A method and system for making friends interface interaction

Abstract

The present invention provides a method and system for interacting with a dating interface, including: obtaining information data of a current user and displaying a dating interface of the current user; generating dating screening conditions based on the information data of the current user and determining one or more recommended users from a preset user database; generating profile cards related to the recommended users and displaying a preset number of profile cards in the dating interface of the current user; and if a first interaction operation with respect to the dating screening conditions is detected, determining one or more recommended users based on the first interaction operation and the user database. The present invention provides a method and system for interacting with a dating interface, which has relatively good dating matching accuracy, can meet the dating needs of users, also improves the speed at which users obtain key information, and can help users accurately and dynamically find dating objects corresponding to their needs.

Description

Technical Field

[0001] The present invention relates to the technical field of dating applications, and particularly to a method and system for interacting with a dating interface. Background Art

[0002] In modern society, with the increasing personalized needs and the accelerating pace of life, the quantity and quality of traditional social ways are limited and it is difficult to meet people's diverse social needs. Thus, some online dating ways have gradually emerged.

[0003] However, in the prior art, due to the characteristics of the network itself, the number of users is huge and the information resources are also huge, which easily brings the problem of information overload. As a result, in the actual social process, users often have difficulty in accurately and effectively obtaining information about dating partners. Moreover, in the actual social process, users' social needs also show a diverse trend, and the existing single dating ways are also difficult to meet users' social needs in different situations.

[0004] Therefore, there is a need for a solution to address at least one of the above problems currently. Summary of the Invention

[0005] In view of the deficiencies in the prior art, the present application proposes a method and system for interacting with a dating interface. To solve the above technical problems, the technical solution of the present application is as follows:

[0006] A method for interacting with a dating interface includes:

[0007] Obtaining information data of the current user and displaying the dating interface of the current user;

[0008] Generating dating screening conditions based on the information data of the current user and determining one or more recommended users from a preset user database;

[0009] Generating profile cards related to the recommended users and displaying a preset number of the profile cards in the dating interface of the current user;

[0010] If a first interaction operation of the current user with respect to the dating screening conditions is detected, determining one or more of the recommended users based on the first interaction operation and the user database.

[0011] In a specific embodiment, the specific process of generating dating screening conditions based on the information data of the current user and determining one or more recommended users from a preset user database includes:

[0012] Obtaining information data of the current user;

[0013] Analyzing the collected data by using a pre-trained algorithm model to generate dating screening conditions;

[0014] Combine to generate friend-making screening conditions, calculate the similarity between the current user and other users in the user database, and obtain the similarity results between the current user and other users;

[0015] Based on the similarity results, determine one or more recommended users.

[0016] In a specific embodiment, the specific process of determining one or more of the recommended users based on the first interaction operation and the user database includes:

[0017] Based on the first interaction operation, determine multiple friend-making target requirements involved;

[0018] Determine one or more users corresponding to the friend-making target requirements from the user database;

[0019] Respectively determine whether each of the users meets the preset number of friend-making target requirements;

[0020] When meeting the requirements, use the user as a recommended user.

[0021] In a specific embodiment, if a second interaction operation related to the current user's friend-making activity is detected, obtain the friend-making activity requirements set by the user and generate friend-making activity information;

[0022] The friend-making activity information includes one or any combination of activity type, activity time, activity location, and friend-making requirements;

[0023] At the same time, based on the friend-making activity information, determine potential social users.

[0024] In a specific embodiment, the process of determining potential social users based on the friend-making activity information at the same time specifically includes:

[0025] Calculate the matching degree between the current user and potential social users, arrange the potential social users in descending order of the matching degree, and display the potential social users in the area range of the friend-making activity information;

[0026] According to the real-time feedback and behavioral operations of the current user, dynamically adjust the display content of the potential social users.

[0027] In a specific embodiment, the method further includes:

[0028] Based on the information data of the current user, extract the main features of the current user;

[0029] The main features include any one or more of basic information categories, hobbies categories, and social needs categories;

[0030] Construct one or more user portraits of the current user based on all the main features and different types of the main features;

[0031] Store the user portraits of all users in the feature data information library related to the social circle;

[0032] Match and recommend social circles that the current user may be interested in based on the user portrait of the current user and the feature data information library.

[0033] In a specific embodiment, after constructing one or more user portraits of the current user, the method further includes: establishing a user feature vector;

[0034] Establishing the user feature vector specifically includes: selecting some features from the user portrait as the first features;

[0035] Convert the first features into numerical form and perform normalization processing on the converted numerical values;

[0036] Combine the features after the normalization processing into a vector to form a user feature vector.

