CN106469205B - Method and device for determining geographical location information of user - Google Patents
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Abstract
The invention aims to provide a method and a device for determining geographical position information of a user. The network equipment acquires historical address information of a current user and determines geographical position information of the current user according to the historical address information. Compared with the prior art, the method and the device can comprehensively consider a plurality of items of historical address information of the user to predict the current geographic position information for the user.
Description
Technical Field
The present invention relates to the field of information processing technologies, and in particular, to a technology for determining geographical location information of a user.
Background
At present, the user is mostly positioned through a GPS or an IP address, but the GPS information of the user is not always available, and with the arrival of the mobile era, the problems of gateway IP, user roaming, browser proxy IP and the like exist in the wireless, so that the wireless IP region identification is not accurate.
Disclosure of Invention
The invention aims to provide a method and a device for determining geographical position information of a user.
According to an aspect of the present invention, there is provided a method of determining geographical location information of a user, wherein the method comprises the steps of:
-obtaining historical address information of a current user, wherein the historical address information comprises at least any one of:
-historical GPS information;
-historical WiFi IP information;
-address information associated with historical search behavior;
-determining geographical location information of the current user based on the historical address information.
According to another aspect of the present invention, there is also provided a method of determining geographical location information of a user, wherein the method comprises the steps of:
performing model training according to the pre-labeled historical address information of a plurality of users and the geographic position information corresponding to each user to obtain a trained geographic identification model,
wherein the historical address information includes at least any one of:
-historical GPS information;
-historical WiFi IP information;
-address information associated with historical search behavior;
wherein, the method also comprises:
-determining geographical location information of a current user by means of said geographical identification model based on historical address information of said current user.
According to still another aspect of the present invention, there is also provided an apparatus for determining geographical location information of a user, wherein the apparatus includes:
-means for obtaining historical address information of a current user, wherein the historical address information comprises at least any one of:
-historical GPS information;
-historical WiFi IP information;
-address information associated with historical search behavior;
-means for determining geographical location information of said current user based on said historical address information.
Compared with the prior art, the method and the device can comprehensively consider a plurality of items of historical address information of the user to predict the current geographic position information for the user. For example, the information such as gps, wifi ip and search behavior of the user in a recent historical period can accurately predict the current geographic position information of the user through the trained geographic recognition model, so that the geographic recognition accuracy of the user is improved. Taking the search words of the user as an example, if a user frequently searches for words of "back dragon viewing a good restaurant" and "shopping place in the middle guan village", the user may be in Beijing, and for example, a user may be GPS positioned in the back dragon every day for the last 7 days, which indicates that the user is likely to be in Beijing.
When the current position of the user cannot be accurately known, the method and the device can predict the current geographic position information of the user for the current user, and can be widely applied to various scenes needing the user positioning information, such as more accurate information pushing and search result providing for the current user. This is clearly beneficial to enhance the user experience. Moreover, when the current geographic position is predicted for the user through the scheme of the invention, the popularization information can be pushed to the user at the relevant position in a more targeted manner according to the predicted geographic position, so that the precision of the popularization information is effectively improved, and the click rate of the popularization information is further influenced.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments made with reference to the following drawings:
FIG. 1 illustrates a flow diagram of a method for determining geographic location information of a user, according to one embodiment of the invention;
fig. 2 shows a schematic diagram of an apparatus for determining geographical location information of a user according to one embodiment of the invention.
The same or similar reference numbers in the drawings identify the same or similar elements.
Detailed Description
Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the operations as a sequential process, many of the operations can be performed in parallel, concurrently, or simultaneously. In addition, the order of the operations may be re-arranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, functions, procedures, subroutines, and the like.
The term "computer device" or "computer" in this context refers to an intelligent electronic device that can execute predetermined processes such as numerical calculation and/or logic calculation by running predetermined programs or instructions, and may include a processor and a memory, wherein the predetermined processes are executed by the processor by executing program instructions prestored in the memory, or the predetermined processes are executed by hardware such as ASIC, FPGA, DSP, or a combination thereof. Computer devices include, but are not limited to, servers, Personal Computers (PCs), laptops, tablets, smart phones, and the like.
