CN108960975A - Personalized Precision Marketing Method, server and storage medium based on user's portrait - Google Patents
Personalized Precision Marketing Method, server and storage medium based on user's portrait Download PDFInfo
- Publication number
- CN108960975A CN108960975A CN201810616629.7A CN201810616629A CN108960975A CN 108960975 A CN108960975 A CN 108960975A CN 201810616629 A CN201810616629 A CN 201810616629A CN 108960975 A CN108960975 A CN 108960975A
- Authority
- CN
- China
- Prior art keywords
- user
- portrait
- data
- internet
- information
- 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
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
- G06Q30/0202—Market predictions or forecasting for commercial activities
Landscapes
- Business, Economics & Management (AREA)
- Accounting & Taxation (AREA)
- Finance (AREA)
- Strategic Management (AREA)
- Engineering & Computer Science (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- General Business, Economics & Management (AREA)
- Economics (AREA)
- Marketing (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- Data Mining & Analysis (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Information Transfer Between Computers (AREA)
Abstract
The present invention provides a kind of personalized Precision Marketing Method, server and storage mediums based on user's portrait, by the label information for obtaining user, user's portrait is established according to the label information, it obtains the current internet behavior of user, and gives a forecast analysis to the behavior, when forecast analysis to user has purchase or consumes wish, portrait based on user, filtering out user may like or interested content, may like according to the user or interested content, carry out personalized recommendation.Since each user has exclusive user's portrait, depth analysis is carried out in conjunction with user's portrait of user and current internet behavior, user can be filtered out to like or content of interest list, it can be good at according to the recommendation for carrying out personalization the case where each user, the accuracy rate of recommendation is improved, while also can be very good the cold start-up for solving the problems, such as new user.
Description
Technical field
The present invention relates to computer processing technical fields, precisely seek more particularly to a kind of personalization based on user's portrait
Pin method, server and storage medium.
Background technique
With the development of internet, people's lives are more and more closely connected together with internet.At this
In the allegro epoch, user wants to quickly find the product oneself needed by internet, but the product number of magnanimity
According to constantly generating in internet daily, this causes Internet user to be difficult to quickly find that oneself is needed or feel emerging
The information of interest.In order to allow user to quickly find oneself interested product, traditional collaborative filtering is according to user
History purchase situation goes to recommend, but cold start-up problem is often faced for new user, hardly results in accurate recommendation,
And the interest for some product users may be disposable, is recommended if only buying situation according to history, is pushed away
The accuracy rate recommended is not often high.
Summary of the invention
Based on this, it is necessary to provide a kind of personalized Precision Marketing Method based on user's portrait, comprising:
Obtain the label information of user；
User's portrait is established according to the label information；
It obtains the current internet behavior of user, and gives a forecast analysis to the behavior；
When forecast analysis to user has purchase or consumption wish, the portrait based on user, filtering out user may like or feel
The content of interest；
It may like according to the user or interested content, carry out personalized recommendation.
The label information for obtaining user in one of the embodiments, comprising:
Internet Various types of data is obtained, and the internet data that will acquire carries out fusion and gets through to form knowledge base；
Obtain the internet log of user；
The internet log match forming user tag information with the knowledge base.
The label information includes: in one of the embodiments,
Model class label information that statistics class label information, the modeling algorithm that data are calculated obtain, single client's label information
With label system user group information；
The statistics class label includes regional information, population essential attribute information；
The model class label includes user behavior preference, customer consumption action value, consumer spending habit prediction.
Acquisition internet Various types of data in one of the embodiments, and the internet data that will acquire is melted
Conjunction gets through to form knowledge base, comprising:
Mode is crawled using distributed reptile and obtains internet Various types of data；
Refinement classification is carried out to the internet Various types of data of the acquisition, then carries out the automatic merger of label, and classification is carried out
It is unified.
The internet data includes portal website, video website, electric business website, tourism in one of the embodiments,
The data such as website, forum, microblogging, wechat, APP.
The label information further includes media label, purchase label, search label, industry in one of the embodiments,
Label, user's gender, age bracket.
Described the step of establishing user's portrait according to the label information, includes: in one of the embodiments,
One or more label information of user is formed into a text vector；
It draws a portrait the text vector as the user of user.
It is described in one of the embodiments, to obtain the current internet behavior of user, and give a forecast analysis to the behavior, it wraps
It includes:
When spying out user access network, internet behavior information and the internet behavior track of user are obtained；
It draws a portrait in conjunction with user, gives a forecast analysis to the internet behavior purpose of user.
The portrait based on user in one of the embodiments, filters out in user may like or is interested
Hold, comprising:
According to historical data of the user in present networks perhaps other networks carry out data analysis recommended user may like or
Interested content；
The historical data include user consumption habit data, browsing web data, purchase data, App use habit data,
User's portrait result data.
It is described in one of the embodiments, to may like according to the user or interested content, it carries out personalized
After recommendation, further includes:
According to the personalized recommendation of user as a result, scene location in combination with active user, recommended user is current desired to be wanted
Information, the information include at least one of cuisines, road conditions, lodging, amusement.
The present invention also provides a kind of server, the server includes: memory, processor and is stored in the storage
On device and can run on the processor based on user portrait personalized precision marketing program, it is described based on user draw a portrait
Personalized precision marketing program be arranged for carrying out as described above based on user portrait personalized Precision Marketing Method step
Suddenly.
The present invention also provides a kind of storage medium, the personalization essence based on user's portrait is stored on the storage medium
Quasi- marketing program, the personalized precision marketing program based on user's portrait realize base as described above when being executed by processor
In user portrait personalized Precision Marketing Method the step of.
