CN111026912B - IPTV-based collaborative recommendation method, device, computer equipment and storage medium - Google Patents
IPTV-based collaborative recommendation method, device, computer equipment and storage medium Download PDFInfo
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Abstract
The invention relates to the technical field of computer technology, in particular to a collaborative recommendation method, a device, computer equipment and a storage medium based on IPTV, wherein the collaborative recommendation method based on IPTV comprises the following steps: acquiring user basic image information and a user identifier corresponding to the user basic image information through a front-end device; the front-end device acquires a corresponding basic video recommendation set from the video cloud according to the basic portrait information of the user; the front-end device acquires user behavior data, acquires video update data from the video cloud according to the user behavior data, and updates the video update data to a basic video recommendation set; if the front-end device acquires a video watching request triggered by the user identifier, calculating user interest data according to the user behavior data and the user basic portrait information, and acquiring a video recommendation result from the basic video recommendation set according to the user interest data. The video recommendation method and device have the effects of reducing the calculation pressure of the background server and improving the efficiency of recommending videos to users.
Description
Technical Field
The invention relates to the technical field of computer technology, in particular to a collaborative recommendation method, a collaborative recommendation device, computer equipment and a storage medium based on IPTV.
Background
Currently, a recommendation algorithm is an algorithm in the computer profession, and by using some behaviors of a user, a mathematical algorithm is adopted to speculatively calculate goods, videos or other contents which the user may like.
Most of the existing video recommendation algorithms are recommendation algorithms based on calculation in a background server, when the number of users is continuously increased, the number of interest degrees of the users to be calculated is also continuously increased, and the problems of network delay and inaccurate pushing are caused while larger calculation pressure and resource consumption are brought to the background server, so that user experience is poor and recommendation effects are not ideal.
Disclosure of Invention
The invention aims to provide an IPTV-based collaborative recommendation method, an IPTV-based collaborative recommendation device, a computer device and a storage medium, wherein the computing pressure of a background server is reduced, and the video recommendation efficiency for users is improved.
The first object of the present invention is achieved by the following technical solutions:
an IPTV-based collaborative recommendation method, the IPTV-based collaborative recommendation method comprising:
s10: acquiring user basic portrait information and a user identifier corresponding to the user basic portrait information through a front-end device;
s20: the front-end device acquires a corresponding basic video recommendation set from a video cloud according to the user basic portrait information;
s30: the front-end device acquires user behavior data, acquires video update data from the video cloud according to the user behavior data, and updates the video update data to the basic video recommendation set;
s40: if the front-end device acquires the video watching request triggered by the user identifier, calculating user interest data according to the user behavior data and the user basic portrait information, and acquiring a video recommendation result from the basic video recommendation set according to the user interest data.
By adopting the technical scheme, the basic video recommendation set can be obtained from the basic user portraits in the video cloud of the background server by obtaining the basic user portraits in the front-end device, such as a television set top box, and after the front-end user behavior data, video update data is obtained from the cloud video in real time according to the user behavior data, so that the basic video recommendation set can be enriched, and the quantity of videos to be recommended is continuously enriched; when a user triggers the video watching request, user interest data can be calculated through the front-end device, then video recommending results are obtained from the basic video recommending set according to the calculated user interest data, and then the free computing capacity of each front-end device can be exerted, and further the back-end server only needs to send corresponding videos to the corresponding front-end devices from cloud videos according to user basic portrait information and user behavior data, and then the back-end server does not need to calculate the video recommending results of each user, so that the pressure calculated by the back-end server is reduced, namely the calculated pressure is shared to the front-end device corresponding to each user, recommending efficiency is improved, and the data throughput capacity of the back-end server can be exerted to a greater extent due to the fact that the pressure calculated by the back-end server is reduced, and the number of accessed front-end devices is also facilitated to be increased.
The invention is further provided with: before step S10, the collaborative recommendation method based on IPTV further includes:
s101: the front-end device is in communication with the video cloud through an Internet of things protocol.
Through adopting above-mentioned technical scheme, establish communication with front-end device and video high in the clouds through thing networking protocol for front-end device can acquire video data from the video high in the clouds.
