A method and system for recommending bullet screen input based on bullet screen heat

Abstract

The application provides a method and system for recommending a barrage input based on a barrage heat, the method comprising: receiving data of a barrage sent by a client; wherein the data of the barrage comprises: barrage content, a sending time of the barrage, and a user ID; grouping barrages according to the sending time; for each group of barrages, calculating the similarity between each barrage in the group and a pre-set target word or sentence; and determining a high-heat target word or sentence according to the calculated similarity; and sending the high-heat target word or sentence to the client for display as an input barrage recommended to the user. The application can enrich the implementation mode of the user input barrage and the interaction between the user and the host, reduce the interaction cost of the user, and improve the interaction quantity of the user.

Description

TECHNICAL FIELD

[0001] Embodiments of the present application relate to the technical field of computer, and particularly relate to a method and system for inputting and recommending a barrage based on barrage heat, a computer device, and a computer readable storage medium. BACKGROUND

[0002] With the development of Internet technology, the application of barrage technology in the field of video playing is also increasing. The barrage refers to various comments of other users that slowly roll on the video screen when a user watches a video. At present, the live video supports user inputting a barrage and interacting with the host.

[0003] In actual application, the present applicant finds that the current implementation of the user inputting a barrage and interacting with the host is limited to relying on the active input of the user, which increases the interaction cost and reduces the interaction quantity.

[0004] Therefore, it is necessary to provide a barrage input recommendation based on barrage heat to enrich the implementation of the user inputting a barrage and interacting with the host, reduce the interaction cost of the user, and improve the interaction quantity of the user. SUMMARY

[0005] An embodiment of the present application aims to provide a method and system for inputting and recommending a barrage based on barrage heat, a computer device, and a computer readable storage medium, which can enrich the implementation of the user inputting a barrage and interacting with the host, reduce the interaction cost of the user, and improve the interaction quantity of the user.

[0006] One aspect of an embodiment of the present application provides a method for inputting and recommending a barrage based on barrage heat, used for a server, comprising:

[0007] receiving data of a barrage sent by a client; wherein the data of the barrage comprises barrage content, sending time of the barrage, and a user ID;

[0008] grouping the barrage according to the sending time;

[0009] for each group of barrage, calculating the similarity between each barrage in the group and a pre-set target sentence; and judging a high-heat target barrage according to the calculated similarity;

[0010] sending the high-heat target barrage to the client for display as an input barrage recommended to the user.

[0011] Optionally, the judging of the high-heat target barrage according to the calculated similarity specifically comprises:

[0012] adding the barrage to a set corresponding to the target sentence with a similarity higher than a pre-set similarity value;

[0013] For each target sentence, determine the heat of the target sentence according to the number of the barrage in the set corresponding to the target sentence;

[0014] The target barrage with the highest heat and higher than the preset heat value is determined as a high-heat target barrage.

[0015] Optionally, the heat of the target sentence is determined according to the number of the barrage in the set corresponding to the target sentence, and specifically includes:

[0016] The ratio value of the number of the barrage in the set corresponding to the target sentence to the total number of the received barrage is calculated;

[0017] The calculated ratio value is taken as the heat of the target sentence.

[0018] Optionally, the barrage is grouped according to the sending time, and specifically includes:

[0019] The barrage with the same sending time is grouped into a group.

[0020] Optionally, the ratio value of the number of the barrage in the set corresponding to the target sentence to the total number of the received barrage is calculated, and specifically includes:

[0021] The ratio value of the number of the barrage in the set corresponding to the target sentence to the total number of the received barrage is calculated, and specifically includes:

[0022] An aspect of an embodiment of the present application further provides a barrage input recommendation device based on barrage heat, used for a server, and including:

[0023] A barrage receiving module is configured to receive data of barrage sent by a client and group the barrage according to the sending time; wherein the data of the barrage includes: barrage content, sending time of the barrage and user ID;

[0024] A similarity calculation module is configured to calculate, for each group of barrage, the similarity between each barrage in the group and a target sentence set in advance;

[0025] A heat calculation module is configured to determine a high-heat target barrage according to the calculated similarity;

[0026] An input barrage recommendation module is configured to send the high-heat target barrage to the client as an input barrage recommended to the user for display.

