CN108306812B - Data processing method and server - Google Patents
Data processing method and server Download PDFInfo
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- CN108306812B CN108306812B CN201710069536.2A CN201710069536A CN108306812B CN 108306812 B CN108306812 B CN 108306812B CN 201710069536 A CN201710069536 A CN 201710069536A CN 108306812 B CN108306812 B CN 108306812B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/52—Network services specially adapted for the location of the user terminal
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L51/00—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail
- H04L51/21—Monitoring or handling of messages
- H04L51/222—Monitoring or handling of messages using geographical location information, e.g. messages transmitted or received in proximity of a certain spot or area
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L51/00—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail
- H04L51/52—User-to-user messaging in packet-switching networks, transmitted according to store-and-forward or real-time protocols, e.g. e-mail for supporting social networking services
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L65/00—Network arrangements, protocols or services for supporting real-time applications in data packet communication
- H04L65/40—Support for services or applications
Abstract
The embodiment of the invention discloses a data processing method and a server, wherein the method comprises the following steps: receiving a user query request sent by a first client, wherein the user query request carries the position information of the first client; determining a client cluster of which the distance from the first client is smaller than a preset distance threshold value based on the position information, wherein the client cluster comprises at least one second client; acquiring user behavior data of each second client contained in the client cluster; and determining a target second client matched with the first client in the client cluster based on the user behavior data of the second clients. By adopting the embodiment of the invention, the accuracy of user query can be improved.
Description
Technical Field
The present invention relates to the field of internet technologies, and in particular, to a data processing method and a server.
Background
In a social network, in some cases, a user may need to find out users nearby and then contact them, so as to make friends, seek help, and the like. The following lookup approach can now be used: for any user, supposing that the user X is a user X, when the user needs to search nearby users, the user X reports own position information (longitude and latitude information) to a server through a first client used by the user X; after receiving the position information reported by the first client, the server searches out a second client of which the distance to the first client is smaller than a preset distance threshold, and returns the client identification of the second client of which the distance to the first client is smaller than the preset distance threshold, the distance between the first client and the second client and the like to the first client. However, the server finds out that the nearby user only refers to one dimension of the location information, and the search result is not accurate enough.
Disclosure of Invention
The technical problem to be solved by the embodiments of the present invention is to provide a data processing method and a server, which can improve the accuracy of user query.
A first aspect of the present invention provides a data processing method, including:
receiving a user query request sent by a first client, wherein the user query request carries position information of the first client;
determining a client cluster of which the distance from the first client is smaller than a preset distance threshold value based on the position information, wherein the client cluster comprises at least one second client;
acquiring user behavior data of each second client contained in the client cluster;
and determining a target second client matched with the first client in the client cluster based on the user behavior data of each second client.
A second aspect of the present invention provides a server, comprising:
the device comprises a query request receiving unit, a query processing unit and a query processing unit, wherein the query request receiving unit is used for receiving a user query request sent by a first client, and the user query request carries the position information of the first client;
a cluster determining unit, configured to determine, based on the location information, a client cluster in which a distance to the first client is smaller than a preset distance threshold, where the client cluster includes at least one second client;
a behavior data obtaining unit, configured to obtain user behavior data of each second client included in the client cluster;
and the client determining unit is used for determining a target second client matched with the first client in the client cluster based on the user behavior data of each second client.
By implementing the embodiment of the invention, the server receives the user query request sent by the first client, determines the client cluster of which the distance to the first client is smaller than the preset distance threshold value based on the position information of the first client carried by the user query request, acquires the user behavior data of each second client contained in the client cluster, and determines the target second client matched with the first client in the client cluster based on the user behavior data of each second client, so that the accuracy of user query can be improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts;
FIG. 1 is a block diagram of a data processing system provided in an embodiment of the present invention;
FIG. 2 is a flow chart of a data processing method provided in an embodiment of the present invention;
FIG. 3 is a schematic illustration of an interface provided in an embodiment of the present invention;
FIG. 4 is an interface diagram of a user representation tree provided in an embodiment of the present invention;
FIG. 5 is a schematic diagram of a feature value and its corresponding method of construction provided in an embodiment of the present invention;
FIG. 6 is a schematic illustration of an interface provided in another embodiment of the present invention;
fig. 7 is a schematic structural diagram of a server provided in an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a server provided in another embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a data processing method.A server receives a user query request sent by a first client, determines a client cluster of which the distance to the first client is smaller than a preset distance threshold value based on position information of the first client carried by the user query request, acquires user behavior data of each second client contained in the client cluster, and determines a target second client matched with the first client in the client cluster based on the user behavior data of each second client. Compared with the traditional server, the server receives the user query request sent by the first client, and determines the client cluster of which the distance from the first client is smaller than the preset distance threshold value based on the position information of the first client carried by the user query request.
