Method and device for identifying friend remarks of social software

Abstract

The invention discloses a friend remark recognition method and device for social software, and relates to the technical field of data processing. Receiving a remark identification request sent by the first user, and selecting a second user in the target friend list according to the remark identification request; determining a target chat frequency from the target chat information; when the target chat frequency is smaller than the preset chat frequency, determining a low intimacy level corresponding to the second user, and obtaining a first number and a second number according to the low intimacy level; acquiring a third number corresponding to the same position; inputting the first quantity, the second quantity and the third quantity into a preset relational database for query to obtain a first relational tag; and generating first remark list information according to the first relation label, so that the first user selects a first remark name from the first remark list information to remark the second user. By implementing the technical scheme, the tedious problem that the user needs to manually query to determine the identity of the friend without remark and remark is solved.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and particularly to a method and device for recognizing friend remarks of social software. Background Art

[0002] Social software has greatly promoted the instant connectivity between people, enabling users to communicate and interact across time and space boundaries at any time. Through the instant messaging function, such platforms have significantly improved the speed and efficiency of information transmission. Users can send and receive information instantaneously, thus accelerating the communication process.

[0003] In the process of using social network applications, users frequently interact and converse through instant messaging tools, which greatly enriches their daily life experiences. Instant messaging tools are not only used for daily chit-chat but are also often used by users to send various types of messages, including but not limited to festival greetings and transactional notifications, reflecting their extensive application in various communication scenarios. However, when browsing the friend list, users may encounter the situation where contacts are not remarked, which directly affects their ability to quickly identify the other party's identity. For unremarked friends, users can only rely on querying chat information or asking others to determine the other party's identity and then make corresponding remarks according to the other party's identity. However, this identification method is overly dependent on users' manual queries, which takes a long time and the identification results are also inaccurate.

[0004] Therefore, there is an urgent need for a method and device for recognizing friend remarks of social software that can solve the above technical problems. Summary of the Invention

[0005] This application provides a method and device for recognizing friend remarks of social software, which solves the cumbersome and inefficient problems of users manually querying to determine the identity of unremarked friends and making remarks.

[0006] In a first aspect, the present application provides a method for identifying friend remarks of a social software, which is applied to a server. The method includes: receiving a remark identification request sent by a first user, selecting a second user from a target friend list according to the remark identification request, where the second user is the user to be identified; obtaining target chat information corresponding to the first user and the second user, and determining a target chat frequency from the target chat information; determining whether the target chat frequency is less than a preset chat frequency; when the target chat frequency is less than the preset chat frequency, determining that the second user corresponds to a low intimacy level, and obtaining a first quantity and a second quantity according to the low intimacy level, where the first quantity is the total quantity corresponding to a third user, and the third user is a user who exists in the friend lists corresponding to the first user and the second user at the same time, and the second quantity is the total quantity of a target group, and the target group is a chat group joined by both the first user and the second user; obtaining a first location corresponding to the first user, and obtaining a second location corresponding to the second user; analyzing the first location and the second location to obtain the same location, and obtaining a third quantity corresponding to the same location; inputting the first quantity, the second quantity, and the third quantity into a preset relational database for querying to obtain a first relationship label, where the first relationship label includes a family relationship label, a colleague relationship label, a classmate relationship label, a friend relationship label, a customer relationship label, and an online relationship label; generating first remark list information according to the first relationship label, and recommending the first remark list information to the first user, so that the first user can select a first remark name from the first remark list information to remark the second user, and the first remark name is a remark name generated according to the first relationship label.

[0007] By adopting the above technical solution, receiving the remark identification request of the first user, automatically selecting the second user to be identified from the target friend list, obtaining and analyzing the chat frequency between the first user and the second user, judging the intimacy degree between the first user and the second user, when the target chat frequency is less than the preset chat frequency, considering that the second user corresponds to a low intimacy level, then obtaining the first quantity of the common friends of the first user and the second user, and the second quantity of the group chats jointly joined by the first user and the second user, and at the same time obtaining the third quantity of the same location of the first user and the second user, by inputting the first quantity, the second quantity, and the third quantity into the preset relational database for querying, the relationship label existing between the first user and the second user can be intelligently predicted. This process greatly reduces the need for users to manually query and judge. Generating and recommending the first remark list information to the first user based on the predicted first relationship label, and the remark name is automatically generated according to the first relationship label, saving the time for the user to think about the remark name and improving the accuracy of the remark.

[0008] Optionally, analyze the first position and the second position to obtain the same position, specifically including: obtaining the target time point, which is the time point when the first user and the second user officially become friends; obtaining multiple first positions corresponding to the first user within the first time period, and summarizing the multiple first positions into the first position set, where the first time period is the interval duration between the target time point and the current time point; obtaining multiple second positions of the second user within the first time period, and summarizing the multiple second positions into the second position set; obtaining the third position from the first position set, and determining whether the third position exists in the second position set; when the third position exists in the second position set, determine that both the first user and the second user have the third position, and output the third position as the same position.

[0009] By adopting the above technical solution, collect multiple position information of the first user and the second user within the first time period respectively, and summarize this information into their respective position sets. By accurately identifying the intersecting positions, that is, the common positions, analyze the relationship between the first user and the second user based on the common positions, providing more accurate data input for subsequent note recognition, thereby improving the accuracy of subsequent recognition.

[0010] Optionally, after analyzing the first position and the second position to obtain the same position, the method further includes: obtaining the second time period, which is the total time period when both the first user and the second user are at the third position; analyzing the second time period to obtain the target time period, which is the time period between the start time and the end time when the first user and the second user are simultaneously at the third position; determining whether the target time period is in the preset time schedule, where the preset time schedule is the time schedule corresponding to normal daytime work; when the target time period is in the preset time schedule, determine the preset first relationship label, where the preset first relationship label includes the colleague relationship label, the classmate relationship label, and the customer relationship label; determining whether the preset first relationship label exists in the first relationship label; when the preset first relationship label exists in the first relationship label, output the preset first relationship label as the first relationship label.

[0011] By adopting the above technical solution, analyze the second time period to obtain the target time period, which indicates the time period when the first user and the second user are at the same position. Then compare the target time period with the preset time schedule, which can accurately predict the relationship label between the first user and the second user. By considering the coexistence duration and the relationship between the coexistence time and the preset time schedule, improve the accuracy of user relationship label prediction.

[0012] Optionally, after determining whether the target time period is in the preset time schedule, the method further includes: when the target time period is not in the preset time schedule, determining a preset second relationship label, where the preset second relationship label includes a relative relationship label, a friend relationship label, and a classmate relationship label; determining whether there is a preset second relationship label in the first relationship label; when there is a preset second relationship label in the first relationship label, outputting the preset second relationship label as the first relationship label.

[0013] By adopting the above technical solution, not only the co-presence situation during working hours is considered to determine the colleague or customer relationship, but also the co-presence situation during non-working hours is considered to determine the relative, friend or classmate relationship. By introducing the preset second relationship label, the direction of relationship prediction can be flexibly adjusted according to different situations of the target time period. This flexibility helps to meet the needs of different user groups and social scenarios and improve the applicability and accuracy of prediction.