[0037] In a specific embodiment, the method further includes:

[0038] In response to a selection trigger operation performed on the dating interface, update the profile card of the recommended user displayed on the dating interface;

[0039] Wherein, the selection trigger operation includes: an addition operation, which is used to represent that the current user agrees to initiate social interaction with the recommended user, and then send an addition request to the recommended user;

[0040] A removal operation, which is used to represent that the current user refuses to initiate social interaction with the recommended user and removes the profile card of the recommended user;

[0041] An acquisition operation, which is used to represent that the current user needs to obtain detailed information about the recommended user.

[0042] In a specific embodiment,

[0043] The process of determining multiple dating target requirements involved based on the first interaction operation includes:

[0044] Detect the controls related to the screening information and detect the states of each screening control, wherein the states of each control include selected and unselected;

[0045] The types represented by the types of the controls include: one or any combination of emotion, interest tags, geographical location plugins, and gender buttons;

[0046] Detect all the controls in sequence, and use the controls with the status of selected as the first control;

[0047] Generate the friend-making target requirements for the first interaction operation based on the filtering content corresponding to the first control;

[0048] If the status of all the controls is unselected, expand the friend-making target requirement history on a preset timeline to obtain the friend-making target requirement history items arranged in sequence on the timeline;

[0049] Determine the friend-making target requirement history items that meet the preset conditions from the friend-making target requirement history items;

[0050] Generate a template based on the preset requirements, and use the friend-making target requirements in the friend-making target requirement history items as the friend-making target requirements for the first interaction operation;

[0051] Among them, the preset conditions include:

[0052] The friend-making target requirement history items on the timeline meet within a preset number before the first interaction operation;

[0053] There are add operations, delete operations or acquisition operations in the friend-making target requirement history items, and the quantity of the add operation and / or the acquisition operation reaches the proportion of the preset total trigger operations;

[0054] The trigger operations include add operations, delete operations and acquisition operations.

[0055] A friend-making interface interaction system for executing a friend-making interface interaction method described in any item of the first part, includes:

[0056] An information collection module for obtaining the information data of the current user,

[0057] An interface display module for displaying the friend-making interface of the current user;

[0058] A friend-making recommendation module for generating friend-making screening conditions according to the information data of the current user, and determining one or more recommended users from a preset user database;

[0059] Generate a profile card related to the recommended user, and display a preset number of the profile cards in the friend-making interface of the current user;

[0060] An interaction detection module for, if detecting a first interaction operation of the current user with respect to the friend-making screening conditions, determining one or more of the recommended users based on the first interaction operation and the user database.

[0061] Beneficial effects: The present invention provides a method and system for interacting with a dating interface, which has relatively good dating matching accuracy, can meet the dating needs of users, and also improves the speed at which users obtain key information, and can help users accurately and dynamically find dating partners corresponding to their needs; specifically, it comprehensively considers the user's information data and dating needs, generates personalized screening conditions, helps to achieve more accurate user recommendations, and improves the relevance of matching; user profiles usually contain a large amount of information. By dynamically generating a profile card, it can quickly help users obtain key information. Through the dynamic response of the dating interface to user interactions, the interactivity of the platform is also enhanced. By adjusting the recommendations in real time, it can continuously provide dynamic and real-time updated dating options, increasing the exploration fun of users and improving the limitations of a single and fixed algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments. It should be understood that the following drawings only show some embodiments of the present invention, and thus should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other relevant drawings can also be obtained based on these drawings.

[0063] Figure 1 Flow schematic of the method for interacting with a dating interface in the embodiment Figure 1 ;

[0064] Figure 2 Flow schematic of the method for interacting with a dating interface in the embodiment Figure 2 ;

[0065] Figure 3 Schematic diagram of the interaction of the dating interface in the embodiment;

[0066] Figure 4 Module composition diagram of the dating interface interaction system in the embodiment.

[0067] Reference numerals:

[0068] 1 - Information acquisition module; 2 - Interface display module; 3 - Dating recommendation module; 4 - Interaction detection module. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0069] In the following, various embodiments of the present disclosure will be described more comprehensively. The present disclosure can have various embodiments, and adjustments and changes can be made therein. However, it should be understood that there is no intention to limit the various embodiments of the present disclosure to the specific embodiments disclosed herein, but rather the present disclosure should be understood to cover all adjustments, equivalents, and / or alternative solutions falling within the spirit and scope of the various embodiments of the present disclosure.

[0070] Hereinafter, the term "comprising" or "may comprise" used in various embodiments of the present disclosure indicates the presence of the disclosed functions, operations, or elements, and does not limit the addition of one or more functions, operations, or elements. Further, as used in various embodiments of the present disclosure, the terms "comprising", "having", and their cognates are only intended to indicate a specific feature, number, step, operation, element, component, or combination of the foregoing items, and should not be construed as precluding the existence or addition of one or more other features, numbers, steps, operations, elements, components, or combinations of the foregoing items first.

