CN112016001A - Friend recommendation method and device and computer readable medium - Google Patents
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Abstract
The application provides a friend recommendation scheme, in the scheme, a server side device pushes a photo of a candidate user to a user side device corresponding to a target user, then user face image information collected and uploaded by the user side device when the user browses the photo of the candidate user is obtained, emotion information of the user is determined according to the user face image information, social willingness information of the target user to the candidate user is determined based on the emotion information, and therefore a friend relationship between the target user and the candidate user is established. Because the social willingness information is determined based on the user face image information which is collected and uploaded when the user browses the candidate user photos, the social willingness does not need to be expressed through specific operation for the user, only the real expression is exposed when the candidate user photos are browsed, the server-side equipment can automatically identify the corresponding social willingness, the user operation is simplified, and the use experience is better.
Description
Technical Field
The present application relates to the field of information technologies, and in particular, to a method and an apparatus for friend recommendation, and a computer-readable medium.
Background
With the continuous development of internet technology, internet companies provide a variety of social networking products. The user can form a social circle through the social network product, for example, the user can contact new friends and old friends through the network, and if the number of the friends of the user is small, the user cannot easily experience the convenience of network social contact brought by the social network product.
At present, a plurality of social platforms have a function of recommending friends in a photo mode, and after a user uploads a photo of the user to the social platform, the user can obtain the right of viewing the photos of other users. When recommending friends, the social platform pushes photos of the candidate users, after receiving the photos, the users can express social willingness of the candidate users through various certain operations (such as leftward sliding or rightward sliding), and the system can establish friend relationships according to the social willingness of the two parties. However, in such a way of recommending friends, the user still needs to actively perform a certain operation to express a social willingness to the server device, and the use experience is not good.
Disclosure of Invention
An object of the present application is to provide a friend recommendation method, device and computer readable medium.
To achieve the above object, some embodiments of the present application provide a friend recommendation method, including:
the server-side equipment pushes the photos of the candidate users to user-side equipment corresponding to the target users;
the server-side equipment acquires user face image information which is acquired and uploaded by user-side equipment when a target user browses photos of candidate users;
the server side equipment determines emotion information of a target user according to the user face image information;
the server-side equipment determines social willingness information of the target user to the candidate user based on the emotion information;
and the server side equipment establishes a friend relationship between the target user and the candidate user according to the social willingness information.
In addition, the embodiment of the application also provides friend recommendation equipment, which comprises a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein when the computer program instructions are executed by the processor, the equipment is triggered to execute the friend recommendation method.
Embodiments of the present application further provide a computer-readable medium, on which computer program instructions are stored, where the computer-readable instructions are executable by a processor to implement the friend recommendation method.
Some embodiments of the present application provide a friend recommendation scheme, in which a server device pushes a photo of a candidate user to a user end device corresponding to a target user, then obtains user face image information collected and uploaded by the user end device when the user browses the photo of the candidate user, determines emotion information of the user according to the user face image information, and determines social willingness information of the target user to the candidate user based on the emotion information, so as to establish a friend relationship between the target user and the candidate user. Because the social willingness information is determined based on the user face image information which is collected and uploaded when the user browses the candidate user photos, the social willingness does not need to be expressed through specific operation for the user, only the real expression is exposed when the candidate user photos are browsed, the server-side equipment can automatically identify the corresponding social willingness, the user operation is simplified, and the use experience is better.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
fig. 1 is a schematic diagram of a system for implementing friend recommendation according to an embodiment of the present application;
FIG. 2 is a processing flow diagram for implementing friend recommendation in an embodiment of the present application;
FIG. 3 is another processing flow diagram for implementing friend recommendation in an embodiment of the present application;
FIG. 4 is a schematic diagram illustrating an implementation of a friend recommendation method in an embodiment of the present application;
fig. 5 is an implementation schematic diagram of another friend recommendation method in the embodiment of the present application;
fig. 6 is an implementation schematic diagram of a friend recommendation method that can be optimized based on feedback information in an embodiment of the present application;
fig. 7 is a schematic structural diagram of a computing device for implementing friend recommendation according to an embodiment of the present application;
the same or similar reference numbers in the drawings identify the same or similar elements.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
In a typical configuration of the present application, the terminal, the devices serving the network each include one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, which include both non-transitory and non-transitory, removable and non-removable media, may implement the information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
Some embodiments of the application provide a friend recommendation method, social willingness information in the method is determined based on user facial image information acquired and uploaded when a user browses photos of candidate users, the social willingness is not required to be expressed through specific operations for the user, only real expressions need to be revealed when the user browses the photos of the candidate users, and the server-side equipment can automatically recognize corresponding social willingness, so that the user operation is simplified, and the use experience is better.