[0014] Optionally, after determining whether the target chat frequency is less than the preset chat frequency, the method further includes: when the target chat frequency is greater than the preset chat frequency, determining a high intimacy level corresponding to the second user; obtaining a first quantity and a second quantity according to the high intimacy level, inputting the first quantity and the second quantity into a preset relationship database for matching to obtain a second relationship label; generating second note list information according to the second relationship label, and recommending the second note list information to the first user, so that the first user can select a second note name from the second note list information to note the second user, and the second note name is a note name generated according to the second relationship label.

[0015] By adopting the above technical solution, when the target chat frequency is greater than the preset chat frequency, by introducing the number of common friends and common group chats as the basis for relationship matching, the relationship label between users can be predicted more accurately. For the first user, quickly and accurately setting notes for friends is the key to improving the social experience.

[0016] Optionally, after determining whether the target chat frequency is less than the preset chat frequency, the method further includes: when the target chat frequency is equal to the preset chat frequency, determining a medium intimacy level corresponding to the second user; performing text recognition on the target chat information to obtain target text information; inputting the target text information into a preset text database for matching to obtain a third relationship label; obtaining first release information corresponding to the first user and second release information corresponding to the second user; calculating the similarity between the first release information and the second release information to obtain a target similarity; determining a fourth relationship label according to the target similarity, combining the third relationship label and the fourth relationship label to obtain a fifth relationship label; generating third note list information according to the fifth relationship label, and recommending the third note list information to the first user.

[0017] By adopting the above technical solution, when the target chat frequency is equal to the preset chat frequency, the medium intimacy level corresponding to the second user is determined, the target text information is obtained from the target chat information according to the medium intimacy level, the target text information is then analyzed to obtain the third relationship label, the similarity between the first release information of the first user and the second release information of the second user is calculated to obtain the target similarity, and then the fourth relationship label is determined according to the target similarity. By comprehensively analyzing the third relationship label and the fourth relationship label, the fifth relationship label is obtained. The generation of the fifth relationship label is more in line with the note list information of the actual relationship between users, reducing the time and effort of manual operation by users and optimizing the user experience.

[0018] Optionally, after generating the first note list information according to the first relationship label, the method further includes: generating a plurality of note names according to the first relationship label, and obtaining a third note name and a fourth note name from the plurality of note names; determining whether the priority of the third note name is higher than the priority of the fourth note name; when the priority of the third note name is higher than the priority of the fourth note name, determining to rank the third note name before the fourth note name in the note list, and sending the sorted first note list information to the first user.

[0019] By adopting the above technical solution, a plurality of note names are generated for the first relationship label, providing diverse choices for users. By providing a sorting function for priorities, the plurality of note names are sorted according to the priority order, enabling users to find the most suitable note name more quickly, reducing the time for users to manually input and filter notes, and improving the convenience of note setting.

[0020] In the second aspect of the present application, a device for identifying friend remarks of a social software is provided. The device is a server, which includes a receiving unit, a processing unit, and a recommending unit; the receiving unit receives a remark identification request sent by a first user, selects a second user from a target friend list according to the remark identification request, and the second user is the user to be identified; obtains the target chat information corresponding to the first user and the second user, and determines the target chat frequency from the target chat information; the processing unit determines whether the target chat frequency is less than a preset chat frequency; when the target chat frequency is less than the preset chat frequency, determines that the second user corresponds to a low intimacy level, obtains a first quantity and a second quantity according to the low intimacy level, the first quantity is the total quantity corresponding to a third user, and the third user is a user who exists in the friend lists corresponding to the first user and the second user at the same time, and the second quantity is the total quantity of the target group, and the target group is a chat group joined by both the first user and the second user; obtains the first position corresponding to the first user, and obtains the second position corresponding to the second user; analyzes the first position and the second position to obtain the same position, and obtains the third quantity corresponding to the same position; inputs the first quantity, the second quantity, and the third quantity into a preset relational database for querying to obtain a first relationship label, and the first relationship label includes a relative relationship label, a colleague relationship label, a classmate relationship label, a friend relationship label, a customer relationship label, and an online relationship label; the recommending unit generates first remark list information according to the first relationship label, and recommends the first remark list information to the first user, so that the first user can select a first remark name from the first remark list information to remark on the second user, and the first remark name is a remark name generated according to the first relationship label.

[0021] Optionally, the receiving unit is used to obtain a target time point, which is the time point when the first user and the second user officially become friends; obtains multiple first positions corresponding to the first user within a first time period, and summarizes the multiple first positions into a first position set, and the first time period is the interval duration between the target time point and the current time point; obtains multiple second positions of the second user within the first time period, and summarizes the multiple second positions into a second position set; obtains a third position from the first position set, and determines whether the third position exists in the second position set; the processing unit is used to determine that both the first user and the second user have the third position when the third position exists in the second position set, and output the third position as the same position.

[0022] Optionally, the receiving unit is used to obtain a second duration, where the second duration is the total duration when the first user and the second user are both at the third location; the processing unit is used to analyze the second duration to obtain a target time period, where the target time period is the time period between the start time and the end time when the first user and the second user are at the third location simultaneously; determine whether the target time period is in a preset time schedule, where the preset time schedule is the time schedule corresponding to normal daytime work; when the target time period is in the preset time schedule, determine a preset first relationship label, where the preset first relationship label includes a colleague relationship label, a classmate relationship label, and a customer relationship label; determine whether there is a preset first relationship label in the first relationship label; when there is a preset first relationship label in the first relationship label, output the preset first relationship label as the first relationship label.

[0023] Optionally, when the target time period is not in the preset time schedule, the processing unit is used to determine a preset second relationship label, where the preset second relationship label includes a family member relationship label, a friend relationship label, and a classmate relationship label; determine whether there is a preset second relationship label in the first relationship label; when there is a preset second relationship label in the first relationship label, output the preset second relationship label as the first relationship label.

[0024] Optionally, when the target chat frequency is greater than a preset chat frequency, the processing unit is used to determine that the second user corresponds to a high intimacy level; obtain a first quantity and a second quantity according to the high intimacy level, input the first quantity and the second quantity into a preset relationship database for matching to obtain a second relationship label; the recommendation unit is used to generate second note list information according to the second relationship label and recommend the second note list information to the first user, so that the first user can select a second note name from the second note list information to note the second user, and the second note name is a note name generated according to the second relationship label.

[0025] Optionally, when the target chat frequency is equal to the preset chat frequency, the processing unit is used to determine that the second user corresponds to a medium intimacy level; perform text recognition on the target chat information to obtain target text information; input the target text information into a preset text database for matching to obtain a third relationship label; the receiving unit is used to obtain a first release information corresponding to the first user and obtain a second release information corresponding to the second user; the processing unit is used to calculate the similarity between the first release information and the second release information to obtain a target similarity; determine a fourth relationship label according to the target similarity, and combine the third relationship label and the fourth relationship label to obtain a fifth relationship label; the recommendation unit is used to generate third note list information according to the fifth relationship label and recommend the third note list information to the first user.

[0026] Optionally, the processing unit is configured to generate multiple note names according to the first relationship tag, obtain a third note name and a fourth note name from the multiple note names; determine whether the priority of the third note name is higher than the priority of the fourth note name; when the priority of the third note name is higher than the priority of the fourth note name, determine that the third note name is ranked before the fourth note name in the note list, and send the sorted first note list information to the first user.

