CN110535669B - Content recommendation method and content recommendation device - Google Patents
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- H—ELECTRICITY
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- H04L12/00—Data switching networks
- H04L12/02—Details
- H04L12/16—Arrangements for providing special services to substations
- H04L12/18—Arrangements for providing special services to substations for broadcast or conference, e.g. multicast
- H04L12/1813—Arrangements for providing special services to substations for broadcast or conference, e.g. multicast for computer conferences, e.g. chat rooms
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L12/00—Data switching networks
- H04L12/02—Details
- H04L12/16—Arrangements for providing special services to substations
- H04L12/18—Arrangements for providing special services to substations for broadcast or conference, e.g. multicast
- H04L12/1859—Arrangements for providing special services to substations for broadcast or conference, e.g. multicast adapted to provide push services, e.g. data channels
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Abstract
The invention provides a content recommendation method and a content recommendation device. First, information input into a first chat room by a first user is obtained. Next, a candidate list associated with the information is determined, wherein the candidate list has a plurality of candidate contents. Then, first usage data of the plurality of candidate contents in the first chat room is calculated. Then, a first content recommendation list is generated based on the first usage data. Finally, a first content recommendation list is provided to the first chat room.
Description
Technical Field
The present invention relates to a content recommendation method and a content recommendation apparatus, and more particularly, to a content recommendation method and a content recommendation apparatus for recommending contents according to a usage data of candidate contents.
Background
Instant chat software, which is a communication tool, can instantly transmit or receive various data such as text, images, or videos, and is commonly used in terminal devices such as personal computers or mobile communication terminals.
However, the above-mentioned techniques have at least the following problems:
contents such as hot pictures, expressions or videos are easily repeatedly posted to the chat rooms, and even the same contents are repeatedly posted among different chat rooms. For the same user, not only is the user's feeling of dislike caused by continuously receiving the same content, but also relevant resources such as bandwidth or storage space are wasted by receiving repeated content, so that the user has poor use experience.
Disclosure of Invention
The invention provides a content recommendation method and a content recommendation device based on at least one embodiment of the invention. The content recommendation method enables a plurality of candidate contents to be arranged according to the use data of the corresponding chat rooms, provides customized content recommendation lists for different chat room members, preferentially recommends the candidate contents with low use rate to the user, and enables the user to transmit relatively novel candidate contents to the corresponding chat rooms, thereby not only increasing the entertainment of the chat rooms, but also enabling other members in the same chat room to have better use experience.
The invention provides a content recommendation method, which comprises the following steps. First, information input into a first chat room by a first user is obtained. Next, a candidate list associated with the information is determined, wherein the candidate list has a plurality of candidate contents. In another aspect, first usage data for a plurality of candidate content in a first chat room is calculated. Then, a first content recommendation list is generated based on the first usage data. Finally, a first content recommendation list is provided to the first chat room.
The invention also provides a content recommendation device, wherein the content recommendation device comprises a device end communication module and a device end processing module. The device side communication module is used for acquiring information input by a first user in a first chat room. The device-side processing module is used for determining a candidate list associated with the information, and the candidate list comprises a plurality of candidate contents. The device end processing module is further used for calculating first usage data of a plurality of candidate contents in a first chat room, generating a first content recommendation list according to the first usage data, and providing the first content recommendation list to the first chat room.
For a better understanding of the features and technical content of the present invention, reference is made to the following detailed description of the invention and accompanying drawings, which are provided for illustration purposes only and are not intended to limit the scope of the invention in any way.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive labor.
FIG. 1 is a schematic illustration of an implementation environment according to an embodiment of the invention;
fig. 2 is a block diagram of an internal structure of a user side according to an embodiment of the present invention;
fig. 3 is a block diagram of an internal structure of a content recommendation device according to an embodiment of the present invention;
FIG. 4 is a flowchart illustrating steps of a content recommendation method according to an embodiment of the present invention;
fig. 5 is a block diagram of an internal structure of a content recommendation device according to another embodiment of the present invention;
FIG. 6 is a flowchart illustrating steps of a content recommendation method according to another embodiment of the present invention;
FIG. 7 is a diagram illustrating a relationship between a first user and a second user according to an embodiment of the invention;
FIG. 8 is a diagram illustrating a relationship between a first user and a second user according to another embodiment of the present invention;
FIG. 9 is a diagram illustrating a first user using a chat group via a dedicated application, in accordance with an embodiment of the present invention; and
fig. 10 is a diagram illustrating first user input information according to an embodiment of the present invention.
