CN104933157A - Method and device used for obtaining user attribute information, and server - Google Patents
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- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- G06F16/90—Details of database functions independent of the retrieved data types
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- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
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Abstract
The invention discloses a method and a device used for obtaining user attribute information, and a server. One implementation mode of the method comprises the following steps: obtaining map log information, positioning log information and the log information of a search engine; preprocessing the map log information, the positioning log information and the log information of the search engine to obtain the relevant data of a user; on the basis of the relevant data of the user, obtaining the behavior characteristics of the user; and on the basis of the behavior characteristics of the user, determining the user attribute information. The implementation mode fully utilizes information including user positioning, map search and the like to analyze the user attribute information, and improves the comprehensiveness and the accuracy of the obtained user attribute information.
Description
Technical field
The application relates to field of computer technology, is specifically related to field of terminal technology, particularly relates to the method for obtaining customer attribute information, device and server.
Background technology
User's portrait can be the set of customer attribute information, can describe the feature of user with a model.In prior art, it is the attribute information of search behavior analysis user on line based on user that user draws a portrait the main method that builds.In this approach, owing to may there is the virtual user search behavior such as search information of forging because of malicious user and the noise caused in search behavior on the line of user, the structure result causing user to draw a portrait is inaccurate.In addition, portrait based on search behavior on the line of user builds the problem that also may there is text semantic difference, and same search word may point to different user characteristicses, such as user search " Mount Lushan ", may be pay close attention to travel information, also may be like the film relevant to Mount Lushan.
In addition, also have in prior art and draw a portrait construction method based on the user of user's real trade data, the online trading data based on user build.The online trading of user is low frequency behavior in the behavior of user, therefore cannot draw comprehensive, complete, customer attribute information accurately according to it.
Summary of the invention
In view of above-mentioned defect of the prior art or deficiency, expect the acquisition methods that a kind of customer attribute information comprehensively and accurately can be provided.This application provides the method for obtaining customer attribute information, device and server.
First aspect, this application provides a kind of method for obtaining customer attribute information, comprising: the log information obtaining map log information, location log information and search engine; Pre-service is carried out, to obtain the related data of user to the log information of map log information, location log information and search engine; Related data based on user obtains the behavioural characteristic of user; And based on the behavioural characteristic determination customer attribute information of user.
In some implementation, pre-service is carried out to the log information of map log information, location log information and search engine, comprising: the data analysis that the log information of map log information, location log information and search engine is comprised; Extract map log information, location log information and data relevant to geographic position and user behavior in the log information of search engine, as the related data of user.
In further implementation, pre-service is carried out to map log information, location log information and the log information of search engine, also comprises: go out the related data of the information relevant to the data that map log information, the log information of locating log information and search engine comprise as user by NetFind.
In some implementation, the related data of user at least comprises position searching data and/or locator data.Wherein, position searching data comprises following at least one item: the line information of target location search data, route search data and correspondence; And the perimeter data of target location.
In further implementation, target location search data comprises following at least one item: the destination of search, the moment of search, user's current geographic position; Route search data comprise following at least one item: the trip mode of the moment of user search route, initial geographic position, target geographic position, track data and correspondence; The perimeter data of target location comprises following at least one item: the buildings data of target location periphery, count certificate, parking data.
In some implementation, the related data based on user obtains the behavioural characteristic of user, comprises following at least one item: carry out statistics and analysis based on locator data to the distribution in the geographic position that user stops, to determine that user fixes movable place; Position-based retrieve data obtains the interest point information of user; Position-based retrieve data carries out statistics and analysis to the trip mode of user, to determine the trip mode of user preference; The degree of correlation between user is calculated, to determine the intimate degree of multiple user based on locator data.
In some implementation, based on the behavioural characteristic determination customer attribute information of user, comprising: based on the behavioural characteristic of user, adopt the model determination customer attribute information of having trained.
In some implementation, customer attribute information comprises following at least one item: the age bracket of user, sex, occupation, interest, income level, consumption habit, health status, social relationships and capital assets situation.
Second aspect, this application provides a kind of device for obtaining customer attribute information, comprising: the first acquiring unit, for obtaining the log information of map log information, location log information and search engine; Pretreatment unit, for carrying out pre-service, to obtain the related data of user to the log information of map log information, location log information and search engine; Second acquisition unit, for obtaining the behavioural characteristic of user based on the related data of user; And determining unit, for the behavioural characteristic determination customer attribute information based on user.
In some implementation, pretreatment unit is used for carrying out pre-service to the log information of map log information, location log information and search engine as follows: the data analysis comprised the log information of map log information, location log information and search engine; Extract map log information, location log information and data relevant to geographic position and user behavior in the log information of search engine, as the related data of user.
In further implementation, pretreatment unit is also for carrying out pre-service to map log information, location log information with the log information of search engine as follows: go out the related data of the information relevant to the data that map log information, the log information of locating log information and search engine comprise as user by NetFind.
In some implementation, the related data of user at least comprises position searching data and/or locator data.Wherein, position searching data comprises following at least one item: the line information of target location search data, route search data and correspondence; And the perimeter data of target location.
In further implementation, target location search data comprises following at least one item: the destination of search, the moment of search, user's current geographic position; Route search data comprise following at least one item: the trip mode of the moment of user search route, initial geographic position, target geographic position, track data and correspondence; The perimeter data of target location comprises following at least one item: the buildings data of target location periphery, count certificate, parking data.