A kind of personalized Precision Marketing Method based on user's portrait is provided in the present invention, comprising: obtain the label letter of user
Breath；User's portrait is established according to the label information；It obtains the current internet behavior of user, and gives a forecast analysis to the behavior；
When forecast analysis to user has purchase or consumption wish, the portrait based on user, filtering out user be may like or interested
Content；It may like according to the user or interested content, carry out personalized recommendation.By being drawn a portrait and being used according to user
The current internet behavior in family, the internet behavior purpose current to user give a forecast analysis, when forecast analysis to user have purchase or
When consuming wish, the portrait based on user, filtering out user be may like or interested content, and may like according to user
Or interested content, personalized recommendation is carried out, since each user has exclusive user's portrait, in conjunction with the use of user
Family portrait and current internet behavior carry out depth analysis, can filter out user and like or content of interest list, can be very
The good recommendation personalized according to progress the case where each user, improves the accuracy rate of recommendation, simultaneously because the recommended method
It is to be recommended based on user's portrait and the current internet behavior of user, new user is also suitable, very good solution
The cold start-up problem of new user.
Detailed description of the invention
Fig. 1 is the server architecture schematic diagram for the hardware running environment that the embodiment of the present invention is related to；
Fig. 2 is the flow diagram of the personalized Precision Marketing Method one embodiment drawn a portrait the present invention is based on user；
Fig. 3 is the subdivision flow diagram of step S10 in one embodiment of the invention Fig. 2；
Fig. 4 is the subdivision flow diagram of step S101 in one embodiment of the invention Fig. 3；
Fig. 5 is the subdivision flow diagram of step S20 in one embodiment of the invention Fig. 2；
The flow diagram for personalized another embodiment of Precision Marketing Method that Fig. 6 draws a portrait the present invention is based on user.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to the accompanying drawings and embodiments, right
The present invention is further elaborated.It should be appreciated that the specific embodiments described herein are merely illustrative of the present invention, and
It is not used in the restriction present invention.
The description of specific distinct unless the context otherwise, the present invention in element and component, the shape that quantity both can be single
Formula exists, and form that can also be multiple exists, and the present invention is defined not to this.Although step in the present invention with label into
It has gone arrangement, but is not used to limit the precedence of step, unless expressly stated the order of step or holding for certain step
Based on row needs other steps, otherwise the relative rank of step is adjustable.It is appreciated that used herein
Term "and/or" one of is related to and covers associated listed item or one or more of any and all possible groups
It closes.
Referring to Fig.1, Fig. 1 is the server architecture schematic diagram for the hardware running environment that the embodiment of the present invention is related to.
As shown in Figure 1, the server may include: processor 1001, such as CPU, communication bus 1002, user interface
1003, network interface 1004, memory 1005.Wherein, communication bus 1002 is for realizing the connection communication between these components.
User interface 1003 may include display screen (Display), optional user interface 1003 can also include standard wireline interface,
Wireless interface, the wireline interface for user interface 1003 can be USB interface in the present invention.Network interface 1004 optionally may be used
To include standard wireline interface and wireless interface (such as WI-FI interface).Memory 1005 can be high speed RAM memory, can also
To be stable memory (non-volatile memory), such as magnetic disk storage.Memory 1005 optionally can also be
Independently of the storage device of aforementioned processor 1001.
It will be understood by those skilled in the art that structure shown in Fig. 1 does not constitute the restriction to server, may include
Than illustrating more or fewer components, certain components or different component layouts are perhaps combined.
As shown in Figure 1, as may include that operating system, network are logical in a kind of memory 1005 of computer storage medium
Believe module, Subscriber Interface Module SIM and the personalized precision marketing program based on user's portrait.
In server shown in Fig. 1, network interface 1004 is mainly used for connecting each network equipment, with each network equipment into
Row data communication；User interface 1003 is mainly used for connecting peripheral hardware；The server calls memory by processor 1001
The personalized precision marketing program based on user's portrait stored in 1005, and execute following operation:
Obtain the label information of user；
User's portrait is established according to the label information；
It obtains the current internet behavior of user, and gives a forecast analysis to the behavior；
When forecast analysis to user has purchase or consumption wish, the portrait based on user, filtering out user may like or feel
The content of interest；
It may like according to the user or interested content, carry out personalized recommendation.
Further, processor 1001 can call the personalization based on user's portrait stored in memory 1005 precisely
Marketing program also executes following operation:
Internet Various types of data is obtained, and the internet data that will acquire carries out fusion and gets through to form knowledge base；
Obtain the internet log of user；
The internet log match forming user tag information with the knowledge base.
Further, the label information includes:
Model class label information that statistics class label information, the modeling algorithm that data are calculated obtain, single client's label information
With label system user group information；
The statistics class label includes regional information, population essential attribute information；
The model class label includes user behavior preference, customer consumption action value, consumer spending habit prediction.
Further, processor 1001 can call the personalization based on user's portrait stored in memory 1005 precisely
Marketing program also executes following operation:
Mode is crawled using distributed reptile and obtains internet Various types of data；
Refinement classification is carried out to the internet Various types of data of the acquisition, then carries out the automatic merger of label, and classification is carried out
It is unified.
Further, processor 1001 can call the personalization based on user's portrait stored in memory 1005 precisely
Marketing program also executes following operation:
One or more label information of user is formed into a text vector；
It draws a portrait the text vector as the user of user.
Further, processor 1001 can call the personalization based on user's portrait stored in memory 1005 precisely
Marketing program also executes following operation:
When spying out user access network, internet behavior information and the internet behavior track of user are obtained；
It draws a portrait in conjunction with user, gives a forecast analysis to the internet behavior purpose of user.
Further, processor 1001 can call the personalization based on user's portrait stored in memory 1005 precisely
Marketing program also executes following operation:
According to historical data of the user in present networks perhaps other networks carry out data analysis recommended user may like or
Interested content；
The historical data include user consumption habit data, browsing web data, purchase data, App use habit data,
User's portrait result data.
Further, processor 1001 can call the personalization based on user's portrait stored in memory 1005 precisely
Marketing program also executes following operation:
According to the personalized recommendation of user as a result, scene location in combination with active user, recommended user is current desired to be wanted
Information, the information include at least one of cuisines, road conditions, lodging, amusement.
