CN113286199A - Program recommendation method, television and storage medium - Google Patents

Program recommendation method, television and storage medium Download PDF

Info

Publication number
CN113286199A
CN113286199A CN202010106804.5A CN202010106804A CN113286199A CN 113286199 A CN113286199 A CN 113286199A CN 202010106804 A CN202010106804 A CN 202010106804A CN 113286199 A CN113286199 A CN 113286199A
Authority
CN
China
Prior art keywords
program
user
programs
recommendation list
playing
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202010106804.5A
Other languages
Chinese (zh)
Inventor
陈小平
于显双
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Foshan Viomi Electrical Technology Co Ltd
Original Assignee
Foshan Viomi Electrical Technology Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Foshan Viomi Electrical Technology Co Ltd filed Critical Foshan Viomi Electrical Technology Co Ltd
Priority to CN202010106804.5A priority Critical patent/CN113286199A/en
Publication of CN113286199A publication Critical patent/CN113286199A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/45—Management operations performed by the client for facilitating the reception of or the interaction with the content or administrating data related to the end-user or to the client device itself, e.g. learning user preferences for recommending movies, resolving scheduling conflicts
    • H04N21/466—Learning process for intelligent management, e.g. learning user preferences for recommending movies
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/16—Human faces, e.g. facial parts, sketches or expressions
    • G06V40/172—Classification, e.g. identification
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
    • H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
    • H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
    • H04N21/441—Acquiring end-user identification, e.g. using personal code sent by the remote control or by inserting a card
    • H04N21/4415—Acquiring end-user identification, e.g. using personal code sent by the remote control or by inserting a card using biometric characteristics of the user, e.g. by voice recognition or fingerprint scanning

Abstract

The application relates to the field of intelligent household appliances, in particular to a program recommendation method, a television and a storage medium, wherein the method comprises the following steps: acquiring an image acquired by the shooting device, wherein the image comprises at least one user; determining the identity information of the user in the image based on the trained face recognition model, and acquiring historical program data of the user according to the identity information of the user; determining interesting programs of the user according to the historical program data of the user, and determining a user group corresponding to the user; obtaining interesting programs of users in the user group, and carrying out fusion processing on the interesting programs to obtain a program recommendation list corresponding to the user group; and playing the programs according to the program recommendation list corresponding to the user group. By determining the user group corresponding to the user and playing the program according to the program recommendation list corresponding to the user group, the method is more intelligent and improves the experience degree of the user.