In further implementation, second acquisition unit is used for the behavioural characteristic obtaining user by following at least one mode: carry out statistics and analysis based on locator data to the distribution in the geographic position that user stops, to determine that user fixes movable place; Position-based retrieve data obtains the interest point information of user; Position-based retrieve data carries out statistics and analysis to the trip mode of user, to determine the trip mode of user preference; The degree of correlation between user is calculated, to determine the intimate degree of multiple user based on locator data.
In some implementation, determining unit, based on the behavioural characteristic of user, adopts the model determination customer attribute information of having trained.
In some implementation, customer attribute information comprises following at least one item: the age bracket of user, sex, occupation, interest, income level, consumption habit, health status, social relationships and capital assets situation.
The third aspect, this application provides a kind of server, comprises the device for obtaining customer attribute information that the application's second aspect provides.
The method for obtaining customer attribute information that the application provides, device and server, by obtaining map log information, the log information of location log information and search engine, subsequently to map log information, the log information of location log information and search engine carries out pre-service, to obtain the related data of user, afterwards based on the behavioural characteristic of the related data acquisition user of user, finally based on the behavioural characteristic determination customer attribute information of user, take full advantage of the location of user, the information analysis customer attribute informations such as map search, improve the comprehensive of obtained customer attribute information and accuracy.
Accompanying drawing explanation
That is done with reference to the following drawings by reading is described in detail non-limiting example, and the other features, objects and advantages of the application will become more obvious:
Fig. 1 is the process flow diagram of an embodiment of the method for obtaining customer attribute information that the embodiment of the present application provides;
Fig. 2 is the process flow diagram log information of map log information, location log information and search engine being carried out to an embodiment of pretreated method that the embodiment of the present application provides;
Fig. 3 is the structural representation of an embodiment of the device for obtaining customer attribute information that the embodiment of the present application provides;
Fig. 4 is the exemplary system architecture schematic diagram can applying the embodiment of the present application;
Fig. 5 is the structural representation of the computer system of the server be suitable for for realizing the embodiment of the present application.
Embodiment
Below in conjunction with drawings and Examples, the application is described in further detail.Be understandable that, specific embodiment described herein is only for explaining related invention, but not the restriction to this invention.In addition, for convenience of description, illustrate only the part relevant to Invention in accompanying drawing.
It should be noted that, when not conflicting, the embodiment in the application and the feature in embodiment can combine mutually.Below with reference to the accompanying drawings and describe the application in detail in conjunction with the embodiments.
Terminal involved by the application can include but not limited to smart mobile phone, panel computer, personal digital assistant, Intelligent worn device and pocket computer on knee etc.Object and is for simplicity described, in ensuing discussion, in conjunction with being provided with electronic chart, browser, there is the terminal of positioning function to describe the exemplary embodiment of the application for example.
Please refer to Fig. 1, it illustrates the flow process of the embodiment for obtaining customer attribute information method according to the application.The method can be performed by server.
As shown in Figure 1, in a step 101, the log information of map log information, location log information and search engine is obtained.
In general, when user is by map inquiry destination address, route information, terminal can establish a communications link with map server, to obtain the map datum that user inquires about; User is at the browser access webpage by terminal or when retrieving, and terminal can establish a communications link with web page server, to obtain the info web that user will access; During the positioning function that user opens a terminal, terminal can establish a communications link with location-server, to obtain current locator data.Terminal is while user feedback Query Result, access result and positioning result, can inquiry, access be saved in the storer of terminal with the relevant information in location, also inquiry, access can be uploaded to corresponding server with the relevant information in location, generating log information is also preserved.Particularly, user can save as map log information by the relevant information (such as query time, query contents) of map inquiry; The relevant information (the three-dimensional geographic position data of such as terminal and positioning time) that terminal is located by modes such as GPS can save as location log information; User can save as the log information of search engine by the relevant information (time of such as retrieval of content, retrieval) that the search engine on browser carries out retrieving.
In the present embodiment, if above-mentioned log information is kept in the storer of terminal, then server can obtain the log information of map log information, location log information and search engine from terminal by network.If above-mentioned log information is kept in corresponding server, then server can obtain map log information from map server respectively, obtains location log information from location-server, obtains the log information of search engine from the server of search engine.In some implementations, server can obtain log information from the server of terminal and corresponding map server, location-server and search engine, and identifies based on the identification information (as IP address, user ID etc.) of user and extract the above-mentioned log information of same user.
Can comprise in the log information of above-mentioned map log information, location log information and search engine: the storage space etc. that the content comprised in temporal information, operation information, status information, network protocol message, inquiry, location or Search Results and result takies.In some implementations, above-mentioned log information is preserved with field form.
In a step 102, pre-service is carried out to the log information of map log information, location log information and search engine.
In the present embodiment, server can carry out pre-service, to obtain the related data of user to the log information of the map log information obtained, location log information and search engine.The related data of user can be the data relevant to user behavior, and also can be the data relevant to the attributive character of user (such as sex, personality), can also be the data relevant to the geographic position of user.As previously described, in above-mentioned log information except comprising temporal information, operation information, inquiry, location or Search Results, also comprise the status information, the network protocol message that have nothing to do with the behavior of user, attributive character, geographic position.In the present embodiment, the information filtering that server can be irrelevant with user behavior in log information, such as, when log information is field form, can carry out by analyzing field contents the field that filtering represents status information and procotol.
With further reference to Fig. 2, it illustrates the process flow diagram log information of map log information, location log information and search engine being carried out to an embodiment of pretreated method that the embodiment of the present application provides.
As shown in Figure 2, in step 201, to the data analysis that the log information of map log information, location log information and search engine comprises.
In the present embodiment, server can be analyzed the map log information obtained, location log information and the log information of search engine, such as, can carry out cutting to the log information of field form, and analyzes each content representated by part field.For example, the URL etc. of the IP address of user, search time, search content, status code, access can be comprised in a log information of search engine.This log information can be analyzed, thus determine search time and the search content of user.