[0071] In various embodiments of the present disclosure, the expression "or" or "at least one of A or / and B" includes any combination or all combinations of the listed words. For example, the expression "A or B" or "at least one of A or / and B" may include A, may include B, or may include both A and B.

[0072] Expressions (such as "first", "second", etc.) used in various embodiments of the present disclosure may modify various constituent elements in the various embodiments, but do not limit the corresponding constituent elements. For example, the above expressions do not limit the order and / or importance of the elements. The above expressions are only for the purpose of distinguishing one element from other elements. For example, the first user device and the second user device indicate different user devices, although both are user devices. For example, without departing from the scope of various embodiments of the present disclosure, the first element may be referred to as the second element, and similarly, the second element may also be referred to as the first element.

[0073] It should be noted that: if it is described that one constituent element is "connected" to another constituent element, the first constituent element may be directly connected to the second constituent element, and a third constituent element may be "connected" between the first constituent element and the second constituent element. Conversely, when one constituent element is "directly connected" to another constituent element, it can be understood that there is no third constituent element between the first constituent element and the second constituent element.

[0074] The term "user" used in various embodiments of the present disclosure may indicate a person who uses an electronic device or a device that uses an electronic device (e.g., an artificial intelligence electronic device).

[0075] The terms used in various embodiments of the present disclosure are for the purpose of describing specific embodiments only and are not intended to limit the various embodiments of the present disclosure. As used herein, the singular forms are intended to include the plural forms as well, unless the context clearly indicates otherwise. Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of the present disclosure pertain. The terms (such as those defined in a commonly used dictionary) will be interpreted as having the same meaning as the contextual meaning in the relevant technical field and will not be interpreted as having an idealized meaning or an overly formal meaning, unless clearly defined in the various embodiments of the present disclosure. Embodiment

[0076] An embodiment of the present application proposes a method for interacting with a friend-making interface, as Figure 1 with Figure 3 shown, including:

[0077] S100, obtaining information data of the current user and displaying the friend-making interface of the current user;

[0078] Specifically, in some embodiments of the present application, obtaining the information data of the current user includes: collecting basic information input by the user during registration, such as name, age, gender, occupation, geographical location, etc.; collecting interest and hobby information; collecting interests and hobbies filled in or selected by the user in the application, such as sports, travel, reading, music, etc.; collecting the social needs of the user based on specific activities, such as looking for dining partners, coffee partners, sports partners, etc.

[0079] In addition, obtaining the information data of the current user may also be to collect the behavior habit information of the current user, such as the active time of the current user, the activity frequency, the social activities that the current user is interested in, etc.

[0080] S200, generating friend-making screening conditions based on the information data of the current user, and determining one or more recommended users from a preset user database;

[0081] Exemplarily, screening conditions similar to the age of the current user can be generated, screening conditions with common interests with the current user can also be generated, and screening conditions for nearby users can be generated based on the geographical location of the current user; of course, no limitation is made on the friend-making screening conditions herein.

[0082] Specifically, in some embodiments of the present application, in the user database, applying the above screening conditions, screening out potential recommended users therefrom, scoring the candidate users according to the matching degree (such as interest similarity, geographical distance, etc.), and sorting to determine the most suitable recommended object.

[0083] Understandably, the filtering conditions based on user characteristics help to make the recommended users more in line with the current user's needs and preferences, thereby improving the matching success rate; the user can find the dating partners they are interested in faster, reducing the filtering time and increasing the satisfaction of using the dating platform; by analyzing user data, the recommendation results are highly personalized, enhancing the user's sense of participation.

[0084] S300, generate a profile card for the recommended users and display a preset number of profile cards on the current user's dating interface;

[0085] S400, if a first interaction operation regarding the dating filtering conditions is detected for the current user, determine one or more recommended users based on the first interaction operation and the user database.

[0086] Finally, generate a profile card for the recommended users involved in the first interaction operation and display a preset number of profile cards on the current user's dating interface;

[0087] Understandably, through the above method, a diversified recommended user recommendation method is realized, which helps to more flexibly and satisfactorily meet the user's dating needs; combined with the user's first interaction operation, filter the candidate users in the user database, and further update the recommended user list in real time and dynamically, so that the displayed recommended users can more accurately meet the user's latest dating needs.

