CN113688328A

CN113688328A - Method, device and storage medium for matching target object

Info

Publication number: CN113688328A
Application number: CN202111251316.4A
Authority: CN
Inventors: 刘瑜晗; 龚鸣; 熊圣尧
Original assignee: Beijing Blueberry Technology Co ltd
Current assignee: Beijing Blueberry Technology Co ltd
Priority date: 2021-10-27
Filing date: 2021-10-27
Publication date: 2021-11-23

Abstract

The application discloses a method and a device for matching a target object and a storage medium. The method for matching the target object comprises the following steps: receiving a request for matching a target object from a terminal device requesting a matching user; matching the target object from a preset user matching pool according to the requested request information; and under the condition that the target object is not matched in the user matching pool, matching the target object from the online user set according to the request information.

Description

Method, device and storage medium for matching target object

Technical Field

The present application relates to the field of internet social contact, and in particular, to a method, an apparatus, and a storage medium for matching a target object.

Background

Nowadays, the internet is spread all over the world, people can not leave the smart phone, and for the fact that the communication distance between people is increased due to the nature of the internet in life, the social mode is basically carried out in an online mode. Due to the diversity of life styles of people and the variety of social software on mobile phones, people have become mainstream ways of making friends by using mobile phone software. Currently, friend-making software generally has a real-time online matching function, and matches users who participate in matching at the same time, but if the number of participants is small, the phenomenon of unsuccessful matching occurs.

Aiming at the technical problem existing in the prior art that the user matching success rate in the real-time online matching scene is low, an effective solution is not provided at present.

Disclosure of Invention

Embodiments of the present application provide a method, an apparatus, and a storage medium for matching a target object, so as to at least solve the technical problem in the prior art that a user matching success rate in a real-time online matching scenario is low.

According to an aspect of an embodiment of the present application, there is provided a method of matching a target object, including: receiving a request for matching a target object from a terminal device requesting a matching user; matching the target object from a preset user matching pool according to the requested request information; and under the condition that the target object is not matched in the user matching pool, matching the target object from the online user set according to the request information.

According to another aspect of embodiments of the present application, there is also provided a storage medium including a stored program, wherein the method of any one of the above is performed by a processor when the program is run.

According to another aspect of the embodiments of the present application, there is also provided an apparatus for matching a target object, including: a request receiving module for receiving a request for matching the target object from a terminal device requesting a matching user; the first matching module is used for matching the target object from a preset user matching pool according to the requested request information; and the second matching module is used for matching the target object from the online user set according to the request information under the condition that the target object is not matched in the user matching pool.

According to another aspect of the embodiments of the present application, there is also provided an apparatus for matching a target object, including: a processor; and a memory coupled to the processor for providing instructions to the processor for processing the following processing steps: receiving a request for matching a target object from a terminal device requesting a matching user; matching the target object from a preset user matching pool according to the requested request information; and under the condition that the target object is not matched in the user matching pool, matching the target object from the online user set according to the request information.

In the embodiment of the application, when the target object is matched, the matching is performed through the user matching pool, and under the condition that the target object is not matched in the user matching pool, the target object is intensively matched from the online user according to the request information. Therefore, the technical scheme can screen out the target object which meets the requirement of the user requesting for matching in a large range by firstly utilizing the user matching pool for matching and then matching the target object by the two modes of the adjustment matching. And when the users are screened, the screening operation is carried out through multiple dimensions such as distance, user characteristics and time, so that the users who are willing to participate in the matching are accurately and efficiently screened, the number of the users who participate in the matching is increased, and the matching rate of the users is improved. And the technical problem that the success rate of user matching in a real-time online matching scene is low in the prior art is solved.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:

fig. 1 is a hardware block diagram of a computing device for implementing the method according to embodiment 1 of the present application;

fig. 2 is a schematic flow chart of a method for matching a target object according to the first aspect of embodiment 1 of the present application;

fig. 3 is a schematic diagram of an apparatus for matching a target object according to embodiment 2 of the present application; and

fig. 4 is a schematic diagram of an apparatus for matching a target object according to embodiment 3 of the present application.

Detailed Description

In order to make those skilled in the art better understand the technical solutions of the present application, 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. It is to be understood that the described embodiments are merely exemplary of some, and not all, of the present application. 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.

It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

Example 1

According to the present embodiment, there is provided an embodiment of a method of matching target objects, it is noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be performed in an order different than here.

The method embodiments provided by the present embodiment may be executed in a mobile terminal, a computer terminal, a server or a similar computing device. FIG. 1 illustrates a block diagram of a hardware architecture of a computing device for implementing a method of matching target objects. As shown in fig. 1, the computing device may include one or more processors (which may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory for storing data, and a transmission device for communication functions. Besides, the method can also comprise the following steps: a display, an input/output interface (I/O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of the I/O interface), a network interface, a power source, and/or a camera. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the electronic device. For example, the computing device may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.