In step 202., map log information, location log information and data relevant to geographic position and user behavior in the log information of search engine are extracted, as the related data of user.
In the present embodiment, whether server whether can analyze the data obtained respectively in determining step 201 relevant to user behavior and relevant with geographic position.When analyzing the data obtained and being relevant to geographic position or user behavior, relevant data can be extracted, as the related data of user.The related data of user can be used for analyzing the attributive character of user, builds user's portrait.
Such as, in the above-described embodiments, map inquiry time, query contents can be extracted as the related data of user from map log information; The related data of geographic position data as user of positioning time and location can be extracted from the log information of location; The related data of the webpage URL in the key word of user search, retrieval time, the result for retrieval clicked as user can also be extracted from the log information of search engine.
In some implementations, the related data of user at least comprises position searching data and/or locator data.Wherein position searching data can extract and draw from the log information of map log information, search engine, and locator data can obtain from the log information of location.Position searching data can comprise following at least one item: the line information of target location search data, route search data and correspondence, and the perimeter data of target location.
Particularly, target location search data can comprise following at least one item: the destination of search, the moment of search, user's current geographic position.When user searches for target location by the electronic chart in terminal, map server can find the geographic position, destination of coupling according to the searching request of user, user's current geographic position can also be obtained, the moment of record searching, and be saved in map log information.Server can obtain these data from map log information, as user-dependent data.
Route search data can comprise following at least one item: the trip mode of the moment of user search route, initial geographic position, target geographic position, track data and correspondence.The wherein track data route track that can inquire for map server, corresponding trip mode can for driving, public transport or walking.In some implementations, route search data can also comprise the distance between initial geographic position and target geographic position.
The line information of the correspondence of route search data can be navigation data.Navigation data can comprise detailed locating information and the displacement in the corresponding moment.
The perimeter data of target location can comprise following at least one item: the buildings data of target location periphery, count certificate, parking data.Such as when a certain tourist attractions of user search, server can obtain subway station near the hotel of tourist attractions periphery, the data in food and drink place, these tourist attractions, the parking lot quantity of bus station's quantity and distance or this tourist attractions periphery and parking stall quantity.
In step 203, the related data of the information relevant to the data that map log information, the log information of locating log information and search engine comprise as user is gone out by NetFind.
Except extract the above-mentioned data relevant to user behavior or geographic position from log information except, the relevant information of the data that server can also be comprised by network inquiry and map log information, the log information of locating log information and search engine is as the related data of user.Such as when server extracts the destination of user search from map log information, server can find the information such as type, attribute of destination.For example, when destination is restaurant, can by the information such as average consumption, the style of cooking, parking stall, evaluation, other branch addresses of NetFind to restaurant, as the related data of user; When destination is shopping place, the information such as Brand, type, price of selling can be searched, as the related data of user.
In the above-described embodiments, server can be analyzed log information, extract wherein relevant to user behavior or geographic position data, the information relevant to the data that the log information obtained in step 101 comprises can also be inquired about, thus obtain the related data of user.
Return Fig. 1, in step 103, the related data based on user obtains the behavioural characteristic of user.
The behavioural characteristic of user can draw by carrying out analysis to the related data of the user obtained in step 102.In the present embodiment, server can infer the behavioural characteristic of user by data statistics.The behavioural characteristic of user can include but not limited to that user fixes movable place, interest point information, the trip mode of preference, social networks.When the related data of user comprises position searching data and locator data, the above-mentioned behavioural characteristic of user can be obtained in several ways respectively.
In some implementations, server can carry out statistics and analysis based on locator data to the distribution in the geographic position that user stops, to determine that user fixes movable place.Particularly, server can add up time and the frequency of stop, show that user's residence time is the longest according to statistics or stop the highest several geographic position of frequency, and the home location of the concrete time information deduction user of combination stop, work place and other regular movable places.Such as, can using the longest for the residence time in all geographic position and stop the moment be that the geographic position of non-working time is as home location; Using the longest for the residence time and to stop the moment be the work place of geographic position as user of working time.In actual applications, user can also be stopped the high and stable geographic position of frequency as the regular movable place (restaurant etc. that such as gymnasium, user like) of user.User fixes the information such as occupation, interest, Housing Price that movable place may be used for analyzing user.
In some implementations, server position-based retrieve data can obtain the interest point information of user.The interest point information of user can be that user may interested information.Particularly, server can obtain the attribute informations such as the destination type of user search, obtains the interest point information of relevant information as user according to attribute informations such as destination types.Further, server can also obtain the frequency information of the destination of the same attribute information of user search, as the interest point information of user.Such as, when comprising the positional information on user search airport in position searching data, server can obtain on-site tourist attractions, this airport information, hotel information etc., as the interest point information of user, to analyze the attribute information such as interest, personality of user.Again such as, when comprising the position in user search hotel in position searching data, server can obtain the information such as star, the level of consumption in hotel, as the interest point information of user, to analyze the condition of assets of user.Can also comprise the information are correlated with in such as refuelling station, parking lot, carwash place in position searching data, whether server can obtain these information has the assets informations such as car to analyze user.
In some implementations, server position-based retrieve data can carry out statistics and analysis to the trip mode of user, to determine the trip mode of user preference.The trip mode of the user's route information selected and correspondence when retrieving route can be comprised in position searching data, server can be added up the trip mode that user selects, analyze trip mode that user commonly uses (drive, public transport, subway, passenger vehicle, train, aircraft etc.), thus infer that user is to the sensitivity of transportation cost, and then analyze the condition of assets of user.