The invention is further provided with: step S20 includes:
s21: building a basic video recommendation information base on the video cloud according to the user basic portrait information;
s22: and sending the basic video recommendation information to the front-end device, and storing the basic video recommendation information into a built-in database of the front-end device as the basic video recommendation set.
By adopting the technical scheme, the basic video recommendation information is acquired from the video cloud, so that a data set can be provided for the follow-up calculation of the video recommendation result; meanwhile, the basic video recommendation information base is built according to the basic portrait information of the user, so that video data in the built-in database is closer to the attribute of the user, and further the accuracy of calculation can be improved.
The invention is further provided with: step S30 includes:
s31: acquiring the user behavior data through program embedded points;
s32: acquiring video features to be recommended from the user behavior data;
s33: and carrying out matching query from the video cloud according to the video features to be recommended, and taking a matching query result as the video update data.
By adopting the technical scheme, the corresponding video update data is matched from the video cloud through the user behavior data, so that the video update data is more in line with the user behavior data, and the recommendation accuracy is improved.
The invention is further provided with: step S40 includes:
s41: acquiring attribute information of each video in the basic video recommendation set;
s42: calculating video similarity according to the video attribute information;
s43: and taking the user behavior data and the user basic image as weighting parameters, and calculating the video similarity by using the weighting parameters to obtain the user interest data.
By adopting the technical scheme, the user interest data conforming to the attribute of the user can be obtained by calculating the video similarity according to each video attribute information and carrying out weighted calculation on the user behavior data and the user basic image.
The second object of the present invention is achieved by the following technical solutions:
an IPTV-based collaborative recommendation device, characterized in that the IPTV-based collaborative recommendation device comprises:
the user attribute acquisition module is used for acquiring user basic portrait information and user identifications corresponding to the user basic portrait information through the front-end device;
the data sending module is used for the front-end device to acquire a corresponding basic video recommendation set from a video cloud according to the user basic portrait information;
the video set updating module is used for acquiring user behavior data by the front-end device, acquiring video updating data from the video cloud according to the user behavior data, and updating the video updating data to the basic video recommendation set;
and the recommendation module is used for calculating user interest data according to the user behavior data and the user basic portrait information and acquiring video recommendation results from the basic video recommendation set according to the user interest data if the front-end device acquires the video viewing request triggered by the user identifier.
By adopting the technical scheme, the basic video recommendation set can be obtained from the basic user portraits in the video cloud of the background server by obtaining the basic user portraits in the front-end device, such as a television set top box, and after the front-end user behavior data, video update data is obtained from the cloud video in real time according to the user behavior data, so that the basic video recommendation set can be enriched, and the quantity of videos to be recommended is continuously enriched; when a user triggers the video watching request, user interest data can be calculated through the front-end device, then video recommending results are obtained from the basic video recommending set according to the calculated user interest data, and then the free computing capacity of each front-end device can be exerted, and further the back-end server only needs to send corresponding videos to the corresponding front-end devices from cloud videos according to user basic portrait information and user behavior data, and then the back-end server does not need to calculate the video recommending results of each user, so that the pressure calculated by the back-end server is reduced, namely the calculated pressure is shared to the front-end device corresponding to each user, recommending efficiency is improved, and the data throughput capacity of the back-end server can be exerted to a greater extent due to the fact that the pressure calculated by the back-end server is reduced, and the number of accessed front-end devices is also facilitated to be increased.
The third object of the present invention is achieved by the following technical solutions:
a computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the IPTV-based collaborative recommendation method described above when the computer program is executed by the processor.
The fourth object of the present invention is achieved by the following technical solutions:
a computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the IPTV-based collaborative recommendation method described above.
In summary, the beneficial technical effects of the invention are as follows:
1. the computing pressure and the resource consumption of the video cloud are reduced;
2. the self computing capacity of the front-end device is fully utilized;
3. more timely and more accurate pushing effect.