The present embodiment establishes user's portrait according to the label information, obtains user by the label information of acquisition user
Current internet behavior, and giving a forecast analysis to the behavior, when forecast analysis to user has purchase or consumption wish, based on using
The portrait at family, filtering out user may like or interested content, may like according to the user or interested content,
Carry out personalized recommendation.Since each user has exclusive user's portrait, in conjunction with user's portrait of user and current
Internet behavior carries out depth analysis, can filter out user and like or content of interest list, can be good at according to each use
The case where family, carries out personalized recommendation, improves the accuracy rate of recommendation, simultaneously because the recommended method is drawn a portrait based on user
With user current internet behavior is also suitable new user come what is recommended, the very good solution cold start-up of new user
Problem.
Based on above-mentioned hardware configuration, the embodiment for the personalized Precision Marketing Method drawn a portrait the present invention is based on user is proposed.
Referring to Fig. 2, Fig. 2 is that the process for the personalized Precision Marketing Method first embodiment drawn a portrait the present invention is based on user is shown
Be intended to, it is described based on user portrait personalized Precision Marketing Method the following steps are included:
Step S10 obtains the label information of user；
In one of the embodiments, as shown in figure 3, step S10 further comprises:
Step S101 obtains internet Various types of data, and the internet data that will acquire carries out fusion and gets through to form knowledge base；
Step S102 obtains the internet log of user；
The internet log match forming user tag information by step S103 with the knowledge base.
In one of the embodiments, as shown in figure 4, step S101 further comprises:
Step S1011 crawls mode using distributed reptile and obtains internet Various types of data；
Step S1012 carries out refinement classification to the internet Various types of data of the acquisition, then carries out the automatic merger of label, and
Classification is subjected to unification.
Specifically, the label information of user can be the build-in attribute of user, it is also possible to the dynamic attribute of user, also
The combination that can be the two, can obtain different label informations according to different business scenarios.Wherein, build-in attribute includes
The attributes such as age, gender, the occupation of user, dynamic attribute include the historical behavior of user's purchase, browse the category such as record of viewing
Property.
Mode is crawled using distributed reptile when obtaining internet Various types of data in one of the embodiments, described point
Cloth crawler is disposed using master slave mode, and main controlled node is by the uniform resource locator of user setting
(UniformResourceLocator, URL) crawl task is distributed to each crawler node, and crawler node is responsible under specific webpage
Parsing task is carried, main controlled node carries out load balancing according to the loading condition of each working node.Meanwhile such mode has well
Scalability share the task of crawling by increasing crawler node when the system is overloaded.Crawler by execute timed task come
Realize constantly automatically updating for climbed content.
The various internet datas obtained include following a few major class: portal website, video website, electric business website, travel network
It stands, forum, microblogging, wechat etc..At this time since data volume is huge, for convenient for constructing user tag, internet data fusion is beaten
When logical, classification is refined to internet data first, then carries out the automatic merger of label, classification is subjected to unification.For example, portal
Knowledge base of standing will refine to most thin category, by taking Sina as an example, will realize such as " science and technology "-" internet " two-stage label；Depending on
Frequency website will refine to some specific programme details, such as " TV play "-" continent is acute "-" thinkling sound Ya list "-" acting the leading role director "；Electric business net
Specific commodity details will be refine to by standing, and " 7kg/ kilograms complete by such as " big household electrical appliances "-" washing machine "-" Haier "-" XQG70-B12866 "-
Automatic frequency-conversion mute rotary drum washing machine, price 2199 ".Since (such as clothes are indicated with clothes for the classification disunity of each website
A kind of things, but it is lengthy and tedious to will cause data when merger, processing is difficult), therefore thesaurus is established in knowledge base, it will be all kinds of
Internet data does fusion when getting through, and carries out the automatic merger of label according to thesaurus first, and classification is carried out unification；It may
Remaining fraction can not merger classification, then additional by manually participating in can carrying out automatic new label after checking, mitigation work significantly
It measures.For crawl data of mobile application end, such as APP application etc., due to can not refine, artificial packet capturing is needed to sort out, with
The classification label that upper crawler crawls will composition user media label and purchase label after matching with internet log.
In the present invention, behavior of the same user on fixed network and movement can also pass through third party's account such as QQ, Taobao
ID is got through.User's online can leave various account informations, such as QQ number, mailbox number, electric business account information, especially QQ number
With electric business account, carry out user data to get through being feasible based on such account.By third party's account identify user this
Internet behavior on tripartite's media domain, for cross-domain internet behavior, mobile terminal internet log passes through the information such as mobile device number
Cross-domain tracking is carried out, for fixed network internet log, the cross-domain tracking of coarseness is carried out by internet account and equipment for surfing the net feature,
A possibility that there may be multiple users due to the same equipment feature of same account, is identified to a certain extent by mining algorithm
Then the behavior of single people out is carried out the mobile Internet data with fixed network as bridge by third party's account as QQ
It gets through, and then calculates user in the label data of the whole network.
Step S20 establishes user's portrait according to the label information；
The label information includes: in one of the embodiments,
Model class label information that statistics class label information, the modeling algorithm that data are calculated obtain, single client's label information
With label system user group information；
The statistics class label includes regional information, population essential attribute information；
The model class label includes user behavior preference, customer consumption action value, consumer spending habit prediction.
Specifically, statistics class label includes regional information, population essential attribute information etc..Model class label includes user's row
For preference, customer consumption action value, consumer spending habit prediction etc..Single client's label refers in entire label system, gives
The label of some specified user's assignment.User group information refers to the user group for meeting certain features.
In one of the embodiments, as shown in figure 5, the step S20 further comprises:
One or more label information of user is formed a text vector by step S201；
Step S202 draws a portrait the text vector as the user of user.
Specifically, user's portrait is a kind of effective tool delineated target user, contact user's demand and design direction.
Often by the attribute of user, behavior and expect to join with the most plain and closeness to life language during practical operation
System gets up.In the present embodiment, user's portrait is made of the multiple label informations obtained, the multiple labels letter that will acquire
Breath group becomes a text vector, draws a portrait the text vector of composition as the user of the user.