In some implementations, server can calculate the degree of correlation between user, to determine the intimate degree of multiple user based on described locator data.Such as server can rest on the cross feature of the locator datas such as immediate geographic location jointly according to user, analyzes the power of social attribute between user, i.e. the intimate degree of multiple user.The temporal information that server can also rest on immediate geographic location jointly based on user analyzes the type of the social networks of user further, such as household, friend, work buddies etc.For example, when the time that multiple user rests on same geographic position on one's own time more than a threshold value time, can think that the intimate degree of multiple user is higher, may be household or friends.
Alternatively, server can also obtain the type information of the mobile terminal that user uses, and such as Android system or ios system, infer the Assets of user.
Below the acquisition methods of several user behavior feature is exemplarily described.Be appreciated that the related data of the user obtained in step 102 may be used for analyzing and obtains the user behavior feature of multiple other types, the location type that such as often can stop based on the locator data of user and position searching data analysis user; Can add up etc. based on the trip rule of the position searching data of user to user.
At step 104, based on the behavioural characteristic determination customer attribute information of user.
In the present embodiment, server can carry out data mining to the behavioural characteristic of user, based on the attribute information of the analysis of statistical results user of step 103.In certain embodiments, customer attribute information can comprise following at least one item: the age bracket of user, sex, occupation, interest, income level, consumption habit, health status, social relationships and capital assets situation.
In some implementations, after server carries out data mining to the behavioural characteristic of user, multiple alternative user attribute information and probability thereof can be inferred, such as, draw the age bracket of multiple different user and probability corresponding to each age bracket.Afterwards using the alternative user attribute information of maximum probability as customer attribute information.
Particularly, the age bracket of user can be fixed movable place according to user and judge, such as user fixes movable place when being university, can infer that the older probability of user is 18-24 year, user fixes movable place when being office building, can infer the older probability of user be 24-50 year, user fixes movable place when being old center, can infer that the older probability of user is for being greater than 50 years old.The age bracket of user also can be inferred according to the interest point information of user.Such as, user by map retrieval recreation ground time, can infer the age of user be 15-30 year probability larger.
The sex of user can judge according to its interest point information, and user's greater probability of such as searching for the addresses such as beauty parlor is women, and searches for user's greater probability of the addresses such as basketball court for the male sex.
The occupation of user can be judged by the locator data of user.Such as in the behavioural characteristic of user, user fixes movable place when comprising the mansion of certain IT enterprises, then can determine that user is engaged in the probability of IT industry larger; In the behavioural characteristic of user, user fixes movable place when comprising unit of government bodies, then can determine that the occupation of user is that the probability of civil servant is larger.
The interest of user can be determined by the interest point information in the behavioural characteristic of user.Such as when the sports center quantity that the interest point information of user comprises exceedes certain value, can think that user likes motion.
The social networks of user can be judged by the position searching data of user.Such as in the position searching data of user, comprise the objectives such as adventure playground, primary school, children's palace, then can determine that user has child.Further, can also whether user has child by there being the interest point information of the user obtained by position searching data to judge.Such as comprise in the interest point information of user a large amount of similar " child ", " children ", " baby " keyword time, can determine that user has child.
The social networks of user can also be determined by the intimate degree between the user that obtained by the locator data of multiple user.Such as when the intimate degree of multiple user is higher, server can determine that multiple user is that the probability of household or friends is higher.
The income level of user, the level of consumption and capital assets situation can be inferred from the multiple behavioural characteristic of user.Such as, income level and the capital assets situation of user can be inferred based on user to the susceptibility of transportation cost, residential price can be inferred based on the home address of user, thus judge the capital assets situation of user; Also income level and the level of consumption of user can be judged according to the consumption price in the consumption place (as hotel, restaurant etc.) of user search; Income level and the level of consumption of user can also be determined according to the frequency in user search high consumption place (such as golf course etc.).Whether often drive when can also go on a journey according to user in addition and whether comprise parking lot in locator data, address, refuelling station judges whether user has car.
The frequency that the health status of user can stop in the medical space such as hospital and pharmacy according to user in the locator data of user is inferred.Such as when user the frequency of hospital stay be weekly 2 ~ 3 times time, can infer that the probability of user health situation difference is larger.
Below the defining method of several customer attribute information is exemplarily described.It should be noted that, customer attribute information can be not limited to several information described above, the method that the application provides also is not limited to for determining several customer attribute information described above, can also be used for the customer attribute information determining other types, what the statistics of the clothes shop that such as often can stop based on user determined that user likes wears style; The personality (export-oriented or introversive) etc. of the analysis of statistical data user of line frequency can be gone out based on user.
In the present embodiment, server can calculate the probability of each customer attribute information according to the rule preset.Such as can preset a frequency threshold, when the frequency that user rests on a certain geographic position exceedes this frequency threshold, be that the probability in this geographic position is set as 80% by subscriber household address.Such as can preset the list of top-grade consumption place again, can be that the probability of booming income level is set to and is greater than 50% by user when comprising the place in default high consumption place list in the locator data of user.
In some optional implementations, server based on the behavioural characteristic of user, can adopt the model determination customer attribute information of having trained.The user behavior feature that the position searching data of the user by known users attribute and locator data obtain can be carried out train classification models as training set by server.This disaggregated model can comprise multiple submodel, and each submodel is used for classifying to a kind of attribute information of user.This disaggregated model also can be a unified model, for classifying to the multiple attribute information of user.Alternatively, the user behavior feature that can also obtain based on position searching data and the locator data by the user different from training set sets up test set, is optimized disaggregated model.When applying, can utilize trained and the model optimized to the behavioural characteristic analysis of user of acquisition in step 103, draw the attribute information of user.