[0027] In a third aspect of the present application, an electronic device is provided. The electronic device includes a processor, a memory, a user interface, and a network interface. The memory is used to store instructions. The user interface and the network interface are used to communicate with other devices. The processor is configured to execute the instructions stored in the memory, so that the electronic device executes the method according to any one of the above in the present application.

[0028] In a fourth aspect of the present application, a computer-readable storage medium is provided. The computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of the above in the present application is executed.

[0029] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. Receive the note recognition request of the first user, automatically select the second user to be recognized in the target friend list, obtain and analyze the chat frequency between the first user and the second user, judge the intimacy between the first user and the second user. When the target chat frequency is less than the preset chat frequency, it is considered that the second user corresponds to a low intimacy level. Then obtain the first number of common friends between the first user and the second user, and the second number of group chats jointly joined by the first user and the second user. At the same time, obtain the third number of the same locations between the first user and the second user. By inputting the first number, the second number, and the third number into the preset relationship database for query, the relationship tag existing between the first user and the second user can be intelligently predicted. This process greatly reduces the need for users to manually query and judge. Based on the predicted first relationship tag, generate and recommend the first note list information to the first user. The note names are automatically generated according to the first relationship tag, saving the user's time to think about the note names and improving the accuracy of the notes.

[0030] 2. Generate multiple note names for the first relationship tag, providing users with diversified choices. By providing a priority sorting function, sort the multiple note names according to the priority order, enabling users to find the most suitable note name more quickly, reducing the time for users to manually input and filter notes, and improving the convenience of note setting. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1It is the first process schematic diagram of a method for identifying friend remarks in a social software provided by an embodiment of the present application; Figure 2 It is a scenario schematic diagram of a method for identifying friend remarks in a social software provided by an embodiment of the present application; Figure 3 It is the second process schematic diagram of a method for identifying friend remarks in a social software provided by an embodiment of the present application; Figure 4 It is a structural schematic diagram of a device for identifying friend remarks in a social software provided by an embodiment of the present application; Figure 5 It is a structural schematic diagram of an electronic device disclosed by an embodiment of the present application.

[0032] Explanation of reference numerals: 401, receiving unit; 401, processing unit; 403, recommendation unit; 500, electronic device; 501, processor; 502, memory; 503, user interface; 504, network interface; 505, communication bus. Detailed implementation manners

[0033] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments.

[0034] In the description of the embodiments of the present application, words such as "for example" or "for illustration" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "for example" or "for illustration" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly, using words such as "for example" or "for illustration" aims to present relevant concepts in a specific manner.

[0035] In the description of the embodiments of the present application, the meaning of the term "plurality" refers to two or more. For example, a plurality of systems refers to two or more systems, and a plurality of screen terminals refers to two or more screen terminals. In addition, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the technical features indicated. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "include", "comprise", "have" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0036] Social software has greatly promoted instant connectivity among people, enabling users to communicate and interact regardless of time and space boundaries. Through instant messaging functions, such platforms have significantly improved the speed and efficiency of information transmission. Users can send and receive information instantly, thus accelerating the communication process.

[0037] In the process of using social network applications, users frequently interact and converse through instant messaging tools, which greatly enriches their daily life experiences. Instant messaging tools are not only used for casual chatting but also often used by users to send various types of messages, including but not limited to festival greetings and transactional notifications, reflecting their wide application in various communication scenarios. However, when browsing the friend list, users may encounter the situation where they have not made remarks on contacts, which directly affects their ability to quickly identify the identity of the other party. For friends without remarks, users can only rely on querying chat information or asking others to determine the identity of the other party and then make corresponding remarks according to the identity of the other party. However, this identification method relies too much on the user's manual query, which takes a long time and the identification result is also inaccurate.

[0038] Therefore, how to solve the cumbersome problem of users manually querying to determine the identity of friends without remarks. An embodiment of the present application provides a method for identifying friend remarks of social software, which is applied to a server. The server of the present application can be a platform that provides friend remark identification services for social software. Figure 1 It is the first process schematic diagram of a method for identifying friend remarks of social software provided by an embodiment of the present application. Refer to Figure 1 , this method includes the following steps S101 - step S107.

[0039] S101: Receive a remark identification request sent by a first user, and select a second user from the target friend list according to the remark identification request.

[0040] In the above S101, when a user is socializing using a social software, they may forget to add a note for a certain friend when initially adding friends, or due to other reasons, the friend note is accidentally deleted. When checking the friend list, the problem to be solved in this application is how to add a note for a friend without a note and then assign a corresponding note name to the friend. This application provides a method for assisting the user in adding a note name for a friend. By inferring various information between the user and the friend, a note name is generated based on the inference result and recommended to the user for selection, saving the process that the user needs to query manually. Next, it will be explained in detail how this application assists the user in identifying the friend note name. First, the first user refers to the user who sends the note identification request. When the first user wants to add a note for a certain user (the second user) in their friend list, the server will receive a note identification request. The note identification request includes the identifier of the second user (such as user ID or username) so that the server can locate the user in the friend list. Locate the corresponding second user in the target friend list according to the note identification request. The target friend list is the friend list of the first user, and search for the user in the target friend list that matches the second user specified in the note identification request.

[0041] For example, after the first user logs in to the corresponding APP of the social software and browses the friend list, when finding a friend without a note during the browsing process, they can click to enter the chat box corresponding to the friend and send a note identification request to the server. After the server receives the note identification request, it can locate the chat box currently corresponding to the user, determine the corresponding user according to the chat box, and define this user as the user to be noted.

[0042] S102: Obtain the target chat information corresponding to the first user and the second user, and determine the target chat frequency from the target chat information.

[0043] In the above S102, after determining the second user from the target friend list, obtain all the chat records between the first user and the second user. These chat records include message content, sending time, etc. By analyzing these chat records, calculate the chat frequency between the first user and the second user, that is, the number of chats they have per day, week, or month on average.

[0044] S103: Determine whether the target chat frequency is less than the preset chat frequency.

[0045] In the above S103, compare the calculated target chat frequency with a preset threshold (preset chat frequency). The preset chat threshold is used to judge the intimacy level between the first user and the second user, and the preset chat threshold is set according to the historical intimacy relationship.

[0046] S104: When the target chat frequency is less than the preset chat frequency, determine the low intimacy level corresponding to the second user, and obtain the first quantity and the second quantity according to the low intimacy level.

