WO2016206196A1

WO2016206196A1 - Method and device for obtaining user attribute information, and server

Info

Publication number: WO2016206196A1
Application number: PCT/CN2015/089823
Authority: WO
Inventors: 吴海山; 汪天一; 武政伟; 李正学; 张潼
Original assignee: 百度在线网络技术（北京）有限公司
Priority date: 2015-06-26
Filing date: 2015-09-17
Publication date: 2016-12-29
Also published as: CN104933157A

Abstract

A method and device for obtaining user attribute information, and server. The method comprises: obtaining map log information, positioning log information and search engine log information (101); pre-processing the map log information, the positioning log information and the search engine log information to obtain user-related data (102); obtaining, on the basis of the user-related data, user behavioral characteristics (103); and determining, on the basis of the user behavioral characteristics, user attribute information (104). Information such as user positioning and map search is fully utilized to analyze the user attribute information, thus improving comprehensiveness and accuracy of the obtained user attribute information.

Description

Method, device and server for obtaining user attribute information

Cross-reference to related applications

This application claims Baidu Online Network Technology (Beijing) Co., Ltd. submitted on June 26, 2015, the invention name is "method for obtaining user, attribute information, device and server", Chinese patent application number "201510363062.3" Priority is hereby incorporated by reference in its entirety.

Technical field

The present invention relates to the field of computer technologies, and in particular, to the field of terminal technologies, and in particular, to a method, an apparatus, and a server for acquiring user attribute information.

Background technique

The user portrait can be a collection of user attribute information, and a model can be used to describe the characteristics of the user. In the prior art, the main method of user portrait construction is to analyze user attribute information based on the user's online search behavior. In this method, since the user's online search behavior may have noise caused by virtual user search behavior such as search information forged by a malicious user, the result of constructing the user portrait is inaccurate. In addition, the image construction based on the user's online search behavior may also have the problem of text semantic divergence. The same search term may point to different user characteristics. For example, the user searches for “庐山”, may be concerned about tourism information, or may like to like Lushan Related movies.

In addition, in the prior art, there is also a user portrait construction method based on actual transaction data of the user, which is constructed based on the online transaction data of the user. The user's online transaction is a low-frequency behavior in the user's behavior, so it is not possible to derive comprehensive, complete and accurate user attribute information.

Summary of the invention

In view of the above-mentioned defects or deficiencies in the prior art, it is desirable to be able to provide a comprehensive and accurate The method of obtaining the user attribute information. The application provides a method, device and server for obtaining user attribute information.

In a first aspect, the present application provides a method for acquiring user attribute information, including: acquiring map log information, locating log information, and search engine log information; and mapping log information, positioning log information, and search engine log information. Performing pre-processing to obtain relevant data of the user; acquiring behavior characteristics of the user based on the relevant data of the user; and determining user attribute information based on the behavior characteristics of the user.

In some implementation manners, preprocessing the map log information, the location log information, and the search engine log information, including: analyzing the log log information, the location log information, and the data included in the search engine log information; extracting the map The data related to the geographical location and user behavior in the log information, the location log information, and the search engine log information are used as related data of the user.

In a further implementation manner, the map log information, the location log information, and the search engine log information are preprocessed, and the method further includes: searching, by the network, the data included in the map log information, the location log information, and the search engine log information. The information is used as relevant data for the user.

In some implementations, the user's related data includes at least location retrieval data and/or positioning data. The location search data includes at least one of the following: target location search data, route search data, and corresponding route information; and peripheral data of the target location.

In a further implementation manner, the target location search data includes at least one of the following: a destination of the search, a time of the search, and a current geographic location of the user; the route search data includes at least one of the following: a time when the user retrieves the route, and a starting geographic location. The target geographical location, the trajectory data, and the corresponding travel mode; the surrounding data of the target location includes at least one of the following: building data around the target location, traffic site data, and parking lot data.

In some implementations, the user's behavior characteristics are obtained based on the user's related data, including at least one of: statistic and analysis of the distribution of the geographic location where the user stays based on the positioning data to determine the location of the user's fixed activity; Retrieving data to obtain the user's point of interest information; performing statistics and analysis on the user's travel mode based on the location search data to determine the user's preferred travel mode; calculating the relevance between the users based on the location data to determine the intimacy of multiple users .

In some implementations, determining user attribute information based on a user's behavior characteristics includes: determining user attribute information using the trained model based on the user's behavior characteristics.

In some implementations, the user attribute information includes at least one of the following: a user's age group, gender, occupation, interest, income level, spending habits, health status, social relationships, and fixed asset status.