[0027] An aspect of an embodiment of the present application further provides a barrage input recommendation method based on barrage heat, used for a client, and including:

[0028] In response to the barrage sending operation, the server is sent barrage data, the barrage data including: barrage content, barrage sending time and user ID;

[0029] The server sends high heat target barrage, wherein the high heat target barrage is determined by the server according to the similarity between the received barrage and the pre-set target sentence;

[0030] In response to the user operation on the target barrage, the target barrage is sent as the user input barrage.

[0031] An aspect of the embodiment of the present application further provides a barrage input recommendation system based on barrage heat, including a client and a server, wherein:

[0032] The server is configured to receive barrage data sent by the client, wherein the barrage data includes barrage content, barrage sending time and user ID; group the barrage according to the sending time; calculate the similarity between each barrage in each group and the pre-set target sentence; determine high heat target barrage according to the calculated similarity; and send the high heat target barrage to the client as the input barrage for display.

[0033] The client is configured to display the input barrage after receiving the input barrage; and send the input barrage as the user input barrage in response to the user operation on the input barrage.

[0034] An aspect of the embodiment of the present application further provides a computer device, including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the barrage input recommendation method based on barrage heat.

[0035] An aspect of the embodiment of the present application further provides a computer readable storage medium, including a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the barrage input recommendation method based on barrage heat.

[0036] The method, system, device and computer readable storage medium for recommending the input of the barrage based on the barrage heat provided by the embodiments of the present application, after receiving the barrage, grouping the barrage according to the sending time of the barrage; wherein the data of the barrage comprises the barrage content, the sending time of the barrage and the user ID; for each group of barrage, calculating the similarity between each barrage in the group and the pre-set target word; and according to the calculated similarity, determining the target word with high heat; sending the target word with high heat to the client as the input barrage recommended to the user for display; thereby enriching the implementation mode of the user input barrage and the interaction between the anchor, reducing the interaction cost of the user, and improving the interaction quantity of the user. BRIEF DESCRIPTION OF DRAWINGS

[0037] Figure 1 The system architecture diagram of the barrage input recommendation system based on the barrage heat according to the embodiments of the present application is schematically shown;

[0038] Figure 2 The working flow chart of the barrage input recommendation system based on the barrage heat according to the first embodiment of the present application is schematically shown;

[0039] Figure 3a The example diagram of the semantic clustering algorithm according to the first embodiment of the present application is schematically shown;

[0040] Figure 3b The method flow chart of determining the target barrage with high heat according to the first embodiment of the present application is schematically shown;

[0041] Figure 4 The internal structure block diagram of the barrage input recommendation device based on the barrage heat according to the second embodiment of the present application is schematically shown;

[0042] Figure 5 The method flow chart of the barrage input recommendation based on the barrage heat according to the third embodiment of the present application is schematically shown;

[0043] Figure 6 The hardware architecture diagram of the computer device suitable for implementing the barrage input recommendation method based on the barrage heat according to the fourth embodiment of the present application is schematically shown. DETAILED DESCRIPTION

[0044] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0045] It should be noted that the descriptions involving "first," "second," etc., in the embodiments of this application are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0046] In the description of this application, it should be understood that the numerical labels before the steps do not indicate the order of the steps, but are only used to facilitate the description of this application and to distinguish each step, and therefore should not be construed as a limitation of this application.

[0047] Figure 1 This illustration schematically depicts the architecture of a bullet screen input recommendation system based on bullet screen popularity according to an embodiment of this application. Figure 1 As shown, the system includes: a server 2, and multiple clients 6 connected via one or more networks 4. Wherein:

[0048] Server 2 can provide online live streaming or video services through a network service system. For example, Server 2 can be a live streaming server.

[0049] In an exemplary embodiment, server 2 may be a rack server, blade server, tower server, or cabinet server (including a standalone server or a server cluster consisting of multiple servers).