Based on the foregoing principle, an embodiment of the present invention provides a data processing system, which may be an internet application system such as an instant messaging application system, an SNS (Social Networking Services) application system, and the like. Referring to FIG. 1, the architecture of the data processing system includes at least: a server 101, at least one first client 102 accessing the server 101, and at least one second client 103 accessing the server 101. Optionally, the server 101 may be an independent service device in the internet, or a cluster service device formed by multiple independent service devices in the internet, for example, the server 101 may include a logical server 1010, a storage server 1011, and a recommendation server 1012, and then at least one first client 102 establishes a communication connection with the logical server 1010 and the recommendation server 1012, at least one second client 103 establishes a communication connection with the recommendation server 1012, and the storage server 1011 may establish a communication connection with the logical server 1010 and the recommendation server 1012, respectively.
The first client 102 or the second client 103 may operate in a terminal such as a notebook computer, a mobile phone, a PAD (tablet computer), a vehicle-mounted terminal, or a smart wearable device, and the first client 102 or the second client 103 may include an instant messaging client (e.g., a WeChat client or a QQ client), a live broadcast client (e.g., a pattern live broadcast client), or a video playing client (e.g., a Tencent video client). At least one type of internet application may be run in the terminal, including but not limited to: instant messaging applications, SNS applications, and the like. The user can use internet applications through the terminal, such as: the user can search nearby users and the like by using instant messaging applications such as WeChat or QQ and the like through the terminal; alternatively, the user may interact with other users in the internet application through the terminal, for example, the user may use an instant messaging application through the terminal, display a conversation interface with other users, the user may input social messages at the conversation interface, and the like. The server 101 may be used to handle various requirements of the internet application in the process of implementing functions of finding nearby users, business processing, information interaction, and the like, and the server 101 may be used to manage data related to users in the internet application, and interaction messages between users, for example, storing user behavior data of the first client 102 and user behavior data of the second client 103.
For example, the first client 102 may obtain the location information of the terminal through a Global Positioning System (GPS) built in the terminal, where the terminal is installed with the first client 102, the location information of the terminal is the location information of the first client 102, and the location information may include latitude and longitude information and the like. Optionally, the first client 102 may obtain the location information of the terminal through a base station positioning system built in the terminal. Optionally, the first client 102 may receive a wireless signal (e.g., a WIFI signal or a bluetooth signal) sent by a specific terminal device through the terminal, and acquire the location information of the terminal according to the signal strength of the wireless signal. Further, the first client 102 may send the location information to the logical server 1010. Optionally, the first client 102 may also send the obtaining time of the location information to the logic server 1010.
The logic server 1010 may process the location information to obtain coordinate information of the first client 102 in a preset map, and the logic server 1010 may transmit the coordinate information to the storage server 1011. Alternatively, the logic server 1010 may also transmit the acquisition time of the location information to the storage server 1011.
The storage server 1011 may search, in a preset map, target coordinate information having a length with the coordinate information smaller than a preset length threshold, and then determine a client cluster according to the target coordinate information, where a distance between each second client 103 and the first client 102 included in the client cluster is smaller than a preset distance threshold, and a ratio between the preset length threshold and the preset distance threshold may be matched with a reduction ratio of the preset map. Further, the storage server 1011 may send the determined client cluster (i.e., the raw data) to the recommendation server 1012.
