CN115374364A - Content recommendation method and device based on third-party video platform - Google Patents
Content recommendation method and device based on third-party video platform Download PDFInfo
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Abstract
The application discloses a content recommendation method and device based on a third-party video platform. The content recommendation method based on the third-party video platform comprises the following steps: acquiring a public video data pool; acquiring user information transmitted by a target vehicle; and generating a private video data pool for the target vehicle according to the public video data and the user information and transmitting the private video data pool to the target vehicle. According to the content recommendation method based on the third-party video platform, the recommended video can be customized for the target vehicle according to the user information transmitted by the target vehicle, so that the purpose of personalized recommendation is achieved, and a driver is prevented from being unsatisfied with the recommended video.
Description
Technical Field
The application relates to the technical field of vehicle-mounted videos, in particular to a content recommendation method based on a third-party video platform, a content recommendation device based on the third-party video platform and a video recommendation method for an automobile.
Background
At present, private vehicles in each big city in China occupy higher and higher proportion, and automobiles gradually enter thousands of households. With the advent of the 5G era, low-cost, high-quality networks have provided a better infrastructure environment for streaming video. The user can trace the drama more conveniently, and the scene of online education can be supported at any time and any place at present. In-vehicle video applications have almost become the standard for in-built system applications.
The accuracy and the fitness of video content recommendation can often improve the stickiness of the application used by a user, and are important for determining the attention and market benefits of a video platform and three parties of the platform.
At present, most of applications of vehicle enterprises in the market are based on the default version of a three-party video platform, the customization capability of user behaviors is not provided, the special attribute capability of combining the user behaviors of the platform and the like is not provided, and the default three-party application version integrated by the vehicle enterprises in the market only refers to the popularity degree under test, the click rate of users and the like, does not consider the conditions of drivers and passengers on the vehicle, and does not consider whether the applications are related to the driving purpose of the vehicle or not.
Accordingly, a solution is desired to solve or at least mitigate the above-mentioned deficiencies of the prior art.
Disclosure of Invention
The present invention is directed to a third-party video platform-based content recommendation method to solve at least one of the above technical problems.
In one aspect of the present invention, a third-party video platform based content recommendation method is provided, where the third-party video platform based content recommendation method includes:
acquiring a public video data pool;
acquiring user information transmitted by a target vehicle;
and generating a private video data pool for the target vehicle according to the public video data and the user information and transmitting the private video data pool to the target vehicle.
Optionally, the obtaining the common video data pool includes:
acquiring a resource interface;
and crawling required video data information from the resource interface in a crawler mode to generate a public video data pool.
Optionally, the user information transmitted by the target vehicle includes a user basic information group and a vehicle driving information group;
the generating and transmitting a private video data pool for the target vehicle from the public video data and the user information to the target vehicle comprises:
scoring each piece of video data information in the public video data pool according to the user basic information group and/or the vehicle running information group, so as to obtain scoring information of each piece of video data information;
and generating a private video data pool for the target vehicle according to the grading information of each piece of video data information.
Optionally, the user basic information group comprises at least one user basic information;
the vehicle driving information group comprises at least one piece of vehicle driving information;
the scoring of each piece of video data information in the public video data pool according to the user basic information group and/or the vehicle driving information group so as to acquire the scoring information of each piece of video data information comprises the following steps:
acquiring a user basic information weight database, wherein the user basic information weight database comprises a plurality of preset user basic information and a basic weight corresponding to each user basic information;
acquiring a vehicle running information weight database, wherein the vehicle running information weight database comprises a plurality of pieces of vehicle running information and a basic weight corresponding to each piece of vehicle running information;
acquiring video data information to be evaluated in a video data pool;
acquiring basic information in video data information;
acquiring user basic information and/or vehicle driving information which is suitable for grading the video data information in a user basic information group according to the basic information;
acquiring a basic weight of each acquired user basic information according to the acquired user basic information suitable for grading the video data information and/or acquiring a basic weight of each acquired vehicle driving information according to the acquired user basic information suitable for grading the video data information;
and generating the score of the video data information to be scored according to the obtained basic weight of the basic information of the user and the basic weight of the vehicle driving information.
Optionally, the generating a private video data pool for the target vehicle according to the score information of each piece of video data information includes:
sequencing the video data information with scores;
and acquiring a preset number of video data information with the scores larger than a preset threshold value so as to form a private video data pool of the target vehicle.