[0047] In the above S104, if the chat frequency is lower than this threshold, it is considered that the intimacy between the first user and the second user is low, and their relationship needs to be further analyzed. Once it is determined that the second user corresponds to the low intimacy level, start collecting information about the common elements between the first user and the second user. The first quantity is the total quantity corresponding to the third user, where the third user is the user who exists simultaneously in the friend lists corresponding to the first user and the second user respectively. The second quantity is the total quantity of the target group, where the target group is the chat group that both the first user and the second user have joined. The first quantity can also be understood as the number of common friends between the first user and the second user, and the second quantity is the total number of group chats that the first user and the second user have joined together. When obtaining the common friends, the authorization to access the friend list of the social software has been obtained in advance, that is, the server can arbitrarily obtain the friend lists of each user in the social software, then obtain the friend list corresponding to the first user, then obtain the friend list corresponding to the second user, and then compare each friend in the friend list corresponding to the first user with the friend list corresponding to the second user in turn. When there is the same friend in the friend list corresponding to the second user and the friend list corresponding to the first user, define this friend as the third user and count the number of the third user, denoted as the first quantity. When obtaining the common group chats, first obtain all the group chats that the first user has joined, and then check the group members (users in the group chat) in each group chat in turn. When checking, it can be based on the unique identifier of the user on the social software, that is, the user's account, to determine whether the second user exists in the group chats that the first user has joined. If the first user and the second user exist simultaneously in a certain group chat, define this group chat as the target group and count the number of the target group, denoted as the second quantity.

[0048] S105: Obtain the first location corresponding to the first user, and obtain the second location corresponding to the second user; analyze the first location and the second location to obtain the same location, and obtain the third quantity corresponding to the same location.

[0049] In the above S105, before obtaining the locations corresponding to the first user and the second user, the authorization of the first user and the second user needs to be obtained. Through the authorization of the first user and the second user for obtaining the geographical location, further obtain the geographical location information of the first user during a certain period, that is, the first location. These location information may come from the device location of the user, social media sharing or other sources. Then obtain the geographical location information of the second user during a certain period, that is, the second location.

[0050] In addition, after obtaining the first location corresponding to the first user and the second location corresponding to the second user respectively, analyze the first location and the second location to obtain the same location, which specifically includes: obtaining the target time point, where the target time point is the time point when the first user and the second user officially become friends; obtaining multiple first locations corresponding to the first user within the first time period, and summarizing the multiple first locations into the first location set, where the first time period is the interval duration between the target time point and the current time point; obtaining multiple second locations of the second user within the first time period, and summarizing the multiple second locations into the second location set; obtaining the third location from the first location set, and determining whether the third location exists in the second location set; when the third location exists in the second location set, it is determined that both the first user and the second user have the third location, and the third location is output as the same location. Specifically, the time point when the first user and the second user officially become friends can be obtained. This time point can be obtained from the user's friend list record, chat record, or other relevant logs. First, access the friend list or chat record database of the first user. Search for records related to the second user and find the exact time point when they became friends. Save this time point as the target time point. Then obtain the current time point, and calculate the interval duration between the target time point and the current time point, that is, the target duration. The target duration is used to determine the locations that the first user and the second user may have jointly visited after becoming friends. All location records of the first user within the first time period can be queried. These records may come from the user's device location, social media sharing, map applications, etc. Similar to the first user, query all location records of the second user within the first time period. Summarize these location records into the second location set. Select a location from the first location set as the third location. This third location can be the first location in chronological order, the last location, or the location selected according to a certain algorithm (such as the most frequent occurrence). Search for the same location as the third location in the second location set. The judgment criterion can be the exact match of the location coordinates or the fuzzy match based on a certain distance threshold. If the same location as the third location is found in the second location set, it is determined that both the first user and the second user have been at this location. Output this location as the same location. According to the above comparison process for the third location, then sequentially obtain other locations from the first location set and compare them with all locations in the second location set, so as to determine the same locations in the first location set and the second location set. It can accurately determine the locations jointly visited or existed by the first user and the second user, and provide useful information for the user. Then obtain the number of times the first user and the second user have the same location, that is, the third quantity.

[0051] For example, if the time when the first user adds the second user as a friend is May 21, 202x, and the current time point is April 21, 202x, the first duration from when the first user adds the second user as a friend is from May 21, 202x to April 21, 202x. Obtain multiple first positions of the first user during the first duration, obtain multiple second positions of the second user during the first duration, and then compare the multiple first positions with the multiple second positions in sequence to determine whether there are the same positions. If the positions of the first user include a, b, c, d, e, and f, and the positions of the second user include c, f, e, d, and h, compare them to determine that the same positions include c, d, e, and f.

[0052] S106: Input the first quantity, the second quantity, and the third quantity into a preset relational database for query to obtain a first relationship label.

[0053] In the above S106, take the first quantity, the second quantity, and the third quantity as inputs and query a preset relational database. This preset relational database may contain the corresponding relationships between various relationship features (such as the number of common friends, the number of common group chats, the number of common positions) and relationship labels (such as relatives, colleagues, classmates, friends, network, and customers, etc.). The preset relational database is trained through historical data to determine the possible relationships between the first user and the second user in terms of the number of common friends, common group chats, and the same positions under different quantities. The first relationship label includes a relative relationship label, a colleague relationship label, a classmate relationship label, a friend relationship label, a customer relationship label, and a network relationship label. By matching these features, assign one or more most likely relationship labels to the relationship between the first user and the second user.

[0054] In addition, in order to make the obtained first relationship label more in line with the actual situation of the first user and the second user, the co-presence time and time distribution of the first user and the second user at the same location can also be used to infer the relationship between them, and the first relationship label can be updated. Specifically, it includes: obtaining the second duration, where the second duration is the total duration when both the first user and the second user are at the third location; analyzing the second duration to obtain the target time period, where the target time period is the time period between the start time and the end time when the first user and the second user are simultaneously at the third location; determining whether the target time period is in the preset time schedule, where the preset time schedule is the time schedule corresponding to normal daytime work; when the target time period is in the preset time schedule, determining the preset first relationship label, where the preset first relationship label includes a colleague relationship label, a classmate relationship label, and a customer relationship label; determining whether the preset first relationship label exists in the first relationship label; when the preset first relationship label exists in the first relationship label, outputting the preset first relationship label as the first relationship label. Specifically, the total duration when both the first user and the second user are at the third location (the same location), that is, the second duration, can be obtained. This duration reflects the co-presence time of the users at the same location and is an important indicator for judging their relationship. First, obtain all the location records of the first user and the second user at the third location. Compare these records in terms of time to calculate the total duration when they are simultaneously at the third location. Then, obtain the time period between the start time and the end time when the first user and the second user are simultaneously at the third location, that is, the target time period. This time period can help us understand the specific time distribution of the users' co-presence at the third location. The second duration can be further analyzed to identify all the continuous time periods when the users are co-present at the third location. From these time periods, determine the longest or the most representative (with the highest repetition rate) time period as the target time period. The preset time schedule refers to the time schedule corresponding to normal daytime work, usually including the working hours on weekdays, such as from 9 am to 6 pm. This time schedule is used to judge whether the time of the users' co-presence conforms to the law of normal working hours. Compare the target time period with the preset time schedule. If most or all of the target time period falls within the preset time schedule, it is considered that this time period conforms to the law of normal working hours. Based on the fact that the target time period conforms to the preset time schedule, determine to output the preset first relationship label, where the preset first relationship label includes a colleague relationship label, a classmate relationship label, and a customer relationship label. These labels are the possible relationships preset based on the time period when the users are co-present during normal working hours. Since the first relationship label has been determined for the first user and the second user before (through common friends, common group chats, and the number of common locations, etc.), it is necessary to determine whether these labels contain the preset first relationship label. Check the current set of first relationship labels to find whether there is a label that matches the preset first relationship label. If there is a match and the target time period conforms to the preset time schedule, output the preset first relationship label as the final first relationship label.In the above example, after determining that the first user and the second user exist in the same location c, the duration corresponding to the first user and the second user being in the same location c at the same time is obtained in sequence, that is, the second duration, and then the second duration is analyzed to determine the continuous time period in which the first user and the second user coexist in the third location, and the time period with the most repetitions is selected from the continuous time period as the target time period for output. If the target time period is from 9:30 to 4:30, the target time period is compared with the preset time period. If the target time period is within the preset time period, it is confirmed that the first user and the second user may correspond to the preset first relationship tag, and the preset first relationship tag can be compared with the first relationship tag, and the same relationship tag is output. When the first relationship tag is colleagues, classmates, customers, and friends, etc., when the preset first relationship tags are colleague relationship tags, classmate relationship tags, and customer relationship tags, the two are compared at this time, and the same relationship tags are colleagues, classmates, and customers, so colleagues, classmates, and customers are output as first relationship tags.