The recommendation server 1012 may obtain a target second client 103 matched with the first client 102 according to the user behavior data of each second client 103 included in the client cluster, where the target second client 103 may include at least one second client 103 in the client cluster. The recommendation server 1012 may further obtain association degrees (for example, recommendation data) between the second clients 103 and the first client 102 according to the user behavior data of the second clients 103 included in the client cluster, and sort the association degrees according to a preset order (for example, from high to low or from low to high), so as to obtain a user list, where for example, the association degree between the first second client 103 and the first client 102 in the user list is the largest, and the association degree between the last second client 103 and the first client 102 is the smallest. Further, the recommendation server 1012 may also send the user list to the first client 102 through the logic server 1010, so that the first client 102 displays the user list, and the first client 102 may perform data interaction with each second client 103 in the user list.
Based on the schematic structural diagram of the data processing system shown in fig. 1, the embodiment of the present invention discloses a schematic flow diagram of a data processing method shown in fig. 2. As shown in fig. 2, the data processing method may include the steps of:
s201, receiving a user query request sent by a first client, wherein the user query request carries the position information of the first client.
The server may receive a user query request sent by the first client, where the user query request carries location information of the first client. Taking the interface schematic diagram shown in fig. 3 as an example, a user may operate a first client (i.e., a QQ client) through a terminal, click a "dynamic" button, the first client may respond to the click operation of the user to display the interface shown in fig. 3, and the user may click a "nearby" button, the first client may respond to the click operation of the user to acquire the location information of the first client, and generate a user query request including the location information of the first client, and the first client may send the user query request to a server.
S202, determining a client cluster of which the distance between the client cluster and the first client is smaller than a preset distance threshold value based on the position information, wherein the client cluster comprises at least one second client.
The server may determine, based on the location information, a cluster of clients having a distance to the first client that is less than a preset distance threshold, where the cluster of clients may include at least one second client. For example, the server may obtain coordinate information of a first client according to the location information, determine, in a preset map, target coordinate information having a length smaller than a preset length threshold with respect to a coordinate of the first client, use a client whose coordinate information is the target coordinate information as a second client, and further determine a client cluster including the second client. The distance between the second client and the first client is smaller than a preset distance threshold, and the ratio of the preset length threshold to the preset distance threshold is matched with the reduction scale of the preset map.
S203, user behavior data of each second client contained in the client cluster is obtained.
After determining the client cluster with the distance from the first client being smaller than the preset distance threshold, the server may obtain user behavior data of each second client included in the client cluster, where the user behavior data may reflect behavior habits of the user.
Optionally, the user behavior data may include at least one of: attribute information for different social domains, wherein the attribute information may include one or more of web page access characteristics, social status, user preference information, identity information, and game attributes; interactive data between the second client and the first client; a friend-making level of the second client.
Taking the interface schematic diagram of the user portrait system tree shown in fig. 4 as an example, the server may collect attribute information of each second client in the basic field, the advertisement field, the recommendation field, the social credit investigation field, or the shopping field, the server may obtain the identity information, the user preference information, the web access feature, or the social status, etc. of the second client in the basic field, the server may obtain the identity information, the web access feature, the social status, or the user preference information, etc. of the second client in the advertisement field, the server may obtain the identity information, the web access feature, or the game attribute, etc. of the second client in the recommendation field, the server may obtain the identity information, etc. of the second client in the social credit investigation field, and the server may obtain the user preference information, etc. of the second client in the shopping field. The web page access feature may include device information (e.g., a device identifier, an operating system or an Internet Protocol (IP) address of an interconnection between networks, etc.) of the terminal on which the second client operates, a period of time for accessing the web page or a frequency of accessing the web page, etc. The social status may include whether the second client is an active numerator, or an opinion leader, or the liveness of the second client in the respective relationship cluster, and so on. The user preference information may include shopping interests, study-keeping tendencies, shopping number business interests, and the like of the second client, the identity information may include natural attributes such as gender or age of the second client, professional attributes, educational attributes, family attributes, region attributes, and the like, and the game attributes may include game liveness or game type preferences of the second client, and the like.
For example, the interaction data between the second client and the first client may include whether the second client views the basic data of the first client, browses photos published by the first client, or initiates a temporary session to the first client. Optionally, the interaction data between the second client and the first client may include whether the first client views the basic data of the second client, browses photos published by the second client, or initiates a temporary session to the second client.