Optionally, the basic information in the video data information includes video type information;
the step of obtaining the user basic information and/or the vehicle driving information which is suitable for grading the video data information in the user basic information group according to the basic information comprises the following steps:
acquiring a type mapping database, wherein the type mapping database comprises video type information and user basic information and/or vehicle running information which are required to be acquired and correspond to each piece of video type information;
and acquiring the user basic information and/or vehicle running information which is required to be acquired and corresponds to the basic information according to the basic information of the video data information to be scored.
Optionally, the content recommendation method based on a third-party video platform further includes:
and randomly replacing user basic information and/or vehicle running information which is required to be acquired and corresponds to one or more video type information in the type mapping database by taking preset time as a period.
Optionally, after the generating a private video data pool for the target vehicle according to the public video data and the user information and transmitting the private video data pool to the target vehicle, the third-party video platform-based content recommendation method further includes:
acquiring a user basic information group and a vehicle running information group of the target vehicle after the private video data pool is transmitted to the target vehicle;
generating a new private video data pool for the target vehicle according to the newly acquired user basic information group and the vehicle running information group of the target vehicle;
updating the previously communicated private video data pool for the target vehicle with a new private video data pool for the target vehicle.
The application also provides a content recommendation device based on the third-party video platform, which comprises:
the public video data pool acquisition module is used for acquiring a public video data pool;
the system comprises a user information acquisition module, a user information acquisition module and a user information transmission module, wherein the user information acquisition module is used for acquiring user information transmitted by a target vehicle;
and the private video data pool generating module is used for generating a private video data pool for the target vehicle according to the public video data and the user information and transmitting the private video data pool to the target vehicle.
The application also provides a video recommendation method for the automobile, which comprises the following steps:
the vehicle end transmits the user information of the vehicle end to the cloud end;
and the cloud generates a private video data pool for the target vehicle according to the user information transmitted by the vehicle end and the public video data pool acquired by the cloud and transmits the private video data pool to the target vehicle.
Advantageous effects
According to the content recommendation method based on the third-party video platform, the recommended video can be customized for the target vehicle according to the user information transmitted by the target vehicle, so that the purpose of personalized recommendation is achieved, and a driver is prevented from being unsatisfied with the recommended video.
Drawings
Fig. 1 is a flowchart illustrating a content recommendation method based on a third-party video platform according to an embodiment of the present application.
Fig. 2 is a schematic diagram of an electronic device capable of implementing a third-party video platform-based content recommendation method according to an embodiment of the present application.
Fig. 3 is a schematic flowchart of acquiring a common video data pool according to an embodiment of the present application.
Fig. 4 is a schematic diagram of preset user basic information and a basic weight corresponding to each user basic information according to an embodiment of the present application.
Fig. 5 is a schematic flow chart of updating the private video data pool previously transferred to the target vehicle by a new private video data pool for the target vehicle according to an embodiment of the present application.
Detailed Description
In order to make the implementation objects, technical solutions and advantages of the present application clearer, the technical solutions in the embodiments of the present application will be described in more detail below with reference to the drawings in the embodiments of the present application. In the drawings, the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The described embodiments are a subset of the embodiments in the present application and not all embodiments in the present application. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application. Embodiments of the present application will be described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flowchart of a content recommendation method based on a third-party video platform according to an embodiment of the present application.
The content recommendation method based on the third-party video platform shown in fig. 1 includes:
step 1: acquiring a public video data pool;
step 2: acquiring user information transmitted by a target vehicle;
and step 3: and generating a private video data pool for the target vehicle according to the public video data and the user information and transmitting the private video data pool to the target vehicle.
According to the content recommendation method based on the third-party video platform, the recommended video can be customized for the target vehicle according to the user information transmitted by the target vehicle, so that the purpose of personalized recommendation is achieved, and the situation that a driver is not satisfied with the recommended video is prevented.
In this embodiment, acquiring the common video data pool includes:
acquiring a resource interface;
and crawling required video data information from the resource interface in a crawler mode to generate a public video data pool.
In particular, videos are crawled through video content websites, such as through interfaces provided by public third parties, public video information conforming to the Robot protocol, and so forth.