Fig. 1 is a schematic diagram illustrating a system for implementing friend recommendation provided in an embodiment of the present application, where the system includes a user-side device 110 and a server-side device 120, where the user-side device is a device used by a user and may include but is not limited to various terminal devices such as a computer, a mobile phone, a tablet computer, and a smart watch, and a client program with a social function may be run on the terminal device to complete corresponding processing and interaction logic between the terminal device and the user and other devices. In this embodiment of the application, the number of the client devices may be multiple, and the multiple client devices respectively correspond to different users, so as to be used by the different users, and implement social requirements among the users.
The server device may include, but is not limited to, a network host, a single network server, a plurality of network server sets, or a computer set based on Cloud Computing, wherein a Cloud is formed by a large number of hosts or network servers based on Cloud Computing (Cloud Computing), wherein Cloud Computing is a kind of distributed Computing, and a virtual computer is formed by a group of loosely coupled computer sets. A server program with a social function can be run in the server device to complete corresponding processing and interaction logic between other devices.
When the friend recommendation method in the embodiment of the application is implemented, the user-side device 110 is configured to implement collection, uploading of user face image information, and present recommended candidate users to a user, and the server-side device 120 is configured to push photos of the candidate users and subsequent related processing based on the user face image information. Fig. 2 shows an interaction processing flow between the client device 110 and the server device 120, which includes the steps of:
step S201, the server device pushes the photo of the candidate user to the client device corresponding to the target user.
Step S202, the user terminal equipment acquires the photo of the candidate user and displays the photo to the target user for the target user to select.
Step S203, the user end device collects and uploads user face image information when the user browses the candidate user' S photo. The user end device may trigger the acquisition and uploading processing based on various ways, for example, when a certain social application is installed on the user end, the user may request the user to acquire the authority of the image acquisition device when the user opens the social application for the first time, so that when the photo of the candidate user is displayed, the image acquisition device of the user end device is called to acquire the facial image information of the user and upload the facial image information to the server end device. The image acquisition device can be a camera carried by or externally connected with the user side equipment, such as a front camera on a mobile phone.
Step S204, the server side equipment acquires user face image information which is acquired and uploaded by the user side equipment when the target user browses the candidate user photos. The user face image information may be a user face image, that is, after the user face image is acquired, data of the face image is directly uploaded to the server device, or only the data of the image is uploaded after being compressed.
In addition, the user face image information may also be image feature information extracted by the user end device based on the user face image, where the image feature information may be color features, texture features, shape features, and the like in the image, and these features can describe features of the image, so as to distinguish different images. At this time, after the user side device collects the user face image, feature extraction processing may be performed, and after image feature information is extracted from the user face image, the image feature information is directly uploaded to the server side device or is uploaded to the server side device after being compressed, instead of directly uploading image data. Therefore, the safety can be improved, and the situation that the privacy of the user is leaked due to the fact that the face image of the user is directly uploaded is avoided.
Step S205, the server side equipment determines emotion information of a target user according to the user face image information.
In an actual scene, in order to improve the accuracy of the server device in recognizing the emotion of the user from the facial image of the user, when the facial image information of the user is provided, a plurality of continuous frames of facial image information of the user may be provided, for example, several key frames in the whole process of making various expressions by the user. Here, the user equipment may adopt the following processing method: firstly, acquiring continuous images containing face images of a user, for example, shooting a video of a complete process of a certain expression made by the user when browsing photos through a camera; then, extracting a plurality of key frames containing the face images of the user from the continuous images, wherein the key frames can be key pictures in the process that the user makes certain expressions, so that the expressions made by the user can be accurately identified; and finally, sending a plurality of key frames containing the face images of the user to the server-side equipment, so that emotion recognition is carried out through the face images of the user contained in the key frames.