It should be noted that the one or more processors and/or other data processing circuitry described above may be referred to generally herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part in software, hardware, firmware, or any combination thereof. Further, the data processing circuitry may be a single, stand-alone processing module, or incorporated in whole or in part into any of the other elements in the computing device. As referred to in the embodiments of the application, the data processing circuit acts as a processor control (e.g. selection of a variable resistance termination path connected to the interface).

The memory may be used to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the method for matching a target object in the embodiment of the present application, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, implementing the method for matching a target object of an application program as described above. The memory may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory located remotely from the processor, which may be connected to the computing device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission device is used for receiving or transmitting data via a network. Specific examples of such networks may include wireless networks provided by communication providers of the computing devices. In one example, the transmission device includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.

The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the computing device.

It should be noted here that in some alternative embodiments, the computing device shown in fig. 1 described above may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that FIG. 1 is only one example of a particular specific example and is intended to illustrate the types of components that may be present in a computing device as described above.

In the above operating environment, according to a first aspect of the present embodiment, a method of matching a target object is provided. Fig. 2 shows a flow diagram of the method, which, with reference to fig. 2, comprises:

s202: receiving a request for matching a target object from a terminal device requesting a matching user;

s204: matching the target object from a preset user matching pool according to the requested request information; and

s206: and under the condition that the target object is not matched in the user matching pool, matching the target object from the online user set according to the request information.

Specifically, when the matching user is requested to make a real-time matching friend, the matching user is requested to send a request for matching with the target object through the terminal device, and the computing device receives the request for matching with the target object (S202).

Further, the computing device may match the target object from a pre-set user matching pool based on the requested request information. The user matching pool stores demand users with matching demands which are using the real-time matching function. For example, user 1 and user 2 both sent a request for a real-time match, and user 1 and user 2 both stored in the user match pool. Taking user 1 (i.e., the request matching user) as an example, the computing device screens out one user matching user 1, for example, user 2 (i.e., the target object), from the user matching pool according to the request information of user 1 (S204).

Further, when none of the demand users in the user matching pool can match user 1, the computing device determines that the target object is not matched in the user matching pool. Specifically, in the event that the target object is not matched in the user matching pool, the computing device matches the target object from the set of online users according to the request information. Wherein all online users in the set of online users are users that are not currently participating in the real-time matching. For example, if the computing device does not find a user matching user 1 (i.e., the matching-requesting user) in the user matching pool, the computing device triggers the scheduling mechanism to match the target object from the online user set, i.e., from the online users that have not participated in the real-time matching but have higher liveness, for user 1 (i.e., the matching-requesting user) (S206).

As described in the background art, nowadays, the internet is spread all over the world, people cannot leave the smart phone, and for people-to-people communication distance in life increases due to the nature of the internet, the social mode is basically performed in an online mode. Due to the diversity of life styles of people and the variety of social software on mobile phones, people have become mainstream ways of making friends by using mobile phone software. Currently, friend-making software generally has a real-time online matching function, and matches users who participate in matching at the same time, but if the number of participants is small, the phenomenon of unsuccessful matching occurs.

For the technical problems, according to the technical scheme of the embodiment of the application, when the target object is matched, the matching is performed through the user matching pool, and when the target object is not matched in the user matching pool, the target object is intensively matched from the online user according to the request information. Therefore, the technical scheme can screen out the target object which meets the requirement of the user requesting for matching in a large range by firstly utilizing the user matching pool for matching and then matching the target object by the two modes of the adjustment matching. Therefore, users who are willing to participate in matching are accurately and efficiently screened, the number of the users who participate in matching is increased, and the matching rate of the users is improved. And the technical problem that the success rate of user matching in a real-time online matching scene is low in the prior art is solved.

Optionally, the operation of matching the target object from a preset user matching pool according to the requested request information includes: ordering the demand users in the user matching pool according to the distance between the demand users and the request matching users in the user matching pool, and determining a first demand user set to be matched; determining a second to-be-matched required user set corresponding to the user characteristics of the request matching user according to the user characteristics of the required users in the first to-be-matched required user set, wherein the user characteristics comprise gender, age, occupation and constellation; determining a third to-be-matched demand user set according to the frequency of the demand users of the second to-be-matched demand user set participating in matching within a preset time; and determining the target object according to the third to-be-matched requirement user set by using a preset filtering mechanism.

