CN110598047A - Movie and television information recommendation method and device, electronic equipment and storage medium - Google Patents
Movie and television information recommendation method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN110598047A CN110598047A CN201910779316.8A CN201910779316A CN110598047A CN 110598047 A CN110598047 A CN 110598047A CN 201910779316 A CN201910779316 A CN 201910779316A CN 110598047 A CN110598047 A CN 110598047A
- Authority
- CN
- China
- Prior art keywords
- program
- movie
- video
- recommended
- list
- 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
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/73—Querying
- G06F16/735—Filtering based on additional data, e.g. user or group profiles
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/73—Querying
- G06F16/738—Presentation of query results
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/78—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/7867—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using information manually generated, e.g. tags, keywords, comments, title and artist information, manually generated time, location and usage information, user ratings
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
Abstract
The application provides a movie and television information recommendation method, a movie and television information recommendation device, electronic equipment and a storage medium. And then acquiring a program list to be recommended corresponding to the first movie label from a preset movie program database according to the first movie label, wherein the program list to be recommended is a list containing the movie programs to be recommended. And then calculating the recommendation index of the film and television programs to be recommended in the program list to be recommended through a preset algorithm. And finally, screening the video programs to be recommended according to the recommendation index to generate a video program recommendation list corresponding to the user and recommend video information to the user. The method has strong flexibility, can track the interest condition of the user at the current time point in time, improves the accuracy of recommendation information, and can quickly update the recommendation.
Description
Technical Field
The application belongs to the technical field of intelligent recommendation, and particularly relates to a movie and television information recommendation method and device, electronic equipment and a storage medium.
Background
With the development of internet technology and information-based services, watching film and television works becomes one of the relaxing ways that people usually choose to relax after working. However, the amount of network video data is increasing exponentially, so that the method brings abundant program resources to people and also brings confusion to people, and people are difficult to screen out the video work information required by people from mass data in a manual mode.
In the related art, the recommendation system is considered as one of the most effective methods for solving this problem. The prior recommendation method has the technical problems that the flexibility is lacked, the interest condition of the user at the current time point cannot be tracked in time, the accuracy of recommending information to the user is low, the updating speed is low, and the like.
Disclosure of Invention
In view of this, embodiments of the present application provide a method and an apparatus for recommending movie information, an electronic device, and a storage medium, so as to solve the problems that the prior art lacks flexibility and cannot track the interest situation of a user at a current time point in time, so that the accuracy of recommending information to the user is low and the updating speed is slow.
A first aspect of an embodiment of the present application provides a movie information recommendation method, where the movie information recommendation method includes:
acquiring portrait information of a user, wherein the portrait information comprises a first movie label corresponding to the user and acquired from historical behavior data;
acquiring a program list to be recommended corresponding to the first movie label from a preset movie program database according to the first movie label, wherein the program list to be recommended is a list containing movie programs to be recommended;
calculating the recommendation index of the film and television programs to be recommended in the program list to be recommended according to a preset algorithm;
and screening the video programs to be recommended according to the recommendation index to generate a video program recommendation list corresponding to the user and recommend video information to the user.
With reference to the first aspect, in a first possible implementation manner of the first aspect, before the step of acquiring, according to the first movie label, a to-be-recommended program list corresponding to the first movie label from a preset movie program database, the step of acquiring, by the to-be-recommended program list, a list including to-be-recommended movie programs further includes:
collecting program information, wherein each piece of program information comprises a video program and a second video label corresponding to the video program;
grouping the program information according to the second video label to generate a plurality of program lists divided by the second video label;
and importing the generated program lists divided by the video labels into a preset database to generate a video program database.
With reference to the first possible implementation manner of the first aspect, in a second possible implementation manner of the first aspect, the step of acquiring, according to the first movie label, a to-be-recommended program list corresponding to the first movie label from a preset movie program database, where the to-be-recommended program list is a list including to-be-recommended movie programs, further includes:
traversing the preset video program database according to the acquired first video label corresponding to the user;
and when a second video label consistent with the first video label is identified, acquiring a program list corresponding to the second video label from the preset video program database as a program list to be recommended.