In a second aspect, the present application provides an apparatus for acquiring user attribute information, including: a first acquiring unit, configured to acquire map log information, positioning log information, and log information of a search engine; and a preprocessing unit, configured to The map log information, the location log information, and the search engine log information are preprocessed to obtain related data of the user; the second obtaining unit is configured to acquire the user's behavior characteristics based on the related data of the user; and the determining unit is configured to be based on the user The behavioral characteristics determine user attribute information.

In some implementations, the pre-processing unit is configured to pre-process the map log information, the location log information, and the search engine log information as follows: the map log information, the location log information, and the search engine log information are included. Data is analyzed; data related to geographic location and user behavior in the log information of the map, the location log information, and the log information of the search engine are extracted as relevant data of the user.

In a further implementation manner, the pre-processing unit is further configured to pre-process the map log information, the location log information, and the log information of the search engine by searching for the information of the map log, the location log information, and the search engine through the network. The log information contains data related information as the user's related data.

In a further implementation, the second obtaining unit is configured to acquire a behavior characteristic of the user according to at least one of the following: the distribution of the geographic location where the user stays based on the positioning data Statistics and analysis to determine the location of the user's fixed activities; based on the location retrieval data to obtain the user's point of interest information; based on the location retrieval data to the user's travel mode statistics and analysis to determine the user's preferred travel mode; based on positioning data calculation Correlation between users to determine the intimacy of multiple users.

In some implementations, the determining unit determines the user attribute information using the trained model based on the behavior characteristics of the user.

In a third aspect, the present application provides a server, including the apparatus for acquiring user attribute information provided by the second aspect of the present application.

The method, device and server for obtaining user attribute information provided by the application obtain the map log information, the location log information and the log information of the search engine, and then pre-stage the map log information, the location log information and the search engine log information. Processing to obtain relevant data of the user, and then acquiring the behavior characteristics of the user based on the relevant data of the user, and finally determining the attribute information of the user based on the behavior characteristics of the user, fully utilizing the information of the user, the location of the map, and the like to analyze the attribute information of the user, and improve the user attribute information. The comprehensiveness and accuracy of the obtained user attribute information.

DRAWINGS

Other features, objects, and advantages of the present application will become more apparent from the detailed description of the accompanying drawings.

1 is a flowchart of an embodiment of a method for acquiring user attribute information provided by an embodiment of the present application;

2 is a flowchart of an embodiment of a method for pre-processing map log information, location log information, and search engine log information provided by an embodiment of the present application;

3 is a schematic structural diagram of an embodiment of an apparatus for acquiring user attribute information provided by an embodiment of the present application;

4 is a schematic diagram of an exemplary system architecture to which an embodiment of the present application may be applied;

Figure 5 is a diagram showing the structure of a computer system suitable for implementing the server of the embodiment of the present application schematic diagram.

detailed description

The present application will be further described in detail below with reference to the accompanying drawings and embodiments. It is understood that the specific embodiments described herein are merely illustrative of the invention, rather than the invention. In addition, for the convenience of description, only parts related to the related invention are shown in the drawings.

It should be noted that the embodiments in the present application and the features in the embodiments may be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings.

The terminals involved in the present application may include, but are not limited to, a smartphone, a tablet, a personal digital assistant, a smart wearable device, a laptop portable computer, and the like. For purposes of example description and for the sake of brevity, in the following discussion, an exemplary embodiment of the present application is described in connection with an electronic map, a browser, and a terminal having a positioning function.

Please refer to FIG. 1, which illustrates a flow of one embodiment of a method for obtaining user attribute information according to the present application. This method can be performed by the server.

As shown in FIG. 1, in step 101, map log information, location log information, and log information of a search engine are acquired.

Generally, when the user queries the target address and the route information through the map, the terminal can establish a communication connection with the map server to obtain the map data that the user queries; when the user accesses the webpage or searches through the browser of the terminal, the terminal can Establishing a communication connection with the web server to obtain the webpage information that the user wants to access; when the user opens the positioning function of the terminal, the terminal can establish a communication connection with the positioning server to obtain the current positioning data. While feeding back the query result, the access result, and the positioning result to the user, the terminal may save the information related to the query, the access, and the location to the memory of the terminal, or upload the information related to the query, the access, and the location to the corresponding server. Generate log information and save it. Specifically, related information (such as query time and query content) that the user queries through the map can be saved as map log information; related information (such as the three-dimensional geographic location data of the terminal and the positioning time) that the terminal locates by means of GPS or the like can be saved as the positioning. Log information; information about the user's search through the search engine on the browser (such as checking The content of the content, the time of the retrieval) can be saved as the log information of the search engine.