Description

Program recommendation method, television and storage medium
Technical Field
The present application relates to the field of television technologies, and in particular, to a program recommendation method, a television, and a storage medium.
Background
With the continuous development and improvement of television technology, more and more people select the smart television to watch programs. However, most of the intelligent televisions are fixed programs, and program channels need to be selected and switched through a remote controller. Although some existing smart televisions can automatically recommend programs by identifying identity information of users or identifying interests and hobbies of the users, the identification efficiency is low, the accuracy of recommended programs is not accurate enough, and the experience of the users cannot be further improved. Therefore, the effectiveness and accuracy of program recommendation become the most important part of the user experience.
Disclosure of Invention
The application provides a program recommendation method, a television and a storage medium, and the experience degree of a user is improved by determining a program recommendation list according to identity information of the user and automatically playing programs for the user.
In a first aspect, the present application provides a program recommendation method, which is applied to a television, where the television includes a shooting device, and the method includes:
acquiring an image acquired by the shooting device, wherein the image comprises at least one user;
determining the identity information of the user in the image based on the trained face recognition model, and acquiring historical program data of the user according to the identity information of the user;
determining interesting programs of the user according to the historical program data of the user, and determining a user group corresponding to the user;
obtaining interesting programs of users in the user group, and carrying out fusion processing on the interesting programs to obtain a program recommendation list corresponding to the user group;
and playing the programs according to the program recommendation list corresponding to the user group.
In a second aspect, the present application further provides a television, including a camera, a memory, and a processor;
the shooting device is used for acquiring images;
the memory for storing a computer program;
the processor is configured to execute the computer program and to implement the program recommendation method as described above when executing the computer program.
In a third aspect, the present application further provides a computer-readable storage medium storing a computer program, which when executed by a processor causes the processor to implement the program recommendation method as described above.
The application discloses a program recommendation method, a television and a storage medium, wherein images including at least one user can be obtained by obtaining images collected by a shooting device; the identity information of the user in the image can be determined based on the trained face recognition model, and further the historical program data of the user can be obtained according to the identity information of the user; determining interesting programs of a user according to historical program data of the user, and determining a user group corresponding to the user; by acquiring the interesting programs of the users in the user group and fusing the interests, a program recommendation list corresponding to the user group can be obtained, so that the method is more humanized; programs in the program recommendation list can be recommended to the user, and the accuracy of recommending the programs is improved; the programs are played according to the program recommendation list corresponding to the user group, so that the proper programs are played for the user, the program recommendation method is more intelligent, and the experience degree of the user is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic structural diagram of a television set provided in an embodiment of the present application;
fig. 2 is a schematic block diagram of a television set provided by an embodiment of the present application;
FIG. 3 is a flowchart illustrating steps of a program recommendation method according to an embodiment of the present application;
FIG. 4 is a diagram illustrating a prediction result of an image provided by an embodiment of the present application;
FIG. 5 is a schematic diagram of a display list selection box provided by an embodiment of the present application;
fig. 6 is a scene diagram for playing a program according to a program recommendation list provided by an embodiment of the present application;
fig. 7 is a schematic diagram of a user leaving a television set according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The flow diagrams depicted in the figures are merely illustrative and do not necessarily include all of the elements and operations/steps, nor do they necessarily have to be performed in the order depicted. For example, some operations/steps may be decomposed, combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
It is to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
Referring to fig. 1, fig. 1 is a schematic block diagram of a television set provided in the present application. The television set in the embodiment of the present application will be described below with reference to fig. 1.
As shown in fig. 1, the television set 10 includes a camera 11. For example, the camera 11 may be disposed in a frame of the television 10, or may be a separate external camera.
The television 10 is a fully-open platform, carries an operating system, and allows a user to install and uninstall various application software while enjoying common television content, so as to continuously expand and upgrade functions of a new television product, thereby continuously providing rich personalized experience for the user.
Illustratively, the television 10 may be an OLED television, an LED television, a curved-surface television, a full-screen television, a 3D television, a smart television, an ultra high definition UHD television, or the like.
In some embodiments, the camera 11 comprises a camera, which may be a conventional camera, but may also be other cameras, such as a depth camera. It can be understood that a common camera is only used for taking an image of a target; the depth camera can be used to capture depth images in addition to the target.
Specifically, the television 10 is provided with a function control device inside. The function control means may comprise a processor and a memory. The memory is used for storing image data and computer programs, and the processor is used for processing the image data and running the computer programs.
In some embodiments, a function control device is electrically connected to the camera 11 for processing images captured by the camera 11.
In fig. 1, the imaging device 11 is exemplified as a general camera, but the imaging device 11 is limited.
Illustratively, in the television set 10, the photographing device 11 is used to capture an image and transmit the captured image to the function control device. A processor in the function control device can determine the identity information of a user based on the trained face recognition model and acquire the historical program data of the user according to the identity information of the user; the processor may determine the programs of interest of the user according to the historical program data of the user and determine a user group corresponding to the user, then obtain the programs of interest of the user in the user group, and perform fusion processing on the programs of interest of the user to obtain a program recommendation list corresponding to the user group. The processor in the function control device can also play the programs according to the program recommendation list corresponding to the user group.
Referring to fig. 2, fig. 2 is a schematic block diagram of a television according to an embodiment of the present disclosure. In fig. 2, the television 10 includes a processor 101, a memory 102, and a camera 103, wherein the processor 101, the memory 102, and the camera 103 are connected by a bus, such as an I2C (Inter-integrated Circuit) bus.