[0055] Further, when the target time period is not in the preset schedule, a preset second relationship tag is determined, and the preset second relationship tag includes a family relationship tag, a friend relationship tag, and a classmate relationship tag; it is determined whether there is a preset second relationship tag in the first relationship tag; when there is a preset second relationship tag in the first relationship tag, the preset second relationship tag is output as the first relationship tag. Specifically, when the target time period corresponding to the first user and the second user at the third position is not in the preset schedule at the same time, it can be determined that the first user and the second user have a preset second relationship tag, and the preset second relationship tag is a family relationship tag, a friend relationship tag, and a classmate relationship tag. These tags are possible relationships preset based on the time period in which the users are together during the non-working time period. When it is determined that the target time period is not in the preset schedule, it is considered that the time period is non-working time. According to this judgment, it is determined to use the preset second relationship tag as a possible relationship tag set. The preset second relationship tag may also include other relationships, which are only listed here for ease of understanding. Since the first relationship tag has been determined for the first user and the second user before, it is necessary to check each relationship tag in the first relationship tag to determine whether the first relationship tag contains any of the preset second relationship tags. When traversing the current set of first relationship tags, compare it with the preset second relationship tags. If a match is found, that is, one or more of the preset second relationship tags exist in the first relationship tag, the next step will be performed. When a preset second relationship tag exists in the first relationship tag, select the tag (or the most suitable tag, if there are multiple matches) as the final first relationship tag for output. That is, the relationship tags that exist in both the first relationship tag and the preset second relationship tag are output, and those that exist in both the first relationship tag and the preset second relationship tag can be defaulted to the most likely relationship tag between the first user and the second user. In addition to the relationship tags mentioned in this application, the relationship tags can also be updated based on different analysis scenarios to better identify user relationships.

[0056] S107: Generate first note list information according to the first relationship tag, and recommend the first note list information to the first user, so that the first user selects a first note name from the first note list information to make a note for the second user.

[0057] In the above S107, based on the first relationship tag obtained from the query, a list of note information is generated, which includes recommended note names corresponding to each relationship tag. When intelligently generating multiple note names according to the first relationship tag, sorting them according to priority to generate list information, and then recommending the list information to the first user as the first annotation list information, it specifically includes: generating multiple note names according to the first relationship tag, and obtaining the third note name and the fourth note name from the multiple note names; determining whether the priority of the third note name is higher than the priority of the fourth note name; when the priority of the third note name is higher than the priority of the fourth note name, determining that in the note list, the third note name is ranked before the fourth note name, and sending the sorted first note list information to the first user. Specifically, the first relationship tag is comprehensively evaluated based on various factors such as the interaction behavior, common interests, and social circles between users, and is a tag used to describe the relationship between the first user and the second user. After determining the first relationship tag, multiple note names matching the first relationship tag are automatically generated according to the corresponding relationship. If the first relationship tag includes a customer relationship and a colleague relationship, generate customer Mr. XX, etc. according to the customer relationship, and generate colleague manager XX, colleague HR XX, and colleague admin XX, etc. according to the colleague relationship. When generating the corresponding note name, the note name can be further determined from the chat information or the nickname in the group chat. If the group chat nickname is Mr. XX, and the first relationship tag is a customer relationship tag, the note can be default generated as Mr. XX. The third note name and the fourth note name can be selected from the generated multiple note names according to a preset selection strategy (such as random selection, screening according to a certain rule, etc.). After generating multiple note names according to the first relationship tag, the matching degree between the note name and the first relationship tag can be evaluated, that is, by various means to evaluate the actual matching degree between the note name and the second user, and the matching degree can be reflected by a numerical value. Then the numerical value is used as the priority of each note name. Priority is an indicator used to describe the sorting order or importance degree of different note names in the recommendation list. After obtaining the third note name and the fourth note name, it is also necessary to obtain the first matching value corresponding to the third note name, and then obtain the second matching value corresponding to the fourth note name, and compare the first matching value with the second matching value. When the first matching value is greater than the second matching value, it is determined that the priority of the third note name is higher than the priority of the fourth note name. When it is determined that the priority of the third note name is higher than the priority of the fourth note name, the third note name will be automatically ranked before the fourth note name in the note list. According to the above comparison method of the third note name and the fourth note name, and then comparing the other note names in the multiple note names in turn. After the sorting is completed, the sorted note list (that is, the first note list information) will be sent to the first user as a recommendation of the note name for the second user.When generating a note name, it is necessary to ensure the diversity and personalization of the name to meet the needs and preferences of different users. At the same time, the generated note name should conform to social norms and cultural backgrounds to avoid causing misunderstandings or offenses. When the first matching value is less than the second matching value, if it is determined that the priority of the third note name is lower than that of the fourth note name, then the fourth note name is ranked before the third note name. When the first matching value is equal to the second matching value, if it is determined that the priority of the third note name is the same as that of the fourth note name, the sorting of the third note name and the fourth note name can be carried out randomly, but only for note names with the same priority. Then, this first note list information is recommended to the first user so that the first user can select a suitable first note name from the first note list information to note the second user, thereby helping the first user to more effectively manage the friend list. For example. Figure 2 As shown, when the first user is not satisfied with the note name in the initially generated first note list information, the first user can click the regenerate button, and then analyze the information between the first user and the second user again to obtain new note name list information.