In this embodiment, if the log information is saved in the memory of the terminal, the server may acquire map log information, location log information, and log information of the search engine from the terminal through the network. If the log information is saved in the corresponding server, the server may obtain the map log information from the map server, obtain the location log information from the location server, and obtain the log information of the search engine from the server of the search engine. In some implementations, the server may obtain log information from the terminal and the corresponding map server, the location server, and the server of the search engine, and identify and extract the log of the same user based on the user's identification information (eg, IP address, user ID, etc.). information.

The map log information, the location log information, and the search engine log information may include: time information, operation information, status information, network protocol information, query, location, or search result, and storage space occupied by content included in the result. In some implementations, the above log information is saved as fields.

In step 102, map log information, location log information, and search engine log information are preprocessed.

In this embodiment, the server may preprocess the acquired map log information, the location log information, and the log information of the search engine to obtain related data of the user. The user's related data may be data related to the user's behavior, data related to the user's attribute characteristics (such as gender, personality), and data related to the user's geographic location. As described above, the log information includes time information, operation information, query, positioning, or search results, and includes status information irrelevant to the user's behavior, attribute characteristics, and geographic location, and network protocol information. In this embodiment, the server may filter information related to user behavior in the log information. For example, when the log information is in the form of a field, the field indicating the status information and the network protocol may be filtered out by analyzing the content of the field.

With further reference to FIG. 2, it is a flowchart of an embodiment of a method for pre-processing map log information, location log information, and search engine log information provided by an embodiment of the present application.

As shown in FIG. 2, in step 201, data included in the map log information, the location log information, and the log information of the search engine are analyzed.

In this embodiment, the server may analyze the acquired map log information, the location log information, and the log information of the search engine. For example, the log information in the form of a field may be segmented, and the content represented by each partial field may be analyzed. For example, a log information of a search engine may include a user's IP address, search time, search content, status code, visited URL, and the like. This log information can be analyzed to determine the user's search time and search content.

In step 202, data related to the geographical location and user behavior in the map log information, the location log information, and the log information of the search engine are extracted as related data of the user.

In this embodiment, the server may separately determine whether the data obtained in the analysis in step 201 is related to the user behavior and related to the geographic location. When the data obtained by the analysis is related to the geographical location or user behavior, the related data can be extracted as relevant data of the user. The user's relevant data can be used to analyze the user's attribute characteristics and construct a user portrait.

For example, in the foregoing embodiment, the map query time and the query content may be extracted from the map log information as related data of the user; the positioning time and the located geographical location data may be extracted from the positioning log information as related data of the user; The keyword retrieved by the user, the search time, and the URL of the clicked search result are extracted from the log information of the search engine as the relevant data of the user.

In some implementations, the user's related data includes at least location retrieval data and/or location data. The location retrieval data can be extracted from the map log information and the log information of the search engine, and the location data can be obtained from the location log information. The location retrieval data may include at least one of the following: target location search data, route search data and corresponding route information, and peripheral data of the target location.

Specifically, the target location search data may include at least one of the following: a destination of the search, a time of the search, and a current geographic location of the user. When the user searches for the target location through the electronic map on the terminal, the map server can find the matching destination geographic location according to the user's search request, and can also obtain the current geographic location of the user, record the search time, and save the information to the map log information. . The server can obtain this data from the map log information as user-related data.

The route search data may include at least one of the following: a time when the user retrieves the route, Start location, target location, trajectory data, and corresponding travel mode. The trajectory data may be a route trajectory queried by the map server, and the corresponding travel mode may be driving, public transportation or walking. In some implementations, the route search data can also include the distance between the starting geographic location and the target geographic location.

The corresponding line information of the route search data may be navigation data. The navigation data may include detailed positioning information and a moving distance at a corresponding time.

The surrounding data of the target location may include at least one of the following: building data around the target location, traffic site data, and parking lot data. For example, when a user searches for a tourist attraction, the server can obtain data of a hotel, a restaurant, a subway station near the tourist attraction, the number and distance of the bus station, or the number of parking lots and the number of parking spaces around the tourist attraction.

In step 203, information related to the map log information, the location log information, and the data included in the log information of the search engine is found through the network as related data of the user.

In addition to extracting the above-mentioned data related to the user behavior or the geographical location from the log information, the server may also query the information related to the map log information, the location log information, and the data contained in the log information of the search engine as the relevant data of the user through the network. . For example, when the server extracts the destination of the user search from the map log information, the server can find the type, attribute, and the like of the destination. For example, when the destination is a restaurant, the average consumption, cuisine, parking space, evaluation, other branch address, etc. of the restaurant can be found through the network as relevant data of the user; when the destination is a shopping place, the sale can be found. The product brand, type, price and other information, as the user's relevant data.