For example, the friend-making level of the second client may be used to indicate the frequency of interaction between the second client and a stranger other than friends included in the address book, and the interaction with the stranger may be, for example, to view basic information of the stranger, access a space of the stranger, or initiate a temporary session with the stranger.
S204, determining a target second client matched with the first client in the client cluster based on the user behavior data of each second client.
The server may determine, in the client cluster, a target second client that matches the first client based on the user behavior data of the respective second clients. Wherein the professional attribute of the target second client may be associated with the professional attribute of the first client, and the user preference information of the target second client may be associated with the user preference information of the first client. For example, if the professional attribute of the first client is an IT engineer, the professional of the target second client may be an IT engineer, a patent agent, or a reviewer, and if the user preference information of the first client includes travel and shopping, the user preference information of the target second client may include travel, shopping, or photography.
Optionally, the server may obtain attribute information of the first client for different social areas, compare the attribute information of the first client for the target social area with the attribute information of the second client for the target social area, to obtain an association degree between the first client and the second client, and when the association degree between the first client and the second client is greater than a preset ratio threshold, use the second client as the target second client.
For example, the server may obtain identity information of a first client for the base domain and identity information of each second client in the client cluster for the base domain, compare the identity information of the first client for the base domain with the identity information of the second client for the base domain, and add 1 to a numerator of a degree of association between the second client and the first client when a similarity between the identity information of the first client for the base domain and the identity information of the second client for the base domain is greater than a preset proportion threshold; when the similarity between the identity information of the first client aiming at the basic field and the identity information of the second client aiming at the basic field is smaller than or equal to a preset proportion threshold value, the server keeps the association degree between the second client and the first client unchanged. Further, the server can also obtain user preference information of the first client for the shopping domain and identity information of each second client in the client cluster for the basic domain, compare the user preference information of the first client for the shopping domain with the user preference information of the second client for the shopping domain, and add 1 to a numerator of the association between the second client and the first client when the similarity between the user preference information of the first client for the shopping domain and the user preference information of the second client for the shopping domain is greater than a preset proportion threshold; when the similarity between the user preference information of the first client aiming at the shopping domain and the user preference information of the second client aiming at the shopping domain is smaller than or equal to a preset proportion threshold value, the server keeps the association degree between the second client and the first client unchanged. By the method, the server compares the first client with the second client aiming at each attribute information of each social field to obtain the association degree between the first client and the second client, and then can judge whether the association degree between the first client and the second client is greater than a preset proportion threshold value, and when the association degree between the first client and the second client is greater than the preset proportion threshold value, the second client is used as a target second client.
Optionally, the server may compare the attribute information of the first client for the target social domain with the attribute information of the second client for the target social domain to obtain a comparison result, obtain a first feature value of the second client based on interaction data between the second client and the first client, obtain a second feature value of the second client based on a friend-making level of the second client, and further obtain an association degree between the first client and the second client based on the comparison result, the first feature value, and the second feature value.
For example, the comparison result obtained by comparing the first client and the second client with respect to each attribute information of each social domain may be 50%, the first feature value of the second client is 30% based on the interaction data between the second client and the first client, the second feature value of the second client is 40% based on the friend-making level of the second client, the comparison result obtained by comparing the first client and the second client, which are configured in advance by the server, with respect to each attribute information of each social domain is 0.7, the weight of the first feature value is 0.2, and the weight of the second feature value is 0.1, and then the server may determine that the association degree between the first client and the second client is 50% × 0.7+ 30% × 0.2+ 20% × 0.1 ═ 43%.
Optionally, the server may process the user behavior data of each second client included in the client cluster through a preset logistic regression algorithm to obtain the association degree between each second client and the first client, and use the second client with a higher association degree in the second clients included in the client cluster as the target second client. For example, the server may take, as the target second client, a second client whose association degree is greater than a preset ratio threshold value among second clients included in the client cluster, and also, for example, may take, as the target second client, a second client whose association degree is greater than a preset number of second clients included in the client cluster, where the association degree between the target second client and the first client is greater than that between other second clients and the first client in the client cluster.
