with characteristic attribute value corresponding to characteristic attribute from high to low.
Step 205, the weight parameter set for the characteristic attribute is obtained；
Further, after mobile terminal shows the m bar recommendation informations, the feature for every recommendation information can be received
The weight parameter that attribute is set, user can be in characteristic attribute " pre-capita consumption information ", " vendor location information ", " surrounding enviroment
Information ", " food taste information ", difference input weight parameter：0.4、0.2、0.2、0.1.
Step 206, the m bars recommendation information is organized as recommendation summary table by the weight parameter according to the characteristic attribute；
In a kind of preferred embodiment of the embodiment of the present invention, the characteristic attribute includes characteristic attribute value, described according to institute
Stating the step of m bars recommendation information is organized as and recommends summary table by the weight parameter of characteristic attribute includes：Join according to the weight
It is several that average computation is weighted to the characteristic attribute value, obtain weighted results parameter；According to the weighted results parameter to institute
State m bar recommendation informations to be ranked up, summary table is recommended in generation.
By above-mentioned " pre-capita consumption ranking list ", " distance ranking list ", " environment scoring ranking list ", " taste scoring
The data of ranking list " are converted into characteristic attribute value, for example, can be by the distance value in " distance ranking list ", with one
Individual characteristic attribute value scope is demarcated.
For example, this feature attribute-value ranges can be the spy that is set according to the sorting position in " above-mentioned ranking list "
Attribute-value ranges are levied, characteristic attribute value is 10 when the 1st of " distance ranking list " is come such as shop A, and shop B comes the 2nd
When characteristic attribute value be 9, characteristic attribute value is spy when 8 shop J come the 10th when shop C comes the 3rd
It is 1 to levy property value, and distance is more remote, and characteristic attribute value is lower, because the consumption wish that distance far represents user is smaller, is obtained
Characteristic attribute value under sorting position under all characteristic attributes, according to the characteristic attribute value under the weight parameter and sorting position
To the average computation that is weighted of characteristic attribute, weighted results parameter is obtained, according to the weighted results parameter and to the recommendation
Breath is ranked up, and obtains final result.
Characteristic attribute value such as shop A 4 characteristic attributes is：" pre-capita consumption information：3 ", " vendor location information：1”、
" surrounding enviroment information：4 ", " food taste information：5 ", then the weighted results parameter that shop A can be calculated is 3*0.4+1*
0.2+4*0.2+5*0.1=2.7, the weighted results parameter is smaller, then corresponding recommendation information sequence more rearward, can obtain more
The weighted results parameter of individual recommendation information；The m bars recommendation information is ranked up according to the weighted results parameter, generation pushes away
Recommend summary table.
Specific to the embodiment of the present invention, the recommendation information is ranked up according to weighted results parameter, for example, pushed away
It is shop A, shop B and shop C to recommend information, and its weighted results parameter is respectively 2.7,1.5,6.3；Then according to weighted results parameter from greatly to
It is small to be ranked up, such as shop C：6.3rd, shop A：2.7th, shop B：1.5, certainly, above-mentioned citing and be that the embodiment of the present invention enumerates it
One, any core idea not departed from the embodiment of the present invention should all fall into the protection domain of the embodiment of the present invention.
Reference picture 4, show a kind of flow chart of the data processing method of recommendation information of the embodiment of the present invention, such as Fig. 4
Shown, mobile terminal receives user for that after characteristic attribute input weight parameter, then can calculate the weighting of every recommendation information
Result parameter, recommendation information is ranked up, obtains and recommend summary table；That is " comprehensive ranking list " in Fig. 4.
Step 207, the recommendation summary table is showed.
Apply in the embodiment of the present invention, mobile terminal can show the recommendation summary table on screen, receive user
For the click commands of some recommendation information, the navigation page of the recommendation information pair is exported.
Specifically, in a kind of preferred embodiment of the embodiment of the present invention, methods described also includes：Receive and pushed away for described
Recommend the click commands of recommendation information in summary table；Respond the click commands and export navigation page corresponding to the recommendation information.