Referring to fig. 3, for example, crawling of video data by the azygos is performed, resource acquisition is performed first, resource sources include the following categories, the first category is an azygos recommendation interface resource, the second category is a popular search resource interface, the third category is a video resource with each classification interface ranked at the front, and the fourth category is a video resource set for mapping the azygos search result according to data of a client user portrait, such as tags of age, sex, hobbies, frequently-used scene modes, and the like.
The specific practical scheme adopts a script framework, the HTTP interface of the conventional fancy art is crawled through a Python code, and the main crawled content is a structural body of Video content and can be named as Video entity.
Firstly, generating part of users to form a user pool of the three-party website, wherein at the moment, video data do not exist in the user pool, and the user obtains an authorization token of the three-party website, and the crawler program needs to have a mechanism for automatically replacing the token because the token has effectiveness.
Secondly, after we obtain the token, we can obtain the interface in the request and obtain the three-party website interface, according to the obtained rule, put the Video entity of the data content into the MongoDB for storage, and store the ID part of the Video as Redis of KEY.
And finally, crawling is carried out, in the crawling process, basic duplicate removal and recording are realized by means of the KEY stored in the Redis database, and the recorded content comprises interface sources, the recognized times, keyword labels and the like. The video can be crawled into a public video data pool through crawling.
In one embodiment, data in the public video data pool may also be flushed, for example, to check for violations, garbage, stale data, and so on.
In the embodiment, the user information transmitted by the target vehicle comprises a user basic information group and a vehicle running information group;
generating a private video data pool for a target vehicle according to public video data and the user information and transmitting the private video data pool to the target vehicle comprises:
scoring each piece of video data information in the public video data pool according to a user basic information group and/or the vehicle running information group, so as to obtain scoring information of each piece of video data information;
and generating a private video data pool for the target vehicle according to the grading information of each piece of video data information.
Through the grading mode, videos more suitable for the target vehicle can be acquired, the videos suitable for the target vehicle are transmitted to the target vehicle, grading is carried out through two dimensions of the basic information dimension of the user and the vehicle running information dimension, and the dimension considered by grading can be more and more close to the actual situation of the user.
In this embodiment, the user basic information group includes at least one user basic information;
the vehicle driving information group comprises at least one piece of vehicle driving information;
according to the user basic information group and/or the vehicle driving information group, scoring each piece of video data information in a public video data pool, so that the step information of each piece of video data information is acquired, and the step information comprises the following steps:
acquiring a user basic information weight database, wherein the user basic information weight database comprises a plurality of preset user basic information and a basic weight corresponding to each user basic information;
acquiring a vehicle running information weight database, wherein the vehicle running information weight database comprises a plurality of pieces of vehicle running information and a basic weight corresponding to each piece of vehicle running information;
acquiring video data information to be evaluated in a video data pool;
acquiring basic information in video data information;
acquiring user basic information and/or vehicle driving information which is suitable for grading the video data information in a user basic information group according to the basic information;
acquiring a basic weight of each acquired user basic information according to the acquired user basic information suitable for grading the video data information and/or acquiring a basic weight of each acquired vehicle driving information according to the acquired user basic information suitable for grading the video data information;
and generating the score of the video data information to be scored according to the obtained basic weight of the basic information of the user and the basic weight of the vehicle driving information.
In this embodiment, the user basic information may include at least one of the following information:
age information, gender information, academic information, hobby information (e.g., the user may preset his own hobbies as basic labels for societies, finance, economics, politics, etc.).
In the present embodiment, the vehicle travel information may include at least one of the following information:
the in-vehicle setting information, for example, a scene mode in which the vehicle is located, such as a sport mode, a nap mode, a child mode, and the like.
Vehicle navigation information, for example, the destination of the vehicle is a basketball court or the like.
The vehicle speed information is, for example, the current speed of the vehicle is 100KM/h or the like.
In this embodiment, the score is obtained using the following formula:
rank represents the score and score x Vn represents the weight.
Referring to fig. 4, fig. 4 shows an example of the weight of the user basic information group and/or the vehicle travel information group.
The scoring method of the present application is described in detail below by way of example, and it should be understood that this example is not intended to limit the present application in any way.