The method for obtaining customer attribute information that above-described embodiment provides, by carrying out pre-service to the log information of the map log information obtained, location log information and search engine, to obtain the related data of user, afterwards based on the behavioural characteristic of the related data acquisition user of user, finally based on the behavioural characteristic determination customer attribute information of user, take full advantage of the information analysis customer attribute informations such as the location of user, map search, improve the comprehensive of obtained customer attribute information and accuracy.
The method for obtaining customer attribute information that the above embodiments of the present application provide, may be used for building user's portrait.Further, can based on user's portrait to user's content recommendation, such as map server can be drawn a portrait according to user and be analyzed user's preferred diet, recommends the personalized cuisines more meeting user's taste when user search cuisines to user.Can also based on user's portrait for addressing provide foundation, such as addressing server can determine the feature such as distribution, preference of target group based on user's portrait, is optimized addressing result.
With further reference to Fig. 3, it illustrates the structural representation of an embodiment of the device for obtaining customer attribute information that the embodiment of the present application provides.As shown in Figure 3, the device 300 for obtaining customer attribute information can comprise the first acquiring unit 301, pretreatment unit 302, second acquisition unit 303 and determining unit 304.Wherein, the first acquiring unit 301 may be used for the log information obtaining map log information, location log information and search engine.Pretreatment unit 302 may be used for carrying out pre-service, to obtain the related data of user to the log information of map log information, location log information and search engine.Second acquisition unit 303 may be used for the behavioural characteristic obtaining user based on the related data of user.Determining unit 304 may be used for the behavioural characteristic determination customer attribute information based on user.
In the present embodiment, the first acquiring unit 301 can obtain the log information of the map log information of same user, location log information and search engine from the server of terminal or map server, location-server and search engine.The data analysis comprised in the log information that pretreatment unit 302 can obtain the first acquiring unit 301, extracts the related data of wherein relevant to user behavior or geographic position data as user.Pretreatment unit 302 can also go out the related data of the information relevant to the data that map log information, the log information of locating log information and search engine comprise as described user by NetFind.
In some implementations, the related data of user can at least comprise position searching data and/or locator data.Wherein, position searching data can comprise following at least one item: the line information of target location search data, route search data and correspondence; And the perimeter data of target location.
Further, target location search data can comprise following at least one item: the destination of search, the moment of search, user's current geographic position.Route search data can comprise following at least one item: the trip mode of the moment of user search route, initial geographic position, target geographic position, track data and correspondence.The perimeter data of target location can comprise following at least one item: the buildings data of target location periphery, count certificate, parking data.
Further, second acquisition unit 303 may be used for the behavioural characteristic obtaining user by following at least one mode: the locator data drawn based on pretreatment unit 302 carries out statistics and analysis to the distribution in the geographic position that user stops, to determine that user fixes movable place; Based on the interest point information of the position searching data acquisition user that pretreatment unit 302 draws; The position searching data drawn based on pretreatment unit 302 carries out statistics and analysis to the trip mode of user, to determine the trip mode of user preference; Based on the degree of correlation between the locator data calculating user that pretreatment unit 302 draws, to determine the intimate degree of multiple user.
The behavioural characteristic of the user that determining unit 304 can obtain based on second acquisition unit 303, adopts the model determination customer attribute information of having trained.Wherein, customer attribute information can comprise following at least one item: the age bracket of user, sex, occupation, interest, income level, consumption habit, health status, social relationships and capital assets situation.
Should be appreciated that in the device 300 for obtaining customer attribute information that each step in the method that all elements reference Fig. 1-2 recorded describe is corresponding.Thus, above for the unit that operation and the feature of method description are equally applicable to the device 300 for obtaining customer attribute information and wherein comprise, do not repeat them here.
With further reference to Fig. 4, it illustrates the exemplary system architecture schematic diagram can applying the embodiment of the present application.As shown in Figure 4, system 400 can comprise terminal 401,402, map server 403, location-server 404, search engine server 405 and for realizing the server 407 for obtaining customer attribute information that the application provides.Server 407 can comprise in above-described embodiment for obtaining the device 300 of customer attribute information.Network 406 can also be comprised in the system architecture is described.Network 406 in order to terminal 401,402, server 403,404, the medium of communication link is provided between 405 and 407.Network 406 can comprise various connection type, such as wired, wireless communication link or fiber optic cables etc.
Terminal 401,402 can be mutual by network 406 and server 403,404,405,407, to receive or to send message etc.Terminal 401,402 has positioning function, map application and browser can be installed, map log information can be sent to map server 403 by network 406, location log information is sent to location-server 404 by network, the log information of search engine is sent to the server 405 of search engine by network 406.Server 407 can obtain log information by network 406 from terminal 401,402 and server 403,404,405, carries out pre-service, extract user behavior feature, determine customer attribute information log information.In actual applications, the customer attribute information determined can also be sent to the server 405 of map server 403 and search engine by server 407 by network 406.Map server 403 can use during map search objective user and recommend relevant information based on customer attribute information to user; The server 405 of search engine can be resequenced to webpage based on customer attribute information, searches to make user the information satisfied the demands quickly.
Terminal 401,402 can be various electronic equipment, includes but not limited to PC, smart mobile phone, intelligent watch, panel computer, personal digital assistant etc.The process such as server 407 can store the data received, analysis, and result is fed back to terminal and server 403,404 and 405.
Should be appreciated that the number of the terminal in Fig. 4, network and server is only schematic.According to realizing needs, the terminal of arbitrary number, network and server can be had.