In the embodiment of the present invention, user profile is obtained；For the m bar recommendations of user profile screening predetermined number
Breath；Wherein, each bar recommendation information has characteristic attribute；The m bars recommendation information is arranged according to the characteristic attribute
Sequence；Show the m bar recommendation informations after the sequence, the weight parameter set for the characteristic attribute is obtained, according to the spy
The m bars recommendation information is organized as recommending summary table by the weight parameter for levying attribute；Show the recommendation summary table, the embodiment of the present invention
In, recommendation information is ranked up according to multiple characteristic attributes, user can be aided in intuitively to carry out decision-making, with existing needs
Check that the process of recommendation information is compared repeatedly, improve user's efficiency of decision-making, improve Consumer's Experience；In addition, user can also be certainly
The weight parameter of defined feature attribute, the importance of multiple characteristic attributes is quantified, be use by way of weighting and handling
Family provides personalized recommendation results, in the case where user needs more precisely to recommend, is provided the user more with the index of quantization
For intuitively decision-making foundation, the individualized experience of user is improved.
It should be noted that for embodiment of the method, in order to be briefly described, therefore it is all expressed as to a series of action group
Close, but those skilled in the art should know, the embodiment of the present invention is not limited by described sequence of movement, because according to
According to the embodiment of the present invention, some steps can use other orders or carry out simultaneously.Secondly, those skilled in the art also should
Know, embodiment described in this description belongs to preferred embodiment, and the involved action not necessarily present invention is implemented
Necessary to example.
Reference picture 5, show a kind of structure of the data processing equipment embodiment three of recommendation information of the embodiment of the present invention
Block diagram, it can specifically include following module：
User profile acquisition module 301, for obtaining user profile；
Recommendation information screening module 302, for screening the m bar recommendation informations of predetermined number for the user profile；Its
In, each bar recommendation information has characteristic attribute；
Order module 303, for being ranked up according to the characteristic attribute to the m bars recommendation information；
Display module 304, for showing the m bar recommendation informations after the sequence.
Preferably, the display module includes：
Recommend sublist tissue submodule, for the m bars recommendation information after being sorted according to the characteristic attribute to be organized respectively
Recommend sublist to be corresponding；
Sublist is recommended to show submodule, for showing the recommendation sublist on demand.
Preferably, described device includes：
Weight parameter acquisition module, for obtaining the weight parameter set for the characteristic attribute；
Recommend summary table molded tissue block, for the weight parameter according to the characteristic attribute by the m bars recommendation information tissue
To recommend summary table；
Recommend summary table display module, for showing the recommendation summary table.
Preferably, the recommendation information is the Business Information recommended；The characteristic attribute include vendor location information and/or
Surrounding enviroment information and/or food taste information and/or pre-capita consumption information.
Preferably, the user profile includes user's location information and user personalized information.
Preferably, the characteristic attribute includes characteristic attribute value, and the recommendation summary table molded tissue block includes：
Weighted results gain of parameter submodule is flat for being weighted according to the weight parameter to the characteristic attribute value
Calculate, obtain weighted results parameter；
Recommend summary table generation submodule, for being ranked up according to the weighted results parameter to the m bars recommendation information,
Summary table is recommended in generation.
Preferably, described device also includes：
Click commands receiving module, for receiving for the recommendation summary table or recommending the click of recommendation information in sublist to refer to
Order；
Navigation page output module, navigation page corresponding to the recommendation information is exported for responding the click commands.
In the embodiment of the present invention, user profile is obtained；For the m bar recommendations of user profile screening predetermined number
Breath；Wherein, each bar recommendation information has characteristic attribute；The m bars recommendation information is arranged according to the characteristic attribute
Sequence；Show the m bar recommendation informations after the sequence, the weight parameter set for the characteristic attribute is obtained, according to the spy
The m bars recommendation information is organized as recommending summary table by the weight parameter for levying attribute；Show the recommendation summary table, the embodiment of the present invention
In, recommendation information is ranked up according to multiple characteristic attributes, user can be aided in intuitively to carry out decision-making, with existing needs
Check that the process of recommendation information is compared repeatedly, improve user's efficiency of decision-making, improve Consumer's Experience；In addition, user can also be certainly
The weight parameter of defined feature attribute, the importance of multiple characteristic attributes is quantified, be use by way of weighting and handling
Family provides personalized recommendation results, in the case where user needs more precisely to recommend, is provided the user more with the index of quantization
For intuitively decision-making foundation, the individualized experience of user is improved.