[0058] In addition, when the target chat frequency is greater than the preset chat frequency, determine that the second user corresponds to a high intimacy level; obtain the first quantity and the second quantity according to the high intimacy level, input the first quantity and the second quantity into the preset relational database for matching to obtain the second relationship label; generate the second note list information according to the second relationship label, and recommend the second note list information to the first user so that the first user can select the second note name from the second note list information to note the second user, and the second note name is the note name generated according to the second relationship label. Specifically, first obtain the chat records between the first user and the second user. Calculate the chat frequency between them, that is, the number of chats per unit time (such as per day or per week). Compare the calculated chat frequency with the preset chat frequency. If the target chat frequency is greater than the preset chat frequency, determine that the second user corresponds to a high intimacy level. The target chat frequency refers to the actual chat frequency between the first user and the second user. The preset chat frequency is a threshold set by the system for judging the high or low chat frequency. Then obtain the first quantity of the common friends of the first user and the second user and the second quantity of the first user and the second user entering the group chat at the same time in the above way. The first quantity and the second quantity are used to further refine the relationship characteristics between users. According to the high intimacy level, obtain the first quantity and the second quantity from data sources such as chat records, friend lists, and group chat information. Input these quantities as input parameters into the preset relational database for matching. The preset relational database stores the corresponding relationships between different quantity combinations and relationship labels. Through the matching algorithm, find the relationship label that best matches the high intimacy level and the quantity combination in the database, that is, the second relationship label. At this time, the second relationship label includes the relative relationship label, the friend relationship label, the colleague relationship label, and the classmate relationship label. The note name can be analyzed from the nature of the group chat and the note information of the common friends about the second user. According to the second relationship label, generate the second note list information from the preset note name library or generation rules. Present the second note list information to the first user as the recommended options for the note name. The first user can select one or more second note names from the list to note the second user. The second note list information is a list of a series of possible note names generated according to the second relationship label. The second note name is one or more specific note names in the list for the first user to select to note the second user. It can intelligently generate and recommend note names according to the chat frequency and other relevant information between the first user and the second user, helping users better manage and identify their social relationships.

[0059] Furthermore, when the target chat frequency is equal to the preset chat frequency, determine the intimacy level corresponding to the second user; perform text recognition on the target chat information to obtain the target text information; input the target text information into the preset text database for matching to obtain the third relationship label; obtain the first release information corresponding to the first user and the second release information corresponding to the second user; calculate the similarity between the first release information and the second release information to obtain the target similarity; determine the fourth relationship label based on the target similarity, combine the third relationship label and the fourth relationship label to obtain the fifth relationship label; generate the third note list information based on the fifth relationship label and recommend the third note list information to the first user. Specifically, calculate the chat frequency between the first user and the second user. Compare the calculated chat frequency with the preset chat frequency. If the target chat frequency is equal to the preset chat frequency, determine the intimacy level corresponding to the second user. Obtain the chat record or conversation content between the first user and the second user, that is, the target chat information. Perform text recognition processing on the target chat information to extract the text content therein, that is, the target text information. Then input the target text information into the preset text database for analysis. The preset text database stores the corresponding relationships between different topics, keywords, and relationship labels. The preset text database needs to be updated regularly to reflect the corresponding relationships between new topics, keywords, and relationship labels. When performing text recognition, it is necessary to ensure the accuracy and integrity of the recognition to avoid information loss or misunderstanding. Input the target text information into the preset text database for matching. By analyzing the features such as topics and keywords in the target text information, find the most matching relationship label in the database, that is, the third relationship label. Obtain the public information released by the first user on platforms such as social media, blogs, and forums. And obtain the public information released by the second user on the same or similar platforms. That is, obtain the release information of the first user and the second user respectively. Calculate the similarity of these information, and algorithms such as cosine similarity and Jaccard similarity can be used. When performing similarity calculation, it is necessary to select a suitable algorithm according to the actual situation and ensure the effectiveness and accuracy of the algorithm. Obtain the target similarity according to the calculation result, and this value reflects the similarity degree of the release information of the first user and the second user. Compare the calculated target similarity with the preset threshold. Determine the fourth relationship label according to the comparison result. When the target similarity is less than the preset threshold, the fourth relationship label is defaulted to customer relationship, colleague relationship, and classmate relationship. When the target similarity is equal to or greater than the preset threshold, the fourth relationship label is defaulted to friend relationship and relative relationship. Then combine or fuse the third relationship label and the fourth relationship label to obtain the fifth relationship label. You can choose to retain the same relationship labels, remove the different relationship labels, or choose to remove the duplicate relationship labels, that is, only retain one of the same relationship labels. A series of possible note names are generated based on the fifth relationship label.Generate the third list of note information from a preset note name library or generation rules according to the fifth relationship tag. Present the generated third list of note information to the first user as recommended options for note names or descriptions. It is possible to intelligently generate and recommend a list of note names based on characteristics such as the chat frequency, chat content, and similarity of posted information between the first user and the second user, helping users better manage and identify their social relationships.

[0060] In a possible implementation, first obtain the initial note name corresponding to the second user by the current first user, and then obtain the registered account name corresponding to the second user, which is unchangeable. Determine whether the initial note name is the same as the registered account name. When they are the same, it is determined that the real name of the second user still needs to be identified. First, obtain the authorization for the social software to obtain registration information, and then query the registration information corresponding to the second user from the registration information library of the social software. It can be queried through the unique identifier corresponding to the second user, and then obtain the real name information corresponding to the second user from the registration information. Then recommend the real name information and the recommended list of note information to the first user at the same time, so that the first user can choose appropriate note information to note the second user. When the initial note name is different from the registered account name, it is determined to send a prompt message to the first user, and the prompt message is used to prompt the user to select an operation request for obtaining the real name of the second user. Then perform corresponding operations according to the user's selection.

[0061] This application can determine a second user from the friend list of the first user, then start obtaining the chat information between the first user and the second user, analyze the number of chat messages and time points of the chat information to obtain the target chat frequency, and then compare the target chat frequency with the preset chat frequency. When the target chat frequency is less than the preset chat frequency, it is determined that the second user corresponds to a low intimacy level. According to the low intimacy level, obtain the first quantity of the common friends of the first user and the second user, the second quantity of the common group chats, and the third quantity of the same location. At the same time, input the first quantity, the second quantity, and the third quantity into the preset relational database for matching to obtain the first relationship label. Then analyze the time period in which the same location is located to determine the preset relationship label. The preset relationship label includes the preset first relationship label and the preset second relationship label. Then compare the preset relationship label with the first relationship label, and use the comparison result as the latest first relationship label for output. Generate the first note list information according to the first relationship label, and then recommend the first note list information to the first user so that the first user can select a suitable note name from the first note list information as the note for the second user. When the target chat frequency is greater than the preset chat frequency, it is determined that the second user corresponds to a high intimacy level. According to the high intimacy level, obtain the first quantity of the common friends of the first user and the second user and the second quantity of the common group chats. Then input the first quantity and the second quantity into the preset relational database for matching to obtain the second relationship label. Then generate the second note list information according to the second relationship label, and then recommend the second note list information to the first user so that the first user can select a suitable note name from the second note list information as the note for the second user. When the target chat frequency is equal to the preset chat frequency, it is determined that the second user corresponds to a medium intimacy level. Perform text recognition on the chat information according to the medium intimacy level to obtain the third relationship label. Then calculate the similarity between the dynamic information published by the first user and the dynamic information published by the second user to obtain the target similarity. Then determine the fourth relationship label according to the target similarity. Combine the third relationship label and the fourth relationship label to obtain the fifth relationship label, and generate the third note list information according to the fifth relationship label. Then recommend the third note list information to the first user so that the first user can select a suitable note name from the third note list information as the note for the second user.

[0062] The embodiment of the present application also provides a friend note recognition device for a social software. Figure 4 It is a schematic structural diagram of a friend note recognition device for a social software provided by the embodiment of the present application. Refer to Figure 4 The device is a server, and the server includes a receiving unit 401, a processing unit 402, and a recommending unit 403.

[0063] A receiving unit 401 receives a note recognition request sent by a first user, selects a second user from a target friend list according to the note recognition request, where the second user is the user to be recognized; obtains target chat information corresponding to the first user and the second user, and determines a target chat frequency from the target chat information.