The memory 102 may include, among other things, a non-volatile storage medium and an internal memory. The non-volatile storage medium may store an operating system and a computer program. The computer program includes program instructions that, when executed, cause a processor to perform any of the program recommendation methods.
The camera 103 is used to take an image and transfer the taken image to the processor 101 and the memory 102.
The processor 101 is used to provide computing and control capabilities to support the operation of the entire television 10.
The Processor may be a Central Processing Unit (CPU), or may be other general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, a discrete hardware component, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Wherein the processor 101 is configured to run a computer program stored in the memory 102, and when executing the computer program, implement the following steps:
acquiring an image acquired by the shooting device, wherein the image comprises at least one user; determining the identity information of the user in the image based on the trained face recognition model, and acquiring historical program data of the user according to the identity information of the user; determining interesting programs of the user according to the historical program data of the user, and determining a user group corresponding to the user; obtaining interesting programs of users in the user group, and carrying out fusion processing on the interesting programs to obtain a program recommendation list corresponding to the user group; and playing the programs according to the program recommendation list corresponding to the user group.
In some embodiments, the processor, when implementing the determination of the identity information of the user in the image based on the trained face recognition model, implements:
inputting the image into the trained face recognition model, and outputting a prediction identity corresponding to a user in the image and a prediction probability corresponding to the prediction identity; and if the prediction probability corresponding to the predicted identity is larger than a first preset threshold value, taking the predicted identity as the identity information of the user.
In some embodiments, the historical program data includes a play time length and a play number; when the interested program of the user is determined according to the historical program data of the user and the user group corresponding to the user is determined, the following steps are realized:
according to the playing time and the playing times of the user on the multiple programs, carrying out interest scoring on the multiple programs to obtain the interest scores of the user on the multiple programs; if the interest score of the user to the program is larger than a second preset threshold value, taking the program as the interest program of the user; and determining a user group corresponding to the user according to the interesting program of the user based on a preset corresponding relation between the type corresponding to the interesting program and the user group.
In some embodiments, when the processor performs fusion processing on the interested program to obtain a program recommendation list corresponding to the user group, the processor performs:
taking an intersection of the interested programs of all users in the user group to obtain a program recommendation list corresponding to the user group, wherein the program recommendation list comprises at least one program; or collecting a union set of the interested programs of all users in the user group to obtain a program recommendation list corresponding to the user group, wherein the program recommendation list comprises at least one program.
In some embodiments, the processor further implements:
if program recommendation lists corresponding to a plurality of user groups exist, displaying a list selection frame to remind the user to select the program recommendation lists; and determining a program recommendation list to be played according to the selection operation of the user.
In some embodiments, the processor, when implementing playing a program according to the program recommendation list corresponding to the user group, implements:
acquiring playing time and playing duration corresponding to each program in the program recommendation list; determining a channel corresponding to each program according to the playing time corresponding to each program; and playing the program according to the channel corresponding to the current program, and switching to the next channel according to the playing duration corresponding to the current program.
In some embodiments, the program recommendation list includes a current program and a candidate program; when the program is played according to the channel corresponding to the current program and the next channel is switched according to the playing duration corresponding to the current program, the following effects are achieved:
displaying a program selection frame according to the playing time corresponding to the candidate program in the program recommendation list; and if the confirmation operation of the user on the candidate program selection frame is obtained, switching the channel corresponding to the candidate program from the channel corresponding to the current program.
In some embodiments, the processor, when implementing playing the programs in the program recommendation list corresponding to the user group, implements:
if the programs with the same playing time exist in the program recommendation list, displaying a program selection frame to remind the user of selecting the playing program; and determining the played program according to the selection operation of the user.
In some embodiments, when the processor implements playing of the programs in the program recommendation list corresponding to the user group, the processor includes:
if the programs with the same playing time exist in the program recommendation list, acquiring the audience rating corresponding to the programs with the same playing time, and determining the played programs according to the audience rating.
In some embodiments, after the playing the programs in the program recommendation list corresponding to the user group is implemented, the processor further includes:
and acquiring the image acquired by the shooting device, and if the image does not have a user, closing the television.
For ease of understanding, the program recommendation method provided by the embodiment of the present application will be described in detail below with reference to the television set in fig. 1 and fig. 2. It should be noted that the television mechanism defines an application scenario of the program recommendation method provided in the embodiment of the present application.
Referring to fig. 3, fig. 3 is a flowchart illustrating steps of a program recommendation method according to an embodiment of the present application. The program recommending method can be applied to a television, and the accuracy of recommending programs can be improved and the experience of the user is improved by determining the user group corresponding to the user and playing the programs according to the program recommending list corresponding to the user group.
As shown in fig. 3, the program recommending method includes steps S10 through S50.
And step S10, acquiring an image collected by the shooting device, wherein the image comprises at least one user.
It is understood that a camera is installed in the television set, by means of which images within the shooting range can be captured. The shooting device may be a camera, or an electronic device such as a camera that can be used to shoot images.
Wherein the shooting range is the maximum range of the angle of view of the shooting device capable of shooting.
In some embodiments, the capturing device may capture one image at intervals, or may capture a video continuously, and the obtained video includes a plurality of images. For example, one image may be taken every 30S, or other times may be used, which is not limited herein.