With reference to the first aspect, in a third possible implementation manner of the first aspect, the step of calculating, according to a preset algorithm, a recommendation index of a movie program to be recommended in the program list to be recommended includes:
and counting the occurrence frequency of each video program in the program set, and taking the occurrence frequency of the video program as a recommendation index corresponding to the video program.
With reference to the first aspect, in a fourth possible implementation manner of the first aspect, the step of calculating a recommendation index of a movie program to be recommended in the program list to be recommended according to a preset algorithm includes:
and calculating the audience number and/or the audience number corresponding to each video program in the program set, and taking the audience number and/or the audience number corresponding to the video program as the recommendation index corresponding to the video program.
With reference to the fourth possible implementation manner of the first aspect, in a fifth possible implementation manner of the first aspect, for a video program that repeatedly appears in the program set, the number of viewing people and/or the number of viewing times corresponding to the video program are accumulated to serve as a recommendation index corresponding to the video program.
With reference to the first aspect, in a sixth possible implementation manner of the first aspect, before the step of filtering, according to the recommendation index, the movie program to be recommended to generate a movie program recommendation list corresponding to the user and recommending movie information to the user, the method further includes:
acquiring a historical watching record of the user;
and deleting the film and television programs watched by the user according to the historical watching records of the user, and recommending the film and television programs not watched by the user for the user.
A second aspect of the embodiments of the present application provides a movie information recommendation device, including:
the portrait information comprises a first movie label corresponding to a user, which is acquired from historical behavior data;
the second obtaining module is used for obtaining a program list to be recommended corresponding to the first movie label from a preset movie program database according to the first movie label, wherein the program list to be recommended is a list containing the movie programs to be recommended;
the processing module is used for calculating the recommendation index of the film and television programs to be recommended in the program list to be recommended according to a preset algorithm;
and the execution module is used for screening the video programs to be recommended according to the recommendation index so as to generate a video program recommendation list corresponding to the user and recommend video information to the user.
A third aspect of the embodiments of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the movie information recommendation method according to any one of the first aspect when executing the computer program.
A fourth aspect of the embodiments of the present application provides a computer-readable storage medium, where a computer program is stored, and the computer program, when executed by a processor, implements the steps of the movie information recommendation method according to any one of the first aspect.
Compared with the prior art, the embodiment of the application has the advantages that:
the method comprises the steps of giving a movie program label attribute to a recommendation system, obtaining a first movie label which is interested by a user recently by analyzing historical behavior data of the user, further obtaining a corresponding program list to be recommended which is possibly interested by the user from a movie program database according to the first movie label, further carrying out recommendation index analysis on movie programs in the program lists, and then carrying out movie information recommendation on the user according to the recommendation index. The method has strong flexibility, can track the interest condition of the user at the current time point in time, improves the accuracy of information recommendation, and can quickly update the recommendation.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only 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 inventive exercise.
Fig. 1 is a schematic flowchart of a basic method of a movie information recommendation method according to an embodiment of the present application;
fig. 2 is a schematic flowchart of a method for establishing a movie program database in a method for recommending movie information according to an embodiment of the present application;
fig. 3 is a schematic flow chart of a method for acquiring a list of programs to be recommended in a movie information recommendation method according to an embodiment of the present application;
fig. 4 is a schematic flowchart of a method for recommending unviewed video programs for a user in a video information recommendation method according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a movie information recommendation device according to an embodiment of the present application;
fig. 6 is a schematic view of an electronic device implementing a movie information recommendation method according to an embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
In order to explain the technical solution described in the present application, the following description will be given by way of specific examples.
The movie information recommendation method provided by the embodiment of the application includes but is not limited to being applied to an integrated broadcast control platform, an IPTV system, an OTT internet video system or an interactive digital television system, and the application mode includes but is not limited to providing on-demand program recommendation for a client in an EPG electronic program list.
Referring to fig. 1, fig. 1 is a schematic flowchart illustrating a basic method of a movie information recommendation method according to an embodiment of the present application, which is detailed as follows:
in step S101, portrait information of a user is obtained, wherein the portrait information includes a first movie label corresponding to the user obtained from historical behavior data.