[0088] Furthermore, the specific process of generating dating filtering conditions based on the current user's information data and determining one or more recommended users from the preset user database includes:

[0089] Obtain the information data of the current user;

[0090] Use a pre-trained algorithm model to analyze the collected data and generate dating filtering conditions;

[0091] Specifically, apply a pre-trained algorithm model to extract user characteristics and convert them into numerical form for subsequent analysis, and generate potential dating filtering conditions for the current user through the current user's information data;

[0092] Exemplarily, if the current user's profile mentions that the hobby is traveling, then the potential dating filtering conditions may be loving traveling, sports, etc.;

[0093] Combine the generated dating filtering conditions to calculate the similarity between the current user and other users in the user database, and obtain the similarity results between the current user and other users;

[0094] Based on the similarity results, determine one or more recommended users.

[0095] Based on the similarity scores, select one or several users with the highest scores as the recommended users, so that the degree of matching between the recommended users and the current user's interests and needs is maximized as much as possible.

[0096] Specifically, in some embodiments of the present application, the pre-trained algorithm model can be a deep learning model such as a deep neural network (DNN), a convolutional neural network (CNN), and a recurrent neural network (RNN) for analyzing user data and activity data;

[0097] More specifically, the input layer of the pre-trained algorithm model includes the user's basic information and hobbies, the middle layer performs non-linear transformation through activation functions (such as ReLU, sigmoid, etc.), and the output layer gives the similarity, matching score, or probability of the user.

[0098] Even more specifically, in some embodiments of the present application, algorithm model training and optimization are also required, including data preprocessing, cleaning and normalizing user data to improve data quality;

[0099] Among them, for the training of the algorithm model, a large amount of user data is used, and the supervised learning method is adopted to train the deep learning model, and the loss function (such as mean square error, cross entropy, etc.) is used to measure the prediction error of the model, and the optimization algorithm (such as gradient descent algorithm, Adam algorithm, etc.) is used to adjust the parameters; algorithm model evaluation is also required, and cross-validation and holdout methods are adopted to evaluate the performance of the model (such as accuracy, recall rate, F1 value, etc.), and the model structure and parameters are adjusted to improve the generalization ability of the model.

[0100] Further, the specific process of determining one or more recommended users based on the first interaction operation and the user database includes:

[0101] Based on the first interaction operation, determine multiple dating target requirements involved;

[0102] Determine one or more users corresponding to the dating target requirements from the user database;

[0103] Respectively determine whether each user meets the preset number of dating target requirements;

[0104] When meeting the requirements, use the user as a recommended user.

[0105] Specifically, in some embodiments of the present application, the first interaction operation is an operation of setting screening conditions and finding social objects;

[0106] Exemplarily, if there are 6 dating target requirements in the first interaction operation, and if there is a user who meets the number of dating target requirements of 6, it means that the user meets the dating requirements set in the first interaction operation of the current user, and can be displayed as a recommended user;

[0107] On the contrary, it means that all users do not meet the current user's friend-making needs.

[0108] Of course, the number of preset friend-making target requirements can be set to 5. Then, only when there is a user who meets 5 friend-making target requirements can this user be used as a recommended user.

[0109] The importance weights of each friend-making target requirement can also be correspondingly set. When the importance weight reaches a certain level, this user can be used as a recommended user.

[0110] It can be understood that the above interaction method further helps the recommended users to better meet the user's friend-making expectations and improve the matching success rate by refining the friend-making target requirements and the recommended user judgment criteria, rather than simply relying on a fixed and unchanging algorithm for user recommendation, thereby enhancing user satisfaction and improving the experience.

[0111] Furthermore, as Figure 2 shown in Figure 3 S500, if a second interaction operation related to the user's friend-making activity is detected,

[0112] S600, then obtain the friend-making activity requirements set by the user and generate friend-making activity information.

[0113] The friend-making activity information includes one or any combination of the activity type, activity time, activity location, and friend-making requirements.

[0114] S700, and at the same time, based on the friend-making activity information, determine potential social users.

[0115] Specifically, in some embodiments of the present application, the second interaction operation is to publish a social activity and find a social partner for the social activity.

[0116] Finally, generate a profile card of the potential social users involved in the second interaction operation, and display a preset number of profile cards on the current user's friend-making interface.

[0117] Understandably, precise matching is carried out according to the specific needs of users (activity type, time, location, and friend-making requirements), which increases the likelihood of users finding suitable activity partners; the second interaction operation provides clear activity information, which can attract more users to participate, increasing the popularity and interactivity of the activity; it provides a more diverse range of activity options to meet the interests and needs of different users, thus enriching the social experience of users; recommends social activities according to user preferences, increasing user satisfaction and retention rate, and meeting the personalized services of users; by collecting activity feedback and user participation data, the recommendation algorithm can be continuously optimized to improve the quality and relevance of subsequent social activities; different types of activities can attract users with different backgrounds, promoting diversified social interactions and enhancing the inclusiveness of communities and circles.