Taking the schematic diagram of the eigenvalues and the corresponding construction methods shown in fig. 5 as an example, the construction method corresponding to the age of the first client (e.g., fromUin) is discretized, the construction method corresponding to the occupation of the second client (e.g., ToUin) is discretized and complemented, the construction method corresponding to the distance between the first client and the second client is complemented and normalized, and the construction method corresponding to the gender cross-correspondence between the first client and the second client is vector inner product.
Optionally, after determining the target second client matched with the first client in the client cluster, the server may obtain the client identifier of the target second client and the association between the target second client and the first client based on the user behavior data, sort the target second clients based on the association between the target second client and the first client, and send the client identifier of the sorted target second client to the first client.
Optionally, the server may send the client identifier of the target second client and the association between the target second client and the first client to the first client, so that the first client sorts the target second client based on the association between the first client and the first client, and displays the client identifier of the sorted target second client.
Taking the interface diagram shown in fig. 6 as an example, the first client may display the client identifier of each target second client, where a distance between the target second client and the first client is smaller than a preset distance threshold, a correlation degree between the first target second client and the first client is larger, and a correlation degree between the last target second client and the first client is smaller.
In the embodiment of the invention, the server receives the user query request sent by the first client, determines the client cluster of which the distance to the first client is less than the preset distance threshold value based on the position information of the first client carried by the user query request, acquires the user behavior data of each second client contained in the client cluster, and determines the target second client matched with the first client in the client cluster based on the user behavior data of each second client, so that the accuracy of user query can be improved.
Referring to fig. 7, fig. 7 is a schematic structural diagram of a server provided in an embodiment of the present invention, where the server may be used to implement part or all of the steps in the method embodiment shown in fig. 2, and as shown in the figure, the server in the embodiment may at least include a query request receiving unit 701, a cluster determining unit 702, a behavior data obtaining unit 703, and a client determining unit 704, where:
the query request receiving unit 701 is configured to receive a user query request sent by a first client, where the user query request carries location information of the first client.
A cluster determining unit 702, configured to determine, based on the location information, a client cluster whose distance from the first client is smaller than a preset distance threshold, where the client cluster includes at least one second client.
A behavior data obtaining unit 703 is configured to obtain user behavior data of each second client included in the client cluster.
A client determining unit 704, configured to determine, in the client cluster, a target second client that matches the first client based on the user behavior data of each second client.
Optionally, the user behavior data includes at least one of:
and attribute information aiming at different social fields, wherein the attribute information comprises one or more of webpage access characteristics, social states, user preference information, identity information and game attributes.
And the second client side and the first client side interact data.
A friend-making level of the second client.
Optionally, the client determining unit 704 determines, based on the user behavior data of each second client, a target second client matched with the first client in the client cluster, and is specifically configured to:
and acquiring attribute information of the first client aiming at different social fields.
And comparing the attribute information of the first client aiming at the target social field with the attribute information of the second client aiming at the target social field to obtain the association degree between the first client and the second client.
And when the association degree between the first client and the second client is greater than a preset proportion threshold value, taking the second client as the target second client.
Optionally, the client determining unit 704 compares the attribute information of the first client for the target social domain with the attribute information of the second client for the target social domain to obtain an association degree between the first client and the second client, and is specifically configured to:
and comparing the attribute information of the first client aiming at the target social domain with the attribute information of the second client aiming at the target social domain to obtain a comparison result.
And acquiring a first characteristic value of the second client based on the interactive data between the second client and the first client.
And acquiring a second characteristic value of the second client based on the friend making level of the second client.
And acquiring the association degree between the first client and the second client based on the comparison result, the first characteristic value and the second characteristic value.
Optionally, the client determining unit 704 is specifically configured to:
and processing the user behavior data of each second client contained in the client cluster through a preset logistic regression algorithm to obtain the association degree between each second client and the first client.
And taking a second client with higher association degree in second clients contained in the client cluster as the target second client.
Optionally, the server in the embodiment of the present invention may further include:
an identifier obtaining unit 705, configured to, after the client determining unit 704 determines, based on the user behavior data, a target second client that matches the first client in the client cluster, obtain a client identifier of the target second client and an association degree between the target second client and the first client.
A sorting unit 706, configured to sort the target second clients based on the association degrees with the first client.