For example, the image captured by the camera may include one user or may include a plurality of users.
In some embodiments, a light supplement lamp is further disposed in the photographing device. When the shooting device collects images, the light supplement lamp can be turned on to control the light supplement lamp to supplement light to the camera; and then acquiring the image after the light supplement.
The light supplement lamp can be a white light lamp or a red light lamp which is arranged in the camera, and can also be an independent white light lamp or an independent red light lamp.
Illustratively, when the light of the environment around the camera is weak, the light supplement lamp is turned on to enhance the image effect of the camera shooting images.
Through the light filling effect of light filling lamp, can be so that the color reduction degree of the image that the shooting obtained is high, the image is lifelike, and the signal-to-noise ratio is high, supports the compensation function in a poor light simultaneously.
And step S20, determining the identity information of the user in the image based on the trained face recognition model, and acquiring the historical program data of the user according to the identity information of the user.
Specifically, the image is input into the trained face recognition model, and a prediction identity corresponding to the user in the image and a prediction probability corresponding to the prediction identity are output.
For example, the face recognition model may include a recognition algorithm based on feature points of the face, a recognition algorithm based on the whole face image, a recognition algorithm based on a template, a recognition algorithm based on a neural network, or a model theory based on illumination estimation.
In some embodiments, an initial face recognition model is trained to converge through a preset sample image, so as to obtain a trained face recognition model; the trained face recognition model may be stored in the television set. Wherein the sample images comprise images of different users. During training, the sample image is processed by an initial face recognition model, and a prediction identity corresponding to a user in the sample image and a prediction probability corresponding to the prediction identity are obtained. The trained face recognition model can more accurately predict the identity information of the user in the sample image.
In the embodiment of the present application, the face recognition model may be a convolutional neural network. Illustratively, the image is input into a trained face recognition model, convolution and pooling are performed on the image for a plurality of times, and then the processed result is subjected to full-connection processing and normalization processing to obtain a prediction identity corresponding to a user in the image and a prediction probability corresponding to the prediction identity through recognition.
As shown in fig. 4, fig. 4 is a schematic diagram of a prediction result of the image. If the image includes a user, the obtained prediction result may include: [ (Xiaoming, 90%) ]. In the prediction result, "Xiaoming" represents the prediction identity, and "90%" represents the prediction probability corresponding to the prediction identity of "Xiaoming".
Specifically, after the predicted identity of the user in the image and the predicted probability corresponding to the predicted identity are obtained, it is required to determine whether the predicted probability corresponding to the predicted identity is greater than a first preset threshold. And if the prediction probability corresponding to the predicted identity is greater than the first preset threshold, taking the predicted identity as the identity information of the user.
Illustratively, the first preset threshold may be 80%.
In some embodiments, if the prediction probability corresponding to the prediction identity of "twilight" is 90% and is greater than 80% of the first preset threshold, it is determined that the user of the image is twilight.
Specifically, after the identity information of the user in the image is determined, the historical program data of the user may be acquired according to the identity information of the user.
The historical program data may be the playing time length and the playing times of the program played by the user. The memory of the television is stored with the historical program data of the user.
Illustratively, the historical program data of the user may be collected through message middleware. Wherein, the message middleware can be ActiveMQ, RabbitMQ, Kafka or RockketMQ, etc. For example, the messaging middleware may collect the duration or number of times a user watches a program.
It should be noted that the message middleware adopts a message transmission mechanism/message queue middleware technology to perform data communication to solve the message transmission between the distributed systems. The message middleware can be applied to asynchronous processing, decoupling of application, peak clipping of traffic, log processing, message communication and other scenes. For example, historical program data in a television can be collected through the message middleware.
In some embodiments, if the user is twilight, the historical program data corresponding to the twilight may be obtained from the memory, for example, the twilight watches the a program for two hours, and the watching times is 10 times.
The identity information of the user can be accurately determined by acquiring the predicted identity of the user based on the trained face recognition model; the historical program data of the user are obtained according to the identity information of the user, the interested program of the user can be determined according to the historical program data of the user, and the accuracy of the follow-up recommended program can be improved.
Step S30, determining the interesting programs of the user according to the historical program data of the user, and determining the user group corresponding to the user.
Specifically, according to the playing time and the playing times of the user on the multiple programs, the interest scores of the multiple programs are obtained, and the interest scores of the user on the multiple programs are obtained.
Illustratively, the programs watched by the user may include an a program, a B program, and a C program.
For example, the interest scores of the programs may be obtained according to a scoring rule, so as to obtain the interest scores of the user for the programs. Wherein the scoring rules are shown in table 1.
Table 1 is a scoring rule table
Program data Scoring
Duration of viewing 10 min/h
Number of views 2 min/time
In the table, 10 minutes/hour represents that the program viewing time is 1 hour, and the corresponding interest score is 10.
In some embodiments, if the user explicitly watches programs including a program a, a program B, and a program C; wherein, the time length for watching the A program is 2 hours, the watching times are 2 times, and the user's mindset interest score for the A program is 24 scores; the time length for watching the program B is 0.5 hour, the watching times are 1 time, and then the user has a small and clear interest score of the program B of 7 points; the time length for watching the C program is 1 hour, the watching times are 1 time, and the user minds the interest score of the C program to be 12.
Specifically, if the interest score of the user to the program is larger than a second preset threshold, the program is used as the user's interest program.
The second preset threshold may be determined according to actual conditions, and is not limited herein.
In some embodiments, if the interest score of the user minuscule for the program a is greater than the second preset threshold, the program a is taken as the user minuscule program of interest; and if the interest score of the user on the B program is smaller than the second preset threshold value, taking the B program as the user-small uninteresting program.