[0118] Furthermore, the process of determining potential social users based on friend-making activity information specifically includes:

[0119] Calculate the matching degree between the current user and potential social users, rank the potential social users from high to low according to the matching degree, and display the potential social users within the regional scope of the friend-making activity information;

[0120] Dynamically adjust the display content of potential social users based on the real-time feedback and behavioral operations of the current user.

[0121] Understandably, by calculating the matching degree between users and potential social users and ranking the potential social users according to the matching degree, more accurate recommendations for suitable friend-making objects can be made, improving the accuracy of friend-making recommendations; enabling users to find friend-making objects of interest more quickly and enhancing the overall friend-making experience.

[0122] Dynamically adjusting the recommended content according to user feedback and behavior can ensure that the recommended users always meet the current user's preferences, enhancing interactivity.

[0123] Specifically, in some embodiments of the present application, calculate the matching degree between the current user and potential social users, and rank the potential social users from high to low according to the matching degree; first, it is necessary to analyze the user profiles, activity records, and friend-making intentions in the friend-making activity information; then construct feature vectors, convert the attributes of users and potential social users into feature vectors, usually represented numerically; and calculate the similarity through similarity algorithms such as cosine similarity, Euclidean distance, weighted scoring, etc.; among them, cosine similarity needs to calculate the cosine value of the included angle between two feature vectors, the closer to 1, the more similar; Euclidean distance needs to calculate the distance between feature vectors, the smaller the distance, the more similar; weighted scoring needs to assign weights to different attributes according to the importance of features and calculate the comprehensive score; finally, create a matching degree list, calculate the matching degree scores for each potential social user to form a list, and sort the list according to the matching degree scores, ranking from high to low.

[0124] Dynamically adjust the recommended content according to the feedback and behavior of the user. Exemplarily, for example, the user's preference settings can be updated based on the user's interactions (such as accepting invitations, sending messages, etc.); recalculate the matching degree regularly or according to changes in the user's behavior.

[0125] Display potential social users within the regional scope of the dating activity information. By limiting the geographical scope, users can be more effectively matched, ensuring that the recommended objects are within an acceptable distance, enhancing the possibility of interaction; closer users are more likely to participate in offline activities, increasing the attendance rate and activity of the event, promoting the connection between users and the local community, enhancing the sense of community belonging, and stimulating more social activities and cooperation; geographical proximity provides a basis for establishing long-term friendships, and users can maintain contact more easily.

[0126] Furthermore, the method further includes:

[0127] Extract the main features of the current user based on the information data of the current user;

[0128] The main features include any one or more of basic information categories, hobbies categories, and social needs categories;

[0129] Construct one or more user portraits of the current user according to all the main features and the main features of different types;

[0130] It can be understood that constructing one or more user portraits of the current user can comprehensively understand the user from different perspectives, helping to identify their diverse needs and preferences; providing more accurate content and activity recommendations according to different portraits to improve user participation; the interests and needs of users may change over time, and multiple portraits can adjust the recommendation strategy in a timely manner; portraits for different social needs help to more effectively match users with people of similar backgrounds or interests, and also help the current user break through the social circle through different user portraits and expand the user's social scope; data analysis of different portraits can reveal potential trends and help the platform formulate more effective marketing strategies.

[0131] Specifically, in some embodiments of the present application, before extracting the main features of the current user, it is necessary to clean the data, remove missing values and outliers in the data. After cleaning the data, it is necessary to standardize the data of different dimensions for comparison and analysis; finally, statistical analysis methods (such as correlation analysis) are used to determine the main features of the current user; similarly, feature engineering techniques and other methods can also be used to construct new features. Exemplarily, the new features can be interest combinations or activity scores.

[0132] More specifically, in some embodiments of the present application, clustering analysis and principal component analysis can be used to divide users into different groups to identify the characteristics of similar users, thereby facilitating the construction of user portraits.

[0133] Store the user portraits of all users in the feature data information library related to the social circle;

[0134] Based on the user portrait of the current user and the feature data information library, match and recommend social circles that the current user may be interested in.

[0135] Specifically, in some embodiments of the present application, the feature data information library is used to store the basic information, hobbies and social needs data of users, and is also used to store the activity information released by users and related matching records.

[0136] Further, after building the user portrait of one or more current users, the method further includes: establishing a user feature vector;

[0137] Establishing a user feature vector specifically includes: selecting some features from the user portrait as the first features;

[0138] Convert the first features into numerical form and perform normalization processing on the converted numerical values;

[0139] Combine the features after normalization processing into a vector to form a user feature vector.