In the above embodiment, the server may analyze the log information, extract data related to the user behavior or the geographical location, and query the information related to the data included in the log information acquired in step 101, thereby acquiring related data of the user.

Returning to Figure 1, in step 103, the user's behavioral characteristics are obtained based on the user's relevant data.

The behavior characteristics of the user can be obtained by analyzing the relevant data of the user obtained in step 102. In this embodiment, the server can infer the behavior characteristics of the user by data statistics. The user's behavioral characteristics may include, but are not limited to, the location of the user's fixed activity, the point of interest information, the preferred mode of travel, and the social relationship. When the user's related data includes bits When the search data and the positioning data are set, the above behavior characteristics of the user can be separately obtained in various ways.

In some implementations, the server can perform statistics and analysis on the distribution of geographic locations that the user stays based on the location data to determine the location of the user's fixed activity. Specifically, the server can count the time and frequency of the stay, and obtain the geographic location with the longest stay time or the highest frequency of staying according to the statistical result, and infer the user's family location, work place and other rules in combination with the specific time information of the stay. The location of sexual activity. For example, a geographical location in which all the staying time is the longest and the staying time is a non-working time can be regarded as a family place; a geographical position where the staying time is the longest and the staying time is the working time is taken as the working place of the user. In practical applications, it is also possible to use a geographical location where the user stays high and is stable as a place for regular activity of the user (for example, a gym, a restaurant that the user likes, etc.). The location of the user's fixed activity can be used to analyze the user's occupation, interest, housing price and other information.

In some implementations, the server can retrieve the user's point of interest information based on the location retrieval data. The user's point of interest information may be information that the user may be interested in. Specifically, the server may acquire attribute information such as a destination type retrieved by the user, and acquire related information as the interest point information of the user according to the attribute information such as the destination type. Further, the server may also acquire frequency information of a destination of the user retrieving the same attribute information as the user's interest point information. For example, when the location search data includes the location information of the user to retrieve an airport, the server may obtain the tourist attraction information, the hotel information, and the like of the location of the airport as the user's point of interest information to analyze the attribute information such as the user's interest and personality. For another example, when the location search data includes the location where the user retrieves the hotel, the server may obtain information such as the star rating and consumption level of the hotel as the user's point of interest information to analyze the user's asset status. The location search data may also contain information related to a gas station, a parking lot, and a car wash location, and the server may obtain such information to analyze whether the user has asset information such as a car.

In some implementations, the server may perform statistics and analysis on the way the user travels based on the location retrieval data to determine the manner in which the user prefers travel. The location search data may include route information selected by the user when the route is retrieved and the corresponding travel mode, and the server may perform statistics on the travel mode selected by the user, and analyze the travel mode commonly used by the user (driving, Buses, subways, buses, trains, airplanes, etc., to infer the user's sensitivity to traffic costs, and then analyze the user's asset status.

In some implementations, the server can calculate the degree of correlation between users based on the positioning data to determine the degree of intimacy of the plurality of users. For example, the server may analyze the strength of the social attributes between the users according to the cross-characteristics of the positioning data of the user staying in the adjacent geographical location, that is, the intimacy of the multiple users. The server may further analyze the type of social relationship of the user based on time information that the user stays in the adjacent geographic location, such as family, friends, work partners, and the like. For example, when a plurality of users stay in the same geographical location for a non-working time for more than one threshold, it may be considered that the plurality of users have a higher degree of intimacy, which may be a family or friend relationship.

Optionally, the server may also obtain type information of the mobile terminal used by the user, such as an Android system or an ios system, to infer the user's asset status.

Several methods of acquiring user behavior features have been exemplarily described above. It can be understood that the related data of the user obtained in step 102 can be used to analyze and acquire various other types of user behavior characteristics, for example, the location type of the user frequently staying based on the user's positioning data and location retrieval data can be analyzed; The location search data is used to count the user's travel rules.

In step 104, user attribute information is determined based on the user's behavior characteristics.

In this embodiment, the server may perform data mining on the behavior characteristics of the user, and analyze the attribute information of the user based on the statistical result of step 103. In some embodiments, the user attribute information may include at least one of the user's age group, gender, occupation, interest, income level, spending habits, health status, social relationship, and fixed asset status.

In some implementations, after the server performs data mining on the behavior characteristics of the user, the plurality of candidate user attribute information and its probability may be inferred, for example, the age groups of the plurality of different users and the probability corresponding to each age group. The candidate user attribute information with the highest probability is then taken as the user attribute information.