In one of the embodiments, as shown in table 1, the multiple label informations for the user that will acquire form a long text
This vector, the label information of user may include the gender of user, age, consumption attribute, occupation etc..According to different industry
Business scene, available different label information:
Table 1
| User ID | label_1 | label_2 | label_3 | label_4 | … | label_n |
| ID001 | Women | 32 | U.S. face | There is child's calcium deficiency | ||
| ID002 | Male | 27 | Increase flesh | It is unmarried |
Specifically, corresponding vector are as follows: first calculate extraction crowd total number of labels V, as shown in table 1, it is assumed that only ID001 and
Two users of ID002;Then total number of tags is 8: women, male, 32,27, U.S. face, increase flesh, be child's calcium deficiency, unmarried；Then with
The mode of one-hot coding indicates.Such as ID001:[1,0,32,0,1,0,1,0]；ID002:[0,1,0,27,1,1,0,1]；
It certainly, here can also be according to gender, age bracket, health demand, family information, whether married according to different business scenarios
Binary vector notation.
The label information further includes media label, purchase label, search label, industry in one of the embodiments,
Label, user's gender, age bracket etc..
In another embodiment, as shown in table 2, the vector of user's history state indicates: corresponding business is sought
Sell the dualization vector representation method of the historical data of active user.Wherein, 1 some movement is indicated, 0 indicates no.
Table 2:
| User ID | Buy product A | Buy combined complete A+E | … |
| ID001 | 1 | 1 | |
| ID002 | 0 | 0 |
Specifically, the text vector of the user of user tag composition is drawn a portrait as the user of user, user draws a portrait as practical
The virtual representations of user are often built according to product and market, have been reacted the feature of real user and have been needed
It asks.
Step S30, obtains the current internet behavior of user, and gives a forecast analysis to the behavior；
It is described in one of the embodiments, to obtain the current internet behavior of user, and give a forecast analysis to the behavior, comprising:
When spying out user access network, internet behavior information and the internet behavior track of user are obtained；
It draws a portrait in conjunction with user, gives a forecast analysis to the internet behavior purpose of user.
Step S40, when forecast analysis to user has purchase or consumption wish, the portrait based on user filters out user
It may like or interested content；
The portrait based on user in one of the embodiments, filtering out user may like or interested content, packet
It includes:
According to historical data of the user in present networks perhaps other networks carry out data analysis recommended user may like or
Interested content；
The historical data include user consumption habit data, browsing web data, purchase data, App use habit data,
User's portrait result data.
Specifically, can judge whether user passes through and be successfully accessed network by the network packet of acquisition user and step on
Application client, such as QQ, wechat, microblogging, Jingdone district, excellent purchase application client are recorded, i.e., will get user's login
Application program judge whether user accesses network as network packet, thus to combining user to draw a portrait the net current to user
Network behavior and internet behavior track give a forecast analysis.For example, user has logged in Jingdone district, then predict that user may have purchase or consumption
Demand；By obtain user portrait in web-based history access data, such as in Jingdone district search key (for example,
Nike, running shoe, Wilson tennis racket etc.), access the Type of website (for example, e-commerce website, news website etc.), search
The information (for example, Nike, running shoe, Wilson tennis racket etc.) of commodity, so that obtaining user may interested information type.
Step S50, may like or interested content according to the user, carry out personalized recommendation.
Specifically, the shopping history data according to user in present networks or other networks, recommended user may like or
The interested content of person.According to the consumption habit data of user, browsing web data, purchase data, App use habit, user
Result of drawing a portrait etc. carries out data analysis, and recommended user may like or interested content.
Comprehensive offer user individual Products Show is drawn by the data between different industries and is led to, and carries out multi-faceted
User subject identification, solves the problems, such as the cold start-up in conventional recommendation industry, thoroughly break enterprise to be cold-started problem bad dream.
In combination with the social networks technology such as knowledge mapping, the current demand and demand psychology of user, personalized recommended products are analyzed.
In one of the embodiments, as shown in fig. 6, after the step S50, further includes:
Step S60, according to the personalized recommendation of user as a result, scene location in combination with active user, recommended user are current
Required information, the information include at least one of cuisines, road conditions, lodging, amusement.
According to the personalized recommendation of above-mentioned user as a result, location information in combination with user, recommends periphery to use to user
It family may interested content.
Specifically, predicting the newest need of user in conjunction with user's history behavior by the location information for recording and analyzing user
It asks, carries out precision marketing, in conjunction with the scene location of active user, the current desired information wanted of recommended user, including but not only
It is limited to: cuisines, road conditions, lodging, amusement etc..Break behavioural analysis of the tradition based on user's history and recommends marketing, comprehensive combination
The location information of user, interest preference, the demand in prediction current 6 hours, 12 hours, 24 hours, in conjunction with different users
Scape carries out scene marketing.
The present invention also provides a kind of storage medium, the precisely battalion of the personalization based on user's portrait is stored on the storage medium
Program is sold, the personalized precision marketing program based on user's portrait is realized when being executed by processor and is based on using as described above
The step of personalized Precision Marketing Method of family portrait.
A kind of personalized Precision Marketing Method, server and storage medium based on user's portrait provided in the present invention, passes through
The label information for obtaining user establishes user's portrait according to the label information, obtains the current internet behavior of user, and to this
Behavior gives a forecast analysis, and when forecast analysis to user has purchase or consumption wish, the portrait based on user, filtering out user can
It can like or interested content, may like according to the user or interested content, carry out personalized recommendation.Due to every
A user has exclusive user's portrait, carries out depth analysis in conjunction with user's portrait of user and current internet behavior,
User can be filtered out to like or content of interest list, can be good at according to carrying out personalized push away the case where each user
Recommend, improve the accuracy rate of recommendation, simultaneously because the recommended method be based on user portrait and the current internet behavior of user come
Recommended, new user is also suitable, the very good solution cold start-up problem of new user.Compared with the prior art, originally
Invention can recommend the product that it is likely to require to each user, according to the consumption habit data of user, browsing web data,
Purchase data, App use habit, user's portrait result, shopping history data etc. carry out data analysis, and recommended user may like
Or interested content, carry out personalized recommendation；Meanwhile by locking a user, according to the individual character of the above-mentioned user of step
Change recommendation results, in combination with the location information of user, recommends the possible interested content of periphery user to user, record simultaneously
The location information for analyzing user predicts the newest demand of user in conjunction with user's history behavior, carries out precision marketing.