[0140] It can be understood that converting user features into numerical form facilitates mathematical calculations and model training; normalization processing eliminates the scale differences between different features and ensures the comparability and effectiveness of data; the combination of feature vectors can more accurately reflect user preferences, which helps to improve the accuracy of the recommendation algorithm. The user feature vector makes the processing and analysis of large-scale user data more efficient, facilitates the application of machine learning models; and can perform personalized recommendations and matching more flexibly according to the feature vector, improving the user experience.

[0141] Further, the method further includes:

[0142] In response to a selection trigger operation performed on the dating interface, update the profile card of the recommended user displayed on the dating interface;

[0143] Among them, the selection trigger operation includes: an add operation, which is used to indicate that the current user agrees to initiate social interaction with the recommended user, and then send an add request to the recommended user;

[0144] A remove operation, which is used to indicate that the current user refuses to initiate social interaction with the recommended user and removes the profile card of the recommended user;

[0145] An obtain operation, which is used to indicate that the current user needs to obtain the detailed information of the recommended user.

[0146] It can be understood that by responding to user operations and updating the profile cards of recommended users in a timely manner, the real-time update of recommended user profiles is achieved, improving the accuracy and relevance of information; the current user can independently choose to add or remove recommended objects, enhancing the autonomy and flexibility of social interaction; the function of obtaining detailed information allows users to understand recommended objects more deeply, thus helping the current user make more accurate social decisions; the add and remove operations promote interaction between users and increase the activity of the platform.

[0147] Furthermore, the process of determining multiple dating target requirements involved based on the first interaction operation includes:

[0148] Detect the controls related to screening information and detect the status of each screening control, where the status of each control includes selected and unselected;

[0149] The types represented by the types of controls include one or any combination of emotions, interest tags, location plugins, and gender buttons;

[0150] It can be understood that by detecting the status of screening controls, the dating needs of users can be more accurately identified, thus improving the relevance of matching; recommendations more in line with their preferences can be provided based on multiple dimensions such as emotions, interests, locations, and genders selected by users; users can screen according to their specific needs, enhancing the autonomy and satisfaction of the dating process.

[0151] Detect all controls in sequence and use the controls with the status of selected as the first controls;

[0152] Generate the dating target requirements of the first interaction operation based on the screening content corresponding to the first controls;

[0153] If the status of all controls is unselected, expand the dating target requirement history on a preset timeline to obtain dating target requirement history items arranged in sequence on the timeline;

[0154] Specifically, in some embodiments of the present application, each dating target requirement history item is generated based on the dating target requirements in each first interaction operation respectively.

[0155] Determine the dating target requirement history items that meet the preset conditions from the dating target requirement history items;

[0156] Generate a template based on preset requirements and use the dating target requirements in the dating target requirement history items as the dating target requirements of the first interaction operation;

[0157] Among them, the preset conditions include:

[0158] The historical items of the friend-making target requirements on the timeline meet within the preset number before the first interaction operation;

[0159] There are addition operations, deletion operations or acquisition operations in the historical items of the friend-making target requirements, and the number of addition operations and / or acquisition operations reaches the proportion of the total preset trigger operations;

[0160] The trigger operations include addition operations, deletion operations and acquisition operations.

[0161] Through the above method, systematic requirement recognition is achieved. By sequentially detecting the control states, the current friend-making target requirements of the user can be systematically recognized. Taking the selected control as the first control helps to clarify the user's priority requirements and makes the recommendation more accurate; when all controls are not selected, the historical friend-making target requirements are displayed through the timeline, enabling the user to review past selections and preferences, assisting the user in making friend-making decisions. The user can view and adjust their requirement history at any time to adapt to changing personal circumstances and preferences; the user can more intuitively understand the change process of their friend-making requirements, enhancing the sense of participation and satisfaction with the platform; the historical records on the timeline can provide valuable data analysis basis for the platform, helping to optimize the recommendation system and user experience.

[0162] The embodiments of the present application at least have the following beneficial effects:

[0163] The embodiments of the present application provide a friend-making interface interaction method, which has relatively good friend-making matching accuracy, can meet the friend-making needs of users, and also improves the speed at which users obtain key information, and can help users accurately and dynamically find friend-making objects corresponding to their needs;

[0164] Specifically, by comprehensively considering the user's information data and friend-making requirements, personalized screening conditions are generated, which helps to achieve more accurate user recommendations and improve the relevance of matching; user profiles usually contain a large amount of information. By dynamically generating profile cards, it can quickly help users obtain key information. Through the dynamic response of the friend-making interface to user interactions, the interactivity of the platform is also enhanced. By adjusting the recommendation in real time, it can continuously provide dynamic and real-time updated friend-making options, increasing the exploration fun of users and improving the limitations of a single and fixed algorithm.