In the embodiment of the application, when the server device determines the social willingness information of the target user to the candidate user based on the emotion information, the emotion information may be sent to the client device, so that the client device sends the social willingness information of the target user to the candidate user to the server device according to the emotion information, that is, a positive will is corresponding to a positive emotion, and a negative will is corresponding to a negative emotion.
Step S206, the server side equipment determines social willingness information of the target user to the candidate user based on the emotion information. The emotion information of the user determined according to the face image information of the user can comprise positive emotions such as 'approval', 'appreciation' and the like, and negative emotions such as 'disapproval', 'disgust' and the like. If the emotion of the target user when browsing the candidate user is a positive emotion, the social willingness of the target user to the candidate user can be considered as a positive willingness, and otherwise, if the emotion of the target user when browsing the candidate user is a negative emotion, the social willingness of the target user to the candidate user can be considered as a negative willingness.
For example, when the target User1 browses the photo of the candidate User5, and the emotion corresponding to the facial image is a positive emotion of the type "agree", "enjoy", etc., it indicates that the social intention information of the target User1 to the candidate User5 is positive intention, i.e., interested in, desiring to establish a friend relationship. When the target User1 browses the photo of the candidate User8, and the emotion corresponding to the facial image of the target User1 is a negative emotion of the type "disapproval", "dislike", or the like, it indicates that the social willingness information of the candidate User5 by the target User1 is a negative willingness, i.e., no interest and no willingness to establish a friend relationship.
In the embodiment of the application, when the server device determines the social willingness information of the target user to the candidate user based on the emotion information, the emotion information may be sent to the client device, so that the client device sends the social willingness information of the target user to the candidate user to the server device according to the emotion information, that is, a positive will is corresponding to a positive emotion, and a negative will is corresponding to a negative emotion. In an actual scenario, the manner of sending the social willingness information may be set according to requirements of the actual scenario, for example, a request for adding a friend may be sent as a manner of expressing a positive willingness. And after the server side equipment receives a request of the target user from the user side equipment side for adding friends to the candidate user, the social willingness information of the target user to the candidate user can be determined.
Step S207, the server device establishes a friend relationship between the target user and the candidate user according to the social willingness information. Because the social willingness information is determined based on the user face image information which is collected and uploaded when the user browses the candidate user photos, the social willingness does not need to be expressed through specific operation for the user, only the real expression is exposed when the candidate user photos are browsed, the server-side equipment can automatically identify the corresponding social willingness, the user operation is simplified, and the use experience is better.
When a friend relationship between two users is established, if the social intention information of the target user to the candidate user is a positive intention, the server side equipment records the positive intention, and when the positive intention is determined to exist between the two users, the friend relationship between the two users is established. For example, when User1 is taken as a target User, 3 candidate users, User5, User8 and User9 are recommended, and the target User, User1, is interested in the candidate User, User5, and feeds back positive will. And 3 candidate users, User1, User8 and User9, are recommended to the User5 when the User is taken as a target User, and the target User, User1, is interested in the candidate users, User1 and User8, and feeds back positive intentions. It follows that User1 and User5 are interested in each other and feed back positive intentions. After the server records the positive intentions, it can be determined that both users, User1 and User5, have positive intentions, and thus a friend relationship can be established for both users, User1 and User 5.
In addition, another way of establishing a friend relationship may also be adopted in the embodiment of the present application, that is, after the server device obtains the feedback information returned by the user side device, if the social willingness information of the target user to the candidate user is a positive willingness, the server device sends a friend addition request of the target user to the candidate user. Taking the aforementioned User1 as an example, after the target User1 is interested in the candidate User5 and feeds back a positive will, the server device may determine that the social will information of the User1 to the User5 is a positive will, at this time, may send a friend addition request of the User1 to the User5, and the User5 selects whether to agree to establish the friend relationship based on the friend addition request.
In some embodiments of the present application, the candidate user pushed by the server device may be from a candidate data set, that is, before the server device pushes the photo of the candidate user to the client device corresponding to the target user, the candidate user may be determined from the candidate data set. For example, in this embodiment, a candidate data set for selecting candidate users may be set for the target user in advance, where a certain number of candidate users may be included, so as to select a suitable candidate user from the candidate data set when a photo needs to be pushed to the target user.