Specifically, to match a target object from a preset user matching pool, the computing device first filters a desired user in the user matching pool by using a matching mechanism. Specifically, the computing device ranks the demand users in the user matching pool according to the distance between the demand users and the request matching users in the user matching pool, and determines a first set of demand users to be matched. For example, the computing device may first obtain location information of the user 1 (i.e., the request matching user) and other demand users in the user matching pool, then calculate a distance between the demand user and the user 1 (i.e., the request matching user) in the user matching pool according to the location information, sort the demand users in the user matching pool according to a distance to the user 1 (i.e., the request matching user), screen out a certain number (e.g., 200) of demand users that are closest to the user 1 (i.e., the request matching user), and form the screened demand users into a first demand user set to be matched.

Further, after distance screening is performed on the demand users in the user matching pool, the computing device determines a second demand user set to be matched, which corresponds to the user characteristics of the demand user in the first demand user set to be matched, wherein the user characteristics include gender, age, occupation and constellation. Specifically, before using the matching function, the user fills out personal information, and the computing device uses personal information such as gender, age, occupation, and constellation in the personal information as the user characteristics. For example, the computing device obtains user characteristics of user 1 (i.e., the request matching user) and all the demanding users in the first set of demanding users to be matched, then screens out a certain number (e.g., 100) of demanding users from the first set of demanding users to be matched, where the number matches the user characteristics of user 1 (i.e., the request matching user), and forms the screened demanding users into a second set of demanding users to be matched.

Further, after the computing device filters the user characteristics of the demand users in the first demand user set to be matched, the computing device determines a third demand user set to be matched according to the frequency of the demand users of the second demand user set to be matched participating in matching within a predetermined time. The frequency participating in matching comprises the frequency of the demand user sending the matching request and the frequency of receiving the matching request of other users. For example, the computing device obtains the number of times that all demand users in the second to-be-matched demand user set use the real-time matching function, and screens out a certain number of demand users with low frequency participating in the real-time matching function within a predetermined time, for example, 7 days. The operation of these demand users using the real-time matching function includes sending matching requests and receiving other user matching requests. The computing device then groups the screened demand users into a third set of demand users to be matched.

Further, the computing device determines the target object according to the third to-be-matched requirement user set by using a preset filtering mechanism. Specifically, the computing device filters out, from the third set of demanding users to be matched, for example, demanding users who have been successfully matched with the requesting matching user today, demanding users who have become friends, demanding users who have history matching, and the like, and selects one demanding user that best matches user 1 (i.e., the requesting matching user) as the target object.

Therefore, when the technical scheme is used for screening the demand users in the user matching pool, the screening operation is carried out through multiple dimensions such as distance, user characteristics and time, so that users meeting requirements can be accurately and efficiently found in the users using the real-time matching function, and the matching efficiency of the users is improved.

Optionally, the operation of matching the target object from a preset user matching pool according to the requested request information further includes: determining the priority order of the plurality of user matching pools according to a preset priority rule under the condition that the user matching pools are the plurality of user matching pools; and sequentially matching the demand users and the request matching users in the plurality of user matching pools according to the priority order of the plurality of user matching pools to determine the target object.

In particular, there is not just one user matching pool, and the computing device may set the user matching pool to a multi-dimensional user matching pool. The user matching pools of multiple dimensions are divided according to actual matching requirements. That is, the computing device may classify the user match pool based on some of the user's labels. For example: the users with high face value are divided into a user matching pool, the users with high popularity are divided into a user matching pool, and the users with high reputation are divided into a user matching pool. The computing device determines a priority order of the plurality of user matching pools according to a preset priority rule. For example, the priority order of the user matching pool is from high to low: a user matching pool 1, a user matching pool 2 and a user matching pool 3. When matching user 1, first, a matching operation is performed for user 1 (i.e., the matching-requesting user) in the user matching pool 1. When all the required users in the user matching pool 1 cannot be matched with the user 1 (namely, the user requesting matching), matching operation is carried out on the user 1 (namely, the user requesting matching) in the user matching pool 2, and the like until the target object is determined. Therefore, according to the technical scheme, the priorities are set for the plurality of user matching pools, so that the users can be screened purposefully, and the users meeting the requirement of the users requiring matching can be screened accurately.

In addition, since a plurality of user matching pools are simultaneously used, when the identity of one user (for example, the user 2) is changed, and the label of the user 2 may be a user with a high face value, the user 2 may be in the user matching pool 1, and then the label of the user 2 becomes a user with a high popularity, and is added to the user matching pool 2. It may happen that when the user 2 in the user matching pool 1 has already matched a user, at this time, the user 2 joins the user matching pool 2 because the label of the user 2 is changed, and at this time, the user 2 in the user matching pool 2 may be matched by other users. To avoid this, the computing device may record whether the user is currently successfully matched during the matching process. For example, the user 2 is in the user matching pool 1 and has been successfully matched, even if the user 2 joins the user matching pool 2 after identity transition, and when the user matching pool 2 is matched by other users, the user 2 can be ignored (i.e., filtered out) at this time because the user 2 has been successfully matched in the user matching pool 1.