[0050] Server 2 may be located in a data center, such as a single location, or distributed across different geographical locations (e.g., multiple locations). Server 2 may provide services via one or more networks 4. Network 4 includes various network devices, such as routers, switches, multiplexers, hubs, modems, bridges, repeaters, firewalls, proxy devices, and / or similar devices. Network 4 may include physical links, such as coaxial cable links, twisted-pair cable links, fiber optic links, combinations thereof, etc. Network 4 may include wireless links, such as cellular links, satellite links, Wi-Fi links, etc.

[0051] In an exemplary embodiment, client 6 may include devices such as mobile devices, tablets, laptops, smart devices (e.g., smart clothing, smartwatches, smart glasses), virtual reality headsets, gaming devices, set-top boxes, digital streaming devices, robots, in-vehicle terminals, smart TVs, TV boxes, and e-book readers.

[0052] Client 6 can be associated with one or more users. A single user can use one or more of clients 6 to access server 2. Client 6 can travel to various locations and use different networks to access server 2.

[0053] There are multiple clients (6), which can include clients for multiple audio and video broadcasters and clients for multiple viewers.

[0054] In the bullet comment input recommendation scheme based on bullet comment popularity provided in this application, the server calculates the similarity between received bullet comments and pre-set target phrases; and based on the calculated similarity, determines the high-popularity target bullet comments; sends the high-popularity target bullet comments to the client; the client displays the input bullet comments after receiving them, and responds to the user's operation on the input bullet comments by sending the input bullet comments as user-input bullet comments. In this process, the user does not need to input bullet comments on the client, reducing the user's interaction cost, increasing the number of user interactions, and enriching the ways in which users input bullet comments and interact with the broadcaster.

[0055] The following will introduce a bullet comment input recommendation scheme based on bullet comment popularity through several embodiments. This scheme can be implemented using computer device 1300, which can be server 2 or its computing node.

[0056] Example 1

[0057] Figure 2 The flowchart illustrates a bullet screen input recommendation method based on bullet screen popularity according to Embodiment 1 of this application.

[0058] like Figure 2 As shown, the bullet screen input recommendation method based on bullet screen popularity may include the following steps S201 to S209, wherein:

[0059] Step S201: Server 2 receives the bullet comments sent by the client.

[0060] Specifically, when a user sends a bullet comment to the live stream, the client responds to the user's bullet comment sending operation by sending the bullet comment data to server 2. The bullet comment data includes information such as user ID (identifier), bullet comment content, and bullet comment sending time.

[0061] After receiving the bullet comments sent by the client, server 2 groups the bullet comments according to their sending time. Specifically, bullet comments sent within the same period can be grouped together.

[0062] In an exemplary embodiment, the server 2 groups the barrage according to the sending time of the barrage and according to a set period. For example, the set period is one hour, and the server 2 places the barrage whose sending time is within the one hour in a group; for example, the barrage whose sending time is 0-1 o'clock is placed in a group, the barrage whose sending time is 1-2 o'clock is placed in a group, and so on; at the end of each period, the number of barrages received in the period is counted.

[0063] Step S202: The server 2 calculates the similarity between the received barrage and the pre-set target sentence.

[0064] Specifically, in the server 2, the technician can pre-set multiple target sentences; the target sentence can be a common sentence used by the user when sending the barrage, that is, a phrase, sentence, vocabulary, etc. frequently used by the user when sending the barrage; for example, the pre-set target sentence in the server 2 includes the sentences such as "goodbye" and "good morning".

[0065] The server 2 calculates the similarity between each barrage in the group and each target sentence for each group of barrages.

[0066] In an exemplary embodiment, the server 2 can calculate the similarity between the barrage and the target sentence based on a semantic clustering algorithm; for example, the server 2 can calculate the similarity between the barrage and the target sentence based on a semantic clustering algorithm as shown in the following table. Figure 3a The server 2 can calculate the similarity between the barrage and the target sentence based on the semantic clustering algorithm as shown in the following table.