In an actual scene, multiple friend recommendations may be performed on the same target user, so that each recommendation can better meet the social requirement of the target user, and the candidate data set can be updated based on the result of the previous recommendation. Namely, after the server device determines the social willingness information of the target user to the candidate user based on the emotion information, the candidate data set can be updated according to the social willingness information.
Fig. 3 shows a processing flow of another friend recommendation method provided in the embodiment of the present application, including the following steps:
step S301, the user terminal device collects and uploads user face image information. The user end device may trigger the acquisition and uploading processing based on various ways, for example, when a certain social application is installed on the user end, when the user opens the social application for the first time, the user may request the user to acquire the authority of the image acquisition device, so as to acquire the facial image information of the user and upload the facial image information to the server end device. The image acquisition device can be a camera carried by or externally connected with the user side equipment, such as a front camera on a mobile phone.
In addition, the user can actively start the image acquisition device on the user side equipment to acquire the face image information of the user through preset operation in the process of using the social application and upload the face image information to the server side equipment. It should be understood by those skilled in the art that the manner of triggering acquisition and uploading on the client device is merely exemplary, and other forms based on similar principles, which are present or later come to be suitable for the application, should be included in the scope of protection of the present application and are incorporated herein by reference.
Step S302, the server side equipment acquires the user face image information collected and uploaded by the user side equipment.
Step S303, the server side equipment acquires basic attribute information of the target user according to the face image information of the user. The target user is any user needing friend recommendation, and the basic attribute information is some basic information capable of representing the identity of the user, such as the age, the gender, and the like of the user. In an actual scene, the user face image information of the target user can be identified through the neural network model so as to obtain the basic attribute information of the target user, and the neural network model can be obtained through training of a pre-marked training set, so that the basic attribute information of different users can be identified more accurately.
Step S304, the server side equipment determines a candidate data set matched with the target user according to the basic attribute information. And the server side equipment is matched with the optional relevant information of other users based on the basic attribute information, wherein the relevant information of other users is used for matching with the basic attribute information of the target user. The related information may be basic attribute information of other users, or information such as a preset tag, and the matching rule may be preset according to an actual scene. For example, other users of similar age may be matched for the target user and added to the candidate data set.
In an actual scene, the basic attribute information of other users can also be obtained by processing by the server-side device after the user-side device used by other users collects and uploads the corresponding user face image information. For example, for a certain social application, any user can upload user face image information to the server device before using the friend recommendation function, so that the server device can acquire user basic attribute information based on the user face image information as related information when matching with a target user.
Step S305, the server device determines a candidate user from the candidate data set, and pushes a photo of the candidate user to a user end device corresponding to the target user.
The candidate users are users recommended to the target user as alternative friends, the number of the candidate users determined by the server side device for the target user can be set according to requirements of an actual scene, for example, one-time recommendation can be to push 3 or 5 candidate users for the target user to select. The selection mode may be random selection, or the matching degree when determining the candidate data set according to matching, that is, the higher the matching degree is, the more preferentially the candidate data set is selected as the candidate user recommended to the target user.
In addition, in order to enable the target user to better know the candidate user, in the embodiment of the application, the server device pushes the photo of the candidate user to the client device corresponding to the target user, so that the target user can view the photo of the user to better judge whether the candidate user is added as a friend.
In an actual scene, if the user face image information acquired by the server device is the face image of the user, the data of the face image can be used as the photo of the corresponding user, and the photo of the candidate user does not need to be acquired through other channels. If the image information of the face of the user acquired by the server-side equipment is the image feature information of the image information of the face of the user, the server-side equipment can acquire photos of each user in advance through other channels, for example, when the user starts a social application for the first time, the user is prompted to upload the photos, and the permission of using the photos when friends recommend is requested, so that the photos of the candidate users are pushed when the friends are recommended.
Step S306, the user end device obtains the photo of the candidate user and displays the photo to the target user for selection by the target user.