[0064] A processing unit 402 determines whether the target chat frequency is less than a preset chat frequency; when the target chat frequency is less than the preset chat frequency, determines that the second user corresponds to a low intimacy level, and obtains a first quantity and a second quantity according to the low intimacy level, where the first quantity is the total quantity corresponding to a third user, and the third user is a user who exists in the friend lists corresponding to the first user and the second user at the same time, and the second quantity is the total quantity of a target group, and the target group is a chat group joined by both the first user and the second user; obtains a first position corresponding to the first user, and obtains a second position corresponding to the second user; analyzes the first position and the second position to obtain a same position, and obtains a third quantity corresponding to the same position; inputs the first quantity, the second quantity, and the third quantity into a preset relational database for querying to obtain a first relationship label, where the first relationship label includes a family member relationship label, a colleague relationship label, a classmate relationship label, a friend relationship label, a customer relationship label, and an online relationship label.

[0065] A recommendation unit 403 generates first note list information according to the first relationship label, and recommends the first note list information to the first user, so that the first user can select a first note name from the first note list information to note the second user, and the first note name is a note name generated according to the first relationship label.

[0066] In a possible implementation manner, the receiving unit 401 is configured to obtain a target time point, where the target time point is the time point when the first user and the second user officially become friends; obtain multiple first positions corresponding to the first user within a first duration, and summarize the multiple first positions into a first position set, where the first duration is the interval duration between the target time point and the current time point; obtain multiple second positions of the second user within the first duration, and summarize the multiple second positions into a second position set; obtain a third position from the first position set, and determine whether the third position exists in the second position set; the processing unit 402 is configured to, when the third position exists in the second position set, determine that both the first user and the second user have the third position, and output the third position as the same position.

[0067] In a possible implementation, the receiving unit 401 is configured to obtain a second duration, where the second duration is the total duration corresponding to both the first user and the second user at the third location; the processing unit 402 is configured to analyze the second duration to obtain a target time period, where the target time period is the time period between the start time and the end time when the first user and the second user are simultaneously at the third location; determine whether the target time period is in a preset time schedule, where the preset time schedule is the time schedule corresponding to normal daytime work; when the target time period is in the preset time schedule, determine a preset first relationship label, where the preset first relationship label includes a colleague relationship label, a classmate relationship label, and a customer relationship label; determine whether there is a preset first relationship label in the first relationship label; when there is a preset first relationship label in the first relationship label, output the preset first relationship label as the first relationship label.

[0068] In a possible implementation, when the target time period is not in the preset time schedule, the processing unit 402 is configured to determine a preset second relationship label, where the preset second relationship label includes a relative relationship label, a friend relationship label, and a classmate relationship label; determine whether there is a preset second relationship label in the first relationship label; when there is a preset second relationship label in the first relationship label, output the preset second relationship label as the first relationship label.

[0069] In a possible implementation, when the target chat frequency is greater than the preset chat frequency, the processing unit 402 is configured to determine that the second user corresponds to a high intimacy level; obtain a first quantity and a second quantity according to the high intimacy level, input the first quantity and the second quantity into a preset relationship database for matching to obtain a second relationship label; the recommendation unit 403 is configured to generate second note list information according to the second relationship label and recommend the second note list information to the first user, so that the first user can select a second note name from the second note list information to note the second user, and the second note name is a note name generated according to the second relationship label.

[0070] In a possible implementation, when the target chat frequency is equal to the preset chat frequency, the processing unit 402 is configured to determine that the second user corresponds to a medium intimacy level; perform text recognition on the target chat information to obtain target text information; input the target text information into a preset text database for matching to obtain a third relationship label; the receiving unit 401 is configured to obtain a first release information corresponding to the first user and obtain a second release information corresponding to the second user; the processing unit is configured to calculate a similarity between the first release information and the second release information to obtain a target similarity; determine a fourth relationship label according to the target similarity, and combine the third relationship label and the fourth relationship label to obtain a fifth relationship label; the recommendation unit 403 is configured to generate third note list information according to the fifth relationship label and recommend the third note list information to the first user.

[0071] In a possible implementation, the processing unit 402 is configured to generate multiple note names according to the first relationship tag, obtain a third note name and a fourth note name from the multiple note names; determine whether the priority of the third note name is higher than that of the fourth note name; when the priority of the third note name is higher than that of the fourth note name, determine to rank the third note name before the fourth note name in the note list, and send the first note list information obtained after sorting to the first user.

[0072] It should be noted that: when the device provided in the above embodiment realizes its functions, only the division of the above function modules is used for illustration. In actual applications, the above functions can be allocated to different function modules according to needs, that is, the internal structure of the device is divided into different function modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiments belong to the same concept, and the specific implementation process can be seen in the method embodiments, which will not be repeated here.

[0073] This application also discloses an electronic device. Refer to Figure 5 , Figure 5 FIG. 11 is a schematic structural diagram of an electronic device provided by an embodiment of this application. The electronic device 500 may include: at least one processor 501, at least one network interface 504, a user interface 503, a memory 502, and at least one communication bus 505.

[0074] Among them, the communication bus 505 is used to realize the connection and communication between these components.

[0075] Among them, the user interface 503 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 503 may further include a standard wired interface and a wireless interface.

[0076] Among them, the network interface 504 may optionally include a standard wired interface and a wireless interface (such as a WI-FI interface).

[0077] Among them, the processor 501 may include one or more processing cores. The processor 501 connects various parts within the entire server through various interfaces and lines. By running or executing instructions, programs, code sets, or instruction sets stored in the memory 502, and by invoking the data stored in the memory 502, it executes various functions of the server and processes data. Optionally, the processor 501 may be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), or programmable logic array (PLA). The processor 501 may integrate a combination of one or several of a central processing unit (CPU), a graphics processing unit (GPU), and a modem, etc. Among them, the CPU mainly processes the operating system, user interface, and application requests, etc.; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 501 and may be implemented separately by a single chip.

[0078] Among them, the memory 502 may include random access memory (RAM) and may also include read-only memory. Optionally, the memory 502 includes a non-transitory computer-readable storage medium. The memory 502 can be used to store instructions, programs, code, code sets, or instruction sets. The memory 502 may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing the operating system, instructions for at least one function (such as touch function, sound playback function, image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area can store the data involved in the above-mentioned various method embodiments. Optionally, the memory 502 may also be at least one storage device located far from the aforementioned processor 501.

[0079] As Figure 5 shown, the memory 502, as a computer storage medium, may include an operating system, a network communication module, a user interface module, and an application program for recognizing friend remarks of social software.

[0080] In Figure 5In the electronic device 500 shown, the user interface 503 is mainly used to provide an interface for the user to input and obtain the data input by the user. The processor 501 can be used to call the application program stored in the memory 502 for identifying the friend remarks of the social software. When executed by one or more processors, the electronic device performs one or more of the methods described in the foregoing embodiments.

[0081] It should be noted that, for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that this application is not limited by the described action sequence, because according to this application, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to this application.