Optionally, the operation of matching the target object from the online user set according to the request information by the users in the online user set who are in a distance request matching with the users at a predetermined distance includes: sorting the online users in the online user set according to the distance between the online users in the online user set and the request matching users, and determining a first to-be-matched online user set; determining a second online user set to be matched corresponding to the user characteristics of the user requesting to be matched according to the user characteristics of the online users in the first online user set to be matched, wherein the user characteristics comprise gender, age, occupation and constellation; determining a third online user set to be matched according to the time when the online users of the second online user set to be matched send matching requests within preset time; and sending the interaction invitation of the request matching user to the online users in the third online user set to be matched, and determining the target object, wherein the interaction invitation comprises voice interaction and text interaction.

Specifically, in the case that the target object is not matched in the user matching pool, that is, the computing device cannot find a demand user matching with user 1 (i.e., the request matching user) from the user matching pool, the computing device will trigger a scheduling mechanism, so that user 1 (i.e., the request matching user) and an online user not participating in the real-time matching (i.e., having no matching demand) can reach a matching. In particular, the computing device determines a number of online users (e.g., 200 online users) within, for example, 5 kilometers from user 1 (i.e., the requesting matching user) from among the online users that are not participating in the real-time matching, and composes an online user set. Specifically, the computing device builds an index based on geographic location, and then searches for online users near USER-1 (i.e., the requesting matching USER) through the location index. That is, the computing device constructs a location index using a preset search algorithm and then encapsulates the search algorithm alone into an LBS service. When the computing device provides a specified point for USER-1 (i.e., the location of USER-1), i.e., the request-matching user, the computing device may efficiently recall nearby points (i.e., the location of the online user) based on the LBS service.

The computing device may then match the target object from the set of online users based on the requested information. Specifically, the calculation ranks the online users in the online user set according to the distance between the online users in the online user set and the matching requesting users, and determines a first to-be-matched online user set. For example, the computing device may first obtain location information of user 1 (i.e., the request matching user) and other online users in the online user set, then calculate a distance between the online users in the online user set and user 1 (i.e., the request matching user) according to the location information, sort the online users in the online user set according to a distance from user 1 (i.e., the request matching user), screen out a certain number (e.g., 100) of online users closest to user 1 (i.e., the request matching user), and form a first to-be-matched online user set with the screened online users.

Further, after distance screening is performed on the online users in the online user set, the computing device determines a second to-be-matched online user set corresponding to the user characteristics of the user requesting to match according to the user characteristics of the online users in the first to-be-matched online user set, where the user characteristics include gender, age, occupation, and constellation. Specifically, before using the matching function, the user fills out personal information, and the computing device uses personal information such as gender, age, occupation, and constellation in the personal information as the user characteristics. For example, the computing device obtains user characteristics of user 1 (i.e., the matching-requesting user) and all online users in the first set of online users to be matched, then screens out a certain number (e.g., 50) of online users from the first set of online users to be matched, where the number matches the user characteristics of user 1 (i.e., the matching-requesting user), and forms these screened online users into a second set of online users to be matched.

Further, after the computing device filters the user characteristics of the online users in the first online user set to be matched, the computing device determines a third online user set to be matched according to the time when the online users in the second online user set to be matched send matching requests within a preset time. For example, the computing device obtains the time when all online users in the second online user set to be matched use the real-time matching function, screens out online users who have sent matching requests within a predetermined time, for example, within 7 days, and forms the screened online users into a third online user set to be matched.

Further, the computing device sends the interaction invitation of the request matching user to the online users in the third online user set to be matched, and determines the target object. Wherein the interaction invitation includes a voice interaction and a text interaction. For example, the computing device sends an interaction invitation for user 1 (i.e., the requesting matching user) to an online user in the third set of online users to be matched. In the same way, the interactive invitation is the request for the text interaction under the condition that the user 1 requests the voice matching. The computing device then first receives a message, such as user 3 agreeing to the interaction invitation, and determines user 3 as the matching user (i.e., target object) for user 1 (i.e., the requesting matching user), establishing a communication interaction between user 1 (i.e., the requesting matching user) and user 3 (i.e., target object).

Further, the matching operation has a fixed duration (e.g., 10 s). In the early stage of matching (e.g., the first 5 s), the computing device only matches the requesting user in the user matching pool. At the later stage of matching (e.g., the last 5 s), the computing device may match the requesting users in the user matching pool and the online users in the online user pool at the same time as the requesting users in the user matching pool are continuously updating. And, in the simultaneous matching phase, once the computing device determines the target object in the user matching pool, immediately ending the matching operation with the online users in the online user set. Or, when a user matching with user 1 (i.e. the request matching user) is found at the same time when the required user in the user matching pool and the online user in the online user set are matched at the same time, the determined user in the user matching pool is preferentially selected as the target object.