Fig. 4 illustrates an implementation principle of a friend recommendation method in an embodiment of the present application. Taking the target User1 as an example, when the target User1 uses the friend recommendation function, a camera on the User terminal device UE1 such as a mobile phone used by the target User can be started to shoot the face image of the User. And performing feature extraction on the face image of the user, and compressing and uploading the image feature information to the server side equipment after the image feature information is obtained. After determining the basic attribute information of the target User1 based on the uploaded image feature information, the server device performs matching in the initial data set D1 based on the basic attribute information to obtain a candidate data set D2. When recommending friends to the target User1, a portion of the candidate data set D2 may be selected as candidate users and the photos of the candidate users may be recommended to the target User 1.
Because when the candidate user is generated, the user face image information is acquired and uploaded on the basis of the user end equipment, for the user, only the image acquisition device such as the camera needs to be started, and some matched information does not need to be set through manual selection or input, so that the operation is simple, and the use experience is better.
In other embodiments of the present application, when obtaining the basic attribute information of the target user according to the user face image information, the server device may obtain the social attribute information of the target user according to the user face image information. Therefore, the server-side equipment can determine a candidate data set matched with a target user according to the basic attribute information and the social attribute information.
Wherein the social attribute information may represent a current state of the target user, such as a current fatigue state, a current skin state, a current mood state, and the like. Because the current state of the user has a certain influence on the current social willingness of the user, for example, when the mood of the user is poor, the user is more likely to socialize with a good user, so that the recommendation effectiveness can be improved for recommending a candidate user with good suitability and good mood for a target user with poor mood, and the experience of the target user is better.
In order to determine the social attribute information of the target user more accurately, the user facial image information in the embodiment of the present application may be continuous images continuously acquired by the user terminal device, or a series of image feature information from the continuous images. When determining the social attribute information, the server device may also use the neural network model for identification, so as to obtain the social attribute information of the target user. In an actual scenario, the user face image information for determining the basic attribute information of the target user and the user face image information for determining the social attribute information of the target user may be from images acquired at different times, for example, the user face image information for determining the basic attribute information of the target user may be from a static image acquired by the target user when the target user uses the friend recommendation function for the first time, and the user face image information for determining the social attribute information of the target user may be from a continuous dynamic image acquired by the target user in real time each time the target user uses the friend recommendation function.
When the social attribute information of the user is obtained, the social attribute information can also be used for screening and matching the user, and the complementary social attribute information is that the current state of the user represented by the social attribute information can be supplemented with each other, so that communication and exchange among the users are facilitated. Therefore, in addition to data matching based on the basic attribute information, the embodiment of the application further screens based on the social attribute information, so that the data set of the candidate user selected by the user is a more accurate social pairing data set, and friend recommendation is more effective.
Thus, in some embodiments of the present application, the server device may determine, by the server device, a candidate data set matching a target user according to the basic attribute information, then determine, according to the social attribute information, a user with complementary social attribute information from the candidate data set, and update the candidate data set based on the user with complementary social attribute information. Therefore, the new candidate data set comprises users with the social attribute information complementary to the target user, and the selected candidate users are more in line with the current social requirements of the target user, so that friend recommendation is more effective.
Fig. 5 illustrates an implementation principle of the friend recommendation process. The server-side equipment determines basic attribute information and social attribute information of the target User1 based on the uploaded User face image information, firstly matches in an initial data set D1 based on the basic attribute information to obtain a candidate data set D2, and then screens and updates the candidate data set D2 based on the social attribute information to obtain a new candidate data set D3. When recommending friends to the target User1, a portion of the new candidate data set D3 may be selected as candidate users and the photos of the candidate users may be recommended to the target User 1.
Step S307, the user end device collects and uploads user face image information when the user browses the candidate user' S photo.
Step S308, the server side equipment acquires user face image information which is acquired and uploaded by the user side equipment when the target user browses the candidate user photos.
Step S309, the server side equipment determines emotion information of the target user according to the user face image information.
Step S310, the server side equipment determines social willingness information of the target user to the candidate user based on the emotion information.
Step S311, the server device establishes a friend relationship between the target user and the candidate user according to the social willingness information, and may update the candidate data set based on feedback information of the target user on the recommended candidate user.
After the user end device displays the photo of the candidate user to the target user, the target user can send feedback information according to the actual social requirement of the target user, and the feedback information is fed back to the candidate user. The feedback information comprises social willingness information of the target user to the candidate user, and the social willingness information comprises positive willingness and negative willingness. The positive will indicates that the target user is interested in the candidate user and wants to establish a friend relationship with the candidate user, and the negative will indicates that the target user is not interested in the candidate user and does not have a desire to establish a friend relationship.