Specifically, the age range of the user may be determined according to the location of the user's fixed activity. For example, when the location of the fixed activity of the user is a university, it may be inferred that the age of the user is 18-24 years old, and when the location of the fixed activity of the user is an office building, It can be inferred that the user's older age is 24-50 years old, and the user can infer the user when the fixed activity location is the elderly center. The older age is greater than 50 years old. The user's age range can also be inferred based on the user's point of interest information. For example, when a user retrieves a playground through a map, it can be inferred that the probability of the user being 15-30 years old is large.

The gender of the user can be judged according to the information of the interest point. For example, the user who searches for an address such as a beauty salon has a higher probability of being a female, and the user who searches for an address such as a basketball court has a higher probability of being a male.

The user's occupation can be judged by the user's positioning data. For example, when the location of the user's fixed activity in the user's behavioral features includes the building of an IT enterprise, the probability that the user is engaged in the IT industry may be determined to be large; when the location of the user's fixed activity in the user's behavioral feature includes the government agency unit, then It can be determined that the probability that the user's occupation is a civil servant is large.

The user's interest can be determined by the point of interest information in the user's behavioral characteristics. For example, when the number of sports places included in the user's point of interest information exceeds a certain value, the user may be considered to like sports.

The user's social relationship can be judged by retrieving data from the user's location. For example, when the location search data of the user includes a target place such as a children's playground, a primary school, a juvenile palace, etc., it may be determined that the user has a child. Further, it is also possible to determine whether the user has a child by having the point of interest information of the user acquired by the location retrieval data. For example, when the user's point of interest information contains a large number of keywords like "child", "young", and "infant", it can be determined that the user has a child.

The social relationship of the user can also be determined by the degree of intimacy between users obtained by the positioning data of a plurality of users. For example, when the degree of intimacy of a plurality of users is high, the server can determine that the probability that a plurality of users are family or friend relationships is high.

The user's income level, consumption level, and fixed asset status can be inferred from multiple behavioral characteristics of the user. For example, the user's income level and the fixed asset status may be inferred based on the user's sensitivity to the transportation cost, and the residential price may be inferred based on the user's home address to determine the user's fixed asset status; or the consumer location (eg, hotel) may be retrieved according to the user. The consumer price level of the user, the restaurant, etc.) to determine the user's income level and consumption level; the user's income level and consumption level can also be determined according to the frequency at which the user searches for a high-consumption place (such as a golf course, etc.). In addition, it is also possible to determine whether the user has a car according to whether the user frequently drives while traveling and whether the parking data or the gas station address is included in the positioning data.

The health status of the user can be inferred based on the frequency of the user's location data in the medical field such as the hospital and the pharmacy. For example, when the frequency of the user staying in the hospital is 2 to 3 times per week, it can be inferred that the probability of the user's health condition being poor is large.

The method of determining several kinds of user attribute information is exemplarily described above. It should be noted that the user attribute information may not be limited to the several types of information described above, and the method provided by the present application is not limited to determining several user attribute information described above, and may also be used to determine other types of user attributes. The information may be determined, for example, based on statistical data of the clothing store where the user frequently stays; the user's favorite wearing style may be analyzed; the user's personality (extroverted or introverted) or the like may be analyzed based on statistical data of the user's travel frequency.

In this embodiment, the server may calculate the probability of each user attribute information according to a preset rule. For example, a frequency threshold may be preset. When the frequency of the user staying in a certain geographic location exceeds the frequency threshold, the probability that the user's home address is the geographic location is set to 80%. For example, the list of high-end consumer places may be preset. When the location data of the user includes a preset location in the high-consumption list, the probability that the user is a high-income level may be set to be greater than 50%.

In some optional implementations, the server may determine the user attribute information using the trained model based on the user's behavioral characteristics. The server may train the classification model as a training set by using the location retrieval data of the user of the known user attribute and the user behavior feature acquired by the positioning data. The classification model may include a plurality of sub-models, each of which is used to classify one attribute information of the user. The classification model can also be a comprehensive model for classifying various attribute information of users. Optionally, the classification model may also be optimized based on a user behavior feature obtained by retrieving data and positioning data acquired by a user different from the training set. In the application, the trained and optimized model can be used to analyze the behavior characteristics of the user acquired in step 103, and the attribute information of the user is obtained.

The method for obtaining user attribute information provided by the foregoing embodiment is performed by pre-processing the acquired map log information, the location log information, and the log information of the search engine to obtain related data of the user, and then acquiring the related data based on the user. The behavior characteristics of the user, and finally determine the user attribute information based on the user's behavior characteristics, fully utilize the user's positioning, map search and other information to analyze the user attribute information, and improve the acquired user attributes. The comprehensiveness and accuracy of the information.