Specifically, based on a preset corresponding relationship between a type corresponding to the program of interest and a user group, the user group corresponding to the user is determined according to the program of interest of the user.
The type corresponding to the interesting program may include current affairs, finance, science and technology, learning, work, leisure, entertainment, sports, games, and the like.
It can be understood that the interests and hobbies of the user can be determined according to the type of the interesting programs corresponding to the user, and then the users with the same interests and hobbies can be classified into the same user group.
For example, the preset correspondence between the type of the interested program and the user group may be represented by table 2.
Table 2 is a type comparison table
User group Program type
First of all Affairs
Second step Science and technology
C3 Entertainment system
Illustratively, in the genre comparison table, the user group corresponding to the program genre "current affairs" is a; the user group corresponding to the science and technology of the program type is B; the user group corresponding to "entertainment" of the program genre is third.
In some embodiments, if the program type corresponding to the program of interest that the user is small and clear is "current affairs", since the user group corresponding to the program type "current affairs" is a, it may be determined that the user group corresponding to the user is small and clear is a first. If the program type corresponding to the program of interest of the user with little brightness is entertainment, the user group corresponding to the program type entertainment is third, so that the user group corresponding to the user with little brightness can be determined to be third.
The interest scores of the various programs can be obtained by carrying out interest scoring on the various programs according to the playing time length and the playing times of the various programs by the user, so that the interest scores of the user on the various programs can be obtained more accurately; the user group corresponding to the user can be determined based on the preset corresponding relation between the type corresponding to the interesting program and the user group.
Step S40, obtaining the programs of interest of the users in the user group, and performing fusion processing on the programs of interest to obtain a program recommendation list corresponding to the user group.
It should be noted that each user group includes at least one user, and each user has at least one program of interest. If a plurality of users exist in the user group, the interested program corresponding to each user needs to be acquired. The user group may include a plurality of interested programs of the same type, wherein there are repeated interested programs and there are non-repeated interested programs.
In some embodiments, programs of interest to users in the user population are obtained. For example, the user group includes users such as zhang san and lie si besides the user xiao ming. Therefore, the interesting programs of users such as Zhang three, Lile four and the like can be determined according to the historical program data of the users such as Zhang three, Lile four and the like.
Specifically, the merging process of the programs of interest of the users in the user group may be performed by taking an intersection or a union of the programs of interest.
In some embodiments, an intersection is taken for interesting programs of all users in the user group to obtain a program recommendation list corresponding to the user group, where the program recommendation list includes at least one program.
Illustratively, if the interested programs of the users in the user group include unrepeated interested programs, for example, the interested program corresponding to the user xiaoming is a program a, the interested program corresponding to the user zhangsan is a program B, and the interested program corresponding to the user liquad is a program C; and reserving all the unrepeated interested programs and generating a program recommendation list corresponding to the user group according to the unrepeated interested programs.
Illustratively, the program recommendation list is shown in table 3.
Table 3 shows a program recommendation list
Program title
A program
B program
C program
In other embodiments, the programs of interest of all users in the user group are collected to obtain a program recommendation list corresponding to the user group, where the program recommendation list includes at least one program.
Illustratively, if the interested programs of the users in the user group include repeated interested programs, for example, the interested program corresponding to the user xiaoming is a program a, the interested program corresponding to the user zhangsan is a program B, and the interested program corresponding to the user lie is a program a; and deleting the repeated interested programs and generating a program recommendation list corresponding to the user group according to the unrepeated interested programs.
Illustratively, the program recommendation list is shown in table 4.
Table 4 shows a program recommendation list
Program title
A program
B program
Specifically, if the image acquired by the photographing device includes a plurality of users, the plurality of users may be the same user group or different user groups. If the plurality of users correspond to different user groups, for example, the user is mingmen as a user group A; and if the third user is a user group B, the plurality of users correspondingly have different program recommendation lists.
In some embodiments, if there are program recommendation lists corresponding to a plurality of user groups, a list selection box is displayed to remind the user to select a program recommendation list; and then determining a program recommendation list to be played according to the selection operation of the user.
Illustratively, the list selection box may be displayed in a display screen of the television set.
The selection operation can be selected through a remote controller or directly touched in a display screen to determine which program recommendation list is selected.
Illustratively, as shown in fig. 5, fig. 5 is a schematic diagram showing the list selection box. If the list selection box includes program recommendation list 1 and program recommendation list 2. The user may select the program recommendation list 1 or the program recommendation list 2 through a remote controller, or may select the program recommendation list 1 or the program recommendation list 2 in a display screen.
By acquiring the interested programs of all users in the user group and taking intersection or union of the interested programs of all users, the program recommendation list corresponding to the user group can be obtained, the programs in the program recommendation list can be recommended to the users, and the accuracy of recommending the programs is improved.
And step S50, playing the program according to the program recommendation list corresponding to the user group.
Illustratively, as shown in fig. 6, fig. 6 is a scene diagram of playing a program according to a program recommendation list corresponding to the user group.
Specifically, after determining the program recommendation list corresponding to the user group, the playing time and the playing duration corresponding to each program in the program recommendation list may be obtained.
Wherein the program recommendation list includes a current program and a candidate program. It should be understood that the current program refers to a program currently being played or playing at the earliest time; the candidate program is a program having a playing time after the current program.
Illustratively, the playing time and the playing duration corresponding to each program in the program recommendation list may be acquired according to a program forecast in the television; and acquiring a channel corresponding to each program in the program recommendation list. The playing time, playing duration and channel corresponding to each program in the program recommendation list are shown in table 5.
Table 5 shows a program recommendation list
Program title Playing time Duration of play (minutes) Channel with a plurality of channels