Reference numerals
100. User terminal
110. User terminal processing module
120. User side storage module
130. Input/output module
140. User terminal communication module
200. 200' content recommendation device
210. Device end processing module
211. Candidate list generation unit
212. Usage data determination unit
213. Recommendation list generation unit
214. Chat room decisioning part
220. Device end communication module
230. Device end storage module
231. Operation system
232. Content recommendation routine
240. Database with a plurality of databases
300. Window
310. Input area
320. Content display window
S410 to S450, S610 to S631, S632, S640 and S650
A-F users
G01-G13 chat room
Contents of C1 to C7
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
Referring to fig. 1, a schematic diagram of an implementation environment according to an embodiment of the present invention is shown, where the implementation environment includes at least one user terminal 100 and a content recommendation device 200, and the at least one user terminal 100 and the content recommendation device 200 are communicatively connected to each other through a wired or wireless network.
The user terminal 100 may be a smart phone, a tablet computer, a notebook computer, a wearable computer, a desktop computer, etc. connected to a website associated with the content recommendation device 200 or all terminal devices configured and running a dedicated application. The user terminal 100 can perform and form a service screen, input data, transmit or receive data, retain data, and the like under the control of a website or a dedicated application. Therefore, in one embodiment, the first user logs in a website or a dedicated application on the user terminal 100 through his account, and the first user can interact with other users, such as the second user, through the website or the dedicated application.
The content recommendation device 200 may be implemented on a software platform for providing a chat software service, and the content recommendation device 200 may provide a function of generating a content recommendation list having a plurality of candidate contents on a dedicated application program according to information input by the first user, wherein the candidate contents are one of images, icons, emoticons, videos, and websites, and the present invention is not limited thereto. The candidate contents are associated with the first user, and further, the candidate contents are stored by the first user through a website or a dedicated application in the user terminal 100, the content recommendation device 200, or other linked storage devices or servers communicatively connected to one of the user terminal 100 and the content recommendation device 200, and the invention is not limited thereto.
The content recommendation device 200 as described above can be implemented as a platform of a chat software server for providing a chat software service, and can also be implemented as a system different from the chat software server to recommend content in cooperation with the chat software server. Further, at least a part of the components of the content recommendation device 200 may be implemented as a dedicated application provided on the user terminal 100, or may be implemented as a platform for providing services in a client server environment.
Fig. 2 is a block diagram illustrating an internal structure of a user terminal 100 according to an embodiment of the present invention. The client 100 may include a client processing module 110, a client storage module 120, an input/output module 130, and a client communication module 140, wherein the client processing module 110 is electrically connected to the client storage module 120, the input/output module 130, and the client communication module 140.
The client memory module 120 may include a high speed random access memory, a magnetic disk, a static random access memory, a dynamic random access memory, a read only memory, a flash memory, or a non-volatile memory. The client storage module 120 may store software modules, instruction sets, or other various data required for the operation of the client 100. The access to the user-side storage module 120 can be controlled by the user-side processing module 110.
The input/output module 130 may be implemented with a display, a keyboard, a mouse, a touch screen, etc., so that a first user or a second user may input information by the input/output module 130 to operate the website or the dedicated application.
The client communication module 140 provides a communication function with other terminal devices. The ue communication module 140 may be implemented by a communication circuit of wireless fidelity (WiFi), a mobile communication technology, long Term Evolution (LTE), bluetooth (Bluetooth), a Near Field Communication (NFC) technology, a Zigbee protocol (Zigbee), and the like, and the invention is not limited thereto.