Drawings
FIG. 1 is a flowchart of an IPTV-based collaborative recommendation method according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating an implementation of step S20 in an IPTV-based collaborative recommendation method according to an embodiment of the present invention;
fig. 3 is a flowchart illustrating an implementation of step S30 in an IPTV-based collaborative recommendation method according to an embodiment of the present invention;
fig. 4 is a flowchart illustrating an implementation of step S40 in an IPTV-based collaborative recommendation method according to an embodiment of the present invention;
FIG. 5 is a schematic block diagram of an IPTV-based collaborative recommendation device according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of a computer device in accordance with an embodiment of the invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
Embodiment one:
in an embodiment, as shown in fig. 1, the invention discloses a collaborative recommendation method based on IPTV, which specifically includes the following steps:
s10: and acquiring user basic image information and user identification corresponding to the user basic image information through the front-end device.
In this embodiment, the front-end device refers to a device installed at a location of actual use of each user and used for connecting a television with a background server. In this embodiment, the front-end device is a set-top box. The front-end device has video software installed therein for playing video. The user basic portrait information refers to information of personal attributes of the user, such as occupation, age, hobbies, location information, and video preferences. User identification refers to a string for distinguishing each user.
Specifically, when the user uses or registers in the video software in the front-end device for the first time, information such as occupation, age, hobbies and interests, positioning information, video preference and the like of the individual is filled in as user basic portrait information of the user. The type of the data in the user basic portrait information can be set according to the requirement of a recommendation algorithm, so that the accuracy of calculation can be ensured.
Further, the user identification is obtained from the user basic portrait information, wherein the user identification can be one or a combination of an account number, a user ID, an identity card number and other identifications after the user is registered.
S20: and the front-end device acquires a corresponding basic video recommendation set from the video cloud according to the basic portrait information of the user.
In this embodiment, the video cloud refers to a platform for storing video in a background server connected to a front-end device. The basic video recommendation set refers to a video set stored in a built-in database of the front-end device.
Specifically, according to data corresponding to the type of the attribute of the user in the user basic portrait information, matching corresponding videos from the video cloud, and sending the matched videos to a front-end device to serve as the basic video recommendation set.
S30: the front-end device acquires user behavior data, acquires video update data from the video cloud according to the user behavior data, and updates the video update data to the basic video recommendation set.
In this embodiment, the user behavior data refers to data of the behavior of the user using the video software in the front-end device. The video update data refers to video data updated from the video cloud.
Specifically, when a user performs operations such as searching, querying, playing, and the like, information of a video corresponding to the operations, such as a type of the video, an international name to which the video belongs, actor information, and the like, is acquired.
Further, according to the information of the video, video update data is obtained from the video cloud, and the video update data is stored in a basic video recommendation set of the front-end device.
S40: if the front-end device acquires a video watching request triggered by the user identifier, calculating user interest data according to the user behavior data and the user basic portrait information, and acquiring a video recommendation result from the basic video recommendation set according to the user interest data.
In this embodiment, the video viewing request refers to a message triggered by a user requesting to view video in video software in the front-end device. The user interest data refers to the types of videos that are obtained through calculation and that the user may be interested in. The video recommendation result is a video that points to a user recommendation.
Specifically, when the video watching request is acquired from the video software of the front-end device, the user interest data is calculated by adopting the existing recommendation algorithm through the user behavior data and the user basic image.
Further, according to the user interest data, corresponding videos are obtained from the basic video recommendation and the basic video recommendation, and the corresponding videos are used as video recommendation results. Because the video data related to the user attribute is continuously updated according to the user basic portrait information and the user behavior data in the step S20 and the step S30, when the final video recommendation result is obtained by calculation, the basic data set is subjected to multiple screening and filtering, and the pushing precision can be further improved.
In this embodiment, a user basic portrait is obtained in a front-end device, for example, a television set top box, so that a basic video recommendation set can be obtained from the user basic portrait in a video cloud of a background server, after front-end user behavior data, video update data is obtained from the cloud video in real time according to the user behavior data, and the basic video recommendation set can be enriched, so that the number of videos to be recommended is continuously enriched; when a user triggers the video watching request, user interest data can be calculated through the front-end device, then video recommending results are obtained from the basic video recommending set according to the calculated user interest data, and then the free computing capacity of each front-end device can be exerted, and further the back-end server only needs to send corresponding videos to the corresponding front-end devices from cloud videos according to user basic portrait information and user behavior data, and then the back-end server does not need to calculate the video recommending results of each user, so that the pressure calculated by the back-end server is reduced, namely the calculated pressure is shared to the front-end device corresponding to each user, recommending efficiency is improved, and the data throughput capacity of the back-end server can be exerted to a greater extent due to the fact that the pressure calculated by the back-end server is reduced, and the number of accessed front-end devices is also facilitated to be increased.