With further reference to Fig. 5, it illustrates the structural representation of the computer system being the server be suitable for for realizing the embodiment of the present application.As shown in Figure 5, computer system 500 comprises CPU (central processing unit) (CPU) 501, and it or can be loaded into the program random access storage device (RAM) 503 from storage area 508 and perform various suitable action and process according to the program be stored in ROM (read-only memory) (ROM) 502.In RAM 503, also store system 500 and operate required various program and data.CPU 501, ROM 502 and RAM 503 are connected with each other by bus 504.I/O (I/O) interface 505 is also connected to bus 504.
I/O interface 505 is connected to: importation 506 with lower component; Output 507; Comprise the storage area 508 of hard disk etc.; And comprise the communications portion 509 of network interface unit of such as LAN card, modulator-demodular unit etc.Communications portion 509 is via the network executive communication process of such as the Internet.Driver 510 is also connected to I/O interface 505 as required.Detachable media 511, such as disk, CD, magneto-optic disk, semiconductor memory etc., be arranged on driver 510 as required, so that the computer program read from it is mounted into storage area 508 as required.
Process flow diagram in accompanying drawing and block diagram, illustrate according to the architectural framework in the cards of the system of various embodiments of the invention, method and computer program product, function and operation.In this, each square frame in process flow diagram or block diagram can represent a part for module, program segment or a code, and a part for described module, program segment or code comprises one or more executable instruction for realizing the logic function specified.Also it should be noted that at some as in the realization of replacing, the function marked in square frame also can be different from occurring in sequence of marking in accompanying drawing.Such as, in fact the square frame that two adjoining lands represent can perform substantially concurrently, and they also can perform by contrary order sometimes, and this determines according to involved function.Also it should be noted that, the combination of the square frame in each square frame in block diagram and/or process flow diagram and block diagram and/or process flow diagram, can realize by the special hardware based system of the function put rules into practice or operation, or can realize with the combination of specialized hardware and computer instruction.
Be described in unit involved in the embodiment of the present application to be realized by the mode of software, also can be realized by the mode of hardware.Described unit also can be arranged within a processor, such as, can be described as: a kind of processor comprises the first acquiring unit, pretreatment unit, second acquisition unit and determining unit.Wherein, the title of these unit does not form the restriction to this unit itself under certain conditions, and such as, pretreatment unit can also be described to " for pretreated unit ".
As another aspect, present invention also provides a kind of computer-readable recording medium, this computer-readable recording medium can be the computer-readable recording medium comprised in device described in above-described embodiment; Also can be individualism, be unkitted the computer-readable recording medium allocated in terminal.Described computer-readable recording medium stores more than one or one program, and described program is used for performance description in the method for obtaining customer attribute information of the application by one or more than one processor.
More than describe and be only the preferred embodiment of the application and the explanation to institute's application technology principle.Those skilled in the art are to be understood that, invention scope involved in the application, be not limited to the technical scheme of the particular combination of above-mentioned technical characteristic, also should be encompassed in when not departing from described inventive concept, other technical scheme of being carried out combination in any by above-mentioned technical characteristic or its equivalent feature and being formed simultaneously.The technical characteristic that such as, disclosed in above-mentioned feature and the application (but being not limited to) has similar functions is replaced mutually and the technical scheme formed.
Claims (17)
1. for obtaining a method for customer attribute information, it is characterized in that, described method comprises:
Obtain the log information of map log information, location log information and search engine;
Pre-service is carried out, to obtain the related data of user to the log information of described map log information, location log information and search engine;
Related data based on described user obtains the behavioural characteristic of user; And
Based on the behavioural characteristic determination customer attribute information of described user.
2. method according to claim 1, is characterized in that, the described log information to described map log information, location log information and search engine carries out pre-service, comprising:
To the data analysis that the log information of described map log information, location log information and search engine comprises;
Extract described map log information, location log information and data relevant to geographic position and user behavior in the log information of search engine, as the related data of described user.
3. method according to claim 2, is characterized in that, the described log information to described map log information, location log information and search engine carries out pre-service, also comprises:
The related data of the information relevant to the data that described map log information, the log information of locating log information and search engine comprise as described user is gone out by NetFind.
4. method according to claim 1, is characterized in that, the related data of described user at least comprises position searching data and/or locator data;
Wherein, described position searching data comprises following at least one item: target location search data, route search data and the line information of correspondence and the perimeter data of target location.
5. method according to claim 4, is characterized in that,
Described target location search data comprises following at least one item: the destination of search, the moment of search, user's current geographic position;
Described route search data comprise following at least one item: the trip mode of the moment of user search route, initial geographic position, target geographic position, track data and correspondence;
The perimeter data of described target location comprises following at least one item: the buildings data of target location periphery, count certificate, parking data.
6. method according to claim 4, is characterized in that, the described related data based on described user obtains the behavioural characteristic of user, comprises following at least one item:
Based on described locator data, statistics and analysis is carried out to the distribution in the geographic position that user stops, to determine that user fixes movable place;
The interest point information of user is obtained based on described position searching data;
Based on described position searching data, statistics and analysis is carried out to the trip mode of user, to determine the trip mode of user preference;
The degree of correlation between user is calculated, to determine the intimate degree of multiple user based on described locator data.
7. method according to claim 1, is characterized in that, the described behavioural characteristic determination customer attribute information based on described user, comprising:
Based on the behavioural characteristic of described user, the model of having trained is adopted to determine described customer attribute information.
8. according to the arbitrary described method of claim 1-7, it is characterized in that, described customer attribute information comprises following at least one item: the age bracket of user, sex, occupation, interest, income level, consumption habit, health status, social relationships and capital assets situation.