[0165] Embodiment 2

[0166] The embodiments of the present application propose a friend-making interface interaction system, as Figure 4 shown, for executing any one of the friend-making interface interaction methods in Embodiment 1, including:

[0167] An information collection module 1 for obtaining the information data of the current user,

[0168] An interface display module 2 for displaying the dating interface of the current user;

[0169] A dating recommendation module 3 that generates dating screening conditions based on the information data of the current user and determines one or more recommended users from a preset user database;

[0170] Generate a profile card for the recommended user and display a preset number of profile cards on the dating interface of the current user;

[0171] An interaction detection module 4 for determining one or more recommended users based on the first interaction operation and the user database if it detects the first interaction operation of the current user with respect to the dating screening conditions.

[0172] Since the technical problem to be solved in this embodiment, the technical solution used to solve the technical problem, and the technical effects achieved are the same as those of the above method. Therefore, in the case where the technical solution has been clearly and completely described in the above method, for the technical details of the system in this embodiment, reference can be made to Embodiment 1 of the above method, and details will not be repeated here.

[0173] Those skilled in the art can understand that the modules in the device in the implementation scenario can be distributed in the device in the implementation scenario according to the description of the implementation scenario, or can be correspondingly changed and located in one or more devices different from this implementation scenario. The modules in the above implementation scenario can be combined into one module, or further split into multiple sub-modules.

[0174] The above serial numbers of the present invention are only for description and do not represent the advantages or disadvantages of the implementation scenario.

[0175] The above discloses only several specific implementation scenarios of the present invention. However, the present invention is not limited thereto, and any changes that can be thought of by those skilled in the art should fall within the protection scope of the present invention.