Demonstration of an algorithm: for example, in video a, if the playing content is a hot social news category, the calculation formula is:
rank (a) =0.05 × 1 (e.g., age 18, 0.5 is a base per year) +0.1 (gender) +0.3 × a (hobbies, a may take values of 0, 1, 2, etc., where 0 may represent noncompliance, 1 represents slight coincidence, 2 represents very coincided, etc., 0.3 is a base of hobbies) + 0.15B (B represents whether the video has been played in the history, B takes a value of 1 may represent that the video has been played in the history, 0 represents not played) +0.1 × C (C represents that the video has been previously viewed, where C takes a value of 1 represents not viewed, 0 may represent not viewed) +0.1 × D (D represents video hotness rating, e.g., D may take a value of 0.1,0.2, etc., e.g., if a video has been played by 1000 people, hotness = 0.1 × 0.1E (E represents that the video has been stored in the rating, e.g., D may take a value of 5363 zxft 0.58, e.g., represents that the video has been currently stored in the rating, F represents a scene, e.g., F, and F represents a rating.
For another example, a video B, the content played is an early education category, such as a piggy cookie, and then the calculation formula is:
rank (a) =0.05 × 0 (age) +0.1 × 0.5 (gender) +0.3 × 0 (hobby, 0 represents not conforming to) +0.15 × 0 (whether video was played over in history) +0.1 × 0 (see over) +0.1 × 1 (heat/total heat) +0.1 (already collected) +0.1 (children scene mode) =0.2.
Each weight value can be set according to the need, for example, each weight can be set according to the situation of the expert scoring, and can be performed through the schemes of KNN machine learning or decision tree, and the like, which is not described herein again.
By means of the scoring, the scoring condition of each video in the video data pool can be obtained.
In this embodiment, generating a private video data pool for the target vehicle according to the rating information of each piece of video data information includes:
sequencing the video data information with scores;
and acquiring a preset number of video data information with the scores larger than a preset threshold value so as to form a private video data pool of the target vehicle.
Specifically, the video data information is dynamically sorted according to the generated scores, and a preset number of video data information with scores larger than a preset threshold value is formed, so that a private video data pool of the target vehicle is formed.
For example, there are 100 videos, the preset threshold is 0.4, 20 videos of the 100 videos are all greater than 0.4, but the preset number is 10, the first 10 videos are acquired and the 10 videos are formed into a private video data pool of the target vehicle.
In the present embodiment, the basic information in the video data information includes video type information;
the step of obtaining the user basic information and/or the vehicle driving information which is suitable for grading the video data information in the user basic information group according to the basic information comprises the following steps:
acquiring a type mapping database, wherein the type mapping database comprises video type information and user basic information and/or vehicle running information which are required to be acquired and correspond to each piece of video type information;
and acquiring the user basic information and/or vehicle running information which is required to be acquired and corresponds to the basic information according to the basic information of the video data information to be scored.
In this embodiment, different video data may require different user basic information and/or vehicle driving information, for example, a basketball video data, and the user basic information and/or vehicle driving information that may need to be considered are mainly: the age information, the sex information, and the navigation destination information are, for example, about 15 to 30 years old, male sex, and stadium, the score is relatively high, but is relatively low, and the score is not substantially affected by the vehicle running speed, and it is not necessary to use the weight of the vehicle running speed as the evaluation of the score.
In this embodiment, the content recommendation method based on a third-party video platform further includes:
and randomly replacing the user basic information and/or the vehicle driving information which are required to be acquired and correspond to one or more video type information in the type mapping database by taking preset time as a period.
It is understood that the user basic information and/or the vehicle driving information to be acquired may be replaced at regular intervals in order to prevent the contents of each recommendation from being the same or the score from being too stiff.
Taking the last basketball video data as an example, if the basic user information and/or the vehicle driving information required for the first scoring are/is age information, gender information and navigation destination information, the basic user information and/or the vehicle driving information can be converted into the age information, the navigation destination information and the user preference information at intervals to score.
In addition, the ranking mode and/or recommendation rule of the scored videos may be changed, for example, the ranking mode of the scored videos in one time period is in a sequential order, and the ranking mode in another time period may be arbitrarily selected or in a reverse order. For another example, in this embodiment, the recommendation rule is to form the private video data pool in a manner that the score is higher than a preset threshold, and in other embodiments, the private video data pool may be formed in a manner that the score is obtained 10 before the ranking and 10 after the ranking.
By adopting the mode, more freshness can be given to the user, and the data acquired by the user every time is prevented from being basically consistent.