[0067] Step S203: The server 2 judges the target barrage with high heat based on the calculated similarity.

[0068] Specifically, the specific method flow of judging the target barrage with high heat can be as shown in the following table, which includes the following sub-steps S301-S303. Figure 3b

[0069] Sub-step S301: The barrage is added to the set corresponding to the target sentence with a similarity higher than a pre-set similarity value.

[0070] In this sub-step, after the server 2 calculates the similarity between the barrage and each target sentence, the target sentence with a similarity higher than a pre-set similarity value is determined, and then the barrage is added to the set corresponding to the target sentence.

[0071] ​In an exemplary embodiment, the server 2 can empty the set corresponding to each target sentence at the beginning of each cycle; and during the cycle, for a target sentence with a similarity higher than a preset similarity value to the barrage, the server 2 can add the barrage to the set corresponding to the target sentence.

[0072] Sub-step S302: The server 2 determines the heat of each target sentence according to the number of barrages in the set corresponding to the target sentence.

[0073] In this sub-step, the server 2 calculates, for each target sentence, a proportion value of the number of barrages in the set corresponding to the target sentence to the total number of received barrages; and then takes the calculated proportion value as the heat of the target sentence. That is, the greater the proportion of the barrages in the set, the higher the heat of the target sentence corresponding to the set.

[0074] In an exemplary embodiment, the server 2 calculates, for each target sentence, a proportion value of the number of barrages in the set corresponding to the target sentence to the total number of received barrages; and then takes the calculated proportion value as the heat of the target sentence. That is, the greater the proportion of the barrages in the set, the higher the heat of the target sentence corresponding to the set.

[0075] Sub-step S303: The server 2 determines the target barrage with high heat.

[0076] In this sub-step, the server 2 determines the target sentence with the highest heat and higher than a preset heat value as the target barrage with high heat.

[0077] In an exemplary embodiment, the server 2 selects the target sentence with the highest heat, and determines whether the heat of the target sentence is higher than a preset heat value; if the heat of the target sentence is higher than the preset heat value, the server 2 can further determine whether the number of barrages in the set corresponding to the target sentence exceeds a preset number value; if so, the server 2 can take the target sentence as the target barrage with high heat. That is, the server 2 can determine the target sentence with the highest heat, and the heat higher than the preset heat value, and the number of barrages in the corresponding set exceeding the preset number value, as the target barrage with high heat.

[0078] Step S204: The server 2 sends the target barrage with high heat determined by the server 2 to the client 6 for recommendation.

[0079] Step S205: After receiving the target barrage recommended by the server 2, the client 6 displays the barrage as input barrage recommended to the user.

[0080] In this step, the client 6 displays the target barrage recommended by the server 2 as the default input barrage of the user. That is, the target barrage with high heat received by the client 6 is determined by the server according to the similarity between the received barrage and the pre-set target sentence.

[0081] Step S206: The client 6 sends the input barrage to the server 2 as the user input barrage in response to the operation of the user on the input barrage.

[0082] Specifically, the client 6 can send the target barrage to the server 2 as the user input barrage in response to the operation of the user on the target barrage after receiving the double-click operation of the user on the target barrage.

[0083] Step S207: The server 2 displays the barrage sent by the client 6 in the live room.

[0084] The method for recommending barrage input based on barrage heat provided in Embodiment One can realize that the user can send the barrage to the server without manual input, which is convenient for the user to send the barrage, reduces the interactive cost of the user, improves the interactive quantity of the user, and enriches the implementation mode of the user input barrage and the interaction with the host.

[0085] Embodiment Two

[0086] Figure 4 A block diagram of a device for recommending barrage input based on barrage heat according to Embodiment Two of the present application is schematically shown, which can be arranged in a server and divided into one or more program modules, the one or more program modules are stored in a storage medium and executed by one or more processors to complete the embodiments of the present application. The program module referred to in the embodiments of the present application refers to a series of computer program instruction segments capable of completing a specific function, and the functions of the program modules in the embodiments of the present application will be specifically described below.