The computer devices include, for example, user devices and network devices. Wherein the user equipment includes but is not limited to Personal Computers (PCs), notebook computers, mobile terminals, etc., and the mobile terminals include but is not limited to smart phones, PDAs, etc.; the network device includes, but is not limited to, a single network server, a server group consisting of a plurality of network servers, or a Cloud Computing (Cloud Computing) based Cloud consisting of a large number of computers or network servers, wherein Cloud Computing is one of distributed Computing, a super virtual computer consisting of a collection of loosely coupled computers. Wherein the computer device can be operated alone to implement the invention, or can be accessed to a network and implement the invention through interoperation with other computer devices in the network. The network in which the computer device is located includes, but is not limited to, the internet, a wide area network, a metropolitan area network, a local area network, a VPN network, and the like.
It should be noted that the user equipment, the network device, the network, etc. are only examples, and other existing or future computer devices or networks may also be included in the scope of the present invention, and are included by reference.
The methodologies discussed hereinafter, some of which are illustrated by flow diagrams, may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks may be stored in a machine or computer readable medium such as a storage medium. The processor(s) may perform the necessary tasks.
Specific structural and functional details disclosed herein are merely representative and are provided for purposes of describing example embodiments of the present invention. The present invention may, however, be embodied in many alternate forms and should not be construed as limited to only the embodiments set forth herein.
It will be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first element may be termed a second element, and, similarly, a second element may be termed a first element, without departing from the scope of example embodiments. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may be present. In contrast, when an element is referred to as being "directly connected" or "directly coupled" to another element, there are no intervening elements present. Other words used to describe the relationship between elements (e.g., "between" versus "directly between", "adjacent" versus "directly adjacent to", etc.) should be interpreted in a similar manner.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used herein, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be noted that, in some alternative implementations, the functions/acts noted may occur out of the order noted in the figures. For example, two figures shown in succession may, in fact, be executed substantially concurrently, or the figures may sometimes be executed in the reverse order, depending upon the functionality/acts involved.
The present invention may be implemented by a computer device. Typically, the present invention may be implemented by a network device, but it will be understood by those skilled in the art that the solution of the present invention may equally be implemented by a user device, provided that it has the computing/processing capabilities required by the present invention. For convenience of description, the following multi-purpose network device implementation is exemplified in the present specification, but those skilled in the art should understand that these examples are only for the purpose of illustrating the present invention, and should not be construed as any limitation to the present invention.
The present invention is described in further detail below with reference to the attached drawing figures.
Fig. 1 illustrates an embodiment according to the present invention, in which a method of determining geographical location information of a user is specifically illustrated. As shown in fig. 1, in step S1, the network device acquires the historical address information of the current user; in step S2, the network device determines the geographical location information of the current user according to the historical address information.
Specifically, in step S1, the network device acquires the historical address information of the current user.
Here, the historical address information of the user may be various address information of the user in a recent period of time, for example, address information within the last 30 days, and the selection of a specific period of time may depend on the needs of the application.
Wherein the historical address information includes at least any one of:
1) historical GPS information.
For example, the network device may obtain GPS information for the current user within the last 30 days.
The user's GPS information may be obtained through various existing manners of obtaining GPS information, for example, obtaining historical GPS information of the user from a GPS module of the user equipment; the present invention is not particularly limited thereto.
2) Historical WiFi IP information;
for example, the network device may obtain WiFi IP information accessed by the current user within the last 30 days, that is, IP address information of WiFi devices accessed by the current user within the last 30 days. Specifically, each user reports WiFi IP information accessed within the last 30 days to the network device, and accordingly, the network device can obtain historical WiFi IP information of the current user.
3) And historical search behavior associated address information.
Here, the historical search behavior of the user includes, but is not limited to, search keywords historically used by the user and search results historically viewed by the user.
For example, the network device may directly use an address keyword included in search keywords used by the user history as associated address information of the user. Or, the network device determines the associated address information corresponding to the address keyword according to the address keyword, and if the address keyword is "xizhu", the network device may determine that the associated address information corresponding to the address keyword is "beijing".
For another example, the network device may directly use the address keyword included in the search result browsed by the user during the historical search as the associated address information of the user. Specifically, the network device extracts address keywords, such as address keywords whose occurrence frequency reaches a certain frequency, from pages pointed by search results of user history browsing, and uses the address keywords as associated address information of the user.
Preferably, the network device obtains the corresponding associated address keywords according to the historical search behavior of the current user, and further determines the corresponding associated address information according to the obtained associated address keywords.
