CN113949931A - IPTV program recommendation method and system - Google Patents
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- 238000012163 sequencing technique Methods 0.000 claims abstract description 4
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- 238000013077 scoring method Methods 0.000 claims description 3
- 238000005516 engineering process Methods 0.000 description 5
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- 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
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- 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
- H04N21/4508—Management of client data or end-user data
- H04N21/4532—Management of client data or end-user data involving end-user characteristics, e.g. viewer profile, preferences
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- 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
- H04N21/4668—Learning process for intelligent management, e.g. learning user preferences for recommending movies for recommending content, e.g. movies
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- 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
- H04N21/47—End-user applications
- H04N21/482—End-user interface for program selection
- H04N21/4826—End-user interface for program selection using recommendation lists, e.g. of programs or channels sorted out according to their score
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- 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]
- H04N21/60—Network structure or processes for video distribution between server and client or between remote clients; Control signalling between clients, server and network components; Transmission of management data between server and client, e.g. sending from server to client commands for recording incoming content stream; Communication details between server and client
- H04N21/63—Control signaling related to video distribution between client, server and network components; Network processes for video distribution between server and clients or between remote clients, e.g. transmitting basic layer and enhancement layers over different transmission paths, setting up a peer-to-peer communication via Internet between remote STB's; Communication protocols; Addressing
- H04N21/643—Communication protocols
- H04N21/64322—IP
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- Two-Way Televisions, Distribution Of Moving Picture Or The Like (AREA)
Abstract
The application relates to an IPTV program recommendation method and a system; before a user enters a film viewing page, identifying the identity of the user; after the identity of a user is identified, acquiring a first viewing characteristic program list of the user; matching a plurality of similar users according to the first film watching characteristic program list; collecting a second viewing characteristic program list of each similar user; obtaining a program set to be recommended according to the second viewing characteristic program lists and the first viewing characteristic program list; carrying out quantitative scoring on the programs in the program set to be recommended to obtain the scores of the programs in the program set to be recommended; sequencing the programs in the program set to be recommended according to the sequence of scores from large to small; and collecting programs before the preset sequence, and displaying in a film viewing page. The method and the device have the effects of recommending the programs meeting the user interests and improving the use experience of the user.
Description
Technical Field
The present application relates to the field of multimedia data processing, and in particular, to a method and system for recommending an IPTV program.
Background
With the continuous development of internet and broadcast television networks, IPTV, i.e. network television with interactive on-demand function, gradually enters people's lives. The IPTV organically combines digital technology, computer technology, internet technology and broadcast television technology into one platform, and provides a variety of network services such as digital broadcast television, interactive entertainment platform, information platform and electronic commerce to users.
With the development of internet technologies, a large number of programs are introduced into IPTV. The traditional method for playing programs according to the program playing list preset by the television station cannot meet the use requirements of users and cannot recommend programs meeting the interests of the users.
Disclosure of Invention
In order to recommend programs meeting the user interest and improve the use experience of the user, the application provides an IPTV program recommendation method and system.
In a first aspect, the present application provides an IPTV program recommendation method, which adopts the following technical solutions:
an IPTV program recommendation method comprises the following steps:
before the user enters the film viewing page, identifying the identity of the user;
after the identity of a user is identified, acquiring a first viewing characteristic program list of the user;
matching a plurality of similar users according to the first film watching characteristic program list;
collecting a second viewing characteristic program list of each similar user;
obtaining a program set to be recommended according to the second viewing characteristic program lists and the first viewing characteristic program list;
carrying out quantitative scoring on the programs in the program set to be recommended to obtain the scores of the programs in the program set to be recommended;
sequencing the programs in the program set to be recommended according to the sequence of scores from large to small; and the number of the first and second groups,
and collecting programs before the preset sequence, and displaying in a film viewing page.
By adopting the technical scheme, the similar users and the users using the method have great similarity in viewing habits, the programs played by the similar users have a greater probability of being the programs in accordance with the interests of the users, the programs watched by the similar users are recommended for the users using the method, the possibility that the recommended programs are in accordance with the interests of the users can be improved, meanwhile, the programs in the program set to be recommended are quantitatively scored, and the programs in the program set to be recommended are sequenced according to the sequence of the scores from large to small; the programs before the preset sequence are collected and displayed in the film viewing page, so that the programs meeting the user interest are recommended, and the use experience of the user is improved.