The method for obtaining user attribute information provided by the above embodiment of the present application can be used to construct a user portrait. Further, the content may be recommended to the user based on the user portrait. For example, the map server may analyze the user's eating preferences according to the user's portrait, and recommend the personalized food that is more in line with the user's taste when the user searches for the food. It is also possible to provide a basis for the location selection based on the user image. For example, the location server can determine the distribution, preference and the like of the target group based on the user image, and optimize the location result.

With reference to FIG. 3, it is a schematic structural diagram of an embodiment of an apparatus for acquiring user attribute information provided by an embodiment of the present application. As shown in FIG. 3, the apparatus 300 for acquiring user attribute information may include a first obtaining unit 301, a pre-processing unit 302, a second obtaining unit 303, and a determining unit 304. The first obtaining unit 301 can be configured to acquire map log information, location log information, and log information of a search engine. The pre-processing unit 302 can be configured to pre-process the map log information, the location log information, and the log information of the search engine to obtain related data of the user. The second obtaining unit 303 can be configured to acquire a behavior characteristic of the user based on the related data of the user. The determining unit 304 can be configured to determine user attribute information based on the behavior characteristics of the user.

In this embodiment, the first obtaining unit 301 may acquire map log information, location log information, and log information of the search engine of the same user from the terminal or the map server, the location server, and the server of the search engine. The pre-processing unit 302 may analyze the data included in the log information acquired by the first obtaining unit 301, and extract data related to the user behavior or the geographical location as related data of the user. The pre-processing unit 302 can also find out, through the network, information related to map log information, location log information, and data included in the log information of the search engine as related data of the user.

In some implementations, the user's related data can include at least location retrieval data and/or positioning data. The location search data may include at least one of the following: target location search data, route search data, and corresponding line information; and peripheral data of the target location.

Further, the target location search data may include at least one of the following: a destination of the search, a time of the search, and a current geographic location of the user. The route search data may include at least one of the following: a time when the user retrieves the route, a starting geographic location, a target geographic location, and a track Trace data and the corresponding travel mode. The surrounding data of the target location may include at least one of the following: building data around the target location, traffic site data, and parking lot data.

Further, the second obtaining unit 303 may be configured to acquire a behavior characteristic of the user according to at least one of the following: perform statistics and analysis on the distribution of the geographic location where the user stays based on the positioning data obtained by the pre-processing unit 302, to determine that the user is fixed. The location of the activity; the location retrieval data obtained by the preprocessing unit 302 is used to obtain the user's point of interest information; based on the location retrieval data obtained by the preprocessing unit 302, the user's travel mode is statistically analyzed and analyzed to determine the user's preferred travel mode. The correlation between the users is calculated based on the positioning data obtained by the pre-processing unit 302 to determine the degree of intimacy of the plurality of users.

The determining unit 304 may determine the user attribute information using the trained model based on the behavior characteristics of the user acquired by the second obtaining unit 303. The user attribute information may include at least one of the following: a user's age group, gender, occupation, interest, income level, consumption habits, health status, social relationship, and fixed asset status.

It should be understood that the units recited in apparatus 300 for obtaining user attribute information correspond to the various steps in the method described in FIGS. 1-2. Thus, the operations and features described above for the method are equally applicable to the apparatus 300 for acquiring user attribute information and the units included therein, and are not described herein again.

With further reference to FIG. 4, a schematic diagram of an exemplary system architecture to which embodiments of the present application may be applied is shown. As shown in FIG. 4, the system 400 can include

terminals

401, 402, a map server 403, a location server 404, a search engine server 405, and a server 407 for implementing user attribute information provided by the present application. The server 407 may include the apparatus 300 for acquiring user attribute information in the above embodiment. Network 406 may also be included in the system architecture. Network 406 is used to provide a medium for communication links between

terminals

401, 402,

servers

403, 404, 405, and 407. Network 406 can include various types of connections, such as wired, wireless communication links, fiber optic cables, and the like.

The

terminals

401, 402 can interact with the

servers

403, 404, 405, 407 over the network 406 to receive or transmit messages and the like. The terminal 401, 402 has a positioning function, and can install a map application and a browser. The map log information can be sent to the map server 403 through the network 406, and the positioning log information is sent to the positioning server 404 through the network, and the log information of the search engine is transmitted through the network. 406 is sent to the server 405 of the search engine. Server 407 The log information can be obtained from the

terminals

401, 402 and the

servers

403, 404, 405 through the network 406, the log information is preprocessed, the user behavior characteristics are extracted, and the user attribute information is determined. In an actual application, the server 407 can also send the determined user attribute information to the map server 403 and the server 405 of the search engine via the network 406. The map server 403 may recommend relevant information to the user based on the user attribute information when the user searches for the target location using the map; the server 405 of the search engine may reorder the web pages based on the user attribute information to enable the user to quickly search for information satisfying the demand. .