[0082] In the foregoing embodiments, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0083] In the several embodiments provided by this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some service interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0084] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place, or they can be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0085] In addition, in each embodiment of this application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0086] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a memory and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The aforementioned memory includes various media that can store program codes, such as USB flash drives, mobile hard disks, magnetic disks, or optical discs.

[0087] The above are only exemplary embodiments of the present disclosure, and the scope of the present disclosure cannot be limited thereby. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. Those skilled in the art will readily think of other embodiments of the present disclosure after considering the specification and the practice of the present disclosure. This application aims to cover any variations, uses, or adaptive changes of the present disclosure that follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not recorded in the present disclosure.

Claims

1. A method for recognizing friend remarks in a social software, characterized in that, Applied to a server, the method includes: Receiving a note recognition request sent by a first user, and selecting a second user from a target friend list according to the note recognition request, where the second user is the user to be recognized; Obtaining target chat information corresponding to the first user and the second user, and determining a target chat frequency from the target chat information; Judging whether the target chat frequency is less than a preset chat frequency; When the target chat frequency is less than the preset chat frequency, determining that the second user corresponds to a low intimacy level, and obtaining a first quantity and a second quantity according to the low intimacy level. The first quantity is the total quantity corresponding to a third user, where the third user is a user who exists simultaneously in the friend lists corresponding to the first user and the second user, and the second quantity is the total quantity of a target group, where the target group is a chat group joined by both the first user and the second user; Obtaining a first location corresponding to the first user, and obtaining a second location corresponding to the second user; Analyzing the first location and the second location to obtain a same location, and obtaining a third quantity corresponding to the same location; Inputting the first quantity, the second quantity, and the third quantity into a preset relational database for query to obtain a first relationship label, where the first relationship label includes a family relationship label, a colleague relationship label, a classmate relationship label, a friend relationship label, a customer relationship label, and an online relationship label; Generating first note list information according to the first relationship label, and recommending the first note list information to the first user, so that the first user can select a first note name from the first note list information to note the second user, where the first note name is a note name generated according to the first relationship label.

2. The method according to claim 1, wherein The analyzing the first location and the second location to obtain a same location specifically includes: Obtaining a target time point, where the target time point is the time point when the first user and the second user officially become friends; Obtaining multiple first locations corresponding to the first user within a first duration, and summarizing the multiple first locations into a first location set, where the first duration is the interval duration between the target time point and the current time point; Obtaining multiple second locations of the second user within the first duration, and summarizing the multiple second locations into a second location set; Obtaining a third location from the first location set, and judging whether the third location exists in the second location set; When the third location exists in the second location set, determining that both the first user and the second user have the third location, and outputting the third location as the same location.

3. The method according to claim 2, wherein After the analyzing the first location and the second location to obtain a same location, the method further includes: Obtaining a second duration, where the second duration is the total duration corresponding to the first user and the second user both being at the third location; Analyze the second duration to obtain a target time period, where the target time period is the time period between the start time and the end time when the first user and the second user are both at the third location; Determine whether the target time period is in a preset time schedule, where the preset time schedule is the time schedule corresponding to normal daytime work; When the target time period is in the preset time schedule, determine a preset first relationship label, where the preset first relationship label includes a colleague relationship label, a classmate relationship label, and a customer relationship label; Determine whether the preset first relationship label exists in the first relationship label; When the preset first relationship label exists in the first relationship label, output the preset first relationship label as the first relationship label.

4. The method according to claim 3, wherein After determining whether the target time period is in the preset time schedule, the method further includes: When the target time period is not in the preset time schedule, determine a preset second relationship label, where the preset second relationship label includes a relative relationship label, a friend relationship label, and a classmate relationship label; Determine whether the preset second relationship label exists in the first relationship label; When the preset second relationship label exists in the first relationship label, output the preset second relationship label as the first relationship label.

5. The method according to claim 1, wherein After determining whether the target chat frequency is less than a preset chat frequency, the method further includes: When the target chat frequency is greater than the preset chat frequency, determine that the second user corresponds to a high intimacy level; Obtain the first quantity and the second quantity according to the high intimacy level, input the first quantity and the second quantity into a preset relationship database for matching, and obtain a second relationship label; Generate second note list information according to the second relationship label, and recommend the second note list information to the first user, so that the first user can select a second note name from the second note list information to note the second user, where the second note name is a note name generated according to the second relationship label.

6. The method according to claim 1, characterized in that, After determining whether the target chat frequency is less than a preset chat frequency, the method further includes: When the target chat frequency is equal to the preset chat frequency, determine that the second user corresponds to a medium intimacy level; Perform text recognition on the target chat information to obtain target text information; Input the target text information into a preset text database for matching, and obtain a third relationship label; Obtain the first release information corresponding to the first user and obtain the second release information corresponding to the second user; Calculate the similarity between the first release information and the second release information to obtain a target similarity; Determine a fourth relationship label according to the target similarity, and combine the third relationship label and the fourth relationship label to obtain a fifth relationship label; Generate third note list information according to the fifth relationship label, and recommend the third note list information to the first user.

7. The method according to claim 1, wherein After generating the first note list information according to the first relationship label, the method further includes: Generate multiple note names according to the first relationship tag, and obtain a third note name and a fourth note name from the multiple note names; Determine whether the priority of the third note name is higher than that of the fourth note name; When the priority of the third note name is higher than that of the fourth note name, determine that the third note name is ranked before the fourth note name in the note list, and send the sorted first note list information to the first user.

8. A friend note recognition device for a social software, characterized in that, The device is a server, and the server includes a receiving unit (401), a processing unit (402), and a recommendation unit (403); The receiving unit (401) receives a note recognition request sent by a first user, selects a second user in the target friend list according to the note recognition request, and the second user is the user to be recognized; obtains the target chat information corresponding to the first user and the second user, and determines the target chat frequency from the target chat information; The processing unit (402) determines whether the target chat frequency is less than a preset chat frequency; when the target chat frequency is less than the preset chat frequency, determines that the second user corresponds to a low intimacy level, and obtains a first quantity and a second quantity according to the low intimacy level, where the first quantity is the total quantity of third users, and the third users are the users who exist simultaneously in the friend lists corresponding to the first user and the second user, and the second quantity is the total quantity of the target group, and the target group is the chat group that both the first user and the second user have joined; obtains the first position corresponding to the first user, and obtains the second position corresponding to the second user; analyzes the first position and the second position to obtain the same position, and obtains the third quantity corresponding to the same position; inputs the first quantity, the second quantity, and the third quantity into a preset relationship database for query to obtain a first relationship tag, and the first relationship tag includes a family relationship tag, a colleague relationship tag, a classmate relationship tag, a friend relationship tag, a customer relationship tag, and a network relationship tag; The recommendation unit (403) generates first note list information according to the first relationship tag, and recommends the first note list information to the first user, so that the first user can select a first note name from the first note list information to note the second user, and the first note name is a note name generated according to the first relationship tag.

9. An electronic device, characterized in that, It includes a processor (501), a memory (502), a user interface (503), and a network interface (504). The memory (502) is used to store instructions, the user interface (503) and the network interface (504) are used to communicate with other devices, and the processor (501) is used to execute the instructions stored in the memory (502) so that the electronic device (500) executes the method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions that, when executed, perform the method according to any one of claims 1-7.

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