In an embodiment, before step S10, the collaborative recommendation method based on IPTV further includes:
s101: the front-end device establishes communication with the video cloud through an Internet of things protocol.
Specifically, an MQTT protocol is adopted for communication protocol, communication is built between the front-end device and the video cloud, and video software in the front-end device and the video cloud are built to communicate through https protocol. The MQTT is a release subscription protocol based on TCP, and is designed for the initial purpose of unreliable communication between extremely limited memory equipment and a network with very low network bandwidth, so that the MQTT is very suitable for the communication of the Internet of things.
In one embodiment, as shown in fig. 2, in step S20, the front-end device obtains a corresponding basic video recommendation set from the video cloud according to the user basic image information, and specifically includes the following steps:
s21: and building a basic video recommendation information base on the video cloud according to the basic portrait information of the user.
In this embodiment, the basic video recommendation information is information of a video which basically matches with information in the user basic portrait information and needs to be recommended to the user.
Specifically, according to the data in the user basic portrait information, a video is obtained from a video library in a video cloud as the basic video recommendation information.
S22: and sending the basic video recommendation information to the front-end device, and storing the basic video recommendation information into a built-in database of the front-end device as a basic video recommendation set.
Specifically, the basic video recommendation information is transmitted to the front-end device and stored in a built-in database of the front-end device as a basic video recommendation set.
In one embodiment, as shown in fig. 3, in step S30, the front-end device acquires user behavior data, acquires video update data from the video cloud according to the user behavior data, and updates the video update data to the basic video recommendation set, which specifically includes the following steps:
s31: and acquiring user behavior data through program embedded points.
In this embodiment, the program embedding point refers to a program that is preset and used to acquire the operation behavior of the user.
Specifically, the program embedded point is preset in the video software of the front-end device according to operation behaviors which may be performed by a user, such as searching, clicking, watching and the like. Further, when the user performs operation behaviors such as searching, clicking and watching of the video in the video software, the user behavior data is obtained through the program embedded point.
S32: and acquiring video features to be recommended from the data according to the user behaviors.
In this embodiment, the video feature to be recommended refers to a feature of a video that is of interest to the user. Such as the type of video, the nationality to which it belongs, and actors, etc.
Specifically, the video operated by the user is obtained from the user behavior data, and different weight values can be set according to the types of the operated behaviors, so that the video features to be recommended are obtained.
S33: and carrying out matching query from a video cloud according to the video features to be recommended, and taking a matching query result as video update data.
Specifically, feature data of a video cloud is obtained, matching query is performed by using the video features to be recommended, and data of the video corresponding to the matching query result is used as video update data.
In one embodiment, as shown in fig. 4, in step S40, if the front-end device acquires a video viewing request triggered by the user identifier, user interest data is calculated according to the user behavior data and the user basic portrait information, which specifically includes the following steps:
s41: each video attribute information is acquired in the basic video recommendation set.
In the present embodiment, the video attribute information refers to information of attributes of videos in the basic video recommendation set.
Specifically, according to the actual use requirement, setting the type of the corresponding acquired attribute information, and acquiring video attribute information from each video of the basic video recommendation set.
S42: video similarity is calculated through the video attribute information.
Specifically, the video attribute information is used, and a cosine similarity algorithm is adopted to calculate the video similarity between videos in the basic video recommendation set.
S43: and taking the user behavior data and the user basic portrait as weighting parameters, and calculating the video similarity by using the weighting parameters to obtain the user interest data.
Specifically, user behavior data and a user basic portrait are used as weighting parameters, and the weighting parameters are used for calculating the similarity of the videos to obtain user interest data.