An identifier sending unit 707, configured to send the client identifier of the ordered target second client to the first client.
In the embodiment of the present invention, an inquiry request receiving unit 701 receives a user inquiry request sent by a first client, where the user inquiry request carries location information of the first client, a cluster determining unit 702 determines, based on the location information, a client cluster whose distance from the first client is smaller than a preset distance threshold, a behavior data obtaining unit 703 obtains user behavior data of each second client included in the client cluster, and a client determining unit 704 determines, based on the user behavior data of each second client, a target second client matched with the first client in the client cluster, so that accuracy of user inquiry can be improved.
Referring to fig. 8, fig. 8 is a schematic structural diagram of a server according to another embodiment of the present invention, where the server according to the embodiment of the present invention may be used to implement the method according to the embodiment of the present invention shown in fig. 2, and for convenience of description, only the part related to the embodiment of the present invention is shown, and details of the specific technology are not disclosed, please refer to the embodiment of the present invention shown in fig. 2.
As shown in fig. 8, the server includes: at least one processor 801, such as a CPU, at least one input device 803, at least one output device 804, memory 805, at least one communication bus 802. Wherein a communication bus 802 is used to enable connective communication between these components. The input device 803 may specifically be a network interface, and the like, and is used for performing data interaction with the first client. The output device 804 may specifically be a network interface, and is used for performing data interaction with the first client. The memory 805 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory, specifically for storing user behavior data of each client. The memory 805 may optionally include at least one memory device located remotely from the processor 801 as previously described. A set of program codes is stored in the memory 805, and the processor 801, the input device 803, and the output device 804 call the program codes stored in the memory 805 for performing the following operations:
the input device 803 receives a user query request sent by a first client, where the user query request carries location information of the first client.
The processor 801 determines, based on the location information, a client cluster having a distance to the first client that is less than a preset distance threshold, the client cluster including at least one second client.
The processor 801 obtains the user behavior data of each second client included in the client cluster.
The processor 801 determines a target second client matching the first client in the client cluster based on the user behavior data of each second client.
Optionally, the user behavior data includes at least one of:
and attribute information aiming at different social fields, wherein the attribute information comprises one or more of webpage access characteristics, social states, user preference information, identity information and game attributes.
And the second client side and the first client side interact data.
A friend-making level of the second client.
Optionally, the processor 801 may determine, in the client cluster, a target second client matched with the first client based on the user behavior data of each second client, specifically:
the processor 801 obtains attribute information of the first client for different social fields.
The processor 801 compares the attribute information of the first client for the target social domain with the attribute information of the second client for the target social domain to obtain the association degree between the first client and the second client.
When the association degree between the first client and the second client is greater than a preset ratio threshold, the processor 801 takes the second client as the target second client.
Optionally, the processor 801 compares the attribute information of the first client for the target social domain with the attribute information of the second client for the target social domain to obtain the association degree between the first client and the second client, which may specifically be:
the processor 801 compares the attribute information of the first client for the target social domain with the attribute information of the second client for the target social domain to obtain a comparison result.
The processor 801 obtains a first feature value of the second client based on the interaction data between the second client and the first client.
The processor 801 obtains a second feature value of the second client based on the friend making level of the second client.
The processor 801 obtains the association degree between the first client and the second client based on the comparison result, the first feature value, and the second feature value.
Optionally, the processor 801 determines, in the client cluster, a target second client matched with the first client based on the user behavior data, specifically may be:
the processor 801 processes the user behavior data of each second client included in the client cluster through a preset logistic regression algorithm, so as to obtain the association degree between each second client and the first client.
The processor 801 takes the second client with the higher association degree in the second clients included in the client cluster as the target second client.
Optionally, after determining, by the processor 801 and based on the user behavior data, a target second client matching the first client in the client cluster, the following operations may be further performed:
the processor 801 obtains the client identifier of the target second client and the association degree between the target second client and the first client.
The output means 804 sends the client identification of the ordered target second client to the first client.
Specifically, the server described in the embodiment of the present invention may be used to implement part or all of the flow in the embodiment of the method described in conjunction with fig. 2 of the present invention.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.