[0087] As shown in Figure 4 An internal structure of the device for recommending barrage input based on barrage heat can include a barrage receiving module 400, a similarity calculation module 401, a heat calculation module 402, and an input barrage recommendation module 403, wherein:

[0088] The barrage receiving module 400 is configured to receive the data of the barrage sent by the client and group the barrage according to the sending time; wherein the data of the barrage includes the content of the barrage, the sending time of the barrage, and the user ID;

[0089] The similarity calculation module 401 is configured to calculate the similarity between each barrage in each group of barrages and the pre-set target sentence.

[0090] The heat calculation module 402 is configured to determine the target barrage with high heat according to the calculated similarity.

[0091] The input barrage recommendation module 403 is configured to send the target barrage with high heat determined to the client as an input barrage displayed to the user.

[0092] Specifically, the heat calculation module 402 can add the barrage to a set corresponding to a target word or phrase with a similarity higher than a preset similarity value; for each target word or phrase, determine the heat of the target word or phrase according to the number of barrages in the set corresponding to the target word or phrase; and determine the target word or phrase with the highest heat and higher than a preset heat value as the target barrage with high heat.

[0093] The barrage input recommendation device based on barrage heat provided in Embodiment Two of the present application can realize that the user of the client can send the barrage to the server without manual input of the barrage, facilitate the operation of the user to send the barrage, reduce the interactive cost of the user, improve the interactive quantity of the user, and enrich the implementation manner of the user to input the barrage and interact with the host.

[0094] Embodiment Three

[0095] This embodiment is described with the server 2 as the execution subject, and the technical details and technical effects can be referred to Embodiment One.

[0096] Figure 5 A flowchart of the barrage input recommendation method based on barrage heat according to Embodiment Three of the present application is schematically shown. As shown in Figure 5 The barrage input recommendation method based on barrage heat can include steps S501-S504, wherein:

[0097] Step S501: receiving and grouping the barrage.

[0098] In this step, after receiving the barrage sent by the client, the server 2 groups the barrage according to the sending time thereof. Specifically, the barrage with the same sending time in the same period can be grouped.

[0099] Step S502: calculating the similarity between the received barrage and the target word or phrase set in advance.

[0100] In this step, the server 2 calculates the similarity between each received barrage and each target word or phrase.

[0101] Step S503: determining the target barrage with high heat according to the calculated similarity.

[0102] Specifically, the method of determining the target barrage with high heat can be as described above Figure 3bThe methods for each step in the process shown are not described in detail here.

[0103] Step S504: Send the target bullet comments that are judged to be highly popular to the client and display them as recommended input bullet comments to the user.

[0104] In this step, server 2 identifies the most popular target phrases that exceed the preset popularity value as high-popularity target bullet comments; then, the high-popularity target bullet comments are sent to the client as input bullet comments recommended to the user.

[0105] Example 4

[0106] Figure 6 This illustration schematically shows a hardware architecture diagram of a computer device 1300 suitable for implementing a bullet screen input recommendation method based on bullet screen popularity, according to Embodiment 3 of this application. In this embodiment, the computer device 1300 is a device capable of automatically performing numerical calculations and / or information processing according to pre-set or stored instructions. For example, it may be a smartphone, tablet computer, laptop computer, desktop computer, rack server, blade server, tower server, or cabinet server (including independent servers or server clusters composed of multiple servers), etc. Figure 6 As shown, the computer device 1300 includes, but is not limited to, at least: a memory 1310, a processor 1320, and a network interface 1330 that can communicate with each other via a system bus. Wherein:

[0107] The memory 1310 includes at least one type of computer-readable storage media, such as a flash memory, a hard disk, a multimedia card (e.g., SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, and the like. In some embodiments, the memory 1310 can be an internal memory module of the computer device 1300, such as a hard disk or a memory of the computer device 1300. In other embodiments, the memory 1310 can also be an external memory device of the computer device 1300, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, and the like. Of course, the memory 1310 can include both an internal memory module and an external memory device of the computer device 1300. In this embodiment, the memory 1310 is generally used to store an operating system and various application programs installed in the computer device 1300, such as program codes of the method for recommending a barrage input based on a barrage heat, and the like. In addition, the memory 1310 can also be used to temporarily store various data that have been output or will be output.