In this embodiment, after generating a private video data pool for the target vehicle according to public video data and the user information and transmitting the private video data pool to the target vehicle, the content recommendation method based on the third-party video platform further includes:
obtaining a user basic information group and a vehicle driving information group of the target vehicle after the private video data pool is transmitted to the target vehicle;
generating a new private video data pool for the target vehicle according to the newly acquired user basic information group and the vehicle running information group of the target vehicle;
updating by a new private video data pool for the target vehicle that was previously transferred to the target vehicle's private video data pool.
Referring to fig. 5, after the target vehicle acquires the private video data pool, the private video data pool is not completely unchanged, but is updated in real time according to the change of the own condition.
The updating of the private video data pool depends on the updating and scoring of the public video data pool, and generally, the user will first upload user information of the target vehicle, for example, some real-time parameters, such as vehicle speed, mode, driving habits, basic information of the user, and the like, by which the private video data pool will be generated and transmitted to the target vehicle, it can be understood that the private video data pool may be stored on the target vehicle for several hours or days, and may be likely to be started after the vehicle stops for several times. For example, when starting at one time, the target vehicle is still in the used private video data pool a, at this time, the target vehicle queries the real-time interface, can normally receive the updated data of the cloud in the state of having the network, and then performs data comparison, for example, checks the validity of the data, and if the video data cache invalidation time length in the private video data pool is set to be 7 days, the data beyond 7 days are deleted from the private video data pool and marked as invalid.
After the data validity check is completed, the new data (the private video data pool B generated by the public video data at this time) and the old data (the private video data pool A stored before the start at this time) can be compared, the contents are removed to be consistent, and then the two pieces of data are fused into a new private video data pool C, so that the updating is completed.
It is understood that if the updating is performed in an offline state, the updating can be completed through other private pools pre-stored in the ontology.
The application also provides a content recommendation device based on the third-party video platform, which comprises a public video data pool acquisition module, a user information acquisition module and a private video data pool generation module, wherein the public video data pool acquisition module is used for acquiring a public video data pool; the user information acquisition module is used for acquiring user information transmitted by the target vehicle; the private video data pool generation module is used for generating a private video data pool for the target vehicle according to the public video data and the user information and transmitting the private video data pool to the target vehicle.
The application also provides a video recommendation method for the automobile, which comprises the following steps:
the vehicle end transmits the user information of the vehicle end to the cloud end;
and the cloud generates a private video data pool for the target vehicle according to the user information transmitted by the vehicle end and the public video data pool acquired by the cloud and transmits the private video data pool to the target vehicle.
It will be appreciated that the above description of the method applies equally to the description of the apparatus.
The application further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and when the processor executes the computer program, the third-party video platform-based content recommendation method as above is implemented.
The application also provides a computer-readable storage medium, in which a computer program is stored, and when the computer program is executed by a processor, the third-party video platform-based content recommendation method as above can be implemented.
Fig. 2 is an exemplary block diagram of an electronic device capable of implementing a third-party video platform-based content recommendation method according to an embodiment of the present application.
As shown in fig. 2, the electronic device includes an input device 501, an input interface 502, a central processor 503, a memory 504, an output interface 505, and an output device 506. The input interface 502, the central processing unit 503, the memory 504 and the output interface 505 are connected to each other through a bus 507, and the input device 501 and the output device 506 are connected to the bus 507 through the input interface 502 and the output interface 505, respectively, and further connected to other components of the electronic device. Specifically, the input device 504 receives input information from the outside and transmits the input information to the central processor 503 through the input interface 502; the central processor 503 processes input information based on computer-executable instructions stored in the memory 504 to generate output information, temporarily or permanently stores the output information in the memory 504, and then transmits the output information to the output device 506 through the output interface 505; the output device 506 outputs the output information to the outside of the electronic device for use by the user.
That is, the electronic device shown in fig. 2 may also be implemented to include: a memory storing computer-executable instructions; and one or more processors which, when executing the computer-executable instructions, may implement the third party video platform based content recommendation method described in connection with fig. 1.