The embodiments described above only express several embodiments of the present invention, and the description thereof is more specific and detailed, but simultaneously
Limitations on the scope of the patent of the present invention therefore cannot be interpreted as.It should be pointed out that for those of ordinary skill in the art
For, without departing from the inventive concept of the premise, various modifications and improvements can be made, these belong to guarantor of the invention
Protect range.Therefore, the scope of protection of the patent of the invention shall be subject to the appended claims.
Claims (10)
1. the personalized Precision Marketing Method based on user's portrait characterized by comprising
Obtain the label information of user；
User's portrait is established according to the label information；
It obtains the current internet behavior of user, and gives a forecast analysis to the behavior；
When forecast analysis to user has purchase or consumption wish, the portrait based on user, filtering out user may like or feel
The content of interest；
It may like according to the user or interested content, carry out personalized recommendation.
2. processing method according to claim 1, which is characterized in that the label information for obtaining user, comprising:
Internet Various types of data is obtained, and the internet data that will acquire carries out fusion and gets through to form knowledge base；
Obtain the internet log of user；
The internet log match forming user tag information with the knowledge base.
3. processing method according to claim 2, which is characterized in that the label information includes:
Model class label information that statistics class label information, the modeling algorithm that data are calculated obtain, single client's label information
With label system user group information；
The statistics class label includes regional information, population essential attribute information；
The model class label includes user behavior preference, customer consumption action value, consumer spending habit prediction.
4. processing method according to claim 2, which is characterized in that acquisition internet Various types of data, and will acquire
Internet data carry out fusion get through to form knowledge base, comprising:
Mode is crawled using distributed reptile and obtains internet Various types of data；
Refinement classification is carried out to the internet Various types of data of the acquisition, then carries out the automatic merger of label, and classification is carried out
It is unified.
5. processing method according to claim 1-4, which is characterized in that described to be established according to the label information
User draw a portrait the step of include:
One or more label information of user is formed into a text vector；
It draws a portrait the text vector as the user of user.
6. processing method according to claim 1, which is characterized in that described to obtain the current internet behavior of user and right
The behavior gives a forecast analysis, comprising:
When spying out user access network, internet behavior information and the internet behavior track of user are obtained；
It draws a portrait in conjunction with user, gives a forecast analysis to the internet behavior purpose of user.
7. processing method according to claim 6, which is characterized in that the portrait based on user, filtering out user can
It can like or interested content, comprising:
According to historical data of the user in present networks perhaps other networks carry out data analysis recommended user may like or
Interested content；
The historical data include user consumption habit data, browsing web data, purchase data, App use habit data,
User's portrait result data.
8. processing method according to claim 7, which is characterized in that described to may like according to the user or interested
Content, carry out personalized recommendation after, further includes:
According to the personalized recommendation of user as a result, scene location in combination with active user, recommended user is current desired to be wanted
Information, the information include at least one of cuisines, road conditions, lodging, amusement.
9. a kind of server, which is characterized in that the server includes: memory, processor and is stored on the memory
And the personalized precision marketing program based on user's portrait that can be run on the processor, based on user's portrait
Property precision marketing program be arranged for carrying out as it is described in any item of the claim 1 to 8 based on user draw a portrait personalization essence
The step of quasi- marketing method.
10. a kind of storage medium, which is characterized in that be stored with the precisely battalion of the personalization based on user's portrait on the storage medium
Program is sold, the personalized precision marketing program based on user's portrait is realized when being executed by processor as in claim 1 to 8
The step of described in any item personalized Precision Marketing Methods based on user's portrait.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810616629.7A CN108960975A (en) | 2018-06-15 | 2018-06-15 | Personalized Precision Marketing Method, server and storage medium based on user's portrait |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810616629.7A CN108960975A (en) | 2018-06-15 | 2018-06-15 | Personalized Precision Marketing Method, server and storage medium based on user's portrait |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108960975A true CN108960975A (en) | 2018-12-07 |
Family
ID=64489156