Claims

1. A friend-making interface interaction system applied to a friend-making interface interaction method, characterized in that The described friend-making interface interaction method includes: Obtain the information data of the current user and display the friend-making interface of the current user; Generate friend-making screening conditions based on the information data of the current user, and determine one or more recommended users from a preset user database; The specific process of generating friend-making screening conditions based on the information data of the current user and determining one or more recommended users from a preset user database includes: Obtain the information data of the current user; Use a pre-trained algorithm model to analyze the collected data and generate friend-making screening conditions; Combined with the generated friend-making screening conditions, calculate the similarity between the current user and other users in the user database to obtain the similarity results between the current user and other users; Based on the similarity results, determine one or more recommended users; Generate profile cards related to the recommended users, and display a preset number of the profile cards in the friend-making interface of the current user; If a first interaction operation of the current user with respect to the friend-making screening conditions is detected, then based on the first interaction operation and the user database, determine one or more of the recommended users; If a second interaction operation of the current user related to a friend-making activity is detected, then obtain the friend-making activity requirements set by the user and generate friend-making activity information; The friend-making activity information includes one or any combination of activity type, activity time, activity location, and friend-making requirements; At the same time, based on the friend-making activity information, determine potential social users; The specific process of determining one or more of the recommended users includes: Based on the first interaction operation, determine multiple friend-making target requirements involved; Determine one or more users corresponding to the friend-making target requirements from the user database; Respectively determine whether each of the users meets the preset number of the friend-making target requirements; When it meets, use the user as a recommended user; The process of determining multiple friend-making target requirements involved based on the first interaction operation includes: Detect the controls related to the screening information, and detect the states of each screening control, where the states of each control include selected and unselected; The types represented by the types of the controls include one or any combination of emotion, interest tags, geographical location plugins, and gender buttons; Detect all the controls in sequence, and use the controls with the state of selected as the first controls; Based on the screening content corresponding to the first controls, generate the friend-making target requirements of the first interaction operation; If the states of all the controls are unselected, then expand the friend-making target requirement history on a preset timeline to obtain friend-making target requirement history items arranged in sequence on the timeline; Determine the friend-making target requirement history items that meet the preset conditions from the friend-making target requirement history items; Based on a preset requirement generation template, use the friend-making target requirements in the friend-making target requirement history items as the friend-making target requirements of the first interaction operation; Wherein, the preset conditions include: The friend-making target requirement history items on the timeline meet within a preset number before this first interaction operation; There are add operations, delete operations, or obtain operations in the friend-making target requirement history items, and the number of the add operations and / or the obtain operations reaches the proportion of the preset total trigger operations; The trigger operations include add operations, delete operations, and obtain operations; In response to the selection trigger operation performed on the friend-making interface, update the profile card of the recommended user displayed on the friend-making interface; Among them, the selection trigger operation includes: an add operation, which is used to represent that the current user agrees to initiate social interaction with the recommended user, and then sends an add request to the recommended user; A removal operation, which is used to represent that the current user refuses to initiate social interaction with the recommended user and removes the profile card of the recommended user; An obtain operation, which is used to represent that the current user needs to obtain the detailed information of the recommended user; At the same time, the process of determining potential social users based on the friend-making activity information specifically includes: Calculate the matching degree between the current user and potential social users, arrange the potential social users in descending order of the matching degree, and display the potential social users in the area range of the friend-making activity information; Dynamically adjust the display content of the potential social users according to the real-time feedback and behavior operations of the current user; The method further includes: Extract the main features of the current user based on the information data of the current user; The main features include any one or more of basic information categories, hobbies categories, and social needs categories; Construct one or more user portraits of the current user according to all the main features and different types of the main features; Store the user portraits of all users in the feature data information library related to the social circle; Based on the user portrait of the current user and the feature data information library, match and recommend social circles that the current user may be interested in; After constructing one or more user portraits of the current user, the method further includes: establishing a user feature vector; Establishing a user feature vector specifically includes: selecting some features from the user portrait as the first features; Convert the first features into numerical forms and perform normalization processing on the converted numerical values; Combine the features after the normalization processing into a vector to form a user feature vector; The friend-making interface interaction system includes: An information collection module, which is used to obtain the information data of the current user, An interface display module, which is used to display the friend-making interface of the current user; A friend-making recommendation module, which is used to generate friend-making screening conditions according to the information data of the current user and determine one or more recommended users from a preset user database; The specific process of generating friend-making screening conditions according to the information data of the current user and determining one or more recommended users from a preset user database includes: Obtain the information data of the current user; Use a pre-trained algorithm model to analyze the collected data and generate friend-making screening conditions; Combined with the generated friend-making screening conditions, calculate the similarity between the current user and other users in the user database to obtain the similarity results between the current user and other users; Based on the similarity results, determine one or more recommended users; Generate a profile card for the recommended user and display a preset number of such profile cards in the current user's dating interface; An interaction detection module, which is used to determine one or more of the recommended users based on the first interaction operation and the user database if it detects the first interaction operation of the current user for the dating screening conditions; if it detects the second interaction operation of the current user related to a dating activity, it obtains the dating activity requirements set by the user and generates dating activity information; The dating activity information includes one or any combination of activity type, activity time, activity location, and dating requirements; At the same time, based on the dating activity information, determine potential social users; the specific process of determining one or more of the recommended users includes: Based on the first interaction operation, determine multiple dating target requirements involved; Determine one or more users corresponding to the dating target requirements from the user database; Respectively determine whether each user meets the preset number of the dating target requirements; When it meets the requirements, use the user as a recommended user; The process of determining multiple dating target requirements involved based on the first interaction operation includes: Detect the controls related to the screening information and detect the status of each screening control, where the status of each control includes selected and unselected; The types represented by the types of the controls include one or any combination of emotion, interest tags, location plug-ins, and gender buttons; Detect all the controls in sequence and use the controls with the status of selected as the first controls; Based on the screening content corresponding to the first controls, generate the dating target requirements of the first interaction operation; If the status of all the controls is unselected, expand the dating target requirement history on a preset timeline to obtain dating target requirement history items arranged in sequence on the timeline; Determine the dating target requirement history items that meet the preset conditions from the dating target requirement history items; Based on a preset requirement generation template, use the dating target requirements in the dating target requirement history items as the dating target requirements of the first interaction operation; Among them, the preset conditions include: The dating target requirement history items on the timeline meet within a preset number before this first interaction operation; There are add operations, delete operations, or obtain operations in the dating target requirement history items, and the proportion of the number of the add operations and / or the obtain operations reaches the preset total trigger operation ratio; The trigger operations include add operations, delete operations, and obtain operations; In response to a selection trigger operation performed on the dating interface, update the display of the profile cards of the recommended users in the dating interface; Among them, the selection trigger operation includes: an add operation, which is used to represent that the current user agrees to initiate social interaction with the recommended user, and then sends an add request to the recommended user; A removal operation, which is used to represent that the current user refuses to initiate social interaction with the recommended user and removes the profile card of the recommended user; An obtain operation, which is used to represent that the current user needs to obtain the detailed information of the recommended user; The process of determining potential social users based on the friend-making activity information specifically includes: Calculating the matching degree between the current user and potential social users, arranging the potential social users in descending order of the matching degree, and displaying the potential social users within the regional scope of the friend-making activity information; Dynamically adjusting the display content of the potential social users according to the real-time feedback and behavioral operations of the current user; This method further includes: Extracting the main features of the current user based on the information data of the current user; The main features include any one or more of basic information categories, hobbies categories, and social needs categories; Constructing one or more user portraits of the current user according to all the main features and different types of the main features; Storing the user portraits of all users in the feature data information library related to the social circle; Based on the user portrait of the current user and the feature data information library, matching and recommending social circles that the current user may be interested in.

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