The embodiments of the invention provide a kind of electronic equipment and a kind of computer-readable recording medium, the electronic equipment bag
The computer program that includes memory, processor and storage on a memory and can run on a processor, the computing device
Following steps are realized during described program：
Obtain user profile；
For the m bar recommendation informations of user profile screening predetermined number；Wherein, each bar recommendation information has spy
Levy attribute；
The m bars recommendation information is ranked up according to the characteristic attribute；
Show the m bar recommendation informations after the sequence.
Preferably, it is described show after the sequence m bar recommendation informations the step of include：
M bars recommendation information after being sorted according to the characteristic attribute is organized as recommending sublist accordingly respectively；
Show the recommendation sublist on demand.
Preferably, methods described includes：
Obtain the weight parameter set for the characteristic attribute；
The m bars recommendation information is organized as recommending summary table by the weight parameter according to the characteristic attribute；
Show the recommendation summary table.
Preferably, the recommendation information is the Business Information recommended；The characteristic attribute include vendor location information and/or
Surrounding enviroment information and/or food taste information and/or pre-capita consumption information.
Preferably, the user profile includes user's location information and user personalized information.
Preferably, the characteristic attribute includes characteristic attribute value, and the weight parameter according to the characteristic attribute is by institute
M bar recommendation informations are stated to be organized as including the step of recommending summary table：
Average computation is weighted to the characteristic attribute value according to the weight parameter, obtains weighted results parameter；
The m bars recommendation information is ranked up according to the weighted results parameter, summary table is recommended in generation.
Preferably, methods described also includes：
Receive for the recommendation summary table or recommend the click commands of recommendation information in sublist；
Respond the click commands and export navigation page corresponding to the recommendation information.
The computer-readable recording medium storage has computer program, can be realized such as when the program is executed by processor
Lower step：
Obtain user profile；
For the m bar recommendation informations of user profile screening predetermined number；Wherein, each bar recommendation information has spy
Levy attribute；
The m bars recommendation information is ranked up according to the characteristic attribute；
Show the m bar recommendation informations after the sequence.
Preferably, it is described show after the sequence m bar recommendation informations the step of include：
M bars recommendation information after being sorted according to the characteristic attribute is organized as recommending sublist accordingly respectively；
Show the recommendation sublist on demand.
Preferably, methods described includes：
Obtain the weight parameter set for the characteristic attribute；
The m bars recommendation information is organized as recommending summary table by the weight parameter according to the characteristic attribute；
Show the recommendation summary table.
Preferably, the recommendation information is the Business Information recommended；The characteristic attribute include vendor location information and/or
Surrounding enviroment information and/or food taste information and/or pre-capita consumption information.
Preferably, the user profile includes user's location information and user personalized information.
Preferably, the characteristic attribute includes characteristic attribute value, and the weight parameter according to the characteristic attribute is by institute
M bar recommendation informations are stated to be organized as including the step of recommending summary table：
Average computation is weighted to the characteristic attribute value according to the weight parameter, obtains weighted results parameter；
The m bars recommendation information is ranked up according to the weighted results parameter, summary table is recommended in generation.
Preferably, methods described also includes：
Receive for the recommendation summary table or recommend the click commands of recommendation information in sublist；
Respond the click commands and export navigation page corresponding to the recommendation information.
For device embodiment, because it is substantially similar to embodiment of the method, so description is fairly simple, it is related
Part illustrates referring to the part of embodiment of the method.
Each embodiment in this specification is described by the way of progressive, what each embodiment stressed be with
The difference of other embodiment, between each embodiment identical similar part mutually referring to.
It should be understood by those skilled in the art that, the embodiment of the embodiment of the present invention can be provided as method, apparatus or calculate
Machine program product.Therefore, the embodiment of the present invention can use complete hardware embodiment, complete software embodiment or combine software and
The form of the embodiment of hardware aspect.Moreover, the embodiment of the present invention can use one or more wherein include computer can
With in the computer-usable storage medium (including but is not limited to magnetic disk storage, CD-ROM, optical memory etc.) of program code
The form of the computer program product of implementation.