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810616629.7A Pending CN108960975A (en) | 2018-06-15 | 2018-06-15 | Personalized Precision Marketing Method, server and storage medium based on user's portrait |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108960975A (en) |
Cited By (41)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109508426A (en) * | 2018-12-21 | 2019-03-22 | 深圳市智搜信息技术有限公司 | A kind of intelligent recommendation method and its system and storage medium based on physical environment |
| CN109582876A (en) * | 2018-12-19 | 2019-04-05 | 广州易起行信息技术有限公司 | Tourism industry user portrait building method, device and computer equipment |
| CN109684330A (en) * | 2018-12-17 | 2019-04-26 | 深圳市华云中盛科技有限公司 | User's portrait base construction method, device, computer equipment and storage medium |
| CN109767264A (en) * | 2018-12-20 | 2019-05-17 | 深圳壹账通智能科技有限公司 | Product data method for pushing, device, computer equipment and storage medium |
| CN109785045A (en) * | 2018-12-14 | 2019-05-21 | 深圳壹账通智能科技有限公司 | A kind of method for pushing and device based on user behavior data |
| CN109816424A (en) * | 2018-12-14 | 2019-05-28 | 深圳壹账通智能科技有限公司 | Credit card favor information recommended method and system based on customer consumption portrait |
| CN109903117A (en) * | 2019-01-04 | 2019-06-18 | 苏宁易购集团股份有限公司 | A kind of knowledge mapping processing method and processing device for commercial product recommending |
| CN109978630A (en) * | 2019-04-02 | 2019-07-05 | 安徽筋斗云机器人科技股份有限公司 | A kind of Precision Marketing Method and system for establishing user's portrait based on big data |
| CN109993649A (en) * | 2019-03-13 | 2019-07-09 | 王亚萍 | A kind of pilot-operated type consultancy relating to payment of tax method for pushing |
| CN110059177A (en) * | 2019-04-24 | 2019-07-26 | 南京传唱软件科技有限公司 | A kind of activity recommendation method and device based on user's portrait |
| CN110097394A (en) * | 2019-03-27 | 2019-08-06 | 青岛高校信息产业股份有限公司 | The latent objective recommended method of product and device |
| CN110245719A (en) * | 2019-03-27 | 2019-09-17 | 中国海洋大学 | A kind of Feature fusion of entity-oriented and user's portrait |
| CN110348895A (en) * | 2019-06-29 | 2019-10-18 | 北京淇瑀信息科技有限公司 | A kind of personalized recommendation method based on user tag, device and electronic equipment |
| CN110400166A (en) * | 2019-06-24 | 2019-11-01 | 阿里巴巴集团控股有限公司 | The method and apparatus for selecting the official documents and correspondence pushed to target user |
| CN110428244A (en) * | 2019-06-14 | 2019-11-08 | 广州乐摇摇信息科技有限公司 | Package recommendation method and device |
| CN110443639A (en) * | 2019-07-16 | 2019-11-12 | 深圳市数位汇聚科技有限公司 | A kind of marketing strategy recommended method and device based on position positioning under line |
| CN110674391A (en) * | 2019-08-15 | 2020-01-10 | 中国平安财产保险股份有限公司 | Product data pushing method and system based on big data and computer equipment |
| CN110727860A (en) * | 2019-09-16 | 2020-01-24 | 武汉安诠加信息技术有限公司 | User portrait method, device, equipment and medium based on internet beauty platform |
| CN110874767A (en) * | 2019-11-19 | 2020-03-10 | 山东爱城市网信息技术有限公司 | Business hall accurate marketing method based on collaborative filtering |
| CN110910199A (en) * | 2019-10-16 | 2020-03-24 | 中国平安人寿保险股份有限公司 | Item information sorting method and device, computer equipment and storage medium |
| CN110942337A (en) * | 2019-10-31 | 2020-03-31 | 天津中科智能识别产业技术研究院有限公司 | Accurate tourism marketing method based on internet big data |
| CN111310028A (en) * | 2020-01-19 | 2020-06-19 | 浙江连信科技有限公司 | Recommendation method and device based on psychological characteristics |
| CN111353089A (en) * | 2018-12-24 | 2020-06-30 | 阿里巴巴集团控股有限公司 | Information recommendation method and device, electronic equipment and computer storage medium |
| CN111353091A (en) * | 2018-12-24 | 2020-06-30 | 北京三星通信技术研究有限公司 | Information processing method and device, electronic equipment and readable storage medium |
| CN111814092A (en) * | 2020-07-21 | 2020-10-23 | 上海数鸣人工智能科技有限公司 | Data preprocessing method for artificial intelligence algorithm based on user internet behavior |
| CN111815407A (en) * | 2020-07-02 | 2020-10-23 | 杭州屏行视界信息科技有限公司 | Method and device for constructing user portrait |
| CN112115364A (en) * | 2020-09-23 | 2020-12-22 | 北京明略昭辉科技有限公司 | Recommendation method and system for cold start based on login operation and computer equipment |
| WO2021003673A1 (en) * | 2019-07-09 | 2021-01-14 | 深圳市欢太科技有限公司 | Content pushing method and related product |
| CN112487285A (en) * | 2020-11-18 | 2021-03-12 | 中国人寿保险股份有限公司 | Message pushing method and device |
| CN112561598A (en) * | 2020-12-23 | 2021-03-26 | 中国农业银行股份有限公司重庆市分行 | Customer loss prediction and retrieval method and system based on customer portrait |
| CN113065891A (en) * | 2021-03-09 | 2021-07-02 | 广东便捷神科技股份有限公司 | Interactive marketing method for retail terminal |
| CN113330475A (en) * | 2019-05-20 | 2021-08-31 | 深圳市欢太科技有限公司 | Information recommendation method and device, electronic equipment and storage medium |
| CN113344635A (en) * | 2021-06-24 | 2021-09-03 | 深圳市小摩豆网络科技有限公司 | Customer consumption behavior habit management and analysis method |
| CN113505309A (en) * | 2021-05-21 | 2021-10-15 | 深圳市蘑菇财富技术有限公司 | Marketing recommendation method, device, equipment and storage medium based on distributed data |
| CN113505295A (en) * | 2021-06-29 | 2021-10-15 | 广州智会云科技发展有限公司 | Enterprise customer acquisition push algorithm implementation method and system |
| CN113763093A (en) * | 2020-11-12 | 2021-12-07 | 北京沃东天骏信息技术有限公司 | User portrait-based item recommendation method and device |
| CN113821715A (en) * | 2020-11-17 | 2021-12-21 | 北京京东尚科信息技术有限公司 | Method and device for determining push information |
| CN114219580A (en) * | 2021-12-31 | 2022-03-22 | 江苏玖益贰信息科技有限公司 | Consumer portrait generation device and portrait analysis method |