For example, the network device obtains the corresponding associated address keyword from the search keyword used by the current user history, and if the current user history searches for "good-going restaurants", the network device can obtain the associated address keyword "good-going restaurants", and further determine corresponding associated address information, such as "beijing", according to the associated address keyword.
Here, the associated address keyword and the associated address information may have a certain region dependency relationship. For example, when the associated address keyword is a specific address, such as "north road of prefecture and creek", the associated address information may be a business circle such as "xu jia hui", a district or county such as "xu hui district", or a province or city such as "shanghai city" according to the local affiliation. The choice of the aforementioned zone affiliation may depend on the needs of the particular application and may also depend on the accuracy of the currently acquired data.
Further, the identification of the current user may be performed in different ways, divided into a logged-in user and a non-logged-in user.
For example, for a login user, the network device may obtain, according to a login account of a current user, address information corresponding to the account at each login in a recent period of time.
For the user who does not log in, the network device may identify the current user according to the cookie information of the current user, so as to obtain the historical address information of the current user.
For example, when a user accesses a network device, the network device may assign cookie information to the user, where the cookie information includes temporary ID information assigned by the network device to the user, and according to the temporary ID information, the network device may identify the current user, and further obtain historical address information of the current user, such as address information of each location where the current user has accessed the network device in the recent period of time.
Subsequently, in step S2, the network device determines the geographical location information of the current user according to the historical address information of the current user.
Here, the manner of determining the geographical location information of the current user by the network device at least includes the following two ways:
1) and determining the geographical position information of the current user according to the priority of the acquired historical address information.
For example, the priority may be set as follows: historical GPS information > historical WiFi IP information > historical associated address information.
Generally, the GPS information is the most realistic reflection of the geographic location of the user, but since the network device cannot always acquire the GPS information of the user, when there is other address information of the user, the network device may also determine the geographic location of the user from the other address information, for example, the IP address information of the WiFi device accessed by the user device, but since the WiFi device may access the network through a proxy or the like, the WiFi IP information may not truly reflect the geographic location of the user. The search behavior of the user, especially the search behavior associated with the address information, often implicitly expresses the geographic location of the user, so that the network device can also predict the current geographic location information of the user from the historical search behavior of the user.
2) And determining the geographical position information of the current user through a trained geographical identification model according to the historical address information.
The training process of the geographic identification model may be, for example, the network device performs model training according to the pre-labeled historical address information of a plurality of users and the geographic location information corresponding to each user, so as to obtain a trained geographic identification model.
For example, for a plurality of users whose geographical location information is known, the network device further obtains at least one item of historical address information of each user, historical GPS information and historical WiFi IP information of user 1, historical WiFi IP information and historical associated address information of user 2, and the like; the network device inputs the training data into the geographic recognition model to train the geographic recognition model, for example, the historical address information of each user is used as labeled input data, the geographic position information corresponding to each user is used as labeled output data, and the geographic recognition model is input one by one so that the geographic recognition model can automatically learn the recognition mode, thereby obtaining the trained geographic recognition model.
Wherein the geographic identification model includes, but is not limited to, 1) a maximum entropy model; 2) DNN (deep neural network) model. The geographic identification model adopted by the invention can automatically learn the weight of each historical address information, the mutual influence of the historical address information and the influence on the finally output geographic position information through a machine learning process, thereby being used for predicting the geographic position information of the current user.
Fig. 2 shows an embodiment according to the invention, in which a device for determining geographical location information of a user is specifically shown. As shown in fig. 2, the apparatus 200 is installed on the network device side, and specifically includes a historical address obtaining apparatus 201 and a geographic location determining apparatus 202.
Wherein, the historical address obtaining device 201 obtains the historical address information of the current user; subsequently, the geographic position determining device 202 determines the geographic position information of the current user according to the historical address information.
Specifically, the history address acquisition means 201 acquires history address information of the current user.
Here, the historical address information of the user may be various address information of the user in a recent period of time, for example, address information within the last 30 days, and the selection of a specific period of time may depend on the needs of the application.
Wherein the historical address information includes at least any one of:
1) historical GPS information.
For example, the historical address acquisition means 201 may acquire GPS information within the last 30 days of the current user.
The GPS information of the user can be obtained by various existing manners for obtaining GPS information, for example, the historical address obtaining device 201 obtains the historical GPS information of the user from a GPS module of the user equipment; the present invention is not particularly limited thereto.