Optionally, the acquiring the first viewing characteristic program list of the user includes the following steps:
sequentially collecting programs belonging to a preset time period in the film watching record of the user from back to front according to the time sequence; the duration of the preset time period is fixed, and one endpoint of the preset time period is the current moment; and the number of the first and second groups,
judging whether the number of the programs collected in the preset time period reaches a first preset number a or not; if yes, collecting a first preset number a of programs from back to front according to the time sequence to generate a first viewing characteristic program list; and if not, additionally collecting a plurality of programs with the highest playing quantity in the preset time period from the server until the quantity of the collected programs reaches a first preset quantity a, and generating a first viewing characteristic program list from the collected programs.
By adopting the technical scheme, the recent program preference of the user can be more clearly known by collecting the programs watched by the user in the preset time period; the method comprises the steps of judging the number of the collected programs in a preset time period, and avoiding the possibility that the programs recommended for a user do not meet the requirements of the user due to the fact that the number of the collected programs is too small; the programs with the highest playing amount in the preset time period are supplemented by supplementing and collecting the programs with the highest playing amount in the preset time period, so that the generated first viewing characteristic program list is more in line with the preference of the user.
Optionally, the step of matching a plurality of similar users according to the first viewing characteristics program list includes the following steps:
acquiring the film watching records of other users stored in the server, and acquiring a programs in the film watching records of the other users from back to front according to the time sequence to be used as a program set to be judged;
judging whether the number of the programs in the first viewing characteristic program list and the program set to be judged, which are the same, reaches a second preset number b; if yes, the user is marked as a similar user; and the number of the first and second groups,
judging whether the number of the similar users reaches a third preset number c; if yes, the film watching records of other users stored in the server are stopped being collected.
By adopting the technical scheme, the similar users and the users using the method have great similarity in film watching habits, and programs can be recommended to the users more accurately by matching a plurality of similar users.
Optionally, the obtaining of the to-be-recommended program set includes the following steps:
obtaining all programs in the second viewing characteristic program lists according to the second viewing characteristic program lists; and the number of the first and second groups,
and filtering out the programs which are coincided with the first viewing characteristic program in all the programs, and reserving one of the parts which are coincided with the same program in all the programs to obtain the program set to be recommended.
By adopting the technical scheme. The program played by the similar user has a higher probability of being the program according with the user interest; and obtaining all programs in the second viewing characteristic program lists according to the second viewing characteristic program lists, wherein all the programs comprise parts which are overlapped with the program set to be judged and the condition that a plurality of programs are overlapped, and the programs are filtered to avoid recommending repeated programs to the user.
Optionally, the performing quantitative scoring on the programs in the set of programs to be recommended to obtain the score of the programs in the set of programs to be recommended includes the following steps:
collecting the total playing time of the same program in a program set to be recommended by a plurality of similar users;
obtaining the average playing time length of the same program based on the total playing time length;
obtaining the proportion of the average playing time length in the total time length based on the average playing time length and the total time length of the same program; and the number of the first and second groups,
a first score for the program is derived based on the ratio.
By adopting the technical scheme, in the process of playing programs by similar users, clicking a certain program is easy to occur, but the program is not a program which is interested by the program, after the user exits from the playing interface of the program, the occupation ratio of the playing time length of the program in the total time length is smaller than that of the program which is interested by the user, a first score is obtained by the average playing time length of the same program and the total time length of the same program, and the first score is used as a part of the quantitative score, so that whether the program is favored by the user can be better judged.
Optionally, the quantitative scoring method further includes:
classifying the current time into one of a morning time period, an afternoon time period and an evening time period according to the time period of the current time; and the number of the first and second groups,
and obtaining a second score of the same program based on the playing time period of the same program and the current time period of the same program played by different similar users.
By adopting the technical scheme, the programs which are favored by the user in different time periods are different, and the programs can be better recommended to the user according to the playing time of the programs.
Optionally, the identifying the user includes verifying an account of the user and/or identifying the user identity through an identity identification device.
In a second aspect, the present application provides an IPTV program recommendation system, which adopts the following technical solutions.