The

terminals

401, 402 can be various electronic devices including, but not limited to, personal computers, smart phones, smart watches, tablets, personal digital assistants, and the like. The server 407 can perform processing of storing, analyzing, and the like on the received data, and feed back the processing result to the terminal and the

servers

403, 404, and 405.

It should be understood that the number of terminals, networks, and servers in Figure 4 is merely illustrative. Depending on the implementation needs, there can be any number of terminals, networks, and servers.

With further reference to FIG. 5, a block diagram of a computer system suitable for use in implementing the server of the embodiments of the present application is shown. As shown in FIG. 5, computer system 500 includes a central processing unit (CPU) 501 that can be loaded into a program in random access memory (RAM) 503 according to a program stored in read only memory (ROM) 502 or from storage portion 508. And perform various appropriate actions and processes. In the RAM 503, various programs and data required for the operation of the system 500 are also stored. The CPU 501, the ROM 502, and the RAM 503 are connected to each other through a bus 504. An input/output (I/O) interface 505 is also coupled to bus 504.

The following components are connected to the I/O interface 505: an input portion 506; an output portion 507; a storage portion 508 including a hard disk or the like; and a communication portion 509 including a network interface card such as a LAN card, a modem, and the like. The communication section 509 performs communication processing via a network such as the Internet. Driver 510 is also coupled to I/O interface 505 as needed. A removable medium 511 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory or the like is mounted on the drive 510 as needed so that a computer program read therefrom is installed into the storage portion 508 as needed.

The flowchart and block diagrams in the Figures illustrate the architecture, functionality and operation of possible implementations of systems, methods and computer program products in accordance with various embodiments of the invention. In this regard, each block in the flowchart or block diagram can represent a module, program segment, or code. In part, the module, program segment, or portion of code includes one or more executable instructions for implementing the specified logical functions. It should also be noted that in some alternative implementations, the functions noted in the blocks may also occur in a different order than that illustrated in the drawings. For example, two successively represented blocks may in fact be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending upon the functionality involved. It is also noted that each block of the block diagrams and/or flowcharts, and combinations of blocks in the block diagrams and/or flowcharts, can be implemented in a dedicated hardware-based system that performs the specified function or operation. Or it can be implemented by a combination of dedicated hardware and computer instructions.

The units involved in the embodiments of the present application may be implemented by software or by hardware. The described unit may also be provided in the processor, for example, as a processor comprising a first acquisition unit, a pre-processing unit, a second acquisition unit and a determination unit. The names of these units do not constitute a limitation on the unit itself under certain circumstances. For example, the pre-processing unit may also be described as "a unit for pre-processing".

In another aspect, the present application further provides a computer readable storage medium, which may be a computer readable storage medium included in the apparatus described in the foregoing embodiment, or may exist separately, not A computer readable storage medium that is assembled into a terminal. The computer readable storage medium stores one or more programs that are used by one or more processors to perform the methods for obtaining user attribute information as described herein.

The above description is only a preferred embodiment of the present application and a description of the principles of the applied technology. It should be understood by those skilled in the art that the scope of the invention referred to in the present application is not limited to the specific combination of the above technical features, and should also be covered by the above technical features without departing from the inventive concept. Other technical solutions formed by any combination of their equivalent features. For example, the above features are combined with the technical features disclosed in the present application, but are not limited to the technical features having similar functions.

Claims

A method for obtaining user attribute information, the method comprising:

Obtain map log information, locate log information, and log information of the search engine;

Pre-processing the map log information, the location log information, and the search engine log information to obtain related data of the user;

Acquiring user behavior characteristics based on relevant data of the user;

User attribute information is determined based on the behavior characteristics of the user.
The method according to claim 1, wherein the pre-processing of the map log information, the location log information, and the log information of the search engine comprises:

And analyzing data included in the map log information, the location log information, and the search engine log information;

Data related to the geographical location and user behavior in the map log information, the location log information, and the log information of the search engine are extracted as related data of the user.
The method according to claim 2, wherein the pre-processing the map log information, the location log information, and the log information of the search engine further includes:

Information related to the map log information, the location log information, and the data included in the log information of the search engine is found through the network as related data of the user.
The method according to any one of claims 1-3, wherein the related data of the user comprises at least location retrieval data and/or positioning data;