Therefore, nearby users can be quickly positioned through the LBS service, the situation that when the number of users is large, the positions of the users are calculated firstly, then the users are sequenced integrally, and finally the mode with low efficiency is found out for the users closest to the users is avoided, so that the screening efficiency is improved. And when the online users are screened, the screening operation is carried out through multiple dimensions such as distance, user characteristics and time, so that the users who are willing to participate in the matching are accurately and efficiently dispatched, the number of the users who participate in the matching is increased, and the matching rate of the users is improved.

Optionally, the method further comprises: and under the condition that the target object is not matched in the online user set, returning matching failure information to the user requesting matching.

Specifically, in the event that the computing device does not receive any consent messages from online users within a predetermined time, the computing device stops matching online users for user 1 (i.e., the requesting matching user) and returns matching failure information to user 1 (i.e., the requesting matching user). Therefore, the prompt is sent to the user in this way, and the user is reminded in time.

Optionally, the method further comprises: the user matching pool updates in real time according to newly added users needing to be updated; and setting the weight ratio of the newly added demand users to be higher than the original demand users in the user matching pool.

Specifically, when there are a certain number of users (e.g., 30 ten thousand users) in the user matching pool, and then the computing device matches user 1 (i.e., the request matching user) with 30 ten thousand users in the user matching pool, it is likely that a required user matching user 1 (i.e., the request matching user) cannot be found in the 30 ten thousand users due to the matching rule. Because the user matching pool can be updated in real time according to newly added users, more and more users can be added in the user matching pool, such as 500 million users, 1 million users, and even 5 million users. When the users in the user matching pool become 500 million people, the computing device needs to filter all the time. When the users in the user matching pool become 1 million people, the computing device still needs to filter all the time. If the computing device is going to filter the part of users that are not matched, when users meeting the matching requirement of user 1 (i.e. users requesting to be matched) appear behind the user matching pool, the computing device is not easy to match the target object due to the extremely large number of users that are not matched in the user matching pool.

Therefore, in order to avoid the problem, the computing equipment reduces the weight of the user which is not matched in the user matching pool all the time, and improves the weight of the user which is newly added into the user matching pool, so that the computing equipment can preferentially screen the newly added user during the matching operation, and the matching success rate is improved.

In addition, matching operations include sorting, filtering, classifying, scheduling, and the like, and a series of processes are not fixed logic, and there are always variations in such requirements. For example, the user matching pool has only one category, or is divided into multiple categories according to the user characteristics. Or sorted by distance and gender and then sorted by distance, gender and constellation. In addition, the dimension and scheduling method set during filtering may also be changed.

Due to the changing demands, the development cost is high. Because there may be a slight minor adjustment, the logic needs to be changed greatly. Therefore, the matching process is packaged into a universal library for solving the matching scene during development. The library abstracts the whole matching process, and developers can easily realize the whole process according to different requirements of products. For example, if the ranking index needs to be changed, the developer only needs to change the parameters of the corresponding configuration file. That is, the whole matching process is generalized, and the library is similar to a general template, so that the development cost can be reduced, and the rapid online can be realized when the requirement of the product is iterated.

In addition, because the library abstracts the whole matching process, the library can be used in a voice matching service scene, and the service of any matching scene can be quickly on-line. Such as video, i.e. a landlord. First a user enters the room and then starts playing with n people. In this case the library can be used directly to match quickly to the user.

Further, referring to fig. 1, according to a second aspect of the present embodiment, there is provided a storage medium. The storage medium comprises a stored program, wherein the method of any of the above is performed by a processor when the program is run.

Therefore, according to the embodiment, when the target object is matched, the matching is performed through the user matching pool, and when the target object is not matched in the user matching pool, the target object is intensively matched from the online user according to the request information. Therefore, the technical scheme can screen out the target object which meets the requirement of the user requesting for matching in a large range by firstly utilizing the user matching pool for matching and then matching the target object by the two modes of the adjustment matching. And when the users are screened, the screening operation is carried out through multiple dimensions such as distance, user characteristics and time, so that the users who are willing to participate in the matching are accurately and efficiently screened, the number of the users who participate in the matching is increased, and the matching rate of the users is improved. And the technical problem that the success rate of user matching in a real-time online matching scene is low in the prior art is solved.

1. When the user A requests to perform online matching (including voice matching and text matching), the computing device finds a required user matched with the user A from the user matching pool according to a preset matching strategy. The computing device sets a user matching pool for storing all required users with matching requirements of the platform. Wherein, the matching strategy comprises a matching mechanism and a filtering mechanism.