Claims (12)
1. A method of data processing, the method comprising:
receiving a user query request sent by a first client, wherein the user query request carries position information of the first client;
determining a client cluster of which the distance from the first client is smaller than a preset distance threshold value based on the position information, wherein the client cluster comprises at least one second client;
acquiring user behavior data of each second client included in the client cluster, wherein the user behavior data comprises attribute information aiming at different social fields, and the attribute information comprises one or more of webpage access characteristics, social contact states, user preference information, identity information and game attributes;
acquiring attribute information of the first client aiming at different social fields and attribute information of each second client in a client cluster aiming at different social fields;
when the similarity between the attribute information of the first client aiming at each social domain and the attribute information of any second client aiming at the social domain is larger than a preset proportion threshold value, adding 1 to the numerator of the association degree between the second client and the first client; when the similarity between the attribute information of the first client for each social domain and the attribute information of the second client for the social domain is smaller than or equal to the preset proportion threshold, keeping the association degree between the second client and the first client unchanged;
and determining a target second client matched with the first client in the client cluster according to the association degree between each second client and the first client.
2. The method of claim 1, wherein the user behavior data further comprises at least one of:
interaction data between the second client and the first client;
a friend-making level of the second client.
3. The method of claim 2, wherein after obtaining the attribute information of the first client for different social areas and the attribute information of each of the second clients in the client cluster for different social areas, further comprising:
comparing the attribute information of the first client aiming at the target social domain with the attribute information of the second client aiming at the target social domain to obtain a comparison result;
acquiring a first characteristic value of the second client based on interaction data between the second client and the first client;
acquiring a second characteristic value of the second client based on the friend making level of the second client;
acquiring the association degree between the first client and the second client based on the comparison result, the first characteristic value and the second characteristic value;
and determining a target second client matched with the first client in the client cluster according to the association degree between each second client and the first client.
4. The method of claim 1, wherein after obtaining the attribute information of the first client for different social areas and the attribute information of each of the second clients in the client cluster for different social areas, further comprising:
processing the user behavior data of each second client contained in the client cluster through a preset logistic regression algorithm to obtain the association degree between each second client and the first client;
taking a second client with a larger association degree in second clients included in the client cluster as the target second client, wherein the second client with the larger association degree refers to: the sum of the number is a preset number, and the association degree between the second client and the first client is greater than that between other second clients in the client cluster and the first client, or the association degree between the second client and the first client is greater than a preset proportion threshold.
5. The method of any one of claims 1 to 4, wherein after determining a target second client matching the first client in the client cluster according to the association degree between each second client and the first client, further comprising:
acquiring a client identifier of the target second client and the association degree between the target second client and the first client;
ranking the target second client based on the degree of association with the first client;
and sending the client identification of the ordered target second client to the first client.
6. A server, characterized in that the server comprises:
the device comprises a query request receiving unit, a query processing unit and a query processing unit, wherein the query request receiving unit is used for receiving a user query request sent by a first client, and the user query request carries the position information of the first client;
a cluster determining unit, configured to determine, based on the location information, a client cluster in which a distance to the first client is smaller than a preset distance threshold, where the client cluster includes at least one second client;
the behavior data acquisition unit is used for acquiring user behavior data of each second client contained in the client cluster, wherein the user behavior data comprises attribute information aiming at different social fields, and the attribute information comprises one or more of webpage access characteristics, social contact states, user preference information, identity information and game attributes;
the client determining unit is used for acquiring the attribute information of the first client aiming at different social fields and the attribute information of each second client in the client cluster aiming at different social fields; when the similarity between the attribute information of the first client aiming at each social domain and the attribute information of any second client aiming at the social domain is larger than a preset proportion threshold value, adding 1 to the numerator of the association degree between the second client and the first client; when the similarity between the attribute information of the first client for each social domain and the attribute information of the second client for the social domain is smaller than or equal to the preset proportion threshold, keeping the association degree between the second client and the first client unchanged; and determining a target second client matched with the first client in the client cluster according to the association degree between each second client and the first client.
7. The server of claim 6, wherein the user behavior data further comprises at least one of:
interaction data between the second client and the first client;
a friend-making level of the second client.