The location retrieval data includes at least one of the following: target location search data, route search data and corresponding route information, and peripheral data of the target location.
The method of claim 4 wherein:

The target location search data includes at least one of: a destination of the search, a time of the search, and a current geographic location of the user;

The route search data includes at least one of: a time when the user retrieves the route, a starting geographic location, a target geographic location, trajectory data, and a corresponding travel mode;

The surrounding data of the target location includes at least one of the following: building data around the target location, traffic site data, and parking lot data.
The method according to claim 4 or 5, wherein the acquiring the behavior characteristics of the user based on the related data of the user comprises at least one of the following:

Performing statistics and analysis on the distribution of the geographic location where the user stays based on the positioning data to determine the location of the user's fixed activity;

Obtaining interest point information of the user based on the location retrieval data;

Calculating and analyzing the travel mode of the user based on the location retrieval data to determine the travel mode of the user preference;

A correlation between users is calculated based on the positioning data to determine the degree of intimacy of the plurality of users.
The method according to any one of claims 1-6, wherein the determining user attribute information based on the behavior characteristics of the user comprises:

The user attribute information is determined using the trained model based on the behavior characteristics of the user.
The method according to any one of claims 1 to 7, wherein the user attribute information comprises at least one of the following: a user's age group, gender, occupation, interest, income level, consumption habits, health status, society Relationship and the status of fixed assets.
An apparatus for acquiring user attribute information, wherein the apparatus comprises:

a first acquiring unit, configured to acquire map log information, locate log information, and log information of a search engine;

a pre-processing unit, configured to perform pre-processing on the map log information, the location log information, and the log information of the search engine to obtain related data of the user;

a second acquiring unit, configured to acquire a behavior characteristic of the user based on the related data of the user;

a determining unit, configured to determine user attribute information based on a behavior characteristic of the user.
The apparatus according to claim 9, wherein the pre-processing unit is configured to pre-process the map log information, the location log information, and the log information of the search engine as follows:

And analyzing data included in the map log information, the location log information, and the search engine log information;

Data related to the geographical location and user behavior in the map log information, the location log information, and the log information of the search engine are extracted as related data of the user.
The apparatus according to claim 10, wherein the pre-processing unit is further configured to pre-process the map log information, the location log information, and the log information of the search engine as follows:

Information related to the map log information, the location log information, and the data included in the log information of the search engine is found through the network as related data of the user.
The apparatus according to any one of claims 9-11, wherein the related data of the user comprises at least location retrieval data and/or positioning data;

The location retrieval data includes at least one of the following: target location search data, route search data, and corresponding line information; and peripheral data of the target location.
The device according to claim 12, characterized in that

The target location search data includes at least one of: a destination of the search, a time of the search, and a current geographic location of the user;

The route search data includes at least one of: a time when the user retrieves the route, a starting geographic location, a target geographic location, trajectory data, and a corresponding travel mode;

The surrounding data of the target location includes at least one of the following: building data around the target location, traffic site data, and parking lot data.
The device according to claim 12 or 13, wherein the second obtaining unit is configured to acquire behavior characteristics of the user in at least one of the following manners:

Performing statistics and analysis on the distribution of the geographic location where the user stays based on the positioning data to determine the location of the user's fixed activity;

Obtaining interest point information of the user based on the location retrieval data;

Calculating and analyzing the travel mode of the user based on the location retrieval data to determine the travel mode of the user preference;

A correlation between users is calculated based on the positioning data to determine the degree of intimacy of the plurality of users.
The apparatus according to any one of claims 9-14, wherein the determining unit determines the user attribute information using a trained model based on behavior characteristics of the user.
The device according to any one of claims 9-15, wherein the user attribute information comprises at least one of the following: a user's age group, gender, occupation, interest, income level, consumption habits, health status, society Relationship and the status of fixed assets.
A server comprising the apparatus of any of claims 9-16.
An apparatus, comprising:

One or more processors;

Memory

One or more programs, the one or more programs being stored in the memory, when executed by the one or more processors:

Obtain map log information, locate log information, and log information of the search engine;

Pre-processing the map log information, the location log information, and the search engine log information to obtain related data of the user;

Acquiring user behavior characteristics based on relevant data of the user;

User attribute information is determined based on the behavior characteristics of the user.
A non-volatile computer storage medium storing one or more programs, when the one or more programs are executed by a device, causing the device to:

Obtain map log information, locate log information, and log information of the search engine;

Pre-processing the map log information, the location log information, and the search engine log information to obtain related data of the user;

Acquiring user behavior characteristics based on relevant data of the user;

User attribute information is determined based on the behavior characteristics of the user.