The embodiment of the present invention is with reference to method according to embodiments of the present invention, terminal device (system) and computer program
The flow chart and/or block diagram of product describes.It should be understood that can be by computer program instructions implementation process figure and/or block diagram
In each flow and/or square frame and the flow in flow chart and/or block diagram and/or the combination of square frame.These can be provided
Computer program instructions are set to all-purpose computer, special-purpose computer, Embedded Processor or other programmable data processing terminals
Standby processor is to produce a machine so that is held by the processor of computer or other programmable data processing terminal equipments
Capable instruction is produced for realizing in one flow of flow chart or multiple flows and/or one square frame of block diagram or multiple square frames
The device for the function of specifying.
These computer program instructions, which may be alternatively stored in, can guide computer or other programmable data processing terminal equipments
In the computer-readable memory to work in a specific way so that the instruction being stored in the computer-readable memory produces bag
The manufacture of command device is included, the command device is realized in one flow of flow chart or multiple flows and/or one side of block diagram
The function of being specified in frame or multiple square frames.
These computer program instructions can be also loaded into computer or other programmable data processing terminal equipments so that
Series of operation steps is performed on computer or other programmable terminal equipments to produce computer implemented processing, so that
The instruction performed on computer or other programmable terminal equipments is provided for realizing in one flow of flow chart or multiple flows
And/or specified in one square frame of block diagram or multiple square frames function the step of.
Although having been described for the preferred embodiment of the embodiment of the present invention, those skilled in the art once know base
This creative concept, then other change and modification can be made to these embodiments.So appended claims are intended to be construed to
Including preferred embodiment and fall into having altered and changing for range of embodiment of the invention.
Finally, it is to be noted that, herein, such as first and second or the like relational terms be used merely to by
One entity or operation make a distinction with another entity or operation, and not necessarily require or imply these entities or operation
Between any this actual relation or order be present.Moreover, term " comprising ", "comprising" or its any other variant meaning
Covering including for nonexcludability, so that process, method, article or terminal device including a series of elements are not only wrapped
Those key elements, but also the other element including being not expressly set out are included, or is also included for this process, method, article
Or the key element that terminal device is intrinsic.In the absence of more restrictions, wanted by what sentence "including a ..." limited
Element, it is not excluded that other identical element in the process including the key element, method, article or terminal device also be present.
Above at the data processing method to a kind of recommendation information provided by the present invention and a kind of data of recommendation information
Device to be managed, is described in detail, specific case used herein is set forth to the principle and embodiment of the present invention,
The explanation of above example is only intended to help the method and its core concept for understanding the present invention；Meanwhile for the one of this area
As technical staff, according to the thought of the present invention, there will be changes in specific embodiments and applications, to sum up institute
State, this specification content should not be construed as limiting the invention.
Claims (10)
1. a kind of data processing method of recommendation information, it is characterised in that methods described includes：
Obtain user profile；
For the m bar recommendation informations of user profile screening predetermined number；Wherein, each bar recommendation information has feature category
Property；
The m bars recommendation information is ranked up according to the characteristic attribute；
Show the m bar recommendation informations after the sequence.
2. according to the method for claim 1, it is characterised in that the step for showing the m bar recommendation informations after the sequence
Suddenly include：
M bars recommendation information after being sorted according to the characteristic attribute is organized as recommending sublist accordingly respectively；
Show the recommendation sublist on demand.
3. according to the method for claim 1, it is characterised in that methods described includes：
Obtain the weight parameter set for the characteristic attribute；
The m bars recommendation information is organized as recommending summary table by the weight parameter according to the characteristic attribute；
Show the recommendation summary table.
4. according to the method for claim 1, it is characterised in that the recommendation information is the Business Information recommended；The spy
Sign attribute includes vendor location information and/or surrounding enviroment information and/or food taste information and/or pre-capita consumption information.
5. according to the method for claim 1, it is characterised in that the user profile includes user's location information and user
Property information.