In some embodiments of the present application, the portrait information of the user is information representing interests and hobbies of the user, and in this embodiment, the portrait information of the user is embodied as a movie label corresponding to a movie program obtained from historical behavior data of the user. For a video program, its corresponding video tag may include tags of actors, directors, keywords, year, etc. of the video program. Moreover, a mapping relationship is set between the movie label of the movie program and the program identifier of the movie program, that is, the movie label corresponding to the movie program can be obtained according to the program identifier of the movie program. In this embodiment, the historical behavior data is historical viewing records of all users stored by the recommendation system, where one historical viewing record corresponds to one video program. The historical watching records comprise program identifiers of the film and television programs in the system, user identifiers for watching the film and television programs and starting time for watching the film and television programs.
In this embodiment, the grouping list is formed by grouping the historical viewing records of all users stored in the recommendation system according to the user identifiers, so that each user corresponds to one grouping list. And for the historical watching records in the same grouping list, performing descending sequencing according to the starting time of watching the film and television programs to generate a watching program sequence of the user. And then, according to the program identification in the viewed program sequence, acquiring a video label which has a mapping relation with the program identification as a first video label corresponding to the user.
For example, the recommendation system may store the following historical viewing records:
user U1; viewing start time 17:35, 2019/7/31; the viewed program identification P1.
User U3; viewing start time 17:31, 2019/7/29; the viewed program identification P3.
User U4; viewing start time 15:22, 2019/7/28; the viewed program identification P2.
User U2; viewing start time 14:04, 2019/7/25; the viewed program identification P5.
User U1; viewing start time 12:46, 2019/7/25; the viewed program identification P2.
User U3; viewing start time 10:35, 2019/7/21; the viewed program identification P5.
User U4; viewing start time 20:17, 2019/7/19; the viewed program identification P1.
User U3; viewing start time 18:15, 2019/7/19; the viewed program identification P2.
User U1; viewing start time 11:58, 2019/7/16; the viewed program identification P3.
User U2; viewing start time 16:56, 2019/7/08; the viewed program identification P4.
According to the historical watching records, at this time, the sequence of the watching programs of the user is generated by sequencing the starting time of watching the video programs in a descending order as follows:
user U1: p1, P2, P3.
User U2: p5, P4.
User U3: p3, P5, P2.
User U4: p2, P1.
In the recommendation system, the information of the related video programs is collected, and the information specifically includes the following video tags:
p1: an actor A, B, C; a director H, J; keywords W1, W2, W3.
P2: an actor B, D, F; a director K, H; keywords W2, W3.
P3: an actor C, E; a director L; keywords W4, W1, W2.
P4: an actor B, F; a director J, K; keywords W2, W4.
P5: an actor A, E; a director L, H; keywords W1, W3, W4.
Therefore, taking user U1 as an example, acquiring, according to the program identifier in the viewing program sequence, a video tag having a mapping relationship with the program identifier as a first video tag corresponding to the user includes: A. b, C, H, J, W1, W2, W3, B, D, F, K, H, W2, W3, C, E, L, W4, W1 and W2.
In some embodiments of the present application, the viewing program sequence of the user may be a video program corresponding to n (n is a positive integer) historical viewing records with the latest retention time, or a video program corresponding to all historical viewing records.
In some embodiments of the present application, the sequence of programs watched by the user may be sorted in a descending order according to the time length of the program watched by the user. In this case, if only the n (n is a positive integer) video programs corresponding to the history viewing records are reserved, the n video programs that have the longest viewing time for the user are obtained.
In step S102, a to-be-recommended program list corresponding to the first movie label is acquired from a preset movie program database according to the first movie label, where the to-be-recommended program list is a list including to-be-recommended movie programs.
In some embodiments of the present application, please refer to fig. 2, and fig. 2 is a schematic flow chart of a method for establishing a movie program database in a movie information recommendation method according to an embodiment of the present application. The details are as follows:
in step S201, collecting program information, where each piece of program information includes a video program and a second video tag corresponding to the video program;
in step S202, grouping the program information according to the second video tag to generate a plurality of program lists divided by the second video tag;
in step S203, the generated program lists divided by the second video tags are imported into a preset database to generate a video program database.