Therefore, in the friend recommendation method in the embodiment of the application, the server device may further obtain feedback information returned by the client device, where the feedback information includes social willingness information of the target user to the candidate user. The server-side device may update the candidate data set according to the social willingness information.
The update mode of the candidate data set based on the feedback information may be: and if the target user has negative will on the social willingness information of the candidate user, determining the candidate user and other candidate users with similar basic attribute information to the candidate user as negative feedback users. Since the candidate user at this time is a user of the same type that the user is not interested in, the same type of user that the user is not interested in, that is, the negative feedback user, can be found from the candidate data set by the above-mentioned method. Then, the server-side equipment deletes the negative feedback user in the candidate data set, so that the candidate data set can be updated, and the user in the candidate data set can better meet the requirement of the target user.
Here, it should be understood by those skilled in the art that the foregoing two ways of updating the candidate data set may be used in combination, that is, after the candidate data set is obtained through the basic attribute information, the candidate data set may be updated through the social attribute information and the feedback information, respectively, so as to obtain a more accurate candidate data set.
Fig. 6 shows the implementation principle of the friend recommendation process. After determining the basic attribute information of the target User1 based on the uploaded image feature information, the server device performs matching in the initial data set D1 based on the basic attribute information to obtain a candidate data set D2. When recommending friends to the target User1, a portion of the candidate data set D2 may be selected as candidate users and the photos of the candidate users may be recommended to the target User 1. Then, the server-side device can also acquire feedback information returned by the user-side device, and then update the candidate data set according to whether the social willingness information in the feedback information is positive willingness or negative willingness, and delete negative feedback users in the feedback information to update and obtain a new candidate data set D4, so that when subsequent friend recommendation is performed to the target user again, candidate users can be selected from the new candidate data set D4, similar users corresponding to the negative willingness are reduced, and the recommendation effectiveness is improved.
Based on the same inventive concept, the embodiment of the application also provides friend recommendation equipment, a corresponding method of the equipment can be the friend recommendation method in the embodiment, and the problem solving principle is similar to that of the method. The device comprises a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein the computer program instructions, when executed by the processor, trigger the device to perform the aforementioned friend recommendation method.
The friend recommendation method can be implemented in the server device, the server device processes data collected by the user terminal device, and then the user terminal device displays recommended candidate users to the target user. The server may include, but is not limited to, a network host, a single network server, a plurality of network server sets, or a computer set based on Cloud Computing, wherein a Cloud is formed by a large number of hosts or network servers based on Cloud Computing (Cloud Computing), wherein Cloud Computing is one type of distributed Computing and is a virtual computer composed of a group of loosely coupled computer sets.
Fig. 7 shows a structure of a device suitable for implementing the method and/or technical solution in the embodiment of the present application, and the device 700 includes a Central Processing Unit (CPU)701, which can execute various suitable actions and processes according to a program stored in a Read Only Memory (ROM) 702 or a program loaded from a storage portion 708 into a Random Access Memory (RAM) 703. In the RAM 703, various programs and data necessary for system operation are also stored. The CPU 701, the ROM 702, and the RAM 703 are connected to each other via a bus 704. An Input/Output (I/O) interface 705 is also connected to the bus 704.
The following components are connected to the I/O interface 705: an input portion 706 including a keyboard, a mouse, a touch screen, a microphone, an infrared sensor, and the like; an output section 707 including a Display panel such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), an LED Display, an OLED Display, and the like, and a speaker; a storage portion 708 comprising one or more computer-readable media such as a hard disk, optical disk, magnetic disk, semiconductor memory, or the like; and a communication section 709 including a Network interface card such as a LAN (Local Area Network) card, a modem, or the like. The communication section 709 performs communication processing via a network such as the internet.
In particular, the methods and/or embodiments in the embodiments of the present application may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. The computer program, when executed by a Central Processing Unit (CPU)701, performs the above-described functions defined in the method of the present application.
It should be noted that the computer readable medium described herein can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart or block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
As another aspect, the present application also provides a computer-readable medium, which may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer-readable medium carries one or more computer-readable instructions executable by a processor to implement the methods and/or aspects of the embodiments of the present application as described above.