6. according to the method for claim 3, it is characterised in that the characteristic attribute includes characteristic attribute value, the foundation
The step of m bars recommendation information is organized as and recommends summary table by the weight parameter of the characteristic attribute includes：
Average computation is weighted to the characteristic attribute value according to the weight parameter, obtains weighted results parameter；
The m bars recommendation information is ranked up according to the weighted results parameter, summary table is recommended in generation.
7. according to the method for claim 6, it is characterised in that methods described also includes：
Receive for the recommendation summary table or recommend the click commands of recommendation information in sublist；
Respond the click commands and export navigation page corresponding to the recommendation information.
8. a kind of data processing equipment of recommendation information, it is characterised in that described device includes：
User profile acquisition module, for obtaining user profile；
Recommendation information screening module, for screening the m bar recommendation informations of predetermined number for the user profile；Wherein, it is described
Each bar recommendation information has characteristic attribute；
Order module, for being ranked up according to the characteristic attribute to the m bars recommendation information；
Display module, for showing the m bar recommendation informations after the sequence.
9. a kind of electronic equipment, the equipment includes memory and processor, and being stored with the memory can be at the place
The computer program run on reason device, it is characterised in that the processor performs aforesaid right when running the computer program
It is required that the method described in 1 to 7 any one.
10. a kind of computer-readable recording medium, is stored thereon with computer program, it is characterised in that the computer program
The method described in any one of the claims 1 to 7 is performed when being run by processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710751911.1A CN107545491A (en) | 2017-08-28 | 2017-08-28 | A kind of data processing method and device of recommendation information |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710751911.1A CN107545491A (en) | 2017-08-28 | 2017-08-28 | A kind of data processing method and device of recommendation information |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107545491A true CN107545491A (en) | 2018-01-05 |
Family
ID=60957532
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710751911.1A Pending CN107545491A (en) | 2017-08-28 | 2017-08-28 | A kind of data processing method and device of recommendation information |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107545491A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108650166A (en) * | 2018-03-19 | 2018-10-12 | 安徽锐欧赛智能科技有限公司 | A kind of timely communication data analysis and message push management system |
| CN108804646A (en) * | 2018-06-06 | 2018-11-13 | 重庆邮电大学 | The point of interest of a kind of fusion deep learning and Factorization machine is registered prediction technique |
| CN110659362A (en) * | 2019-09-17 | 2020-01-07 | 武汉鼎森电子科技有限公司 | Recommendation method and system for automatically adjusting candidate range |
| CN110909267A (en) * | 2019-11-29 | 2020-03-24 | 口碑(上海)信息技术有限公司 | Method and device for displaying entity object side, electronic equipment and storage medium |
| CN111368150A (en) * | 2018-12-25 | 2020-07-03 | 北京奇虎科技有限公司 | Training method and device of prediction model and electronic equipment |
| CN113283960A (en) * | 2021-05-18 | 2021-08-20 | 武汉畅来电子商务有限公司 | Vertical e-commerce platform commodity intelligent recommendation method based on big data analysis and cloud computing and cloud service platform |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102799593A (en) * | 2011-05-24 | 2012-11-28 | 一零四资讯科技股份有限公司 | Personalized searching and sequencing method and system |
| CN103390031A (en) * | 2013-07-01 | 2013-11-13 | 招商银行股份有限公司 | Intelligent recommending method and device |
| CN103886090A (en) * | 2014-03-31 | 2014-06-25 | 北京搜狗科技发展有限公司 | Content recommendation method and device based on user favorites |
| CN104881807A (en) * | 2015-06-26 | 2015-09-02 | 陆健 | Method and device for recommending goods information |
| CN107016595A (en) * | 2017-03-16 | 2017-08-04 | 腾讯科技（深圳）有限公司 | A kind of information recommendation method, device and system |
-
2017