1.1 the computing equipment sorts all users in the user matching pool according to the distance degree with the user A based on the distance dimension, and screens out a certain number of required users closest to the user A;

1.2 the computing device further screens out demand users corresponding to the user A characteristics from the demand users screened out in the step 1.1 based on the demand user characteristic dimension, wherein the demand user characteristics are, for example and without limitation, characteristics of gender, age, occupation, constellation and the like of the demand users;

1.3 the computing device further screens out the demand users who use the matching function within a preset time period (for example, demand users who use the matching function in about 7 days) from the demand users screened out in the step 1.2 based on the matching function use time dimension;

1.4 the computing device selects a best matching one of the demand users screened in step 1.3 as the matching user of user A according to a filtering mechanism. Wherein the filtering mechanism includes filtering out the demand users belonging to the friends, filtering out the demand users having matched history, and so on.

2. In the event that the computing device cannot find a user from the user matching pool that matches user a, the computing device will trigger a scheduling mechanism to help user a reach a match with other online users that are not participating in the match (i.e., do not have the matching requirements).

2.1 the system determines a certain number of online users (e.g., 200 online users) closest to user A from the other online users that never participate in the match;

2.2 the computing device screens out a plurality of online users (for example, 10 online users) meeting the scheduling matching requirement from the 200 online users according to the principle of the above steps 1.1 to 1.3;

2.3 the computing device sends the interactive invitation to the plurality of online users at the same time, wherein the interactive invitation is the request voice interaction under the condition that the user A requests to perform voice matching, and similarly, the interactive invitation is the request text interaction under the condition that the user A requests to perform text matching;

2.4 the computing device, upon first receiving a consent of the online user to the interaction invitation, determines the online user as a matching user to user A and establishes a communication interaction between user A and the online user.

2.5 in the event that the computing device does not receive any consent message within a predetermined time, the operation of matching users for user A is stopped and a notification of a failed match is returned to user A.

It should be noted that, the matching has a fixed duration (e.g. 10 s), and in the early stage of matching (e.g. the first 5 s), the computing device only performs step 1 above, i.e. the system only performs on-line demand matching. At the later stage of matching (e.g., the last 5 s), the computing device performs both online demand matching and schedule matching, i.e., performs step 1 and step 2 simultaneously. And, in the simultaneous execution phase, once the computing device finds a user matching user a through online demand matching, the scheduling matching is ended. Or when the online requirement matching and the scheduling matching simultaneously find the user matched with the user A, the user matched with the online requirement is preferentially selected.

(1) Searching which people are nearby from the current position, if according to the common practice, fishing out all users of the APP from the APP, then calculating the positions of the users, sequencing the users integrally, and finally taking out the people closest to the users. This approach is particularly slow, however, because there are tens of millions of users in APP. To address this problem, we construct an index based on geographic location, and then from this location index, the computing device can quickly search for which people are in fact in the vicinity of the user.

Specifically, an existing search algorithm is used to construct the position index, and then the algorithm is packaged into an LBS service separately. When a point is provided that can efficiently recall nearby points, which are other nearest points, based on the LBS service according to the designated point.

(2) When the online requirement matching is carried out, the user matching pool is not only one pool, but also at least two pools and multi-dimensional pools. The pool is divided according to the requirements of the product. That is, the user matching pool is classified according to some labels of the users. For example: and the users with high face value are divided into one pool, the users with high popularity are divided into another pool, the users with high reputation are divided into another pool, and the like.

For example, there are now two pools, a class a pool and a class b pool. The matching of two pools has a priority score. For example, when the user a performs online matching, the user a can only match with people in the class a pool according to the priority of the user a. The user A can go to the b-type pool to match only when the a-type pool can not match the user. Since these pools are in use at the same time, when the identity of a user (e.g., user B) is changing, it is possible that user B has just been the a-identity, inside the class a pool, and then he has become the B-identity, and so joins the class B pool. It may happen at this time that when the user B in the class a pool has already been matched to a person, the user B joins the class B pool because the identity of the user B has changed, and at this time, the user B in the class B pool may be matched by others.

To avoid this, the computing device records whether the user has been matched currently during the matching process, for example, the user is currently in the class a pool and has been successfully matched, even when the user joins the class b pool after the identity transition and is successfully matched by other users in the class b pool, the user can be ignored (i.e., filtered out) at this time because the user has been successfully matched elsewhere.