A 19:00 120 CCTV-1 synthesis
B 20:30 60 CCTV-2 finance and economics
C 20:30 70 CCTV-5 sports
Specifically, the channel corresponding to the current program is determined according to the playing time corresponding to each program.
For example, the program with the earliest playing time may be determined as the current program. For example, if the playing time corresponding to the program A is the earliest, the program A is taken as the current program and the channel corresponding to the program A is determined to be CCTV-1 integrated.
In some embodiments, the program is played according to the channel corresponding to the current program, and the next channel is switched according to the playing duration corresponding to the current program.
Illustratively, if the channel corresponding to the current program is a CCTV-1 integrated channel, the program is played in the CCTV-1 integrated channel. For example, at 19:00, the a program starts playing.
It can be understood that, since the playing time duration corresponding to the current program is 120 minutes, after the current program is played, it is necessary to switch to the next channel to play the program. For example, after the a program is played, the channel corresponding to the B program or the C program is switched to play the program.
It should be noted that, when the current program is the program a, the candidate programs are the program B and the program C.
In some embodiments, during the playing of the current program, if the time reaches the playing time of the candidate program, the channel corresponding to the candidate program may be switched to.
For example, a program selection frame may be displayed according to the playing time corresponding to the candidate program in the program recommendation list, and if the confirmation operation of the user on the candidate program selection frame is obtained, the channel corresponding to the candidate program is switched from the channel corresponding to the current program.
The confirmation operation may be that the user performs selection through a remote controller or directly performs touch selection in a display screen of the television.
It can be understood that, when the program a is not played yet and the playing time corresponding to the program B or the program C is about to reach, a program selection box may be displayed in the display screen of the television set, so that the user may determine whether to switch to the channel corresponding to the program B or the program C.
By acquiring the playing time and the playing duration corresponding to each program in the program recommendation list, the channel corresponding to the current program can be determined and the program can be played, and the next channel can be switched to according to the playing duration corresponding to the current program, so that a proper program can be played for a user, and the experience of the user is improved.
In some examples, if there are programs with the same playing time in the program recommendation list, displaying a program selection frame to remind the user to select a playing program; and then determining the played program according to the selection operation of the user.
Wherein, the program selection frame comprises programs with the same playing time. For example, if the programs with the same playing time include a program B and a program C, two options of the program B and the program C are displayed in the program selection frame. The user can perform selection operation in the program selection frame. For example, if the user selects the program B, the user switches to the channel corresponding to the program B to play the program B.
When programs with the same playing time exist, the user can select the programs to be played by himself, and therefore the playing method is more humanized.
In other embodiments, if there are programs with the same playing time in the program recommendation list, the audience rating corresponding to the program with the same playing time is obtained, and the played program is determined according to the audience rating.
The audience rating is the percentage of the number of people (or the number of households) watching a certain television program in a certain period of time to the total number of television audiences (or the number of households).
It will be appreciated that the rating may reflect the popularity of the program to some extent. For example, when determining a program to be played according to the audience rating, a program with a higher audience rating may be played, so as to recommend popular programs to the user.
Exemplarily, if the programs with the same playing time include a program B and a program C, obtaining respective audience ratings of the program B and the program C; for example, the audience rating corresponding to the program B is B, and the audience rating corresponding to the program C is C. And if the audience rating B is greater than the audience rating c, determining that the played program is the program B and switching to a channel corresponding to the program B to play the program B.
In some embodiments, after the programs in the program recommendation list corresponding to the user group are played, the image acquired by the shooting device is acquired, and if the image does not have a user, the television is turned off. Fig. 7 is a schematic diagram of the user leaving the television set, as shown in fig. 7.
It should be noted that, if there is no user in the image captured by the shooting device, it indicates that the user has left the television or temporarily does not want to watch the program; at this time, the television can be turned off, and resource waste is avoided.
According to the program recommendation method provided by the embodiment, the predicted identity and the predicted probability of the user in the image can be determined by inputting the image into the trained face recognition model, so that the identity information of the user is determined, and the prediction accuracy can be effectively improved; the interesting programs of the user can be accurately determined by carrying out interest scoring according to the playing time length and the playing times of the user on various programs; according to the corresponding relation between the type corresponding to the interesting program and the user group, the user group corresponding to the user can be determined; by taking intersection or union of the interested programs of the users in the user group, the program recommendation list corresponding to the user group can be obtained, the programs in the program recommendation list are recommended to the users, the accuracy of recommending the programs is improved, and the program recommendation method is more intelligent; by acquiring the playing time and the playing duration corresponding to each program in the program recommendation list, the channel corresponding to the current program can be determined and the program can be played, and the next channel can be switched to according to the playing duration corresponding to the current program, so that a proper program can be played for a user, and the experience of the user is improved.
The embodiment of the application further provides a computer-readable storage medium, wherein a computer program is stored in the computer-readable storage medium, the computer program comprises program instructions, and the processor executes the program instructions to implement any program recommendation method provided by the embodiment of the application.
The computer-readable storage medium may be an internal storage unit of the television set described in the foregoing embodiment, for example, a hard disk or a memory of the television set. The computer readable storage medium may also be an external storage device of the television, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital Card (SD), a Flash memory Card (Flash Card), and the like, which are provided on the television.
While the invention has been described with reference to specific embodiments, the scope of the invention is not limited thereto, and those skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims (12)