The embodiment of fig. 2 is merely an example of the user terminal 100, and the user terminal 100 may omit some components in fig. 2 or have other components not shown, or have a structure or configuration in which more than 2 components are combined. In other embodiments, the user terminal 200 may be implemented by hardware, software, or a combination of hardware and software.
Fig. 3 is a block diagram of an internal structure of a content recommendation device 200 according to an embodiment of the present invention.
The content recommendation device 200 of the present embodiment may include a device-side processing module 210, a device-side communication module 220, a device-side storage module 230, and a database 240. The device-side processing module 210 may include a candidate list generating part 211, a usage data determining part 212, and a recommendation list generating part 213. In other embodiments, the content recommendation device 200 may include more structural elements than those shown in fig. 3, but most of the structural elements in the prior art need not be explicitly shown, for example, the content recommendation device 200 may further include other structural elements such as a display or a transceiver.
The device-side communication module 220 may be a hardware structural element that connects the content recommendation device 200 with a computer network. The device-side communication module 220 may communicatively connect the content recommendation device 200 with a computer network through a wireless or wired connection. The device-side communication module 220 may be implemented by a communication circuit of wireless fidelity (WiFi), ethernet protocol, etc., and the invention is not limited thereto.
The device-side storage module 230 is a computer-readable storage medium, and may include a random access memory, a read only memory, a disk drive, and other permanent mass storage devices. The device-side storage module 230 may be used to store program codes of an operating system 231 and content recommendation routines 232 required to operate the content recommendation device 200.
The database 240 may store data of multiple conversations of multiple chat rooms, multiple contents transmitted/received, candidate contents stored by each user, wherein the contents are one of images, pictures, expressions, videos, and websites, and the invention is not limited thereto.
The embodiment of the content recommendation device 200 illustrated in fig. 3 includes the database 240, but the present invention is not limited thereto, and in other embodiments, the embodiment may be omitted according to the implementation manner or environment of the content recommendation device 200. Alternatively, in other embodiments, part or all of database 240 may be implemented in additional other systems.
The device-side processing module 210 performs basic arithmetic, logic, and input/output operations of the content recommendation device 200, and is configured to process instructions of a computer program. The device-side processing module 210 is, for example, a digital signal processor, but the invention is not limited thereto. The instructions may be provided to the device-side processing module 210 by the device-side storage module 230 or by the device-side communication module 220. The device-side processing module 210 may run the operating system 231 and program code for the content recommendation routine 232, such as the candidate list generation portion 211, the usage data determination portion 212, the recommendation list generation portion 213, and the like, where the candidate list generation portion 211, the usage data determination portion 212, and the recommendation list generation portion 213 may represent different programs run by the device-side processing module 210. The candidate list generation section 211, the usage data determination section 212, and the recommendation list generation section 213 may be software units resulting from execution of codes by a digital signal processor without loss of generality. However, the present invention is not limited thereto, and those skilled in the art can design the candidate list generating unit 211, the usage data determining unit 212, and the recommendation list generating unit 213 as hardware Circuit units such as Field-Programmable Gate arrays (FPGAs) or Application Specific Integrated Circuits (ASICs).
The candidate list generating unit 211, the usage data determining unit 212, and the recommendation list generating unit 213 are configured to execute a plurality of steps of fig. 4, and will be further described with reference to fig. 4.
In step S410, the device-side communication module 220 obtains information input by the first user into the first chat room. Further, the first user enters text or symbols as information in the first chat room.
In step S420, the candidate list generation section 211 decides a candidate list associated with the information, wherein the candidate list has a plurality of candidate contents. Further, since each candidate content is associated with a category to which a character or a symbol as information is assigned, the candidate list generating unit 211 generates a candidate list by collecting a plurality of candidate contents associated with the same category according to the category represented by the information. In other words, the candidate list includes a plurality of candidate contents associated with the same category.