[0108] The processor 1320 can be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor 1320 is generally used to control the overall operation of the computer device 1300, such as performing control and processing related to data interaction or communication of the computer device 1300, and the like. In this embodiment, the processor 1320 is used to run program codes or process data stored in the memory 1310.

[0109] Network interface 1330 may include a wireless network interface or a wired network interface, which is typically used to establish a communication link between computer device 1300 and other computer devices. For example, network interface 1330 is used to connect computer device 1300 to an external terminal via a network, establishing a data transmission channel and communication link between computer device 1300 and the external terminal. The network may be an intranet, the Internet, Global System for Mobile Communication (GSM), Wideband Code Division Multiple Access (WCDMA), 4G network, 5G network, Bluetooth, Wi-Fi, or other wireless or wired networks.

[0110] It should be pointed out that, Figure 6 Only a computer device with components 1310-1330 is shown; however, it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0111] In this embodiment, the overdraw test method based on color feature recognition stored in memory 1310 can be further divided into one or more program modules and executed by one or more processors (processor 1320 in this embodiment) to complete the embodiment of this application.

[0112] Example 5

[0113] This application also provides a computer-readable storage medium storing a computer program thereon. When the computer program is executed by a processor, it implements the steps of the bullet screen input recommendation method based on bullet screen popularity in the embodiments.

[0114] In this embodiment, the computer readable storage medium includes a flash memory, a hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a random access memory (RAM), a static random access memory (SRAM), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), a programmable read-only memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the computer readable storage medium can be an internal storage unit of the computer device, for example, a hard disk or a memory of the computer device. In other embodiments, the computer readable storage medium can also be an external storage device of the computer device, for example, a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the computer device. Of course, the computer readable storage medium can also include both the internal storage unit and the external storage device of the computer device. In this embodiment, the computer readable storage medium is usually used to store the operating system and various application software installed on the computer device, for example, the program code of the bullet input recommendation method based on the bullet heat in the embodiment, etc. In addition, the computer readable storage medium can also be used to temporarily store various data that have been output or will be output.

[0115] Obviously, those skilled in the art should understand that each module or each step of the above-mentioned embodiments of the present application can be realized by a general computing device, which can be concentrated on a single computing device or distributed on a network composed of multiple computing devices, and optionally, they can be realized by program codes executable by the computing device, so that they can be stored in the storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in different order, or they can be manufactured into each integrated circuit module, or multiple modules or steps can be manufactured into a single integrated circuit module. Therefore, the embodiments of the present application are not limited to any specific combination of hardware and software.

[0116] The above is only the preferred embodiment of the present application, and does not limit the patent scope of the present application, and any equivalent structure or equivalent process transformation using the content of the specification and drawings of the present application, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for recommending bullet screen input based on bullet screen popularity, used on a server, characterized in that, The method comprises the following steps: receiving data of the sent barrage by the client; wherein the data of the barrage comprises barrage content, sending time of the barrage and user ID; grouping the barrage according to the sending time; for each group of barrage, calculating the similarity between each barrage in the group and a pre-set target sentence; and judging the target barrage with high heat degree according to the calculated similarity; sending the target barrage with high heat degree to the client as input barrage recommended to the user for display; wherein the judging of the target barrage with high heat degree according to the calculated similarity comprises: adding the barrage to a set corresponding to the target sentence with similarity higher than a pre-set similarity value; for each target sentence, determining the heat degree of the target sentence according to the number of barrage in the set corresponding to the target sentence; judging the target sentence with the highest heat degree and higher than a pre-set heat degree value as the target barrage with high heat degree.