In one embodiment, the electronic device shown in fig. 2 may be implemented to include: a memory 504 configured to store executable program code; one or more processors 503 configured to execute the executable program code stored in the memory 504 to perform the third party video platform based content recommendation method in the above embodiments.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media include both non-transitory and non-transitory, removable and non-removable media that implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
Furthermore, it will be obvious that the term "comprising" does not exclude other elements or steps. A plurality of units, modules or devices recited in the device claims may also be implemented by one unit or overall device by software or hardware.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks identified in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The Processor in this embodiment 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, a discrete Gate or transistor logic device, a discrete hardware component, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may be used to store computer programs and/or modules, and the processor may implement various functions of the apparatus/terminal device by running or executing the computer programs and/or modules stored in the memory, as well as by invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, etc. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
In this embodiment, the module/unit integrated with the apparatus/terminal device may be stored in a computer-readable storage medium if it is implemented in the form of a software functional unit and sold or used as a separate product. Based on such understanding, all or part of the flow in the method according to the embodiments of the present invention may also be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of the embodiments of the method. 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 computer program code, recording medium, U.S. disk, removable hard disk, magnetic diskette, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signal, telecommunications signal, software distribution medium, etc. It should be noted that the computer readable medium may contain content that is appropriately increased or decreased as required by legislation and patent practice in the jurisdiction. Although the present application has been described with reference to the preferred embodiments, it is not intended to limit the present application, and those skilled in the art can make variations and modifications without departing from the spirit and scope of the present application.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Furthermore, it will be obvious that the term "comprising" does not exclude other elements or steps. A plurality of units, modules or devices recited in the device claims may also be implemented by one unit or overall device by software or hardware.
Although the invention has been described in detail hereinabove with respect to a general description and specific embodiments thereof, it will be apparent to those skilled in the art that modifications or improvements may be made thereto based on the invention. Accordingly, such modifications and improvements are intended to be within the scope of the invention as claimed.
Claims (10)
1. A content recommendation method based on a third-party video platform is characterized by comprising the following steps:
acquiring a public video data pool;
acquiring user information transmitted by a target vehicle;
and generating a private video data pool for the target vehicle according to the public video data and the user information and transmitting the private video data pool to the target vehicle.
2. The third-party video platform-based content recommendation method according to claim 1, wherein the obtaining a common video data pool comprises:
acquiring a resource interface;
and crawling required video data information from the resource interface in a crawler mode to generate a public video data pool.
3. The third-party video platform-based content recommendation method according to claim 2, wherein the user information transmitted by the target vehicle comprises a user basic information group and a vehicle driving information group;
the generating and transmitting a private video data pool for the target vehicle from the public video data and the user information to the target vehicle comprises:
scoring each piece of video data information in the public video data pool according to the user basic information group and/or the vehicle running information group, so as to obtain scoring information of each piece of video data information;
and generating a private video data pool for the target vehicle according to the grading information of each piece of video data information.
4. The third party video platform based content recommendation method according to claim 3,
the user basic information group comprises at least one piece of user basic information;
the vehicle travel information set includes at least one vehicle travel information;
the scoring of each piece of video data information in the public video data pool according to the user basic information group and/or the vehicle driving information group so as to acquire the scoring information of each piece of video data information comprises the following steps:
acquiring a user basic information weight database, wherein the user basic information weight database comprises a plurality of preset user basic information and a basic weight corresponding to each user basic information;
acquiring a vehicle running information weight database, wherein the vehicle running information weight database comprises a plurality of pieces of vehicle running information and a basic weight corresponding to each piece of vehicle running information;
acquiring video data information to be evaluated in a video data pool;
acquiring basic information in video data information;
acquiring user basic information and/or vehicle driving information which is suitable for grading the video data information in a user basic information group according to the basic information;
acquiring basic weight of each acquired user basic information according to the acquired user basic information suitable for grading the video data information and/or acquiring basic weight of each acquired vehicle driving information according to the acquired user basic information suitable for grading the video data information;
and generating the score of the video data information to be scored according to the obtained basic weight of the user basic information and the basic weight of the vehicle driving information.
5. The third-party video platform-based content recommendation method according to claim 4, wherein the generating a private video data pool for the target vehicle according to the rating information of each video data information comprises:
sequencing the video data information with scores;
and acquiring a preset number of video data information with the scores larger than a preset threshold value so as to form a private video data pool of the target vehicle.