2) Historical WiFi IP information;
for example, the historical address obtaining device 201 may obtain WiFi IP information accessed by the current user within the last 30 days, that is, IP address information of WiFi devices accessed by the current user within the last 30 days. Specifically, each user reports WiFi IP information accessed within the last 30 days to the network device, and accordingly, the historical address obtaining device 201 can obtain historical WiFi IP information of the current user.
3) And historical search behavior associated address information.
Here, the historical search behavior of the user includes, but is not limited to, search keywords historically used by the user and search results historically viewed by the user.
For example, the history address acquisition means 201 may directly use, as the address information associated with the user, an address keyword included in search keywords used by the user in history. Alternatively, the historical address obtaining device 201 may determine the associated address information corresponding to the address keyword according to the address keyword, and if the address keyword is "xizhu", the associated address information corresponding to the address keyword may be determined to be "beijing".
For another example, the history address acquisition device 201 may directly use the address keyword included in the search result browsed by the user in the history search as the associated address information of the user. Specifically, the historical address obtaining device 201 extracts address keywords, such as address keywords whose occurrence frequency reaches a certain frequency, from pages pointed to by search results of user historical browsing, and uses the address keywords as associated address information of the user.
Preferably, the historical address obtaining device 201 obtains the corresponding associated address keywords according to the historical search behavior of the current user, and further determines the corresponding associated address information according to the obtained associated address keywords.
For example, the historical address obtaining device 201 obtains the corresponding associated address keyword from the search keyword used by the current user history, and if the current user history searches for "good restaurant in dragon view", the historical address obtaining device may obtain the associated address keyword "dragon view", and further determine the corresponding associated address information, such as "beijing", according to the associated address keyword.
Here, the associated address keyword and the associated address information may have a certain region dependency relationship. For example, when the associated address keyword is a specific address, such as "north road of prefecture and creek", the associated address information may be a business circle such as "xu jia hui", a district or county such as "xu hui district", or a province or city such as "shanghai city" according to the local affiliation. The choice of the aforementioned zone affiliation may depend on the needs of the particular application and may also depend on the accuracy of the currently acquired data.
Further, the identification of the current user may be performed in different ways, divided into a logged-in user and a non-logged-in user.
For example, for a login user, the historical address obtaining device 201 may obtain, according to a login account of a current user, address information corresponding to the account at each login in a recent period of time.
For the user who does not log in, the historical address obtaining device 201 may identify the current user according to the cookie information of the current user to obtain the historical address information of the current user.
For example, when the user accesses the network device, the network device may assign cookie information to the user, where the cookie information includes temporary ID information assigned by the network device to the user, and according to the temporary ID information, the historical address obtaining apparatus 201 may identify the current user, and further obtain historical address information of the current user, such as address information where the current user has accessed the network device in the recent period of time.
Subsequently, the geographic position determination device 202 determines the geographic position information of the current user according to the historical address information of the current user.
Here, the manner of determining the geographical location information of the current user by the geographical location determining apparatus 202 includes at least the following two ways:
1) and determining the geographical position information of the current user according to the priority of the acquired historical address information.
For example, the priority may be set as follows: historical GPS information > historical WiFi IP information > historical associated address information.
Usually, the GPS information is the most realistic reflection of the geographical location of the user, but since the network device cannot always acquire the GPS information of the user, when there is other address information of the user, the geographical location determining apparatus 202 may also determine the geographical location of the user from the other address information, for example, the IP address information of the WiFi device accessed by the user device, but since the WiFi device may access the network through a proxy or the like, the WiFi IP information may not truly reflect the geographical location of the user. The search behavior of the user, especially the search behavior associated with the address information, often implicitly expresses the geographic location of the user, so that the geographic location determining apparatus 202 can predict the current geographic location information of the user from the historical search behavior of the user.
2) And determining the geographical position information of the current user through a trained geographical identification model according to the historical address information.
The apparatus 200 may further include a model training device 203, and the training process of the model training device 203 on the geographic identification model may be, for example, the model training device 203 performs model training according to the pre-labeled historical address information of a plurality of users and the geographic location information corresponding to each of the users, so as to obtain a trained geographic identification model.