In step S430, usage data determination unit 212 calculates first usage data of each of the plurality of candidate contents in the first chat room. For example, referring to fig. 10, the candidate list includes candidate contents C1 to C7, and the usage data determining unit 212 calculates first usage data of the candidate content C1 in the chat room G01, first usage data of the candidate content C2 in the chat room G01, first usage data of the candidate content C3 in the chat room G01, first usage data of the candidate content C4 in the chat room G01, first usage data of the candidate content C5 in the chat room G01, first usage data of the candidate content C6 in the chat room G01, and first usage data of the candidate content C7 in the chat room G01.
In other embodiments, the first usage data is a recommended content usage rate, and the recommended content usage rate is a usage frequency or a usage number of times that the recommended content is provided to the chat room, and the invention is not limited thereto.
In an embodiment where the first usage data is a recommended content usage rate, the first usage data is calculated based on a number of times each candidate content is provided to the first chat room. Further, the usage data determining unit 212 compares at least one of the content identification data, the content name, and the content size of the candidate content to calculate the number of times of transmission and/or reception of the candidate content in the first chat room.
For example, the usage data determining unit 212 searches the first chat room with the content identification data of the candidate content, and calculates the number of occurrences of the content identification data in the chat room, where the number represents the recommended content usage rate.
Alternatively, the usage data determination unit 212 calculates first usage data of the candidate content for a specific time period. The specific time period is, for example, 10 days, two weeks, or one month, and the present invention is not limited thereto.
For example, the usage data determining unit 212 searches for and calculates the number of times that the candidate content is provided to the first chat room from the current time point to the previous two weeks.
In step S440, the recommendation list generating section 213 generates a first content recommendation list based on the first usage data. The recommendation list generation unit 213 sorts the candidate contents based on the first usage data for each candidate content, and the recommendation list generation unit 213 generates the first content recommendation list based on the sorting result.
In the embodiment in which the first usage data is the recommended content usage rate, the recommendation list generating section 213 sorts the candidate contents according to the recommended content usage rate of each candidate content, and generates the first content recommendation list according to the result of the sorting.
In another embodiment, the recommendation list generating section 213 sorts the candidate contents in ascending order according to the recommended content usage rate of each candidate content.
In step S450, a first content recommendation list is provided to a first chat room. Further, the first content recommendation list is transmitted to the user terminal 100 of the first user, so that the first content recommendation list can be provided to the first chat room.
In the embodiment where the candidate contents are sorted in an ascending order, the candidate contents with lower first usage data are displayed in front of the service screen of the user terminal 100 of the first user.
In other embodiments, such as the content recommendation device 200' shown in fig. 5, the device-side processing module 210 further comprises a chat room decider 214, and the device-side processing module 210 may execute program code for a content recommendation routine 232 associated with the chat room decider 214. Wherein the chat room decision 214 may represent different programs run by the device-side processing module 210. Without loss of generality, the chat room decider 214 may be a software unit generated by a digital signal processor executing code. However, the present invention is not limited thereto, and those skilled in the art can design the chat room decision part 214 as a hardware Circuit unit such as a Field-Programmable Gate Array (FPGA) or an Application Specific Integrated Circuit (ASIC).
The candidate list generating unit 211, the usage data determining unit 212, the recommendation list generating unit 213, and the chat room deciding unit 214 are configured to execute a plurality of steps of fig. 6, and will be further described below with reference to fig. 6.
Some steps in fig. 6 are the same as those in fig. 4, and therefore will not be described in detail below. The difference between fig. 6 and fig. 4 is that the steps of fig. 6 further include step S631 and step S632.
In step S631, the chat room decision unit 214 decides a second chat room, which is a chat room to which the second user in the first chat room belongs. For example, referring to FIG. 7, the first user is
In fig. 7, the second user is user B in fig. 7, the first chat room is chat room G01, and user a and user B belong to members of chat room G01, so the second chat room is chat room G03 to which user B and user F belong and which is not the first chat room.
In step S632, the usage data determination unit 212 calculates second usage data of the plurality of candidate contents in the second chat room. Step S632 calculates the second usage data of each candidate content in the second chat room in the manner described in step S430, please refer to step S430 for a detailed calculation manner, which will not be described in detail below.
In step S640, the recommendation list generation section 213 generates a first content recommendation list based on the first usage data and the second usage data. Further, taking one candidate content as an example, the recommendation list generation section 213 generates statistical usage data from the first usage data of the candidate content and the second usage data of the candidate content. The statistical usage data may be a weighted average of the first usage data and the second usage data, and the invention is not limited thereto. The recommendation list generating unit 213 sorts the statistical usage data for each candidate content and generates a first content recommendation list.
In other embodiments, user B further belongs to other chat rooms (as shown in fig. 8, user B further belongs to chat room G04 and chat room G05) in addition to chat room G01 and chat room G03 described above. In this embodiment, step S631 and step S632 are repeated until the second usage data of each candidate content in chat room G04 and the second usage data in chat room G05 are calculated. In step S640, recommendation list generating unit 213 generates a first content recommendation list based on the first usage data of chat room G01 and the second usage data of chat room G03, the second usage data of chat room G04, and the second usage data of chat room G05 as candidates.
A content recommendation method according to another embodiment of the present invention will be described below with reference to the drawings. Referring to fig. 8, fig. 8 includes users a-E and chat rooms G01-G13. User a belongs to chat room G01 and chat room G02, user B belongs to chat rooms G01 and G03 to G05, user C belongs to chat rooms G01, G04, G06 and G07, user D belongs to chat rooms G01 and G08 to G12, and user E belongs to chat rooms G01 and G13.
In this embodiment, when user a inputs information in chat room G01, content recommendation device 200 generates a candidate list including a plurality of candidate contents according to the information, and calculates first usage data of each candidate content in chat room G01 and second usage data of chat rooms G03-G12, and content recommendation device 200 sorts the plurality of candidate contents according to the first usage data of each candidate content in chat room G01 and the second usage data of chat rooms G03-G12 to generate a first content recommendation list.
When the user a inputs the same information as the above in the chat room G02, the content recommendation device 200 generates the candidate list according to the information, and calculates the first usage data of each candidate content in the chat room G02 and the second usage data in the chat room G13, and the content recommendation device 200 sorts the plurality of candidate contents according to the first usage data of each candidate content in the chat room G02 and the second usage data in the chat room G13 to generate the second content recommendation list.
Since the chat room to which the first usage data and the second usage data used to generate the second content recommendation list are associated is different from the chat room to which the first usage data and the second usage data used to generate the first content recommendation list are associated, the ranking result of the plurality of candidate contents in the second content recommendation list is different from that of the first content recommendation list. In other words, the same information input into different chat rooms by the content recommendation apparatus 200 results in content recommendation lists with different ranking results.
Referring to fig. 9, fig. 9 is a diagram illustrating a window 300 of a user a using a chat room G01 by a dedicated application at a user terminal 100. User a may receive information transmitted by user B, user C, and user D in chat room G01 through window 300. User a can interact with user B, user C, and user D in chat room G01 in an informational manner in input area 310 of window 300.
Referring to fig. 10, fig. 10 is a schematic diagram of the user a inputting information. In fig. 10, the user a inputs information corresponding to a specific content category, for example, love. The window 300 displays the candidate contents C1-C7 corresponding to the information in the content display window 320 from left to right and from top to bottom according to the aforementioned first content recommendation list. The first content recommendation list is generated based on first usage data or/and second usage data of a plurality of candidate contents C1 to C7 corresponding to information. Thus, in this embodiment, for user a in chat room G01, candidate content C1 has a lower frequency or number of uses than candidate content C2, candidate content C2 has a lower frequency or number of uses than candidate content C3, candidate content C3 has a lower frequency or number of uses than candidate content C4, candidate content C4 has a lower frequency or number of uses than candidate content C5, candidate content C5 has a lower frequency or number of uses than candidate content C6, and candidate content C6 has a lower frequency or number of uses than candidate content C7.
In summary, the content recommendation method and the content recommendation apparatus provided in the embodiments of the present invention enable a plurality of candidate contents associated with the same information to be sorted in an ascending order for different chat rooms, so that the plurality of candidate contents associated with the same information have different sorting orders in different chat rooms. The invention carries out ascending arrangement on the candidate contents, and leads the candidate contents with lower use frequency or use times to be preferentially recommended to the user, therefore, the user can select to transmit the relatively novel candidate contents to the corresponding chat room, thereby not only increasing the entertainment of the chat room, but also leading other members in the same chat room to have better use experience.