9. for obtaining a device for customer attribute information, it is characterized in that, described device comprises:
First acquiring unit, for obtaining the log information of map log information, location log information and search engine;
Pretreatment unit, for carrying out pre-service, to obtain the related data of user to the log information of described map log information, location log information and search engine;
Second acquisition unit, for obtaining the behavioural characteristic of user based on the related data of described user; And
Determining unit, for the behavioural characteristic determination customer attribute information based on described user.
10. device according to claim 9, is characterized in that, described pretreatment unit is used for carrying out pre-service to the log information of described map log information, location log information and search engine as follows:
To the data analysis that the log information of described map log information, location log information and search engine comprises;
Extract described map log information, location log information and data relevant to geographic position and user behavior in the log information of search engine, as the related data of described user.
11. devices according to claim 10, is characterized in that, described pretreatment unit is also for carrying out pre-service to the log information of described map log information, location log information and search engine as follows:
The related data of the information relevant to the data that described map log information, the log information of locating log information and search engine comprise as described user is gone out by NetFind.
12. devices according to claim 9, is characterized in that, the related data of described user at least comprises position searching data and/or locator data;
Wherein, described position searching data comprises following at least one item: the line information of target location search data, route search data and correspondence; And the perimeter data of target location.
13. devices according to claim 12, is characterized in that,
Described target location search data comprises following at least one item: the destination of search, the moment of search, user's current geographic position;
Described route search data comprise following at least one item: the trip mode of the moment of user search route, initial geographic position, target geographic position, track data and correspondence;
The perimeter data of described target location comprises following at least one item: the buildings data of target location periphery, count certificate, parking data.
14. devices according to claim 12, is characterized in that, described second acquisition unit is used for the behavioural characteristic obtaining user by following at least one mode:
Based on described locator data, statistics and analysis is carried out to the distribution in the geographic position that user stops, to determine that user fixes movable place;
The interest point information of user is obtained based on described position searching data;
Based on described position searching data, statistics and analysis is carried out to the trip mode of user, to determine the trip mode of user preference;
The degree of correlation between user is calculated, to determine the intimate degree of multiple user based on described locator data.
15. devices according to claim 9, is characterized in that, described determining unit, based on the behavioural characteristic of described user, adopts the model of having trained to determine described customer attribute information.
16. according to the arbitrary described device of claim 9-15, it is characterized in that, described customer attribute information comprises following at least one item: the age bracket of user, sex, occupation, interest, income level, consumption habit, health status, social relationships and capital assets situation.
17. 1 kinds of servers, is characterized in that, comprise as arbitrary in claim 9-16 as described in device.
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| CN201510363062.3A CN104933157A (en) | 2015-06-26 | 2015-06-26 | Method and device used for obtaining user attribute information, and server |
| PCT/CN2015/089823 WO2016206196A1 (en) | 2015-06-26 | 2015-09-17 | Method and device for obtaining user attribute information, and server |
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Cited By (39)
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| CN105160016A (en) * | 2015-09-25 | 2015-12-16 | 百度在线网络技术（北京）有限公司 | Method and device for acquiring user attributes |
| CN105488149A (en) * | 2015-11-26 | 2016-04-13 | 上海晶赞科技发展有限公司 | Data processing method and device |
| CN105630951A (en) * | 2015-12-23 | 2016-06-01 | 北京奇虎科技有限公司 | Method and device for judging occupation distribution of clustered users |
| CN105678457A (en) * | 2016-01-06 | 2016-06-15 | 成都小步创想畅联科技有限公司 | Method for evaluating user behavior on the basis of position mining |
| CN106096653A (en) * | 2016-06-12 | 2016-11-09 | 中国科学院自动化研究所 | Ascribed characteristics of population estimating method based on cross-platform user social contact multimedia behavior |
| CN106503123A (en) * | 2016-10-20 | 2017-03-15 | 宁波江东大金佰汇信息技术有限公司 | A kind of deep learning intelligent response system based on computer cloud data |
| CN106611017A (en) * | 2015-10-27 | 2017-05-03 | 滴滴（中国）科技有限公司 | User identity identification method and device |
| CN106649524A (en) * | 2016-10-20 | 2017-05-10 | 宁波江东大金佰汇信息技术有限公司 | Improved advanced study intelligent response system based on computer cloud data |
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| CN106919579A (en) * | 2015-12-24 | 2017-07-04 | 腾讯科技（深圳）有限公司 | A kind of information processing method and device, equipment |
| CN107247593A (en) * | 2017-06-09 | 2017-10-13 | 泰康保险集团股份有限公司 | User interface switching method, device, electronic equipment and storage medium |