8. The server according to claim 7,
the client determining unit is further configured to compare the attribute information of the first client for the target social domain with the attribute information of the second client for the target social domain after acquiring the attribute information of the first client for different social domains and the attribute information of each second client in the client cluster for different social domains, so as to obtain a comparison result; acquiring a first characteristic value of the second client based on interaction data between the second client and the first client; acquiring a second characteristic value of the second client based on the friend making level of the second client; acquiring the association degree between the first client and the second client based on the comparison result, the first characteristic value and the second characteristic value; and determining a target second client matched with the first client in the client cluster according to the association degree between each second client and the first client.
9. The server according to claim 6,
the client determining unit is further configured to, after obtaining attribute information of the first client for different social fields and attribute information of each second client in a client cluster for different social fields, process, by using a preset logistic regression algorithm, user behavior data of each second client included in the client cluster to obtain a degree of association between each second client and the first client;
taking a second client with a larger association degree in second clients included in the client cluster as the target second client, wherein the second client with the larger association degree refers to: the sum of the number is a preset number, and the association degree between the second client and the first client is greater than that between other second clients in the client cluster and the first client, or the association degree between the second client and the first client is greater than a preset proportion threshold.
10. The server according to any one of claims 6 to 9, wherein the server further comprises:
an identifier obtaining unit, configured to, by the client determining unit, obtain, after determining, according to the association between each second client and the first client, a target second client that matches the first client in the client cluster, a client identifier of the target second client and an association between the target second client and the first client;
the ordering unit is used for ordering the target second client based on the association degree with the first client;
and the identification sending unit is used for sending the client identification of the ordered target second client to the first client.
11. A server, characterized in that the server comprises:
a memory for storing program code;
a processor for calling the program code stored in the memory to execute the data processing method of any one of claims 1 to 5.
12. A computer-readable storage medium, characterized in that it stores a computer program comprising program instructions that, when executed by a server, cause the server to perform the data processing method according to any one of claims 1 to 5.
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| CN112632406B (en) * | 2020-10-10 | 2024-04-09 | 咪咕文化科技有限公司 | Query method, query device, electronic equipment and storage medium |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101287082A (en) * | 2008-05-16 | 2008-10-15 | 华东师范大学 | Collaborative filtered recommendation method introducing hotness degree weight of program |
| CN103377250A (en) * | 2012-04-27 | 2013-10-30 | 杭州载言网络技术有限公司 | Top-k recommendation method based on neighborhood |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100268661A1 (en) * | 2009-04-20 | 2010-10-21 | 4-Tell, Inc | Recommendation Systems |
| CN102752708A (en) * | 2011-04-20 | 2012-10-24 | 曹晓刚 | Parallel friend recommendation system and method capable of serving on basis of geographic positions |
| CN103096242B (en) * | 2011-11-03 | 2015-05-20 | 腾讯科技（深圳）有限公司 | User recommendation method and system based on location based services |
| CN103166834A (en) * | 2011-12-15 | 2013-06-19 | 北京千橡网景科技发展有限公司 | Method and equipment recommending friends to social network user based on location |
| CN103188139B (en) * | 2011-12-31 | 2016-04-13 | 腾讯科技（深圳）有限公司 | A kind of information displaying method of recommending friends and device |
| CN102629945B (en) * | 2012-04-16 | 2014-07-23 | 腾讯科技（深圳）有限公司 | Method for user information acquisition and server as well as terminal |
| CN103581270B (en) * | 2012-08-08 | 2015-12-16 | 腾讯科技（深圳）有限公司 | User's recommend method and system |
| US20140143352A1 (en) * | 2012-11-20 | 2014-05-22 | Linkedln Corporation | User profile and geography-based meetings |
| EP2852105A1 (en) * | 2013-09-20 | 2015-03-25 | Ignazio Di Tocco | Computer system and related process supporting the communication of users located in the same geographical area, in order to establish a starting contact leading to a personal communication |
-
2017
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101287082A (en) * | 2008-05-16 | 2008-10-15 | 华东师范大学 | Collaborative filtered recommendation method introducing hotness degree weight of program |
| CN103377250A (en) * | 2012-04-27 | 2013-10-30 | 杭州载言网络技术有限公司 | Top-k recommendation method based on neighborhood |
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