2. The method of claim 1, wherein, The determining of the heat degree of the target sentence according to the number of barrage in the set corresponding to the target sentence comprises: calculating the ratio value of the number of barrage in the set corresponding to the target sentence to the total number of received barrage; taking the calculated ratio value as the heat degree of the target sentence.

3. The method of claim 2, wherein, The grouping of the barrage according to the sending time comprises: grouping the barrage with the same sending time into a group.

4. The method of claim 3, wherein, The calculating of the ratio value of the number of barrage in the set corresponding to the target sentence to the total number of received barrage comprises: calculating the ratio value of the number of barrage added to the set corresponding to the target sentence in the group to the total number of the group.

5. The method of claim 1, wherein, The method further comprises: displaying the input barrage by the client after receiving the input barrage; and sending the input barrage as the barrage input by the user in response to the operation of the user on the input barrage. The method comprises: 6.A device for recommending a barrage input based on a barrage heat, for a server, characterized by, a barrage receiving module for receiving data of the sent barrage by the client and grouping the barrage according to the sending time; wherein the data of the barrage comprises barrage content, sending time of the barrage and user ID; a similarity calculating module for calculating the similarity between each barrage in each group and a pre-set target sentence; a heat degree calculating module for judging the target barrage with high heat degree according to the calculated similarity; an input barrage recommending module for sending the target barrage with high heat degree to the client as input barrage recommended to the user for display; wherein the judging of the target barrage with high heat degree according to the calculated similarity comprises: adding the barrage to a set corresponding to the target sentence with similarity higher than a pre-set similarity value; for each target sentence, determining the heat degree of the target sentence according to the number of barrage in the set corresponding to the target sentence; judging the target sentence with the highest heat degree and higher than a pre-set heat degree value as the target barrage with high heat degree. The method comprises:

7. A method for recommending a barrage input based on a barrage heat, for a client, the method comprising: receiving a barrage input request from a client; determining a barrage input based on a barrage heat; and sending the determined barrage input to the client. sending data of the barrage to the server in response to the barrage sending operation; wherein the data of the barrage comprises barrage content, sending time of the barrage and user ID; ​ Receive the high-heat target barrage sent by the server; wherein the high-heat target barrage is determined by the server according to the similarity between the received barrage and the pre-set target sentence; In response to the user's operation on the target barrage, the target barrage is sent as the user input barrage; Wherein, the high-heat target barrage is determined by the server according to the similarity between the received barrage and the pre-set target sentence, specifically including: The server groups the received barrage according to the sending time; and For each group of barrage, calculate the similarity between each barrage in the group and the pre-set target sentence; and according to the calculated similarity, determine the high-heat target barrage.

8. A barrage input recommendation system based on barrage heat, characterized by, Including: Client, and server; wherein: The server is used for receiving the data of the barrage sent by the client; wherein, the data of the barrage includes: barrage content, sending time of the barrage and user ID; group the barrage according to the sending time; for each group of barrage, calculate the similarity between each barrage in the group and the pre-set target sentence; and according to the calculated similarity, determine the high-heat target barrage; send the high-heat target barrage to the client as the input barrage recommended to the user for display; The client is used for displaying after receiving the input barrage; and in response to the user's operation on the input barrage, the input barrage is sent as the user input barrage; Wherein, according to the calculated similarity, determine the high-heat target barrage, specifically including: The barrage is added to the set corresponding to the target sentence with a similarity higher than the preset similarity value; For each target sentence, determine the heat of the target sentence according to the number of barrage in the set corresponding to the target sentence; The target sentence with the highest heat and higher than the preset heat value is determined as the high-heat target barrage.

9. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor executes the computer program to implement the steps of the barrage input recommendation method based on the barrage heat according to any one of claims 1 to 5 or 7.

10. A computer readable storage medium characterized by The computer readable storage medium stores a computer program, which can be executed by at least one processor to make the at least one processor execute the steps of the barrage input recommendation method based on the barrage heat according to any one of claims 1 to 5 or 7.

11. A computer program product comprising computer instructions, characterized in that, The computer instructions are executed by the processor to implement the steps of the method according to any one of claims 1 to 5 or 7.

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