1. A program recommendation method is applied to a television, and is characterized in that the television comprises a shooting device, and the method comprises the following steps:
acquiring an image acquired by the shooting device, wherein the image comprises at least one user;
determining the identity information of the user in the image based on the trained face recognition model, and acquiring historical program data of the user according to the identity information of the user;
determining interesting programs of the user according to the historical program data of the user, and determining a user group corresponding to the user;
obtaining interesting programs of users in the user group, and carrying out fusion processing on the interesting programs to obtain a program recommendation list corresponding to the user group;
and playing the programs according to the program recommendation list corresponding to the user group.
2. The method of claim 1, wherein the determining the identity information of the user in the image based on the trained face recognition model comprises:
inputting the image into the trained face recognition model, and outputting a prediction identity corresponding to a user in the image and a prediction probability corresponding to the prediction identity;
and if the prediction probability corresponding to the predicted identity is larger than a first preset threshold value, taking the predicted identity as the identity information of the user.
3. The program recommendation method of claim 1, wherein said historical program data comprises a length of play time and a number of plays;
the determining the interesting programs of the user according to the historical program data of the user and determining the user group corresponding to the user comprises the following steps:
according to the playing time and the playing times of the user on the multiple programs, carrying out interest scoring on the multiple programs to obtain the interest scores of the user on the multiple programs;
if the interest score of the user to the program is larger than a second preset threshold value, taking the program as the interest program of the user;
and determining a user group corresponding to the user according to the interesting program of the user based on a preset corresponding relation between the type corresponding to the interesting program and the user group.
4. The method of recommending program according to claim 1, wherein said fusing said programs of interest to obtain a program recommendation list corresponding to said user group comprises:
taking an intersection of the interested programs of all users in the user group to obtain a program recommendation list corresponding to the user group, wherein the program recommendation list comprises at least one program; or
And collecting the interested programs of all users in the user group to obtain a program recommendation list corresponding to the user group, wherein the program recommendation list comprises at least one program.
5. The program recommendation method of claim 1, further comprising:
if program recommendation lists corresponding to a plurality of user groups exist, displaying a list selection frame to remind the user to select the program recommendation lists;
and determining a program recommendation list to be played according to the selection operation of the user.
6. The program recommendation method of claim 1, wherein said program recommendation list comprises a current program and a candidate program; the playing the program according to the program recommendation list corresponding to the user group includes:
acquiring playing time and playing duration corresponding to each program in the program recommendation list;
determining a channel corresponding to the current program according to the playing time corresponding to each program;
and playing the program according to the channel corresponding to the current program, and switching to the next channel according to the playing duration corresponding to the current program.
7. The method of claim 6, wherein the playing the program according to the channel corresponding to the current program and switching to the next channel according to the playing duration corresponding to the current program comprises:
displaying a program selection frame according to the playing time corresponding to the candidate program in the program recommendation list;
and if the confirmation operation of the user on the candidate program selection frame is obtained, switching the channel corresponding to the candidate program from the channel corresponding to the current program.
8. The method of any one of claims 1 to 7, wherein the playing the programs in the program recommendation list corresponding to the user group comprises:
if the programs with the same playing time exist in the program recommendation list, displaying a program selection frame to remind the user of selecting the playing program;
and determining the played program according to the selection operation of the user.
9. The method of any one of claims 1 to 7, wherein the playing the programs in the program recommendation list corresponding to the user group comprises:
if the programs with the same playing time exist in the program recommendation list, acquiring the audience rating corresponding to the programs with the same playing time, and determining the played programs according to the audience rating.
10. The method of recommending program according to claim 1, further comprising, after said playing back the programs in the program recommendation list corresponding to said user group:
and acquiring the image acquired by the shooting device, and if the image does not have a user, closing the television.
11. A television set, characterized in that the television set comprises a camera, a memory and a processor;
the shooting device is used for acquiring images;
the memory is used for storing a computer program;
the processor for executing the computer program and implementing the program recommendation method according to any one of claims 1 to 10 when executing the computer program.
12. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, causes the processor to implement the program recommendation method according to any one of claims 1 to 10.
CN202010106804.5A 2020-02-20 2020-02-20 Program recommendation method, television and storage medium Pending CN113286199A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010106804.5A CN113286199A (en) 2020-02-20 2020-02-20 Program recommendation method, television and storage medium