For example, for a plurality of users whose geographical location information is known, the model training device 203 also obtains at least one item of historical address information of each user, historical GPS information and historical WiFi IP information of the user 1, historical WiFi IP information and historical associated address information of the user 2, and so on; the model training device 203 inputs the training data into the geographic recognition model to train the geographic recognition model, for example, the historical address information of each user is used as labeled input data, the geographic position information corresponding to each user is used as labeled output data, and the geographic recognition model is input one by one so that the geographic recognition model can automatically learn the recognition mode, thereby obtaining the trained geographic recognition model.
Wherein the geographic identification model includes, but is not limited to, 1) a maximum entropy model; 2) DNN (deep neural network) model. The geographic identification model adopted by the invention can automatically learn the weight of each historical address information, the mutual influence of the historical address information and the influence on the finally output geographic position information through a machine learning process, thereby being used for predicting the geographic position information of the current user.
It is noted that the present invention may be implemented in software and/or in a combination of software and hardware, for example, the various means of the invention may be implemented using Application Specific Integrated Circuits (ASICs) or any other similar hardware devices. In one embodiment, the software program of the present invention may be executed by a processor to implement the steps or functions described above. Also, the software programs (including associated data structures) of the present invention can be stored in a computer readable recording medium, such as RAM memory, magnetic or optical drive or diskette and the like. Further, some of the steps or functions of the present invention may be implemented in hardware, for example, as circuitry that cooperates with the processor to perform various steps or functions.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Claims (9)
1. A method of determining geographical location information of a user, wherein the method comprises the steps of:
-obtaining historical address information of a current user, including address information associated with historical search behavior; wherein the historical address information further comprises at least any one of:
-historical GPS information;
-historical WiFi IP information;
-determining geographical location information of the current user by means of a trained geographical recognition model based on the historical address information;
the geographic identification model is obtained through model training according to pre-labeled historical address information of a plurality of users and geographic position information corresponding to each user.
2. The method according to claim 1, wherein the step of acquiring historical address information of the current user specifically includes:
-identifying the current user according to the cookie information of the current user to obtain historical address information of the current user.
3. The method of claim 1, wherein the historical address information comprises address information associated with historical search behavior;
wherein, the method also comprises:
-obtaining corresponding associated address keywords according to the historical search behavior of the current user;
-determining corresponding associated address information based on said associated address key.
4. The method of any of claims 1 to 3, wherein the geographic identification model comprises any of:
-a maximum entropy model;
-DNN model.
5. A method of determining geographical location information of a user, wherein the method comprises the steps of:
performing model training according to the pre-labeled historical address information of a plurality of users and the geographic position information corresponding to each user to obtain a trained geographic identification model,
wherein the historical address information comprises associated address information of historical search behavior, and at least one of the following items:
-historical GPS information;
-historical WiFi IP information;
wherein, the method also comprises:
-determining geographical location information of a current user by means of said geographical identification model based on historical address information of said current user.
6. An apparatus for determining geographical location information of a user, wherein the apparatus comprises:
-means for obtaining historical address information of a current user, wherein the historical address information comprises address information associated with historical search behavior, and at least further comprising any of:
-historical GPS information;
-historical WiFi IP information;
-means for determining geographical location information of the current user from the historical address information by means of a trained geographical recognition model;
the geographic identification model is obtained through model training according to pre-labeled historical address information of a plurality of users and geographic position information corresponding to each user.
7. The apparatus according to claim 6, wherein the operation of acquiring the historical address information of the current user specifically includes:
-identifying the current user according to the cookie information of the current user to obtain historical address information of the current user.
8. The apparatus of claim 6, wherein the historical address information comprises address information associated with historical search behavior;
wherein, the device still includes:
-obtaining corresponding associated address keywords according to the historical search behavior of the current user;
-determining corresponding associated address information based on said associated address key.
9. The apparatus of any of claims 6 to 8, wherein the geographic identification model comprises any of:
-a maximum entropy model;
-DNN model.
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| CN107295073B (en) * | 2017-06-13 | 2018-06-22 | 腾讯科技（深圳）有限公司 | A kind of localization method, positioning device and computer storage media |
| CN109995884B (en) * | 2017-12-29 | 2021-01-26 | 北京京东尚科信息技术有限公司 | Method and apparatus for determining precise geographic location |
| CN111327721B (en) * | 2020-02-28 | 2023-01-10 | 加和（北京）信息科技有限公司 | IP address positioning method and device, storage medium and electronic device |
| CN111859177A (en) * | 2020-04-10 | 2020-10-30 | 北京嘀嘀无限科技发展有限公司 | Positioning method and system |
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