It should be noted that the present application may be implemented in software and/or a combination of software and hardware, for example, implemented using Application Specific Integrated Circuits (ASICs), general purpose computers or any other similar hardware devices. In some embodiments, the software programs of the present application may be executed by a processor to implement the above steps or functions. Likewise, the software programs (including associated data structures) of the present application may be stored in a computer readable recording medium, such as RAM memory, magnetic or optical drive or diskette and the like. Additionally, some of the steps or functions of the present application may be implemented in hardware, for example, as circuitry that cooperates with the processor to perform various steps or functions.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the apparatus claims may also be implemented by one unit or means in software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.
Claims (13)
1. A friend recommendation method, wherein the method comprises the following steps:
the server-side equipment pushes the photos of the candidate users to user-side equipment corresponding to the target users;
the server-side equipment acquires user face image information which is acquired and uploaded by user-side equipment when a target user browses photos of candidate users;
the server side equipment determines emotion information of a target user according to the user face image information;
the server-side equipment determines social willingness information of the target user to the candidate user based on the emotion information;
and the server side equipment establishes a friend relationship between the target user and the candidate user according to the social willingness information.
2. The method of claim 1, wherein the server device establishes a friend relationship between the target user and the candidate user according to the social willingness information, and the method comprises the following steps:
if the social willingness information of the target user to the candidate user is a positive willingness, the server side equipment records the positive willingness;
and when the server side equipment determines that the target user and the candidate user both have positive intentions, establishing a friend relationship between the target user and the candidate user.
3. The method of claim 1, wherein the server device establishes a friend relationship between the target user and the candidate user according to the social willingness information, and the method comprises the following steps:
and if the social willingness information of the target user to the candidate user is positive willingness, the server-side equipment sends a friend adding request of the target user to the candidate user.
4. The method of claim 1, wherein the server device determines social willingness information of the target user to candidate users based on the emotion information, comprising:
the server-side equipment sends the emotion information to user-side equipment so that the user-side equipment sends social willingness information of a target user to candidate users to the server-side equipment according to the emotion information;
the server-side equipment receives social willingness information of a target user to the candidate user from a user-side equipment side.
5. The method of claim 1, wherein before the server device pushes the photo of the candidate user to the client device corresponding to the target user, the method further comprises:
and the server-side equipment determines candidate users from the candidate data set.
6. The method of claim 5, wherein the method further comprises:
the server-side equipment acquires user face image information collected and uploaded by the user-side equipment;
the server side equipment acquires basic attribute information of a target user according to the user face image information;
and the server side equipment determines a candidate data set matched with the target user according to the basic attribute information.
7. The method of claim 6, wherein the method further comprises:
the server side equipment acquires social attribute information of a target user according to the user face image information;
the server side equipment determines a candidate data set matched with a target user according to the basic attribute information, and the method comprises the following steps:
and the server side equipment determines a candidate data set matched with the target user according to the basic attribute information and the social attribute information.
8. The method of claim 7, wherein the server device determining a candidate data set matching a target user according to the basic attribute information and social attribute information comprises:
the server side equipment determines a candidate data set matched with a target user according to the basic attribute information;
the server-side equipment determines candidate users with complementary social attribute information from the candidate data set according to the social attribute information;
the server device updates the candidate data set based on the candidate users with the complementary social attribute information.
9. The method of claim 5, wherein after the server device determines social willingness information of the target user to the candidate user based on the emotion information, the method further comprises:
and the server-side equipment updates the candidate data set according to the social willingness information.
10. The method of claim 9, wherein the server device updates the candidate data set according to the social willingness information, including:
if the target user has negative will on the social willingness information of the candidate user, the server-side equipment determines the candidate user and other candidate users with similar basic attribute information to the candidate user as negative feedback users;
and the server-side equipment deletes the negative feedback user in the candidate data set.
11. The method according to any one of claims 1 to 10, wherein the user facial image information is image feature information extracted by the user end device based on the user facial image.
12. A buddy recommendation device comprising a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein the computer program instructions, when executed by the processor, trigger the device to perform the method of any of claims 1 to 11.
13. A computer readable medium having stored thereon computer program instructions executable by a processor to implement the method of any one of claims 1 to 11.
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