6. The third party video platform based content recommendation method according to claim 5, wherein the basic information in the video data information comprises video type information;
the step of obtaining the user basic information and/or the vehicle driving information which is suitable for grading the video data information in the user basic information group according to the basic information comprises the following steps:
acquiring a type mapping database, wherein the type mapping database comprises video type information and user basic information and/or vehicle running information which are required to be acquired and correspond to each piece of video type information;
and acquiring the user basic information and/or vehicle running information which is required to be acquired and corresponds to the basic information according to the basic information of the video data information to be scored.
7. The third-party video platform based content recommendation method according to claim 6, wherein the third-party video platform based content recommendation method further comprises:
and randomly replacing user basic information and/or vehicle running information which is required to be acquired and corresponds to one or more video type information in the type mapping database by taking preset time as a period.
8. The third party video platform based content recommendation method of claim 7, wherein after the generating and delivering a private video data pool for the target vehicle to the target vehicle based on the public video data and the user information, the third party video platform based content recommendation method further comprises:
acquiring a user basic information group and a vehicle driving information group of the target vehicle after the private video data pool is transmitted to the target vehicle;
generating a new private video data pool for the target vehicle according to the newly acquired user basic information group and the vehicle running information group of the target vehicle;
updating the previously communicated private video data pool for the target vehicle with a new private video data pool for the target vehicle.
9. A third-party video platform-based content recommendation device is characterized in that the third-party video platform-based content recommendation device comprises:
the public video data pool acquisition module is used for acquiring a public video data pool;
the system comprises a user information acquisition module, a user information acquisition module and a user information processing module, wherein the user information acquisition module is used for acquiring user information transmitted by a target vehicle;
and the private video data pool generating module is used for generating a private video data pool for the target vehicle according to the public video data and the user information and transmitting the private video data pool to the target vehicle.
10. The video recommendation method for the automobile is characterized by comprising the following steps:
the vehicle end transmits the user information of the vehicle end to the cloud end;
and the cloud generates a private video data pool for the target vehicle according to the user information transmitted by the vehicle end and the public video data pool acquired by the cloud and transmits the private video data pool to the target vehicle.
Priority Applications (2)
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| CN202211096988.7A CN115374364A (en) | 2022-09-08 | 2022-09-08 | Content recommendation method and device based on third-party video platform |
| PCT/CN2023/093846 WO2024051202A1 (en) | 2022-09-08 | 2023-05-12 | Content recommendation method and apparatus based on third-party video platform |
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| CN202211096988.7A CN115374364A (en) | 2022-09-08 | 2022-09-08 | Content recommendation method and device based on third-party video platform |
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Cited By (2)
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| CN117336539A (en) * | 2023-09-28 | 2024-01-02 | 北京风平智能科技有限公司 | Video script production method and system for short video IP (Internet protocol) construction |
| WO2024051202A1 (en) * | 2022-09-08 | 2024-03-14 | 中国第一汽车股份有限公司 | Content recommendation method and apparatus based on third-party video platform |
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| WO2013191334A1 (en) * | 2012-06-22 | 2013-12-27 | 에스케이플래닛 주식회사 | System and method for recommending contents |
| CN105245956A (en) * | 2015-09-30 | 2016-01-13 | 上海车音网络科技有限公司 | Audio and video data recommendation method, device and system |
| CN107426610B (en) * | 2017-03-29 | 2020-04-28 | 聚好看科技股份有限公司 | Video information synchronization method and device |
| CN111556348A (en) * | 2020-03-13 | 2020-08-18 | 东风小康汽车有限公司重庆分公司 | Video pushing method and device and computer system |
| CN114065051A (en) * | 2021-11-25 | 2022-02-18 | 中国建设银行股份有限公司 | Private domain platform video recommendation method and device, electronic equipment and medium |
| CN115374364A (en) * | 2022-09-08 | 2022-11-22 | 中国第一汽车股份有限公司 | Content recommendation method and device based on third-party video platform |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024051202A1 (en) * | 2022-09-08 | 2024-03-14 | 中国第一汽车股份有限公司 | Content recommendation method and apparatus based on third-party video platform |
| CN117336539A (en) * | 2023-09-28 | 2024-01-02 | 北京风平智能科技有限公司 | Video script production method and system for short video IP (Internet protocol) construction |
| CN117336539B (en) * | 2023-09-28 | 2024-05-14 | 北京风平智能科技有限公司 | Video script production method and system for short video IP (Internet protocol) construction |
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