In this embodiment, a movie program database is preset in the recommendation system, and is configured to store collected program information, where the collected program information is program information that can be used as a recommended program, each piece of program information includes movie labels of actors, directors, keywords, and the like of the program, and the movie label included in each piece of program information in the collected program information is marked as a second movie label. In this embodiment, the program information collected by the movie program database may be locally stored program information or program information acquired in real time through an access network.
In this embodiment, a plurality of program lists divided by the second video tags are generated by grouping the collected program information according to the second video tags, where one second video tag corresponds to one program list. And then, importing the generated program lists divided by the second video label into a preset database to generate a video program database, so that the collected program information is stored in the preset video program database in the form of a plurality of program lists. For example, five programs, i.e., P1, P2, P3, P4 and P5, listed in step S101, may be obtained by grouping the program information in the movie program database according to the second movie label:
A：P1、P5。
B：P2、P1、P4。
C：P1、P3。
D：P2。
E：P3、P5。
F：P2、P4。
H：P1、P5、P4。
J：P1、P4。
K：P2、P4。
L：P3、P5。
W1：P1、P3、P5。
W2：P2、P1、P3。
W3：P2、P1、P5。
W4：P3、P4、P5。
therefore, according to the first movie label corresponding to the user obtained in step S101, the program list to be recommended corresponding to the first movie label can be obtained from the movie program database. Referring to fig. 3 in detail, fig. 3 is a schematic flow chart of a method for obtaining a list of programs to be recommended in a movie information recommendation method according to an embodiment of the present application. The details are as follows:
in step S301, traversing the preset movie program database according to the acquired first movie label corresponding to the user;
in step S302, when a second video tag consistent with the first video tag is identified, a program list corresponding to the second video tag is acquired from the preset video program database as a program list to be recommended.
In this embodiment, the storage form of the video programs in the preset video program database is that one second video tag corresponds to one program list, that is, mapping relationships are set in the preset video program database in a one-to-one correspondence manner between the second video tag and the program list divided by the second video tag. At this time, traversing the preset video program database according to the acquired first video label corresponding to the user to identify a second video label consistent with the first video label from the preset video program database, and then obtaining a program list corresponding to the second video label as a program list to be recommended according to a mapping relation between the second video label and the program list.
In some embodiments of the present application, after grouping all the video programs according to the second video label in the video program database to form a plurality of program lists, it is also possible to correlate historical viewing records of all users previously maintained by the recommender system, for each program list, counting the number of viewers and/or the number of viewers of each video program in the program list, and then sorting in descending order according to the counted number of the audience and/or the number of the audience, if the program list has more film and television programs, when the program list to be recommended corresponding to the first movie label is acquired from the movie program database according to the first movie label, and only the first M (M is a positive integer) video programs sorted in the descending order of the program list can be acquired as the program list to be recommended in an intercepting manner. The step specifically analyzes the relevance between each video program in the program list to be recommended and the user according to the historical watching records of the user, so that recommendation information is reduced, and recommendation efficiency and recommendation accuracy are improved.
In step S103, a recommendation index of a to-be-recommended movie program in the to-be-recommended program list is calculated according to a preset algorithm.
In some embodiments of the present application, since at least one first video tag corresponding to a user can be obtained from historical behavior data, usually a plurality of first video tags are obtained, at least one program list to be recommended, which is obtained from the video program database, is also at least one program list to be recommended, and usually a plurality of program lists to be recommended. Specifically, in the recommendation system, an empty preselected program set is set for the user in advance, all the obtained video programs in each list to be recommended are stored in the preselected program set, and then statistical calculation is performed on the video programs stored in the program set according to a preset algorithm so as to calculate the recommendation index of the video programs to be recommended. In this embodiment, the preset algorithm may be to count the occurrence frequency of each video program in the program set.
For example, taking the user U1 as an example, obtaining, according to the program identifier in the viewing program sequence, a video tag having a mapping relationship with the program identifier as a first video tag corresponding to the user includes: A. b, C, H, J, W1, W2, W3, B, D, F, K, H, W2, W3, C, E, L, W4, W1 and W2. If the preset algorithm is to count the occurrence frequency of each video program in the program set, at this time, statistics is performed according to the program list obtained by grouping according to the second video label in step S102, and it can be obtained that the recommendation index corresponding to each video program is as follows: p1: 15 times; p2: 9 times; p3: 10 times; p4: 7 times; p5: 10 times.
In some embodiments of the present application, the preset algorithm may further be configured to calculate the number of viewers and/or the number of viewing times corresponding to each video program in the program set. And when the number of the viewers and/or the number of the viewers corresponding to each video program in the program set are calculated, if the video programs which repeatedly appear exist in the program set, the number of the viewers and/or the number of the viewers corresponding to the video programs are accumulated to be used as the recommendation index corresponding to the video programs.
In step S104, the to-be-recommended movie programs are screened according to the recommendation index to generate a movie program recommendation list corresponding to the user and to recommend movie information to the user.
In some embodiments of the application, after the recommendation index of each to-be-recommended movie program in the to-be-recommended program list is calculated, a recommendation index threshold value may be preset in a recommendation system, the recommendation index of the to-be-recommended movie program is compared with a preset recommendation index threshold value, and then the movie programs meeting the preset recommendation index threshold value are screened out, so that a movie program recommendation list is generated and recommended to a user.
In some embodiments of the application, after the recommendation index of each to-be-recommended movie program in the to-be-recommended program list is calculated, the recommended movie programs may be sorted in a descending order according to the recommendation index from high to low, and at this time, the program list generated through the descending order is the movie program recommendation list corresponding to the user. Further, in some embodiments of the application, a part of the movie programs in the movie program recommendation list can be obtained in an intercepting manner and fed back to the user as recommended programs, so that the situation that the recommendation effect cannot be achieved due to too much recommendation information and the user experience is influenced is avoided. The program number of the partial film and television programs may be a number customized by a user or a number default by a recommendation system.
For example, as shown in the above example in step S103, the recommendation index corresponding to each video program is statistically obtained as: p1: 15 times; p2: 9 times; p3: 10 times; p4: 7 times; p5: 10 times. At this time, the result of the recommended list of the film and television programs obtained by descending order is as follows: p1, P3, P5, P2 and P4. If the recommendation number is limited to 3, the recommendation system recommends the programs P1, P3 and P5 to the user U1.
In some embodiments of the present application, please refer to fig. 4, and fig. 4 is a flowchart illustrating a method for recommending unviewed tv programs for a user in a tv information recommendation method according to an embodiment of the present application. The details are as follows:
in step S401, a history viewing record of the user is acquired;
in step S402, the program watched by the user is deleted according to the historical watching record of the user, so as to recommend the non-watched program for the user.
In this embodiment, before generating the recommendation list of video programs corresponding to the user, the historical viewing record of the user is obtained from the historical behavior data stored in the recommendation system, and then the video programs watched by the user are deleted according to the historical viewing record of the user, so that the recommendation list of video programs recommended to the user only contains video programs that the user has not watched, thereby improving the effectiveness of recommendation information.
In the method for recommending movie information provided in the above embodiment, the attribute of the movie program label is given to the recommendation system, then the movie label that the user is interested in the near term is obtained by analyzing the historical behavior data of the user, and then the corresponding program list that the user may be interested in is obtained from the movie program database according to the movie label, and further the recommendation index analysis is performed on the movie programs in the program lists, and then the movie information recommendation is performed on the user according to the recommendation index. The method has strong process flexibility, can track the interest condition of the user at the current time point in time, improves the accuracy of information recommendation, and can quickly update the recommendation.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
In some embodiments of the present application, please refer to fig. 5, and fig. 5 is a schematic structural diagram of a movie information recommendation device according to an embodiment of the present application, which is detailed as follows:
the movie information recommendation device comprises: a first obtaining module 501, a second obtaining module 502, a processing module 503, and an executing module 504. The first obtaining module 501 is configured to obtain portrait information of a user, where the portrait information includes a first movie label corresponding to the user, which is obtained from historical behavior data; the second obtaining module 502 is configured to obtain, according to the first movie label, a to-be-recommended program list corresponding to the first movie label from a preset movie program database, where the to-be-recommended program list is a list including movie programs to be recommended; the processing module 503 is configured to calculate a recommendation index of a to-be-recommended movie program in the to-be-recommended program list according to a preset algorithm; the execution module 504 is configured to filter the to-be-recommended movie programs according to the recommendation index, so as to generate a movie program recommendation list corresponding to the user and recommend movie information to the user.
The movie information recommendation device corresponds to the movie information recommendation method one by one.
In some embodiments of the present application, please refer to fig. 6, and fig. 6 is a schematic diagram of an electronic device implementing a movie information recommendation method according to an embodiment of the present application. As shown in fig. 6, the electronic apparatus 6 of this embodiment includes: a processor 60, a memory 61 and a computer program 62, such as a movie information recommendation program, stored in said memory 61 and operable on said processor 60. The processor 60 executes the computer program 62 to implement the steps in the above-mentioned embodiments of the movie information recommendation method. Alternatively, the processor 60 implements the functions of the modules/units in the above-described device embodiments when executing the computer program 62.
Illustratively, the computer program 62 may be partitioned into one or more modules/units that are stored in the memory 61 and executed by the processor 60 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 62 in the electronic device 6. For example, the computer program 62 may be divided into:
the portrait information comprises a first movie label corresponding to a user, which is acquired from historical behavior data;
the second obtaining module is used for obtaining a program list to be recommended corresponding to the first movie label from a preset movie program database according to the first movie label, wherein the program list to be recommended is a list containing the movie programs to be recommended;
the processing module is used for calculating the recommendation index of the film and television programs to be recommended in the program list to be recommended according to a preset algorithm;
and the execution module is used for screening the video programs to be recommended according to the recommendation index so as to generate a video program recommendation list corresponding to the user and recommend video information to the user.
The electronic device may include, but is not limited to, a processor 60, a memory 61. Those skilled in the art will appreciate that fig. 6 is merely an example of an electronic device 6, and does not constitute a limitation of the electronic device 6, and may include more or fewer components than shown, or some components in combination, or different components, e.g., the electronic device may also include input-output devices, network access devices, buses, etc.
The Processor 60 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 61 may be an internal storage unit of the electronic device 6, such as a hard disk or a memory of the electronic device 6. The memory 61 may also be an external storage device of the electronic device 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the electronic device 6. Further, the memory 61 may also include both an internal storage unit and an external storage device of the electronic device 6. The memory 61 is used for storing the computer program and other programs and data required by the electronic device. The memory 61 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. . Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain other components which may be suitably increased or decreased as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media which may not include electrical carrier signals and telecommunications signals in accordance with legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (10)
1. A movie information recommendation method is characterized by comprising the following steps:
acquiring portrait information of a user, wherein the portrait information comprises a first movie label corresponding to the user and acquired from historical behavior data;
acquiring a program list to be recommended corresponding to the first movie label from a preset movie program database according to the first movie label, wherein the program list to be recommended is a list containing movie programs to be recommended;
calculating the recommendation index of the film and television programs to be recommended in the program list to be recommended according to a preset algorithm;
and screening the video programs to be recommended according to the recommendation index to generate a video program recommendation list corresponding to the user and recommend video information to the user.
2. The method for recommending movie information according to claim 1, wherein before the step of acquiring a list of programs to be recommended corresponding to the first movie label from a preset movie program database according to the first movie label, the list of programs to be recommended is a list including the movie programs to be recommended, the method further comprises:
collecting program information, wherein each piece of program information comprises a video program and a second video label corresponding to the video program;
grouping the program information according to the second video label to generate a plurality of program lists divided by the second video label;
and importing the generated program lists divided by the video labels into a preset database to generate a video program database.
3. The method for recommending movie information according to claim 2, wherein the step of obtaining a list of programs to be recommended corresponding to the first movie label from a preset movie program database according to the first movie label, wherein the list of programs to be recommended is a list including the movie programs to be recommended, further comprises:
traversing the preset video program database according to the acquired first video label corresponding to the user;
and when a second video label consistent with the first video label is identified, acquiring a program list corresponding to the second video label from the preset video program database as a program list to be recommended.
4. The method for recommending movie information according to claim 1, wherein said step of calculating a recommendation index of a movie program to be recommended in said list of programs to be recommended according to a preset algorithm comprises:
and counting the occurrence frequency of each video program in the program set, and taking the occurrence frequency of the video program as a recommendation index corresponding to the video program.
5. The method for recommending movie information according to claim 1, wherein said step of calculating a recommendation index of a movie program to be recommended in said list of programs to be recommended according to a preset algorithm comprises:
and calculating the audience number and/or the audience number corresponding to each video program in the program set, and taking the audience number and/or the audience number corresponding to the video program as the recommendation index corresponding to the video program.
6. The method of claim 5, wherein for the video programs that occur repeatedly in the program set, the recommendation index corresponding to the video program is obtained by accumulating the number of viewers and/or the number of viewing times corresponding to the video programs.
7. The method for recommending movie information according to claim 1, wherein before the step of filtering the movie programs to be recommended according to the recommendation index to generate a movie program recommendation list corresponding to the user and recommending movie information to the user, the method further comprises:
acquiring a historical watching record of the user;
and deleting the film and television programs watched by the user according to the historical watching records of the user, and recommending the film and television programs not watched by the user for the user.
8. A movie information recommendation device, comprising:
the portrait information comprises a first movie label corresponding to a user, which is acquired from historical behavior data;
the second obtaining module is used for obtaining a program list to be recommended corresponding to the first movie label from a preset movie program database according to the first movie label, wherein the program list to be recommended is a list containing the movie programs to be recommended;
the processing module is used for calculating the recommendation index of the film and television programs to be recommended in the program list to be recommended according to a preset algorithm;
and the execution module is used for screening the video programs to be recommended according to the recommendation index so as to generate a video program recommendation list corresponding to the user and recommend video information to the user.
9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the movie information recommendation method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, which stores a computer program, wherein the computer program, when executed by a processor, implements the steps of the movie information recommendation method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910779316.8A CN110598047A (en) | 2019-08-22 | 2019-08-22 | Movie and television information recommendation method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910779316.8A CN110598047A (en) | 2019-08-22 | 2019-08-22 | Movie and television information recommendation method and device, electronic equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110598047A true CN110598047A (en) | 2019-12-20 |
Family
ID=68855140
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910779316.8A Pending CN110598047A (en) | 2019-08-22 | 2019-08-22 | Movie and television information recommendation method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110598047A (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111669654A (en) * | 2020-06-15 | 2020-09-15 | 未来电视有限公司 | Program recommendation method and device, electronic equipment and readable storage medium |
| CN111935204A (en) * | 2020-06-11 | 2020-11-13 | 杭州情咖网络技术有限公司 | Program recommendation method and device and electronic equipment |
| CN112015736A (en) * | 2020-08-21 | 2020-12-01 | 广州欢网科技有限责任公司 | Spark Mllib-based multifunctional recommendation method and device |
| CN112040318A (en) * | 2020-08-21 | 2020-12-04 | 广州欢网科技有限责任公司 | Personalized program recommendation method and device, storage medium and electronic equipment |
| CN113010794A (en) * | 2021-04-12 | 2021-06-22 | 北京明略软件系统有限公司 | Method and device for information recommendation, electronic equipment and storage medium |
| CN114093091A (en) * | 2021-11-30 | 2022-02-25 | 惠州市鑫吉顺电子实业有限公司 | Sharing exchange method, system and storage medium based on sharing lease equipment |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1600022A (en) * | 2001-11-30 | 2005-03-23 | 皇家飞利浦电子股份有限公司 | Media recommender which presents the user with rationale for the recommendation |
| US20160088358A1 (en) * | 2014-09-22 | 2016-03-24 | Echostar Technologies L.L.C. | Scheduled programming recommendation system |
| CN108363730A (en) * | 2018-01-12 | 2018-08-03 | 优地网络有限公司 | A kind of content recommendation method, system and terminal device |
| CN109246450A (en) * | 2018-08-06 | 2019-01-18 | 上海大学 | A kind of video display preferentially recommender system and method based on implicit information scoring |
-
2019
- 2019-08-22 CN CN201910779316.8A patent/CN110598047A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1600022A (en) * | 2001-11-30 | 2005-03-23 | 皇家飞利浦电子股份有限公司 | Media recommender which presents the user with rationale for the recommendation |
| US20160088358A1 (en) * | 2014-09-22 | 2016-03-24 | Echostar Technologies L.L.C. | Scheduled programming recommendation system |
| CN108363730A (en) * | 2018-01-12 | 2018-08-03 | 优地网络有限公司 | A kind of content recommendation method, system and terminal device |
| CN109246450A (en) * | 2018-08-06 | 2019-01-18 | 上海大学 | A kind of video display preferentially recommender system and method based on implicit information scoring |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111935204A (en) * | 2020-06-11 | 2020-11-13 | 杭州情咖网络技术有限公司 | Program recommendation method and device and electronic equipment |
| CN111669654A (en) * | 2020-06-15 | 2020-09-15 | 未来电视有限公司 | Program recommendation method and device, electronic equipment and readable storage medium |
| CN112015736A (en) * | 2020-08-21 | 2020-12-01 | 广州欢网科技有限责任公司 | Spark Mllib-based multifunctional recommendation method and device |
| CN112040318A (en) * | 2020-08-21 | 2020-12-04 | 广州欢网科技有限责任公司 | Personalized program recommendation method and device, storage medium and electronic equipment |
| CN112015736B (en) * | 2020-08-21 | 2024-04-05 | 广州欢网科技有限责任公司 | Multi-functional recommendation method and device based on Spark Mllib |
| CN113010794A (en) * | 2021-04-12 | 2021-06-22 | 北京明略软件系统有限公司 | Method and device for information recommendation, electronic equipment and storage medium |
| CN114093091A (en) * | 2021-11-30 | 2022-02-25 | 惠州市鑫吉顺电子实业有限公司 | Sharing exchange method, system and storage medium based on sharing lease equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110598047A (en) | Movie and television information recommendation method and device, electronic equipment and storage medium | |
| CN108108491B (en) | Multimedia data recommendation method and device | |
| CN105245958B (en) | Live television program recommendation method and device | |
| CN103686237B (en) | Recommend the method and system of video resource | |
| CN109429103B (en) | Method and device for recommending information, computer readable storage medium and terminal equipment | |
| US8875186B2 (en) | Apparatus and method of providing a recommended broadcast program | |
| CN104053023B (en) | A kind of method and device of determining video similarity | |
| US9615117B2 (en) | Content recording and storage allocation | |
| CN103763585A (en) | User characteristic information obtaining method and device and terminal device | |
| CN108600820A (en) | The system and method for video flowing for rendering | |
| CN112235614B (en) | Personalized recommendation method and system for ranking list data of television programs | |
| CN103428541B (en) | Method and system for integrating social media and broadcast data | |
| CN111767430B (en) | Video resource pushing method, video resource pushing device and storage medium | |
| CN105812937A (en) | Television program recommending method and television program recommending device | |
| CN108197336B (en) | Video searching method and device | |
| CN111385606A (en) | Video preview method and device and intelligent terminal | |
| CN108476344A (en) | The content selection of networked media device | |
| CN114071237A (en) | Intelligent television personalized topic recommendation method based on user portrait | |
| CN109348261A (en) | Data statistical approach, device and electronic equipment in a kind of live streaming | |
| CN106534984B (en) | Television program pushing method and device | |
| CN110225374B (en) | User information collecting and processing method for interactive network television | |
| CN105956061B (en) | Method and device for determining similarity between users | |
| CN109063080B (en) | Video recommendation method and device | |
| CN103916705A (en) | Method and device for achieving mosaic navigation at electronic terminal | |
| CN109729428B (en) | Television channel video audience statistical method and device |
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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191220 |