It should be understood that the sequence number of each step in the foregoing embodiment does not mean that the execution sequence of each process should be determined by the function and the internal logic, and should not limit the implementation process of the embodiment of the present invention.
Embodiment two:
in an embodiment, an IPTV-based collaborative recommendation device is provided, where the IPTV-based collaborative recommendation device corresponds to the IPTV-based collaborative recommendation method in the above embodiment one by one. As shown in fig. 5, the IPTV-based collaborative recommendation apparatus includes a user attribute acquisition module 10, a data transmission module 20, a video set update module 30, and a recommendation module 40. The functional modules are described in detail as follows:
a user attribute obtaining module 10, configured to obtain user basic portrait information and a user identifier corresponding to the user basic portrait information through a front-end device;
the data sending module 20 is used for the front-end device to acquire a corresponding basic video recommendation set from the video cloud according to the basic image information of the user;
the video set updating module 30 is configured to obtain user behavior data from the front-end device, obtain video update data from the video cloud according to the user behavior data, and update the video update data to the basic video recommendation set;
and the recommendation module 40 is configured to calculate user interest data according to the user behavior data and the user basic portrait information, and obtain a video recommendation result from the basic video recommendation set according to the user interest data if the front-end device obtains a video viewing request triggered by the user identifier.
Preferably, the IPTV-based collaborative recommendation device further includes:
the network building module 101 is used for building communication between the front-end device and the video cloud through an internet of things protocol.
Preferably, the data transmission module 20 includes:
the video set acquisition sub-module 21 is used for building a basic video recommendation information base on a video cloud according to the basic portrait information of the user;
the data storage sub-module 22 is configured to send the basic video recommendation information to the front-end device, and store the basic video recommendation information in a built-in database of the front-end device as a basic video recommendation set.
Preferably, the video set update module 30 includes:
the behavior data acquisition sub-module is used for acquiring user behavior data through program embedded points;
the feature acquisition sub-module is used for acquiring video features to be recommended from the behavior data according to the user;
and the matching query sub-module is used for carrying out matching query from the video cloud according to the video characteristics to be recommended, and taking the matching query result as video update data.
Preferably, the recommendation module 40 includes:
a video information acquisition sub-module 41, configured to acquire each video attribute information in the basic video recommendation set;
a calculation sub-module 42 for calculating video similarity from the video attribute information;
the recommendation sub-module 43 is configured to use the user behavior data and the user basic portrait as weighting parameters, and calculate the video similarity by using the weighting parameters to obtain user interest data.
For specific limitations of the IPTV-based collaborative recommendation device, reference may be made to the above limitation of the IPTV-based collaborative recommendation method, and no further description is given here. The above-mentioned respective modules in the IPTV-based collaborative recommendation device may be implemented in whole or in part by software, hardware, and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
Embodiment III:
in one embodiment, a computer device is provided, which may be a server, the internal structure of which may be as shown in fig. 6. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing a basic set of video recommendations. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements an IPTV-based collaborative recommendation method.
In one embodiment, a computer device is provided comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps of when executing the computer program:
s10: acquiring user basic image information and a user identifier corresponding to the user basic image information through a front-end device;
s20: the front-end device acquires a corresponding basic video recommendation set from the video cloud according to the basic portrait information of the user;
s30: the front-end device acquires user behavior data, acquires video update data from the video cloud according to the user behavior data, and updates the video update data to a basic video recommendation set;
s40: if the front-end device acquires a video watching request triggered by the user identifier, calculating user interest data according to the user behavior data and the user basic portrait information, and acquiring a video recommendation result from the basic video recommendation set according to the user interest data.
Embodiment four:
in one embodiment, a computer readable storage medium is provided having a computer program stored thereon, which when executed by a processor, performs the steps of:
s10: acquiring user basic image information and a user identifier corresponding to the user basic image information through a front-end device;
s20: the front-end device acquires a corresponding basic video recommendation set from the video cloud according to the basic portrait information of the user;
s30: the front-end device acquires user behavior data, acquires video update data from the video cloud according to the user behavior data, and updates the video update data to a basic video recommendation set;
s40: if the front-end device acquires a video watching request triggered by the user identifier, calculating user interest data according to the user behavior data and the user basic portrait information, and acquiring a video recommendation result from the basic video recommendation set according to the user interest data.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the various embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.