| CN114693245A (en) * | 2022-03-02 | 2022-07-01 | 深圳市小满科技有限公司 | User portrait generation method and device, electronic equipment and readable storage medium |
| CN117726359A (en) * | 2024-02-08 | 2024-03-19 | 成都纳宝科技有限公司 | Interactive marketing method, system and equipment |
| US12099558B2 (en) | 2018-12-24 | 2024-09-24 | Samsung Electronics Co., Ltd. | Method and apparatus for providing content based on user activity |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105608171A (en) * | 2015-12-22 | 2016-05-25 | 青岛海贝易通信息技术有限公司 | User portrait construction method |
| CN105824912A (en) * | 2016-03-15 | 2016-08-03 | 平安科技（深圳）有限公司 | Personalized recommending method and device based on user portrait |
| CN107403335A (en) * | 2017-06-19 | 2017-11-28 | 北京至信普林科技有限公司 | A kind of drawn a portrait based on depth user carries out the system and implementation method of precision marketing |
| CN108021929A (en) * | 2017-11-16 | 2018-05-11 | 华南理工大学 | Mobile terminal electric business user based on big data, which draws a portrait, to establish and analysis method and system |
-
2018
- 2018-06-15 CN CN201810616629.7A patent/CN108960975A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105608171A (en) * | 2015-12-22 | 2016-05-25 | 青岛海贝易通信息技术有限公司 | User portrait construction method |
| CN105824912A (en) * | 2016-03-15 | 2016-08-03 | 平安科技（深圳）有限公司 | Personalized recommending method and device based on user portrait |
| CN107403335A (en) * | 2017-06-19 | 2017-11-28 | 北京至信普林科技有限公司 | A kind of drawn a portrait based on depth user carries out the system and implementation method of precision marketing |
| CN108021929A (en) * | 2017-11-16 | 2018-05-11 | 华南理工大学 | Mobile terminal electric business user based on big data, which draws a portrait, to establish and analysis method and system |
Cited By (52)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109785045A (en) * | 2018-12-14 | 2019-05-21 | 深圳壹账通智能科技有限公司 | A kind of method for pushing and device based on user behavior data |
| CN109816424A (en) * | 2018-12-14 | 2019-05-28 | 深圳壹账通智能科技有限公司 | Credit card favor information recommended method and system based on customer consumption portrait |
| CN109684330A (en) * | 2018-12-17 | 2019-04-26 | 深圳市华云中盛科技有限公司 | User's portrait base construction method, device, computer equipment and storage medium |
| CN109582876A (en) * | 2018-12-19 | 2019-04-05 | 广州易起行信息技术有限公司 | Tourism industry user portrait building method, device and computer equipment |
| CN109582876B (en) * | 2018-12-19 | 2021-05-11 | 广州易起行信息技术有限公司 | Tourist industry user portrait construction method and device and computer equipment |
| CN109767264A (en) * | 2018-12-20 | 2019-05-17 | 深圳壹账通智能科技有限公司 | Product data method for pushing, device, computer equipment and storage medium |
| CN109508426A (en) * | 2018-12-21 | 2019-03-22 | 深圳市智搜信息技术有限公司 | A kind of intelligent recommendation method and its system and storage medium based on physical environment |
| CN111353089A (en) * | 2018-12-24 | 2020-06-30 | 阿里巴巴集团控股有限公司 | Information recommendation method and device, electronic equipment and computer storage medium |
| US12099558B2 (en) | 2018-12-24 | 2024-09-24 | Samsung Electronics Co., Ltd. | Method and apparatus for providing content based on user activity |
| CN111353091A (en) * | 2018-12-24 | 2020-06-30 | 北京三星通信技术研究有限公司 | Information processing method and device, electronic equipment and readable storage medium |
| CN109903117A (en) * | 2019-01-04 | 2019-06-18 | 苏宁易购集团股份有限公司 | A kind of knowledge mapping processing method and processing device for commercial product recommending |
| CN109903117B (en) * | 2019-01-04 | 2022-10-25 | 苏宁易购集团股份有限公司 | Knowledge graph processing method and device for commodity recommendation |
| CN109993649A (en) * | 2019-03-13 | 2019-07-09 | 王亚萍 | A kind of pilot-operated type consultancy relating to payment of tax method for pushing |
| CN110245719B (en) * | 2019-03-27 | 2024-05-07 | 中国海洋大学 | Feature fusion method for entity and user portraits |
| CN110097394A (en) * | 2019-03-27 | 2019-08-06 | 青岛高校信息产业股份有限公司 | The latent objective recommended method of product and device |
| CN110245719A (en) * | 2019-03-27 | 2019-09-17 | 中国海洋大学 | A kind of Feature fusion of entity-oriented and user's portrait |
| CN109978630A (en) * | 2019-04-02 | 2019-07-05 | 安徽筋斗云机器人科技股份有限公司 | A kind of Precision Marketing Method and system for establishing user's portrait based on big data |
| CN110059177A (en) * | 2019-04-24 | 2019-07-26 | 南京传唱软件科技有限公司 | A kind of activity recommendation method and device based on user's portrait |
| CN110059177B (en) * | 2019-04-24 | 2023-09-26 | 南京传唱软件科技有限公司 | Activity recommendation method and device based on user portrait |
| CN113330475A (en) * | 2019-05-20 | 2021-08-31 | 深圳市欢太科技有限公司 | Information recommendation method and device, electronic equipment and storage medium |
| CN113330475B (en) * | 2019-05-20 | 2024-06-04 | 深圳市欢太科技有限公司 | Information recommendation method, device, electronic equipment and storage medium |
| CN110428244A (en) * | 2019-06-14 | 2019-11-08 | 广州乐摇摇信息科技有限公司 | Package recommendation method and device |
| CN110400166A (en) * | 2019-06-24 | 2019-11-01 | 阿里巴巴集团控股有限公司 | The method and apparatus for selecting the official documents and correspondence pushed to target user |
| CN110348895A (en) * | 2019-06-29 | 2019-10-18 | 北京淇瑀信息科技有限公司 | A kind of personalized recommendation method based on user tag, device and electronic equipment |
| WO2021003673A1 (en) * | 2019-07-09 | 2021-01-14 | 深圳市欢太科技有限公司 | Content pushing method and related product |
| CN110443639A (en) * | 2019-07-16 | 2019-11-12 | 深圳市数位汇聚科技有限公司 | A kind of marketing strategy recommended method and device based on position positioning under line |
| CN110674391B (en) * | 2019-08-15 | 2024-05-03 | 中国平安财产保险股份有限公司 | Product data pushing method and system based on big data and computer equipment |