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010106804.5A CN113286199A (en) 2020-02-20 2020-02-20 Program recommendation method, television and storage medium

Publications (1)

Publication Number Publication Date
CN113286199A true CN113286199A (en) 2021-08-20

Family

ID=77275339

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010106804.5A Pending CN113286199A (en) 2020-02-20 2020-02-20 Program recommendation method, television and storage medium

Country Status (1)

Country Link
CN (1) CN113286199A (en)

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030093329A1 (en) * 2001-11-13 2003-05-15 Koninklijke Philips Electronics N.V. Method and apparatus for recommending items of interest based on preferences of a selected third party
US20080216117A1 (en) * 2006-12-07 2008-09-04 Samsung Electronics Co., Ltd Method and apparatus for collecting user interest information
US20120167127A1 (en) * 2010-12-27 2012-06-28 Hiroaki Uchida Video Output Device and Video Output Method
CN104731954A (en) * 2015-04-01 2015-06-24 天翼爱音乐文化科技有限公司 Music recommendation method and system based on group perspective
US20160205438A1 (en) * 2013-08-30 2016-07-14 Lg Electronics Inc. Image display device and operation method thereof
US20170188103A1 (en) * 2015-12-29 2017-06-29 Le Holdings (Beijing) Co., Ltd. Method and device for video recommendation based on face recognition
WO2019134246A1 (en) * 2018-01-03 2019-07-11 平安科技（深圳）有限公司 Facial recognition-based security monitoring method, device, and storage medium
CN110717097A (en) * 2019-09-06 2020-01-21 中国平安财产保险股份有限公司 Service recommendation method and device, computer equipment and storage medium
US20200320646A1 (en) * 2018-04-26 2020-10-08 Tencent Technology (Shenzhen) Company Limited Interest recommendation method, computer device, and storage medium

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20030093329A1 (en) * 2001-11-13 2003-05-15 Koninklijke Philips Electronics N.V. Method and apparatus for recommending items of interest based on preferences of a selected third party
US20080216117A1 (en) * 2006-12-07 2008-09-04 Samsung Electronics Co., Ltd Method and apparatus for collecting user interest information
US20120167127A1 (en) * 2010-12-27 2012-06-28 Hiroaki Uchida Video Output Device and Video Output Method
US20160205438A1 (en) * 2013-08-30 2016-07-14 Lg Electronics Inc. Image display device and operation method thereof
CN104731954A (en) * 2015-04-01 2015-06-24 天翼爱音乐文化科技有限公司 Music recommendation method and system based on group perspective
US20170188103A1 (en) * 2015-12-29 2017-06-29 Le Holdings (Beijing) Co., Ltd. Method and device for video recommendation based on face recognition
WO2019134246A1 (en) * 2018-01-03 2019-07-11 平安科技（深圳）有限公司 Facial recognition-based security monitoring method, device, and storage medium
US20200320646A1 (en) * 2018-04-26 2020-10-08 Tencent Technology (Shenzhen) Company Limited Interest recommendation method, computer device, and storage medium
CN110717097A (en) * 2019-09-06 2020-01-21 中国平安财产保险股份有限公司 Service recommendation method and device, computer equipment and storage medium

Similar Documents

Publication Publication Date Title
CA2810511C (en) Smart media selection based on viewer user presence
CN107566907A (en) video clipping method, device, storage medium and terminal
CN109429103B (en) Method and device for recommending information, computer readable storage medium and terminal equipment
CN105045115A (en) Control method and intelligent household equipment
CN110324679B (en) Video data processing method and device
JP2008542870A (en) Method and apparatus for estimating the overall interest of a group of users for content
CN104994426A (en) Method and system of program video recognition
CN107888985A (en) Intelligent television commending system and method based on multi-user
CN107895161B (en) Real-time attitude identification method and device based on video data and computing equipment
CN108289251A (en) A kind of system and method for the self-defined plot of video display
US9930412B2 (en) Network set-top box and its operating method
US10629239B1 (en) Computing system with DVE template selection and video content item generation feature
WO2019001153A1 (en) Multi-viewer television program recommendation method and system
CN112330371A (en) AI-based intelligent advertisement pushing method, device, system and storage medium
CN112784069B (en) IPTV content intelligent recommendation system and method
WO2016206035A1 (en) Information recommendation method and user terminal
WO2014014720A1 (en) Intuitive image-based program guide for controlling display device such as a television
CN109769130A (en) Remote controler customized setting method and system based on rating big data
CN112312142B (en) Video playing control method and device and computer readable storage medium
CN110287934B (en) Object detection method and device, client and server
CN113286199A (en) Program recommendation method, television and storage medium
US11252375B2 (en) Electronic apparatus and control method thereof
CN112866793B (en) Media mode switching method, device, equipment and storage medium
CN108966007B (en) Method and device for distinguishing video scenes under HDMI
CN114339371A (en) Video display method, device, equipment and storage medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
RJ01 Rejection of invention patent application after publication

Application publication date: 20210820

RJ01 Rejection of invention patent application after publication