| CN107274217A (en) * | 2017-05-27 | 2017-10-20 | 冯小平 | Determine user's current behavior and the method and apparatus for predicting user view |
| CN107316098A (en) * | 2017-05-19 | 2017-11-03 | 芜湖恒天易开软件科技股份有限公司 | A kind of automobile leasing point site selecting method based on user behavior analysis |
| CN107316204A (en) * | 2017-05-27 | 2017-11-03 | 银联智惠信息服务（上海）有限公司 | Recognize humanized method, device, computer-readable medium and the system of holding |
| CN107391603A (en) * | 2017-06-30 | 2017-11-24 | 北京奇虎科技有限公司 | User's portrait method for building up and device for mobile terminal |
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| JP2018535475A (en) * | 2015-09-30 | 2018-11-29 | バイドゥ オンライン ネットワーク テクノロジー （ベイジン） カンパニー リミテッド | User relationship identification method, apparatus, storage medium, and server |
| CN108984555A (en) * | 2017-06-01 | 2018-12-11 | 腾讯科技（深圳）有限公司 | User Status is excavated and information recommendation method, device and equipment |
| CN109063059A (en) * | 2018-07-20 | 2018-12-21 | 腾讯科技（深圳）有限公司 | User behaviors log processing method, device and electronic equipment |
| CN109086384A (en) * | 2018-07-26 | 2018-12-25 | 珠海卓邦科技有限公司 | Water affairs management method and system based on user's portrait |
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| CN110347936A (en) * | 2019-06-21 | 2019-10-18 | 上海淇馥信息技术有限公司 | Data digging method, device, system and recording medium based on LBS information |
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| CN110737690A (en) * | 2018-07-03 | 2020-01-31 | 百度在线网络技术（北京）有限公司 | User label mining method and device, computer equipment and storage medium |
| CN110895587A (en) * | 2018-08-23 | 2020-03-20 | 百度在线网络技术（北京）有限公司 | Method and device for determining target user |
| CN111104609A (en) * | 2018-10-26 | 2020-05-05 | 百度在线网络技术（北京）有限公司 | Interpersonal relationship prediction method, interpersonal relationship prediction device, computer program, and storage medium |
| CN111127064A (en) * | 2018-11-01 | 2020-05-08 | 百度在线网络技术（北京）有限公司 | Method and device for determining social attributes of users and electronic equipment |
| CN111127065A (en) * | 2018-11-01 | 2020-05-08 | 百度在线网络技术（北京）有限公司 | Method and device for acquiring user occupation place |
| CN111310882A (en) * | 2018-12-11 | 2020-06-19 | 北京京东尚科信息技术有限公司 | Method and apparatus for generating information |
Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110300084B (en) * | 2018-03-22 | 2023-09-01 | 北京京东尚科信息技术有限公司 | IP address-based portrait method and apparatus, electronic device, and readable medium |
| CN110781374A (en) * | 2018-07-13 | 2020-02-11 | 北京字节跳动网络技术有限公司 | User data matching method and device, electronic equipment and computer readable medium |
| CN110019916A (en) * | 2018-08-17 | 2019-07-16 | 平安普惠企业管理有限公司 | Event-handling method, device, equipment and storage medium based on user's portrait |
| CN111178925B (en) * | 2018-11-09 | 2023-07-25 | 百度在线网络技术（北京）有限公司 | Method, apparatus, server and computer readable medium for predicting attribute of user portrait |
| CN112330351B (en) * | 2020-02-28 | 2023-09-26 | 北京京东振世信息技术有限公司 | Method for selecting address, address selecting system and electronic equipment |
| CN111400567B (en) * | 2020-03-11 | 2023-06-27 | 北京古杉数据科技有限公司 | AI-based user data processing method, device and system |
| CN111782611B (en) * | 2020-06-30 | 2024-01-23 | 北京百度网讯科技有限公司 | Prediction model modeling method, device, equipment and storage medium |
| CN112184388B (en) * | 2020-10-12 | 2024-02-13 | 上海燕汐软件信息科技有限公司 | Home decoration service list distribution method, device and storage medium |
| CN113836195B (en) * | 2021-09-02 | 2023-09-01 | 国家电网有限公司客户服务中心 | Knowledge recommendation method and system based on user portrait |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040204821A1 (en) * | 2002-07-18 | 2004-10-14 | Tu Ihung S. | Navigation method and system for extracting, sorting and displaying POI information |
| CN104239526A (en) * | 2014-09-18 | 2014-12-24 | 百度在线网络技术（北京）有限公司 | POI (Point of Interest) labeling method and device for electronic map |
| CN104391853A (en) * | 2014-09-25 | 2015-03-04 | 深圳大学 | POI (point of interest) recommending method, POI information processing method and server |
| CN104537027A (en) * | 2014-12-19 | 2015-04-22 | 百度在线网络技术（北京）有限公司 | Information recommendation method and device |
| CN104732062A (en) * | 2015-01-30 | 2015-06-24 | 上海语镜汽车信息技术有限公司 | On-road user socialization attribute automatic judgment method based on characteristic event, movement behavior, traveling track and geographic position |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20100058833A (en) * | 2008-11-25 | 2010-06-04 | 삼성전자주식회사 | Interest mining based on user's behavior sensible by mobile device |
-
2015
- 2015-06-26 CN CN201510363062.3A patent/CN104933157A/en active Pending
- 2015-09-17 WO PCT/CN2015/089823 patent/WO2016206196A1/en active Application Filing
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040204821A1 (en) * | 2002-07-18 | 2004-10-14 | Tu Ihung S. | Navigation method and system for extracting, sorting and displaying POI information |
| CN104239526A (en) * | 2014-09-18 | 2014-12-24 | 百度在线网络技术（北京）有限公司 | POI (Point of Interest) labeling method and device for electronic map |
| CN104391853A (en) * | 2014-09-25 | 2015-03-04 | 深圳大学 | POI (point of interest) recommending method, POI information processing method and server |
| CN104537027A (en) * | 2014-12-19 | 2015-04-22 | 百度在线网络技术（北京）有限公司 | Information recommendation method and device |
| CN104732062A (en) * | 2015-01-30 | 2015-06-24 | 上海语镜汽车信息技术有限公司 | On-road user socialization attribute automatic judgment method based on characteristic event, movement behavior, traveling track and geographic position |
Non-Patent Citations (2)
| Title |
|---|
| 刘启华著: "基于情境计算的用户需求模型", 《泛在商务环境下的信息聚合与推荐》 * |
| 高红波: "《新世纪普通高校广播电视艺术学系列教材 新媒体节目形态》", 31 August 2013 * |
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