| CN110674391A (en) * | 2019-08-15 | 2020-01-10 | 中国平安财产保险股份有限公司 | Product data pushing method and system based on big data and computer equipment |
| CN110727860A (en) * | 2019-09-16 | 2020-01-24 | 武汉安诠加信息技术有限公司 | User portrait method, device, equipment and medium based on internet beauty platform |
| CN110910199B (en) * | 2019-10-16 | 2024-05-28 | 中国平安人寿保险股份有限公司 | Method, device, computer equipment and storage medium for ordering project information |
| CN110910199A (en) * | 2019-10-16 | 2020-03-24 | 中国平安人寿保险股份有限公司 | Item information sorting method and device, computer equipment and storage medium |
| CN110942337A (en) * | 2019-10-31 | 2020-03-31 | 天津中科智能识别产业技术研究院有限公司 | Accurate tourism marketing method based on internet big data |
| CN110874767A (en) * | 2019-11-19 | 2020-03-10 | 山东爱城市网信息技术有限公司 | Business hall accurate marketing method based on collaborative filtering |
| CN111310028A (en) * | 2020-01-19 | 2020-06-19 | 浙江连信科技有限公司 | Recommendation method and device based on psychological characteristics |
| CN111815407A (en) * | 2020-07-02 | 2020-10-23 | 杭州屏行视界信息科技有限公司 | Method and device for constructing user portrait |
| CN111814092A (en) * | 2020-07-21 | 2020-10-23 | 上海数鸣人工智能科技有限公司 | Data preprocessing method for artificial intelligence algorithm based on user internet behavior |
| CN112115364A (en) * | 2020-09-23 | 2020-12-22 | 北京明略昭辉科技有限公司 | Recommendation method and system for cold start based on login operation and computer equipment |
| CN113763093A (en) * | 2020-11-12 | 2021-12-07 | 北京沃东天骏信息技术有限公司 | User portrait-based item recommendation method and device |
| WO2022100518A1 (en) * | 2020-11-12 | 2022-05-19 | 北京沃东天骏信息技术有限公司 | User profile-based object recommendation method and device |
| CN113821715A (en) * | 2020-11-17 | 2021-12-21 | 北京京东尚科信息技术有限公司 | Method and device for determining push information |
| CN112487285A (en) * | 2020-11-18 | 2021-03-12 | 中国人寿保险股份有限公司 | Message pushing method and device |
| CN112561598A (en) * | 2020-12-23 | 2021-03-26 | 中国农业银行股份有限公司重庆市分行 | Customer loss prediction and retrieval method and system based on customer portrait |
| CN113065891A (en) * | 2021-03-09 | 2021-07-02 | 广东便捷神科技股份有限公司 | Interactive marketing method for retail terminal |
| CN113065891B (en) * | 2021-03-09 | 2023-07-18 | 广东便捷神科技股份有限公司 | Interactive marketing method for retail terminal |
| CN113505309B (en) * | 2021-05-21 | 2024-02-09 | 深圳市蘑菇财富技术有限公司 | Marketing recommendation method, device, equipment and storage medium based on distributed data |
| CN113505309A (en) * | 2021-05-21 | 2021-10-15 | 深圳市蘑菇财富技术有限公司 | Marketing recommendation method, device, equipment and storage medium based on distributed data |
| CN113344635A (en) * | 2021-06-24 | 2021-09-03 | 深圳市小摩豆网络科技有限公司 | Customer consumption behavior habit management and analysis method |
| CN113505295A (en) * | 2021-06-29 | 2021-10-15 | 广州智会云科技发展有限公司 | Enterprise customer acquisition push algorithm implementation method and system |
| CN114219580A (en) * | 2021-12-31 | 2022-03-22 | 江苏玖益贰信息科技有限公司 | Consumer portrait generation device and portrait analysis method |
| CN114693245A (en) * | 2022-03-02 | 2022-07-01 | 深圳市小满科技有限公司 | User portrait generation method and device, electronic equipment and readable storage medium |
| CN117726359A (en) * | 2024-02-08 | 2024-03-19 | 成都纳宝科技有限公司 | Interactive marketing method, system and equipment |
| CN117726359B (en) * | 2024-02-08 | 2024-04-26 | 成都纳宝科技有限公司 | Interactive marketing method, system and equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108960975A (en) | Personalized Precision Marketing Method, server and storage medium based on user's portrait | |
| US20210133817A1 (en) | Information Recommendation Method and Apparatus | |
| CN107862553B (en) | Advertisement real-time recommendation method and device, terminal equipment and storage medium | |
| US10095771B1 (en) | Clustering and recommending items based upon keyword analysis | |
| US11037222B1 (en) | Dynamic recommendations personalized by historical data | |
| Costa-Montenegro et al. | Which App? A recommender system of applications in markets: Implementation of the service for monitoring users’ interaction | |
| US9195753B1 (en) | Displaying interest information | |
| TWI533246B (en) | Method and system for discovery of user unknown interests | |
| US8725559B1 (en) | Attribute based advertisement categorization | |
| CN107426328B (en) | Information pushing method and device | |
| CN109977296B (en) | Information pushing method, device, equipment and storage medium | |
| CN106791970A (en) | The method and device of merchandise news is presented in video playback | |
| Boutet et al. | Hyrec: Leveraging browsers for scalable recommenders | |
| CN109033149A (en) | Information recommendation method, device, server and storage medium | |
| CN110020162B (en) | User identification method and device | |
| US20110137904A1 (en) | Clickstreams and website classification | |
| Kagan et al. | Predicting purchase behavior of website audiences | |
| US20180307733A1 (en) | User characteristic extraction method and apparatus, and storage medium | |
| CN107507041A (en) | The construction method and construction device of user model | |
| Costa et al. | Emotion-based recommender system for overcoming the problem of information overload | |
| CN109978580A (en) | Object recommendation method, apparatus and computer readable storage medium | |
| US20160196579A1 (en) | Dynamic deep links based on user activity of a particular user | |
| Anagnostopoulos et al. | Targeted interest-driven advertising in cities using Twitter | |
| Huang et al. | A cross-platform consumer behavior analysis of large-scale mobile shopping data | |
| CN111552835A (en) | File recommendation method and device and server |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181207 |