- 2017-08-28 CN CN201710751911.1A patent/CN107545491A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102799593A (en) * | 2011-05-24 | 2012-11-28 | 一零四资讯科技股份有限公司 | Personalized searching and sequencing method and system |
| CN103390031A (en) * | 2013-07-01 | 2013-11-13 | 招商银行股份有限公司 | Intelligent recommending method and device |
| CN103886090A (en) * | 2014-03-31 | 2014-06-25 | 北京搜狗科技发展有限公司 | Content recommendation method and device based on user favorites |
| CN104881807A (en) * | 2015-06-26 | 2015-09-02 | 陆健 | Method and device for recommending goods information |
| CN107016595A (en) * | 2017-03-16 | 2017-08-04 | 腾讯科技（深圳）有限公司 | A kind of information recommendation method, device and system |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108650166A (en) * | 2018-03-19 | 2018-10-12 | 安徽锐欧赛智能科技有限公司 | A kind of timely communication data analysis and message push management system |
| CN108804646A (en) * | 2018-06-06 | 2018-11-13 | 重庆邮电大学 | The point of interest of a kind of fusion deep learning and Factorization machine is registered prediction technique |
| CN108804646B (en) * | 2018-06-06 | 2021-05-11 | 重庆邮电大学 | Point of interest sign-in prediction method integrating deep learning and factorization machine |
| CN111368150A (en) * | 2018-12-25 | 2020-07-03 | 北京奇虎科技有限公司 | Training method and device of prediction model and electronic equipment |
| CN111368150B (en) * | 2018-12-25 | 2024-04-02 | 北京奇虎科技有限公司 | Training method and device of prediction model and electronic equipment |
| CN110659362A (en) * | 2019-09-17 | 2020-01-07 | 武汉鼎森电子科技有限公司 | Recommendation method and system for automatically adjusting candidate range |
| CN110909267A (en) * | 2019-11-29 | 2020-03-24 | 口碑(上海)信息技术有限公司 | Method and device for displaying entity object side, electronic equipment and storage medium |
| CN113283960A (en) * | 2021-05-18 | 2021-08-20 | 武汉畅来电子商务有限公司 | Vertical e-commerce platform commodity intelligent recommendation method based on big data analysis and cloud computing and cloud service platform |
| CN113283960B (en) * | 2021-05-18 | 2022-08-19 | 河南中钢网科技集团股份有限公司 | Vertical e-commerce platform commodity intelligent recommendation method based on big data analysis and cloud computing and cloud service platform |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107545491A (en) | A kind of data processing method and device of recommendation information | |
| US10402917B2 (en) | Color-related social networking recommendations using affiliated colors | |
| US20170364522A1 (en) | Real-Time Search Tuning | |
| US9043351B1 (en) | Determining search query specificity | |
| CN109313542A (en) | Identify the browsing node of missing | |
| CA3028710A1 (en) | Systems and methods for automated object recognition | |
| CN107220852A (en) | Method, device and server for determining target recommended user | |
| CN107977405A (en) | Data reordering method, data sorting device, electronic equipment and readable storage medium storing program for executing | |
| US20130024449A1 (en) | Method and apparatus for allowing users to augment searches | |
| US8285715B2 (en) | System and method for the structured display of items | |
| CN110060090A (en) | Method, apparatus, electronic equipment and the readable storage medium storing program for executing of Recommendations combination | |
| CN109447713A (en) | A kind of recommended method and device of knowledge based map | |
| CN105677846A (en) | Recommendation system and construction method thereof | |
| CN109597973A (en) | A kind of recommendation, generation method and the device of official documents and correspondence information | |
| CN107332910A (en) | Information-pushing method and device | |
| CN108510373A (en) | Paintings recommend method, paintings recommendation apparatus, equipment and storage medium | |
| CN109872796A (en) | A kind of menu recommended method and device | |
| CN107786943A (en) | A kind of tenant group method and computing device | |
| CN112256977B (en) | Componentized commodity recommendation method, computing device and computer storage medium | |
| US20170329840A1 (en) | Computerized system and method for performing a feature-based search and displaying an interactive dynamically updatable, multidimensional user interface therefrom | |
| US20130166416A1 (en) | Dynamic catalogs on mobile wireless devices | |
| CN110427564A (en) | Deep learning is integrated into broad sense additivity melange effect (GAME) frame | |
| US20210090105A1 (en) | Technology opportunity mapping | |
| JP2023525747A (en) | Method and apparatus for analyzing information | |
| CN109840788A (en) | For analyzing the method and device of user behavior data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |