KR20210115928A - Method and apparatus for recommending content to user - Google Patents

Abstract

The present invention relates to a technology for recommending content to a user based on the number of views of content. According to an embodiment of the present invention, a method for recommending content to a user updates the number of views for each of the plurality of content; calculates a viewing score for each of the plurality of content based on the updated number of times of viewing; determines target content of a preset number of the plurality of content based on the viewing score; and recommends target content to the user.

Description

METHOD AND APPARATUS FOR RECOMMENDING CONTENT TO USER}

The following embodiments relate to a technology for recommending content to a user, and more specifically, to a technology for recommending content to a user based on the number of views of the content.

Currently, services for providing content to user terminals through wired and wireless Internet are common, and as such, the types of content provided to user terminals include image content, music content, video content, game content, real-time information content, etc. and the variety of its types is gradually increasing.

However, in order to use such content, the user has to pay a lot of time and communication cost to access the content providing server and search for the content he or she wants. Conventionally, various methods for selecting and recommending content satisfactory to a user have been proposed. For example, there is a simple recommendation method using content sales volume, purchaser's recommendation information, and the like. As another example, there is a customized recommendation method using preference genre information or preference category information input by a user, or a purchase history of the user.

An embodiment may provide a method and apparatus for recommending content to a user.

An embodiment may provide a method and apparatus for providing content to a user.

According to one aspect, a content recommendation method, performed by a server, includes updating the number of views for each of a plurality of contents, and calculating a viewing score for each of the plurality of contents based on the updated number of views step, determining a preset number of target contents from among the plurality of contents based on the viewing score, and recommending the target contents to a user.

Calculating the viewing score for each of the plurality of contents based on the updated viewing frequency may include calculating a first score based on a viewing ranking for the viewing frequency of the target content, and the viewing of the target content. It may include calculating a second score based on the target group of the target content determined based on the ranking.

The calculating of the first score based on the viewing rank with respect to the number of views of the target content may include calculating a first partial score of the first score for a first period of a preset period, and the preset period of time. calculating a second partial score of the first score for a second time period different from the first time period, and calculating the first score based on the first partial score and the second partial score. can

The calculating of a second score based on a target group of the target content determined based on the viewing rank of the target content may include calculating a first partial score of the second score for a first period among a preset period. , calculating a second subscore of the second score for a second time period of the preset time period different from the first time period, and based on the first partial score and the second partial score, the second score It may include the step of calculating

Calculating the viewing score for each of the plurality of contents based on the updated viewing frequency may include calculating a third score based on a ranking of a rate of change with respect to the number of views of the target content, and the rate of change of the target content It may include calculating a fourth score based on the second target group of the target content determined based on the ranking.

The calculating of the third score based on the ranking of the rate of change with respect to the number of views of the target content may include calculating a first partial score of the third score for a first period of a preset period, and the preset period of time. calculating a second partial score of the third score for a second time period different from the first time period, and calculating the third score based on the first partial score and the second partial score. can

The calculating of a fourth score based on a second target group of the target content determined based on the change rate ranking of the target content includes calculating a first partial score of the fourth score for a first period among a preset period. calculating a second partial score of the fourth score for a second period of the preset period different from the first period, and based on the first partial score and the second partial score, the second partial score. 4 calculating the score.

The determining of a preset number of target contents among the plurality of contents based on the viewing score may include determining the contents viewed by the user from among the plurality of contents, and selecting the plurality of contents excluding the viewing contents. Sorting based on the viewing score, and determining the preset number of target content among the sorted plurality of content may include.

The plurality of contents may be video on demand (VOD).

According to another aspect, a server for performing a content recommendation method includes a memory in which a program for recommending content is recorded, and a processor for executing the program, wherein the program counts the number of views for each of a plurality of content. updating, calculating a viewing score for each of the plurality of contents based on the updated number of views, determining a preset number of target contents among the plurality of contents based on the viewing score, and the A step of recommending target content to the user is performed.

Calculating the viewing score for each of the plurality of contents based on the updated viewing frequency may include calculating a first score based on a viewing ranking for the viewing frequency of the target content, and the viewing of the target content. It may include calculating a second score based on the target group of the target content determined based on the ranking.

Calculating the viewing score for each of the plurality of contents based on the updated viewing frequency may include calculating a third score based on a ranking of a rate of change with respect to the number of views of the target content, and the rate of change of the target content It may include calculating a fourth score based on the second target group of the target content determined based on the ranking.

The determining of a preset number of target contents among the plurality of contents based on the viewing score may include determining the contents viewed by the user from among the plurality of contents, and selecting the plurality of contents excluding the viewing contents. Sorting based on the viewing score, and determining the preset number of target content among the sorted plurality of content may include.

The plurality of contents may be video on demand (VOD).

A method and apparatus for recommending content to a user may be provided.

A method and apparatus for providing content to a user may be provided.

1 illustrates a system for providing content to a user according to an example.
2 is a configuration diagram of a server according to an embodiment.
3 is a flowchart of a method of recommending content to a user according to an exemplary embodiment.
4 is a flowchart of a method of calculating a viewing score for content according to an example.
5 is a flowchart of a method of calculating a first score according to an example.
6 is a flowchart of a method of calculating a second score according to an example.
7 is a flowchart of a method of calculating a third score according to an example.
8 is a flowchart of a method of calculating a fourth score according to an example.
9 is a flowchart of a method of determining a preset number of target contents according to an example.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, the scope of the patent application is not limited or limited by these examples. Like reference numerals in each figure indicate like elements.

Various modifications may be made to the embodiments described below. It should be understood that the embodiments described below are not intended to limit the embodiments, and include all modifications, equivalents or substitutes thereto.

Terms used in the examples are only used to describe specific examples, and are not intended to limit the examples. The singular expression includes the plural expression unless the context clearly dictates otherwise. In the present specification, terms such as “comprise” or “have” are intended to designate that a feature, number, step, operation, component, part, or combination thereof described in the specification exists, but one or more other features It should be understood that this does not preclude the existence or addition of numbers, steps, operations, components, parts, or combinations thereof.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiment belongs. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related art, and should not be interpreted in an ideal or excessively formal meaning unless explicitly defined in the present application. does not

In addition, in the description with reference to the accompanying drawings, the same components are given the same reference numerals regardless of the reference numerals, and the overlapping description thereof will be omitted. In the description of the embodiment, if it is determined that a detailed description of a related known technology may unnecessarily obscure the gist of the embodiment, the detailed description thereof will be omitted.

1 illustrates a system for providing content to a user according to an example.

According to one aspect, the content providing system may include a server 110 that provides content and a user terminal 100 that outputs the provided content. Although the user terminal 100 is illustrated as a mobile device such as a smart phone, it may be an Internet Protocol Television (IPTV) and is not limited to the described embodiment.

The server 110 may provide the content selected by the user of the user terminal 100 to the user through the user terminal 100 , and may additionally recommend one or more contents 101 to 106 to the user. The recommended contents 101 to 106 may be output to the user terminal 100 . The user may select and watch any one of the output contents 101 to 106 .

There are various methods in which the server 110 determines the contents 101 to 106 from among the plurality of contents. For example, among the plurality of contents, a content with a high number of views by users may be determined. As another example, content having a good evaluation by users may be determined from among the plurality of contents.

However, since the above methods determine content based on the accumulated result (ie, the number of views or evaluation) of the content, the user's recent viewing trend cannot be immediately reflected. Therefore, a method that can reflect the recent viewing trend together may be considered.

Hereinafter, a content recommendation method that can reflect a recent viewing trend will be described in detail with reference to FIGS. 2 to 6 .

2 is a configuration diagram of a server according to an embodiment.

The server 200 includes a communication unit 210 , a processor 220 , and a memory 230 . For example, the server 200 may be the server 110 with reference to FIG. 1 .

The communication unit 210 is connected to the processor 220 and the memory 230 to transmit and receive data. The communication unit 210 may be connected to another external device to transmit/receive data. Hereinafter, the expression "transmitting and receiving "A" may indicate transmitting and receiving "information or data representing A".

The communication unit 210 may be implemented as circuitry in the server 200 . For example, the communication unit 210 may include an internal bus and an external bus. As another example, the communication unit 210 may be an element that connects the server 200 and an external device. The communication unit 210 may be an interface. The communication unit 210 may receive data from an external device and transmit the data to the processor 220 and the memory 230 .

The processor 220 processes data received by the communication unit 210 and data stored in the memory 230 . A “processor” may be a data processing device implemented in hardware having circuitry having a physical structure for performing desired operations. For example, desired operations may include code or instructions included in a program. For example, a data processing device implemented as hardware includes a microprocessor, a central processing unit, a processor core, a multi-core processor, and a multiprocessor. , an Application-Specific Integrated Circuit (ASIC), and a Field Programmable Gate Array (FPGA).

Processor 220 executes computer readable code (eg, software) stored in memory (eg, memory 230 ) and instructions issued by processor 220 .

The memory 230 stores data received by the communication unit 210 and data processed by the processor 220 . For example, the memory 230 may store a program (or an application, software). The stored program may be a set of syntaxes coded to recommend content and executable by the processor 220 .

According to one aspect, memory 230 may include one or more volatile memory, non-volatile memory and random access memory (RAM), flash memory, hard disk drive, and optical disk drive.

The memory 230 stores an instruction set (eg, software) for operating the server 200 . The instruction set for operating the server 200 is executed by the processor 220 .

The communication unit 210 , the processor 220 , and the memory 230 will be described in detail below with reference to FIGS. 3 to 6 .

3 is a flowchart of a method of recommending content to a user according to an exemplary embodiment.

The following steps 310 to 360 may be performed by the server 200 described above with reference to FIG. 2 .

In step 310 , the server 200 updates the number of views for each of the plurality of contents. For example, the content may be Video on Demand (VOD) and is not limited to the described embodiment.

The number of views of content viewed by users provided with a service through the server 200 increases, and the number of views of each of the plurality of content is updated based on this. The number of viewing times of the content may be individually accumulated for each of a plurality of preset times. For example, the number of views of content may be individually accumulated on a daily basis.

The number of viewing times of the content may be used to determine the recognition or universality of the content.

In step 320 , the server 200 calculates a viewing score for each of the plurality of contents based on the updated viewing frequency. For example, a score for the recognition of content and a score for a recent viewing trend may be calculated, and a viewing score may be calculated based thereon.

The score calculated based on the ranking of the number of viewings individually accumulated for each of the plurality of times is a score for the recognition of the content, and based on the ranking of the rate of change of the number of viewings individually accumulated for each of the plurality of times The calculated score may be used to determine a recent viewing trend of the content.

A method of calculating the viewing score is described in detail below with reference to FIGS. 4 and 5 .

In step 330 , the server 200 determines a preset number of target contents from among a plurality of contents based on the viewing score. For example, a content having a high viewing score among a plurality of content may be determined as the target content. As another example, the target content may be determined by considering the user's viewing history. A method of determining target content in consideration of a user's viewing history will be described in detail below with reference to FIG. 6 .

In step 340 , the server 200 recommends the determined target content to the user. The recommended target content may be provided to the user through the user terminal.

4 is a flowchart of a method of calculating a viewing score for content according to an example.

According to one aspect, the step 320 described above with reference to FIG. 3 may include the following steps 410 to 440 . Steps 410 to 440 may be performed in parallel and independently.

In step 410 , the server 200 calculates a first score based on the viewing rank with respect to the number of views of the target content. The plurality of contents may be sorted based on the viewing ranking for a preset period (eg, 1 day), and a first score of the target content may be calculated based on the viewing ranking of the target content.

A method of calculating the first score is described in detail below with reference to FIG. 5 .

In step 420, the server 200 calculates a second score based on the target group of the target content determined based on the viewing rank of the target content. The plurality of contents may be sorted based on the viewing order for a preset period (eg, 1 day), and may be divided into a plurality of groups. For example, when the plurality of contents is 1000 and the plurality of groups is 50, one group may include 20 contents. For example, the first group may include contents from 1st to 20th in the viewing ranking, and the 50th group may include contents from 981 to 1000th in the viewing ranking. The second score may be a score for supplementing the first score according to individual viewing rankings.

A method of calculating the second score is described in detail below with reference to FIG. 6 .

In step 430 , the server 200 calculates a third score based on the ranking of the rate of change with respect to the number of views of the target content. The rate of change for the number of views means the difference between the number of views of the content for the previous period and the number of views of the content for this time, and the greater the difference in the direction of increasing the number of views, the higher the ranking of the rate of change for the period .

A method of calculating the third score is described in detail below with reference to FIG. 7 .

In step 440 , the server 200 calculates a fourth score based on the second target group of the target content determined based on the ranking of the rate of change of the target content. The plurality of contents may be sorted based on a change rate ranking for a preset period (eg, 1 day), and may be divided into a plurality of groups. The description of the plurality of groups may be replaced with the description of the plurality of groups described in step 420 .

A method of calculating the fourth score is described in detail below with reference to FIG. 8 .

5 is a flowchart of a method of calculating a first score according to an example.

According to one aspect, the step 410 described above with reference to FIG. 4 may include the following steps 510 to 530 .

In step 510, the server 200 calculates a first partial score of the first score for a first period of the preset period. For example, the first partial score of the first score is calculated using the following [Equation 1].

[Equation 1]

In [Equation 1], AR _XD(1) represents the viewing order of the target content for the first period, and K _XD may be a constant. K _XD may be appropriately adjusted by the operator of the server 200 . The higher the rank _{, the smaller the value of AR XD(1)} , so the first partial score of the first score increases.

In step 520 , the server 200 calculates a second partial score of the first score for a second period different from the first period of the preset period. For example, the second partial score of the first score is calculated similarly to [Equation 1] described above. _{AR XD(2)} is used instead of AR _XD(1) for the second partial score. AR _XD(2) indicates the viewing ranking of the target content for the second period.

In step 530 , the server 200 calculates a first score based on the first partial score and the second partial score. For example, the first score is calculated using the following [Equation 2].

[Equation 2]

In [Equation 2], WR may be a weight for the first score, and D may be the number of the entire period. For example, when D is one week and the period is set to one day, the first score may be calculated based on a total of seven partial scores.

6 is a flowchart of a method of calculating a second score according to an example.

According to one aspect, the step 420 described above with reference to FIG. 4 may include the following steps 610 to 630 .

In step 610, the server 200 calculates a first partial score of the second score for the first period of the preset period. For example, the first partial score of the second score is calculated using the following [Equation 3].

[Equation 3]

In [Equation 3], ART _XD(1) represents the rank of the target group determined based on the viewing rank of the target content for the first period, and K _XD may be a constant. For example, when the plurality of contents is 1000, there are 50 groups, and the viewing order of the target content is 15th, the group ranking of the target group including the target content may be 1st. When the viewing ranking of the target content is 28th, the group ranking of the target group including the target content may be the 2nd. By calculating the second score based on the ranking of the target group and reflecting the second score in the overall score, it is possible to prevent an abnormal score that may be indicated by the first score.

In step 620 , the server 200 calculates a second partial score of the second score for a second period different from the first period of the preset period. For example, the second partial score of the second score is calculated similarly to [Equation 3] described above. _{ART XD(2)} is used instead of ART _XD(1) for the second partial score. ART _XD(2) indicates the ranking of the target group of target content for the second period.

In step 630 , the server 200 calculates a second score based on the first partial score and the second partial score. For example, the second score is calculated using the following [Equation 4].

[Equation 4]

In [Equation 4], WRT may be a weight for the second score.

7 is a flowchart of a method of calculating a third score according to an example.

According to one aspect, the step 430 described above with reference to FIG. 4 may include the following steps 710 to 730 .

In step 710 , the server 200 calculates a first partial score of the third score for the first period of the preset period. For example, the first partial score of the third score is calculated using the following [Equation 5].

[Equation 5]

In [Equation 5], FR _XD(1) represents a rank of change rate with respect to the number of views of the target content for the first period, and K _XD may be a constant.

In step 720 , the server 200 calculates a second partial score of the third score for a second period different from the first period of the preset period. For example, the second partial score of the third score is calculated similarly to [Equation 5] described above. _{FR XD(2)} is used instead of FR _XD(1) for the second partial score. FR _XD(2) indicates the ranking of the rate of change with respect to the number of views of the target content in the second period.

In step 730, the server 200 calculates a third score based on the first partial score and the second partial score. For example, the third score is calculated using the following [Equation 6].

[Equation 6]

In [Equation 6], WF may be a weight for the third score.

8 is a flowchart of a method of calculating a fourth score according to an example.

According to one aspect, step 440 described above with reference to FIG. 4 may include steps 810 to 830 below.

In step 810 , the server 200 calculates a first partial score of the fourth score for the first period of the preset period. For example, the first partial score of the fourth score is calculated using the following [Equation 7].

[Equation 7]

In [Equation 7], AFT _XD(1) represents the ranking of the second target group determined based on the ranking of the rate of change of the target content for the first period. For example, when the plurality of contents is 1000, there are 50 groups, and the change rate of the target contents is 15th, the group ranking of the second target group including the target contents may be 1st. When the ranking of the change rate of the target content is 28th, the group ranking of the second target group including the target content may be the 2nd. By calculating the fourth score based on the ranking of the target group and reflecting the fourth score in the overall score, it is possible to prevent an abnormal score that may be indicated by the third score.

In step 820 , the server 200 calculates a second partial score of the fourth score for a second period different from the first period of the preset period. For example, the second partial score of the fourth score is calculated similarly to [Equation 7] described above. _{AFT XD(2)} is used instead of AFT _XD(1) for the second partial score. AFT _XD(2) indicates the ranking of the second target group of target content for the second time period.

In step 830 , the server 200 calculates a fourth score based on the first partial score and the second partial score. For example, the fourth score is calculated using the following [Equation 8].

[Equation 8]

In [Equation 8], WFT may be a weight for the fourth score.

Finally, the server 200 calculates a viewing score for the target content using Equation 9 below.

[Equation 9]

9 is a flowchart of a method of determining a preset number of target contents according to an example.

According to one aspect, the step 330 described above with reference to FIG. 4 may include the following steps 910 to 930 .

In step 910 , the server 200 determines the content viewed by the user from among the plurality of content. For example, when a user accesses the server 200 through a user identifier, the server 200 may determine viewing content based on the user's viewing history.

In operation 920, the server 200 sorts the plurality of contents excluding the viewing contents based on the viewing score.

In step 930 , the server 200 determines a preset number of target contents from among a plurality of sorted contents.

The device described above may be implemented as a hardware component, a software component, and/or a combination of the hardware component and the software component. For example, devices and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA), It may be implemented using one or more general purpose or special purpose computers, such as a programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For convenience of understanding, although one processing device is sometimes described as being used, one of ordinary skill in the art will recognize that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that can include For example, the processing device may include a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as parallel processors.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic such as floppy disks. - includes magneto-optical media, and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

As described above, although the embodiments have been described with reference to the limited embodiments and drawings, various modifications and variations are possible from the above description by those skilled in the art. For example, the described techniques are performed in a different order than the described method, and/or the described components of the system, structure, apparatus, circuit, etc. are combined or combined in a different form than the described method, or other components Or substituted or substituted by equivalents may achieve an appropriate result.

200: server
210: communication unit
220: processor
230: memory

Claims (15)

The content recommendation method, performed by the server,
updating the number of views for each of the plurality of contents;
calculating a viewing score for each of the plurality of contents based on the updated viewing frequency;
determining a preset number of target contents from among the plurality of contents based on the viewing score; and
Recommending the target content to a user
containing,
How to recommend content.
According to claim 1,
Calculating the viewing score for each of the plurality of contents based on the updated viewing frequency includes:
calculating a first score based on a viewing ranking with respect to the number of viewings of the target content; and
calculating a second score based on a target group of the target content determined based on the viewing rank of the target content
containing,
How to recommend content.
3. The method of claim 2,
Calculating a first score based on a viewing ranking for the number of viewing of the target content includes:
calculating a first partial score of the first score for a first period of a preset period;
calculating a second partial score of the first score for a second period of time different from the first period of the preset period; and
calculating the first score based on the first partial score and the second partial score;
containing,
How to recommend content.
3. The method of claim 2,
Calculating a second score based on a target group of the target content determined based on the viewing rank of the target content,
calculating a first partial score of the second score for a first period of a preset period;
calculating a second partial score of the second score for a second period of the preset period different from the first period; and
calculating the second score based on the first partial score and the second partial score;
containing,
How to recommend content.
According to claim 1,
Calculating the viewing score for each of the plurality of contents based on the updated viewing frequency includes:
calculating a third score based on the ranking of the rate of change with respect to the number of views of the target content; and
calculating a fourth score based on a second target group of the target content determined based on the ranking of the rate of change of the target content
containing,
How to recommend content.
6. The method of claim 5,
Calculating a third score based on the ranking of the rate of change with respect to the number of views of the target content,
calculating a first partial score of the third score for a first period of a preset period;
calculating a second partial score of the third score for a second period of the preset period different from the first period; and
calculating the third score based on the first partial score and the second partial score;
containing,
How to recommend content.
6. The method of claim 5,
Calculating a fourth score based on the second target group of the target content determined based on the ranking of the rate of change of the target content,
calculating a first partial score of the fourth score for a first period of a preset period;
calculating a second partial score of the fourth score for a second period of the preset period different from the first period; and
calculating the fourth score based on the first partial score and the second partial score;
containing,
How to recommend content.
According to claim 1,
The step of determining a preset number of target contents among the plurality of contents based on the viewing score includes:
determining the content viewed by the user from among the plurality of contents;
sorting the plurality of contents excluding the viewing contents based on the viewing score; and
Determining the preset number of target contents among the sorted plurality of contents
containing,
How to recommend content.
According to claim 1,
The plurality of contents is VOD (Video on Demand),
How to recommend content.
A computer-readable recording medium containing a program for performing the method of any one of claims 1 to 9.
The server performing the content recommendation method,
a memory in which a program for recommending content is recorded; and
a processor that executes the program
including,
The program is
updating the number of views for each of the plurality of contents;
calculating a viewing score for each of the plurality of contents based on the updated viewing frequency;
determining a preset number of target contents from among the plurality of contents based on the viewing score; and
Recommending the target content to a user
to do,
server.
12. The method of claim 11,
Calculating the viewing score for each of the plurality of contents based on the updated viewing frequency includes:
calculating a first score based on a viewing ranking with respect to the number of viewings of the target content; and
calculating a second score based on a target group of the target content determined based on the viewing rank of the target content
containing,
server.
12. The method of claim 11,
Calculating the viewing score for each of the plurality of contents based on the updated viewing frequency includes:
calculating a third score based on the ranking of the rate of change with respect to the number of views of the target content; and
calculating a fourth score based on a second target group of the target content determined based on the ranking of the rate of change of the target content
containing,
server.
12. The method of claim 11,
The step of determining a preset number of target contents among the plurality of contents based on the viewing score includes:
determining the content viewed by the user from among the plurality of contents;
sorting the plurality of contents excluding the viewing contents based on the viewing score; and
Determining the preset number of target contents among the sorted plurality of contents
containing,
server.
12. The method of claim 11,
The plurality of contents is VOD (Video on Demand),
server.

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