(3) In the process of matching, for example, when there are some people (e.g., 30 ten thousand people) in the pool now and then the 30 ten thousand people in the pool are matched, since the logic of splitting has some matching rules, it is likely that the user matching with the user a cannot be found in the 30 ten thousand people all the time. The larger the pool may then be, e.g. 500 million people, 1 million people, 5 million people. When the number of people in the pool becomes 500 million, the computing device needs to screen from beginning to end, and when the number of people in the pool becomes 1 million, the computing device needs to screen from beginning to end. A problem that may arise if the computing device is constantly scanning the front portion of people is that when users are present behind the pool that meet the user a's matching requirements, the system may not easily match the users because there are too many people in the front of the pool that are stacked to meet the requirements.

In order to avoid the problem, the computing device reduces the weight of the user which is not matched in the pool all the time, and increases the weight of the user which is newly added in the pool, so that the computing device can scan the new user preferentially during matching, and the matching success rate is increased.

(4) The matching process comprises sorting, filtering, pooling, classifying and scheduling, the whole set of logic is not a fixed logic and is a universal set of flow, but the requirements of products are constantly changed, and the products need to be pooled for classifying only one type and then are divided into multiple types according to the characteristics of users. Or the ordering may now be by distance and gender, and then by distance, gender and constellation. In addition, the filtering dimension may be constantly changing, and the scheduling may also be changing.

As product requirements are constantly changing, development costs are higher. This logic is greatly changed because there may be a slight minor adjustment. Therefore, when the code is developed, the whole matching process is abstracted. I.e. encapsulated into a generic library that resolves matching scenarios. The library abstracts the whole matching process, developers can change the sequencing indexes according to different requirements of products, for example, the sequencing indexes need to be changed, and the developers can change the parameters of the corresponding configuration files. That is to say, the whole matching process is generalized, and the library is a universal template, so that the development cost of developers can be reduced, and then the online operation can be performed quickly when the requirements of products are iterated.

In addition, the library can be used in voice matching services, and services in any matching scene can be directly used for meeting the requirement of quick online. Such as video. Similar to a hopper owner, the hopper owner is a person who enters to start playing games together with n persons. In this case, the library can be directly used, so that the user can be quickly matched with the user to take the ground together.

It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required by the invention.

Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.

Example 2

Fig. 3 shows an apparatus 300 for matching a target object according to the present embodiment, the apparatus 300 corresponding to the method according to the first aspect of embodiment 1. Referring to fig. 3, the apparatus 300 includes: a request receiving module 310, configured to receive a request for matching a target object from a terminal device of a user requesting matching; a first matching module 320, configured to match a target object from a preset user matching pool according to the requested request information; and a second matching module 330, configured to, when the target object is not matched in the user matching pool, collectively match the target object from the online users according to the request information.

Optionally, the first matching module 320 includes: the first determining submodule is used for sequencing the demand users in the user matching pool according to the distance between the demand users and the request matching users in the user matching pool, and determining a first set of demand users to be matched; the second determining submodule determines a second to-be-matched required user set corresponding to the user characteristics of the user requesting to be matched according to the user characteristics of the required users in the first to-be-matched required user set, wherein the user characteristics comprise gender, age, occupation and constellation; the third determining submodule is used for determining a third to-be-matched required user set according to the frequency of the required users of the second to-be-matched required user set participating in matching in preset time; and the fourth determining submodule is used for determining the target object according to the third to-be-matched requirement user set by utilizing a preset filtering mechanism.

Optionally, the first matching module 320 further includes: the fifth determining submodule is used for determining the priority sequence of the plurality of user matching pools according to a preset priority rule under the condition that the user matching pools are the plurality of user matching pools; and the sixth determining submodule is used for sequentially matching the required users in the plurality of user matching pools with the request matching users according to the priority order of the plurality of user matching pools to determine the target object.

Optionally, the users in the online user set are online users who match the user at a predetermined distance from the request, and the second matching module 330 includes: a seventh determining submodule, configured to sort the online users in the online user set according to a distance between the online user in the online user set and the matching-requesting user, and determine a first to-be-matched online user set; the eighth determining submodule is used for determining a second online user set to be matched corresponding to the user characteristics of the user requesting to be matched according to the user characteristics of the online users in the first online user set to be matched, wherein the user characteristics comprise gender, age, occupation and constellation; a ninth determining submodule, configured to determine a third to-be-matched online user set according to time when an online user of the second to-be-matched online user set sends a matching request within a predetermined time; and a tenth determining submodule, configured to send the interaction invitation requesting the matching user to an online user in the third to-be-matched online user set, and determine the target object, where the interaction invitation includes voice interaction and text interaction.

Optionally, the apparatus 300 further comprises: and the information returning module is used for returning matching failure information to the user requesting matching under the condition that the target object is not matched in the online user set.

Optionally, the apparatus 300 further comprises: the updating module is used for updating the user matching pool in real time according to newly added required users; and the weight ratio setting module is used for setting the weight ratio of the newly added demand users to be higher than the weight ratio of the original demand users in the user matching pool.