Claims (8)
1. The IPTV-based collaborative recommendation method is characterized by comprising the following steps:
s10: acquiring user basic portrait information and a user identifier corresponding to the user basic portrait information through a front-end device;
s20: the front-end device acquires a corresponding basic video recommendation set from a video cloud according to the user basic portrait information;
s30: the front-end device obtains user behavior data, obtains video update data from the video cloud according to the user behavior data, and updates the video update data to the basic video recommendation set, and step S30 includes:
s31: acquiring the user behavior data through program embedded points;
s32: acquiring video features to be recommended from the user behavior data;
s33: according to the video features to be recommended, carrying out matching query from the video cloud, and taking a matching query result as the video update data;
s40: if the front-end device acquires the video watching request triggered by the user identifier, calculating user interest data according to the user behavior data and the user basic portrait information, and acquiring a video recommendation result from the basic video recommendation set according to the user interest data, wherein step S40 includes:
s41: acquiring attribute information of each video in the basic video recommendation set;
s42: calculating video similarity according to the video attribute information;
s43: and taking the user behavior data and the user basic image as weighting parameters, and calculating the video similarity by using the weighting parameters to obtain the user interest data.
2. The IPTV-based collaborative recommendation method according to claim 1, wherein prior to step S10, the IPTV-based collaborative recommendation method further comprises:
s101: the front-end device is in communication with the video cloud through an Internet of things protocol.
3. The IPTV-based collaborative recommendation method according to claim 1, wherein step S20 comprises:
s21: building a basic video recommendation information base on the video cloud according to the user basic portrait information;
s22: and sending the basic video recommendation information to the front-end device, and storing the basic video recommendation information into a built-in database of the front-end device as the basic video recommendation set.
4. An IPTV-based collaborative recommendation device, characterized in that the IPTV-based collaborative recommendation device comprises:
the user attribute acquisition module is used for acquiring user basic portrait information and user identifications corresponding to the user basic portrait information through the front-end device;
the data sending module is used for the front-end device to acquire a corresponding basic video recommendation set from a video cloud according to the user basic portrait information;
the video set updating module is configured to obtain user behavior data from the front-end device, obtain video updating data from the video cloud according to the user behavior data, and update the video updating data to the basic video recommendation set, where the video set updating module includes:
the behavior data acquisition sub-module is used for acquiring user behavior data through program embedded points;
the feature acquisition sub-module is used for acquiring video features to be recommended from the behavior data according to the user;
the matching query sub-module is used for carrying out matching query from the video cloud according to the video characteristics to be recommended, and taking a matching query result as video update data;
the recommendation module is configured to calculate user interest data according to the user behavior data and the user basic portrait information if the front-end device obtains a video viewing request triggered by the user identifier, and obtain a video recommendation result from the basic video recommendation set according to the user interest data, where the recommendation module includes:
a video information acquisition sub-module 41, configured to acquire each video attribute information in the basic video recommendation set;
a calculation sub-module 42 for calculating video similarity from the video attribute information;
the recommendation sub-module 43 is configured to use the user behavior data and the user basic portrait as weighting parameters, and calculate the video similarity by using the weighting parameters to obtain user interest data.
5. The IPTV-based collaborative recommendation device according to claim 4, wherein the IPTV-based collaborative recommendation device further comprises:
the network building module is used for building communication between the front-end device and the video cloud through an Internet of things protocol.
6. The IPTV-based collaborative recommendation device according to claim 4, wherein the data transmission module comprises:
the video set acquisition sub-module is used for building a basic video recommendation information base on the video cloud according to the user basic portrait information;
and the data storage sub-module is used for sending the basic video recommendation information to the front-end device and storing the basic video recommendation information into a built-in database of the front-end device to serve as the basic video recommendation set.
7. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the IPTV-based collaborative recommendation method according to any of claims 1 to 3 when the computer program is executed.
8. A computer readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the IPTV-based collaborative recommendation method according to any of claims 1 to 3.
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