The above description is only an embodiment of the present invention, and is not intended to limit the scope of the present invention.
Claims (11)
1. A method for recommending content, the method comprising:
acquiring information input into a first chat room by a first user, wherein the first user and a second user belong to the first chat room;
determining a candidate list associated with the information, wherein the candidate list has a plurality of candidate contents;
calculating first usage data for the plurality of candidate content in the first chat room;
determining a second chat room, the second chat room being a chat room to which the second user belongs but to which the first user does not belong, and the second chat room being different from the first chat room;
calculating second usage data of the candidate contents in the second chat room;
generating a first content recommendation list based on the first usage data and the second usage data; and
providing the first content recommendation list to the first chat room.
2. The content recommendation method according to claim 1, wherein said first usage data is calculated based on a number of times each candidate content is provided to said first chat room.
3. The method of claim 2, wherein the number of times each candidate content is provided to the first chat room is determined by comparing at least one of content identification, content name or content size of each candidate content.
4. The content recommendation method according to claim 1, wherein the candidate content is one of an image, a map, an expression, a video, and a website.
5. The content recommendation method of claim 1, wherein when a third chat room receives the information input by the first user, a second content recommendation list provided to the third chat room is generated based on the plurality of candidate contents, wherein the second content recommendation list is different from the first content recommendation list.
6. A computer-readable storage medium containing instructions that, when executed by a processing module of a computer system, perform the content recommendation method of any of claims 1 to 5 to cause the computer system to generate the first content recommendation list.
7. A content recommendation apparatus characterized by comprising:
the device side communication module is used for acquiring information input by a first user in a first chat room, wherein the first user and a second user belong to the first chat room; and
the device side processing module is used for determining a candidate list associated with the information, wherein the candidate list has a plurality of candidate contents, calculating first use data of the candidate contents in the first chat room, generating a first content recommendation list according to the first use data, and providing the first content recommendation list to the first chat room;
the device-side processing module is further configured to determine a second chat room, and calculate second usage data of the candidate contents in the second chat room, wherein the second chat room is a chat room to which the second user belongs but to which the first user does not belong, the second chat room is different from the first chat room, and the first content recommendation list is generated according to the second usage data and the first usage data.
8. The content recommendation device of claim 7, wherein the device-side processing module calculates the first usage data based on a number of times each candidate content is provided to the first chat room.
9. The device-side processing module of claim 8, wherein the device-side processing module determines the number of times each candidate content is provided to the first chat room by comparing at least one of content identification data, content name or content size of each candidate content.
10. The content recommendation device of claim 7, wherein the candidate content is one of an image, a map, an expression, a video, and a website.
11. The content recommendation device of claim 7, wherein when the device-side processing module obtains the information input by the first user in a third chat room, the device-side processing module generates a second content recommendation list provided to the third chat room based on the candidate contents, wherein the second content recommendation list is different from the first content recommendation list.
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| WO2016045465A1 (en) * | 2014-09-25 | 2016-03-31 | 北京搜狗科技发展有限公司 | Information presentation method based on input and input method system |
| CN104618222A (en) * | 2015-01-07 | 2015-05-13 | 腾讯科技（深圳）有限公司 | Method and device for matching expression image |
| CN104866116A (en) * | 2015-03-25 | 2015-08-26 | 百度在线网络技术（北京）有限公司 | Method and device for outputting expression information |
| CN105975563A (en) * | 2016-04-29 | 2016-09-28 | 腾讯科技（深圳）有限公司 | Facial expression recommendation method and apparatus |
| CN108038102A (en) * | 2017-12-08 | 2018-05-15 | 北京小米移动软件有限公司 | Recommendation method, apparatus, terminal and the storage medium of facial expression image |
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| CN110535669A (en) | 2019-12-03 |
| JP2019204508A (en) | 2019-11-28 |
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