An IPTV program recommendation system comprising:
the identity recognition module is used for carrying out identity recognition on the user before the user enters the film viewing page;
the first acquisition module is used for acquiring a first film watching characteristic program list of a user;
the similar user matching module is used for matching a plurality of similar users according to the first film watching characteristic program list;
the second acquisition module is used for acquiring a second viewing characteristic program list of each similar user;
the to-be-recommended program set generating module is used for obtaining a to-be-recommended program set according to the plurality of second viewing characteristic program lists and the first viewing characteristic program list of the user;
the quantitative scoring module is used for performing quantitative scoring on the programs in the to-be-recommended program set to obtain the scores of the programs in the to-be-recommended program set;
the sorting module sorts the programs in the program set to be recommended according to the order of scores from large to small; and the number of the first and second groups,
and the display module is used for collecting programs before the preset sequence and displaying the programs in the film viewing page. .
Optionally, the first acquisition module includes:
the first acquisition subunit is used for sequentially acquiring programs belonging to a preset time period in the film watching record of the user from back to front according to the time sequence;
the first judging subunit is used for judging whether the number of the programs collected in the preset time period reaches a first preset number a or not;
the second acquisition subunit is used for acquiring a first preset number a of programs to generate a first viewing characteristic program list when the number of the acquired programs in the preset time period reaches a first preset number a; and the number of the first and second groups,
and the third acquisition subunit is used for supplementing and acquiring a plurality of programs with the highest playing quantity in the preset time period from the server when the quantity of the acquired programs in the preset time period does not reach the first preset number a until the quantity of the acquired programs reaches the first preset number a, and generating the acquired programs into a first viewing characteristic program list.
In a third aspect, the present application provides a computer device comprising a memory and a processor, the memory having stored thereon a computer program of any of the above methods loaded and executed by the processor.
Drawings
Fig. 1 is a flowchart of an IPTV program recommendation method according to an embodiment of the present application.
Fig. 2 is a system block diagram of an IPTV program recommendation system according to an embodiment of the present application.
Fig. 3 is a block diagram of a first acquisition module according to an embodiment of the present application.
Description of reference numerals: 1. an identity recognition module; 2. a first acquisition module; 3. a similar user matching module; 4. a second acquisition module; 5. a program set generation module to be recommended; 6. a quantitative scoring module; 7. a sorting module; 8. a display module; 9. a first acquisition subunit; 10. a first judgment subunit; 11. a second acquisition subunit; 12. and a third acquisition subunit.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is further described in detail below with reference to fig. 1-3 and the embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The embodiment of the application discloses an IPTV program recommendation method.
Referring to fig. 1, as an embodiment of an IPTV program recommending method, an IPTV program recommending method includes the following steps:
and S101, before the user enters the film viewing page, identifying the identity of the user.
Specifically, before the user enters the viewing page, the IPTV may be powered on and started, or the user may click a button entering the viewing page in the IPTV. The above viewing interface is an interface for the user to select a program for playing. Identifying the user includes verifying an account of the user and/or identifying the user identity through an identity recognition device. The identification device may be an IPTV device or an external device. The identity recognition device may be a face recognition device or a fingerprint recognition device, and is not limited in particular here.
Step S102, after the user is identified, a first viewing characteristic program list of the user is collected.
And S103, matching a plurality of similar users according to the first viewing characteristic program list.
And step S104, collecting a second viewing characteristic program list of each similar user.
And S105, obtaining a program set to be recommended according to the plurality of second viewing characteristic program lists and the first viewing characteristic program list.
And S106, carrying out quantitative scoring on the programs in the program set to be recommended to obtain the scores of the programs in the program set to be recommended.
And S107, sequencing the programs in the program set to be recommended according to the sequence of scores from large to small.
And S108, collecting programs before the preset sequence, and displaying the programs in a film viewing page.
Specifically, the similar users and the users using the method have great similarity in viewing habits, the programs played by the similar users are programs conforming to the interests of the users with great probability, and the programs watched by the similar users are recommended for the users using the method, so that the possibility that the recommended programs conform to the interests of the users can be improved.
As one implementation of the IPTV program recommendation method, the step of collecting the first viewing characteristic program list of the user in step S102 may include the following steps:
s1021, sequentially collecting programs belonging to a preset time period in the film watching record of the user from back to front according to the time sequence; the duration of the preset time period is fixed, and one endpoint of the preset time period is the current moment.
Specifically, the current time is taken as one of the endpoints, and the other endpoint is obtained by adding a fixed duration to the endpoint; according to the time sequence, the endpoint of the current moment is positioned behind the other endpoint, and the time period between the two endpoints is the preset time period. For example, the current time is 9/24/17/19 in 2021, and the fixed time duration is 14 days, the preset time period is 9/10/17/19 in 2021 to 9/24/17/19 in 2021. After the preset time period is determined, the programs belonging to the preset time period in the user watching record are sequentially collected from back to front according to the time sequence. By collecting programs watched by a user in a preset time period, the recent program preference of the user can be known more clearly, so that programs meeting the requirements of the user are recommended to the user.
Step S1022, determining whether the number of programs collected in the preset time period reaches a first preset number a; if yes, go to step S1023; if not, S1024 is performed.
Specifically, since the user may use the IPTV for the first time or the number of times that the user watches programs using the IPTV in the latest period of time is small, the number of programs collected in the preset period of time is determined, so as to avoid that the number of programs collected is too small, thereby reducing the possibility that the programs recommended for the user do not meet the requirements of the user.
And S1023, collecting a first preset number a of programs from back to front according to the time sequence to generate a first viewing characteristic program list.
Specifically, if the number of the collected programs in the preset time period reaches a first preset number a, the first preset number a of the programs in the front row are collected from back to front according to the time sequence, and a viewing characteristic program list is generated.
Step S1024, a plurality of programs with the highest playing quantity in a preset time period are collected from the server in a supplementing mode until the quantity of the collected programs reaches a first preset quantity a, and the collected programs are generated into a first viewing characteristic program list.
Specifically, if the number of the programs collected in the preset time period does not reach the first preset number a, the number of the collected program samples is small, and the recommended programs are prone to not meet the requirements of the user. The programs with the highest playing amount in the preset time period are supplemented by supplementing and collecting the programs with the highest playing amount in the preset time period, so that the generated first viewing characteristic program list is more in line with the preference of the user.
And step S1025, collecting a first viewing characteristic program list.
As another implementation manner of the IPTV program recommending method, the step S103 of matching a plurality of similar users according to the first viewing characteristic program list includes the following steps:
and step S1031, collecting the film watching records of other users stored in the server, and collecting a programs in the film watching records of other users as a program set to be judged from back to front according to the time sequence.
Step S1032, judging whether the number of the first film watching characteristic program and the number of the programs in the program set to be judged are the same as a second preset number b or not; if so, the user is marked as a similar user.
Step S1033, judging whether the number of the similar users reaches a third preset number c; if yes, the film watching records of other users stored in the server are stopped being collected.
Specifically, the playing time of the programs in the program set to be determined is close to the current time, so that the favorite programs of the user at the current time can be more accurately reflected. The number of the programs contained in the first viewing characteristic program list and the number of the programs contained in the program set to be judged are the same, when the number of the programs contained in the first viewing characteristic program list and the number of the programs contained in the program set to be judged are equal to a second preset number b, the fact that the similarity between the user collected from the server and the user using the system is higher on favorite programs shows that the similarity between the similar user and the user using the system is very high in viewing habits, and the programs can be recommended to the user more accurately by matching a plurality of similar users.
As one implementation of the IPTV program recommendation method, in step S105, obtaining a set of programs to be recommended includes the following steps:
and S1051, obtaining all programs in the second viewing characteristic program lists according to the second viewing characteristic program lists.
Step S1052, filtering out the program that coincides with the first viewing characteristic program in a single phase among all the programs, and retaining one of the parts that coincide with the same program among all the programs to obtain a program set to be recommended.
Specifically, all programs in the second viewing characteristic program lists are obtained according to the second viewing characteristic program lists, the programs include parts which are overlapped with the program to be judged in a centralized mode, and the condition that a plurality of programs are overlapped is also included, and the programs are filtered to avoid recommending the repeated programs to the user. The programs played by similar users have a higher probability of being programs that meet the user's interests.
As one implementation of the IPTV program recommendation method, in step S106, performing quantitative scoring on the programs in the set of programs to be recommended to obtain scores of the programs in the set of programs to be recommended, includes the following steps:
step S1061, collecting the total playing time of the same program in a set of programs to be recommended by a plurality of similar users.
Step S1062, obtaining the average playing time length of the same program based on the total playing time length.
Step S1063, obtaining the proportion of the average playing time length in the total time length based on the average playing time length and the total time length of the same program; and the number of the first and second groups,
and step S1064, obtaining a first score of the program based on the proportion.
Specifically, in the process of playing a program by a similar user, a certain program is easy to click, but the program is not a program which is interested by the program, after the user exits from a playing interface of the program, the proportion of the playing time length of the program in the total time length is smaller than that of the program which is interested by the user, a first score is obtained through the average playing time of the same program and the total time length of the same program, and the first score is used as a part of the quantitative score.
The quantitative scoring method further comprises the following steps:
classifying the current time into one of a morning time period, an afternoon time period and an evening time period according to the time period of the current time;
and obtaining a second score of the same program based on the time period of playing the same program by different similar users and the time period of the current time.
Specifically, the programs preferred by the user in different time periods are different, and the programs can be better recommended to the user according to the playing time of the programs.
Based on the IPTV program recommendation method, the application also provides an IPTV program recommendation system.
An IPTV program recommendation system comprising:
the identity recognition module 1 is used for carrying out identity recognition on the user before the user enters the film viewing page;
the first acquisition module 2 is used for acquiring a first viewing characteristic program list of a user;
the similar user matching module 3 is used for matching a plurality of similar users according to the first viewing characteristic program list;
the second acquisition module 4 is used for acquiring a second viewing characteristic program list of each similar user;
the program set to be recommended generating module 5 is used for obtaining a program set to be recommended according to the plurality of second viewing characteristic program lists and the first viewing characteristic program list of the user;
the quantitative scoring module 6 is used for performing quantitative scoring on the programs in the to-be-recommended program set to obtain scores of the programs in the to-be-recommended program set;
the sorting module 7 sorts the programs in the program set to be recommended according to the order of scores from large to small; and the number of the first and second groups,
and the display module 8 is used for collecting programs before the preset sequence and displaying the programs in the viewing page.
As one of the embodiments of the first acquisition module 2, the first acquisition module 2 includes:
the first acquisition subunit 9 is configured to sequentially acquire, from back to front according to a time sequence, programs belonging to a preset time period in the film watching record of the user;
the first judging subunit 10 is configured to judge whether the number of the programs collected in the preset time period reaches a first preset number a;
the second acquisition subunit 11 is configured to acquire a first preset number a of programs to generate a first viewing feature program list when the number of the acquired programs in the preset time period reaches a first preset number a; and the number of the first and second groups,
the third collecting subunit 12 is configured to, when the number of the programs collected in the preset time period does not reach the first preset number a, supplementarily collect, from the server, a plurality of programs with the highest playing amount in the preset time period until the number of the collected programs reaches the first preset number a, and generate the collected programs into a first viewing characteristic program list.
The embodiment of the application also discloses computer equipment.
In particular, the computer device comprises a memory and a processor, the memory having stored thereon a computer program that can be loaded by the processor and executed to perform any of the methods described above.
The foregoing is a preferred embodiment of the present application and is not intended to limit the scope of the present application in any way, and any features disclosed in this specification (including the abstract and drawings) may be replaced by alternative features serving equivalent or similar purposes, unless expressly stated otherwise. That is, unless expressly stated otherwise, each feature is only an example of a generic series of equivalent or similar features.
Claims (10)
1. An IPTV program recommendation method is characterized in that: the method comprises the following steps:
before the user enters the film viewing page, identifying the identity of the user;
after the identity of a user is identified, acquiring a first viewing characteristic program list of the user;
matching a plurality of similar users according to the first film watching characteristic program list;
collecting a second viewing characteristic program list of each similar user;
obtaining a program set to be recommended according to the second viewing characteristic program lists and the first viewing characteristic program list;
carrying out quantitative scoring on the programs in the program set to be recommended to obtain the scores of the programs in the program set to be recommended;
sequencing the programs in the program set to be recommended according to the sequence of scores from large to small; and the number of the first and second groups,
and collecting programs before the preset sequence, and displaying in a film viewing page.
2. The IPTV program recommendation method of claim 1, wherein: the method for acquiring the first viewing characteristic program list of the user comprises the following steps:
sequentially collecting programs belonging to a preset time period in the film watching record of the user from back to front according to the time sequence; the duration of the preset time period is fixed, and one endpoint of the preset time period is the current moment; and the number of the first and second groups,
judging whether the number of the programs collected in the preset time period reaches a first preset number a or not; if yes, collecting a first preset number a of programs from back to front according to the time sequence to generate a first viewing characteristic program list; and if not, additionally collecting a plurality of programs with the highest playing quantity in the preset time period from the server until the quantity of the collected programs reaches a first preset quantity a, and generating a first viewing characteristic program list from the collected programs.
3. The IPTV program recommending method as claimed in claim 2, wherein said matching a plurality of similar users according to said first viewing characteristics menu comprises the steps of:
acquiring the film watching records of other users stored in the server, and acquiring a programs in the film watching records of the other users from back to front according to the time sequence to be used as a program set to be judged;
judging whether the number of the programs in the first viewing characteristic program list and the program set to be judged, which are the same, reaches a second preset number b; if yes, the user is marked as a similar user; and the number of the first and second groups,
judging whether the number of the similar users reaches a third preset number c; if yes, the film watching records of other users stored in the server are stopped being collected.
4. The IPTV program recommendation method according to claim 3, wherein said obtaining a set of programs to be recommended comprises the steps of:
obtaining all programs in the second viewing characteristic program lists according to the second viewing characteristic program lists; and the number of the first and second groups,
and filtering out the programs which are coincided with the first viewing characteristic program in all the programs, and reserving one of the parts which are coincided with the same program in all the programs to obtain the program set to be recommended.
5. The IPTV program recommending method according to claim 4, wherein said quantitatively scoring the programs in said set of programs to be recommended to obtain the score of the programs in the set of programs to be recommended, comprises the steps of:
collecting the total playing time of the same program in a program set to be recommended by a plurality of similar users;
obtaining the average playing time length of the same program based on the total playing time length;
obtaining the proportion of the average playing time length in the total time length based on the average playing time length and the total time length of the same program; and the number of the first and second groups,
a first score for the program is derived based on the ratio.
6. The IPTV program recommending method according to claim 5, wherein said quantitative scoring method further comprises:
classifying the current time into one of a morning time period, an afternoon time period and an evening time period according to the time period of the current time; and the number of the first and second groups,
and obtaining a second score of the same program based on the playing time period of the same program and the current time period of the same program played by different similar users.
7. The IPTV program recommendation method according to claim 1, wherein said identifying the user comprises verifying an account of the user and/or identifying the user identity through an identity recognition device.
8. An IPTV program recommendation system based on the method of claim 6, comprising:
the identity recognition module (1) is used for carrying out identity recognition on the user before the user enters the film viewing page;
the first acquisition module (2) is used for acquiring a first viewing characteristic program list of a user;
the similar user matching module (3) is used for matching a plurality of similar users according to the first viewing characteristic program list;
the second acquisition module (4) is used for acquiring a second viewing characteristic program list of each similar user;
a to-be-recommended program set generating module (5) for obtaining a to-be-recommended program set according to the plurality of second viewing characteristic program lists and the first viewing characteristic program list of the user;
the quantitative scoring module (6) is used for performing quantitative scoring on the programs in the program set to be recommended to obtain the scores of the programs in the program set to be recommended;
the sorting module (7) sorts the programs in the program set to be recommended according to the order of scores from large to small; and the number of the first and second groups,
and the display module (8) is used for collecting programs before the preset sequence and displaying the programs in the viewing page.
9. The IPTV program recommendation system of claim 8, wherein the first collecting module (2) comprises:
the first acquisition subunit (9) is used for sequentially acquiring programs belonging to a preset time period in the film watching record of the user from back to front according to the time sequence;
the first judging subunit (10) is used for judging whether the number of the programs collected in the preset time period reaches a first preset number a or not;
the second acquisition subunit (11) is used for acquiring a first preset number a of programs to generate a first viewing characteristic program list when the number of the acquired programs in the preset time period reaches a first preset number a; and the number of the first and second groups,
and the third acquisition subunit (12) is used for complementarily acquiring a plurality of programs with the highest playing quantity in the preset time period from the server when the number of the acquired programs in the preset time period does not reach the first preset number a until the number of the acquired programs reaches the first preset number a, and generating a first viewing characteristic program list from the acquired programs.
10. A computer device, characterized by: comprising a memory and a processor, the memory having stored thereon a computer program for the IPTV program recommendation method of any of claims 1-6, which is loaded and executed by the processor.
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