Example 3

Fig. 4 shows an apparatus 400 for matching a target object according to the present embodiment, the apparatus 400 corresponding to the method according to the first aspect of embodiment 1. Referring to fig. 4, the apparatus 400 includes: a processor 410; and a memory 420 coupled to the processor 410 for providing instructions to the processor 410 to process the following process steps: receiving a request for matching a target object from a terminal device requesting a matching user; matching the target object from a preset user matching pool according to the requested request information; and under the condition that the target object is not matched in the user matching pool, matching the target object from the online user set according to the request information.

Optionally, the memory 420 is further configured to provide the processor 410 with instructions to process the following processing steps: and under the condition that the target object is not matched in the online user set, returning matching failure information to the user requesting matching.

Optionally, the memory 420 is further configured to provide the processor 410 with instructions to process the following processing steps: the user matching pool updates in real time according to newly added users needing to be updated; and setting the weight ratio of the newly added demand users to be higher than the original demand users in the user matching pool.

The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.

In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.

The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims

1. A method of matching a target object, comprising:

receiving a request for matching the target object from a terminal device requesting a matching user;

matching the target object from a preset user matching pool according to the request information of the request; and

and under the condition that the target object is not matched in the user matching pool, matching the target object from an online user set according to the request information.

2. The method according to claim 1, wherein the operation of matching the target object from a preset user matching pool according to the requested information of the request comprises:

sorting the demand users in the user matching pool according to the distance between the demand users and the request matching users in the user matching pool, and determining a first set of demand users to be matched;

determining a second required user set to be matched corresponding to the user characteristics of the users requiring matching according to the user characteristics of the required users in the first required user set to be matched, wherein the user characteristics comprise gender, age, occupation and constellation;

determining a third to-be-matched demand user set according to the frequency of the demand users of the second to-be-matched demand user set participating in matching within a preset time; and

and determining the target object according to the third to-be-matched requirement user set by using a preset filtering mechanism.

3. The method according to claim 2, wherein the operation of matching the target object from a preset user matching pool according to the requested information of the request further comprises:

determining the priority order of the plurality of user matching pools according to a preset priority rule under the condition that the user matching pools are a plurality of user matching pools; and

and matching the required users in the user matching pools with the request matching users in sequence according to the priority order of the user matching pools, and determining the target object.

4. The method of claim 1, wherein the operation of matching the target object from the set of online users according to the request information comprises:

sorting the online users in the online user set according to the distance between the online users in the online user set and the request matching user, and determining a first to-be-matched online user set;

determining a second online user set to be matched corresponding to the user characteristics of the user requesting to be matched according to the user characteristics of the online users in the first online user set to be matched, wherein the user characteristics comprise gender, age, occupation and constellation;

determining a third online user set to be matched according to the time when the online users of the second online user set to be matched send matching requests within preset time; and

and sending the interaction invitation of the request matching user to the online users in the third online user set to be matched, and determining the target object, wherein the interaction invitation comprises voice interaction and text interaction.

5. The method of claim 1, further comprising:

and under the condition that the target object is not matched in the online user set, returning matching failure information to the user requesting matching.

6. The method of claim 1, further comprising:

the user matching pool is updated in real time according to newly added users needing to be updated; and

and setting the weight proportion of the newly added demand users to be higher than that of the original demand users in the user matching pool.

7. A storage medium comprising a stored program, wherein the method of any one of claims 1 to 6 is performed by a processor when the program is run.

8. An apparatus for matching a target object, comprising:

the request receiving module is used for receiving a request for matching the target object from the terminal equipment of the user requesting matching;

the first matching module is used for matching the target object from a preset user matching pool according to the request information of the request; and

and the second matching module is used for matching the target object from the online user set according to the request information under the condition that the target object is not matched in the user matching pool.

9. The apparatus of claim 8, wherein the first matching module comprises:

the first determining submodule is used for sequencing the demand users in the user matching pool according to the distance between the demand users in the user matching pool and the request matching user to determine a first set of demand users to be matched;

a second determining submodule, configured to determine, according to user features of demand users in the first to-be-matched demand user set, a second to-be-matched demand user set corresponding to the user features of the demand matching user, where the user features include gender, age, occupation, and constellation;

a third determining submodule, configured to determine a third set of demand users to be matched according to time for a demand user of the second set of demand users to be matched to send a matching request within a predetermined time; and

and the fourth determining submodule is used for determining the target object according to the third to-be-matched demand user set by using a preset filtering mechanism.

10. An apparatus for matching a target object, comprising:

a processor; and

a memory coupled to the processor for providing instructions to the processor for processing the following processing steps: