CN112380389A - Video recommendation method and device, electronic equipment and readable storage medium - Google Patents
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Abstract
The invention relates to the technical field of multimedia information processing, in particular to a video recommendation method and device. The method comprises the following steps: acquiring sample video evaluation set; the sample video scoring set is a scoring data matrix R of a scoring user on the sample video set; processing the sample video scoring set to respectively obtain a sample video hidden feature matrix A and a scoring user hidden feature matrix B; respectively acquiring a sample video display characteristic and a grading user display characteristic; according to the matrixes A and B, respectively processing the obtained sample video display characteristics and the grading user display characteristics into a sample video display characteristic matrix C and a grading user display characteristic matrix D; processing the matrix C and the matrix D according to a preset processing strategy to obtain a matrix G; and performing score prediction on all videos according to the matrix G and recommending the videos to the target user according to the score prediction result.
Description
Technical Field
The invention relates to the technical field of multimedia information processing, in particular to a video recommendation method, a video recommendation device, electronic equipment and a readable storage medium.
Background
At present, the internet video content resources are rich, the requirement that a user is helped to screen videos which are interesting or needed by the user from the rich video resources is increasingly highlighted, a recommendation system is an effective way for solving the requirement, and a recommendation algorithm is a key for influencing the realization of the functions of the recommendation system. At present, the mainstream successful recommendation scheme is a recommendation scheme based on a collaborative filtering algorithm, and although the push collaborative filtering algorithm achieves brilliant achievements, a number of problems still exist to be solved urgently, such as the problem of data sparsity. Because the number of movies watched by users is large and the types of movies are various on the current network, most users only watch a small part of the movies, so that the problem of data sparsity of the users on the rating matrix of the movies exists, and the accuracy of movie recommendation can be seriously influenced by the problem of data sparsity.
Disclosure of Invention
The present application aims to solve at least one of the above technical drawbacks. The technical scheme adopted by the application is as follows:
in a first aspect, an embodiment of the present application discloses a video recommendation method, where the method includes:
acquiring sample video evaluation set; the sample video scoring set is a scoring data matrix R of a scoring user on the sample video set;
processing the sample video scoring set to respectively obtain a sample video hidden feature matrix A and a scoring user hidden feature matrix B;
respectively acquiring a sample video display characteristic and a grading user display characteristic;
according to the matrixes A and B, respectively processing the obtained sample video display characteristics and the grading user display characteristics into a sample video display characteristic matrix C and a grading user display characteristic matrix D;
processing the matrix C and the matrix D according to a preset processing strategy to obtain a matrix G;
and performing score prediction on all videos according to the matrix G and recommending the videos to the target user according to the score prediction result.
Optionally, the sample video scoring set is a scoring matrix table in a MovieLens 1M dataset.
Further, the processing the sample video scoring set to respectively obtain a sample video hidden feature matrix a and a scoring user hidden feature matrix B includes:
decomposing the matrix R by using an MF matrix decomposition algorithm to obtain a sample video hidden feature matrix A and a grading user hidden feature matrix B;
wherein R ∈ RN×MWherein N is the number of users and M is the number of movies;
A∈AK×M；B∈BN×K(ii) a Where K represents the number of hidden feature dimensions.
Further, the respectively obtaining the sample video display characteristics and the scoring user display characteristics comprises: and respectively analyzing the scoring user display characteristics and the sample video display characteristics from the user information and the video information text of the MovieLens 1M data set.
Further, the processing the obtained sample video display characteristics and the obtained scoring user display characteristics into a sample video display characteristic matrix C and a scoring user display characteristic matrix D respectively according to the matrices a and B includes:
processing the obtained sample video display characteristics and the obtained grading user display characteristics into a sample video display characteristic matrix C and a grading user display characteristic matrix D; wherein the content of the first and second substances,
the sample video display characteristic matrix C belongs to CI×M(ii) a Wherein I is the dimension number of the display characteristics of the sample video;
the scoring user display characteristic matrix D E DN×J(ii) a Where J displays the number of feature dimensions for the scoring user.
Further, the processing the matrix C and the matrix D according to a preset processing strategy to obtain a matrix G includes:
performing dimensionality reduction treatment on the matrix C and the matrix D respectively by using an LR linear regression algorithm to obtain a matrix E and a matrix F respectively; wherein the matrix E ∈ EH×M，F∈FN×HIn which H is<I，H<J；
And multiplying the matrix E and the matrix F to obtain a matrix G.
In a second aspect, an embodiment of the present application provides a video recommendation apparatus, where the apparatus includes: the device comprises an interface module, a processing module, a prediction module and a recommendation module; wherein the content of the first and second substances,
the interface module is used for acquiring sample video evaluation sets; the sample video scoring set is a scoring data matrix R of a scoring user on the sample video set;
the processing module is used for processing the sample video scoring set to respectively obtain a sample video hidden feature matrix A and a scoring user hidden feature matrix B;
the interface module is also used for respectively acquiring the display characteristics of the sample video and the display characteristics of the scoring user;
the processing module is used for respectively processing the acquired sample video display characteristics and the grading user display characteristics into a sample video display characteristic matrix C and a grading user display characteristic matrix D according to the matrixes A and B;
the processing module is further configured to process the matrix C and the matrix D according to a preset processing strategy to obtain a matrix G;
the prediction module is used for carrying out grading prediction on all videos according to the matrix G;
and the recommending module is used for recommending videos to the target user according to the grading prediction result.
Optionally, the sample video scoring set acquired by the interface module is a scoring matrix table in a MovieLens 1M dataset.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor and a memory;
the memory is used for storing operation instructions;
the processor is configured to execute the method in any of the embodiments by calling the operation instruction.
In a fourth aspect, the present application provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the computer program implements the method of any one of the above embodiments.
The method has the advantages that based on an optimized dimension reduction algorithm, the two dimension reduction methods are used for carrying out secondary dimension reduction on the acquired sample video evaluation diversity matrix, and the explicit characteristics of the user are quantitatively added into the recommendation process, so that the aims of better coping with data sparseness, fully utilizing user information and improving recommendation accuracy are fulfilled.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the description of the embodiments of the present application will be briefly described below.
Fig. 1 is a schematic flowchart of a video recommendation method according to an embodiment of the present application;
fig. 2 is a schematic diagram of a video recommendation apparatus according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present invention.
It will be understood by those skilled in the art that, unless otherwise specified, the singular forms "a", "an", "the" and "the" may include the plural forms, and the plural forms "a", "an", "a", and "the" are merely intended to illustrate the object definition for clarity and do not limit the object itself, and certainly, the object definition for "a" and "an" may be the same terminal, device, user, etc., and may also be the same terminal, device, user, etc. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. As used herein, the term "and/or" includes all or any element and all combinations of one or more of the associated listed items.
In addition, it is to be understood that "at least one" in the embodiments of the present application means one or more, "a plurality" means two or more. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a alone, both A and B, and B alone, where A, B may be singular or plural. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of the singular or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, a and b, a and c, b and c, or a, b and c, wherein a, b and c can be single or multiple.
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
The following describes some concepts, terms or devices that may be used or are used to aid in the explanation of the embodiments' technical solutions:
the movileens 1M dataset contains 100 ten thousand pieces of rating data from 6000 users for 4000 movies. It is divided into three tables: rating, user information, and movie information. After the data is decompressed from the zip file, the tables can be read into a data frame object through the data.
Spark is a distributed iterative computation framework based on a memory, and certainly, it can also perform iterative computation based on a disk, Spark is an iterative computation framework based on a memory, and the processed data can be from any storage medium, such as a relational database, a local file system, distributed storage, a network Socket byte stream, and the like.
The MF (matrix factorization) matrix decomposition MF algorithm has the characteristics of collaborative filtering, implicit analysis and supervised learning, and is a very classical algorithm in the recommendation field due to the realizability and high expansibility of matrix decomposition.
The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
The following describes the technical solutions of the present application and how to solve the above technical problems with specific embodiments. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 1 shows a schematic flowchart of a video recommendation provided in an embodiment of the present application, and as shown in fig. 1, the method mainly includes:
s101, acquiring sample video evaluation set; the sample video scoring set is a scoring data matrix R of a scoring user on the sample video set; wherein R ∈ RN×MWherein N is the number of users and M is the number of movies;
in the embodiment of the present application, the sample video scoring set is a scoring matrix table in a MovieLens 1M dataset.
S102, processing the sample video scoring set to respectively obtain a sample video hidden feature matrix A and a scoring user hidden feature matrix B;
specifically, the processing the sample video scoring set to respectively obtain a sample video hidden feature matrix a and a scoring user hidden feature matrix B includes: decomposing the matrix R by using an MF matrix decomposition algorithm to obtain a sample video hidden feature matrix A and a scoring user hidden feature matrix B, wherein A belongs to AK×M；B∈BN×K(ii) a Where K represents the number of hidden feature dimensions.
S103, respectively obtaining a sample video display characteristic and a grading user display characteristic;
specifically, the scoring user display characteristics and the sample video display characteristics are respectively analyzed from the user information and the video information text of the MovieLens 1M data set. The display characteristics of the user include, but are not limited to, the user's gender, age, occupation, etc. dimensions. The display characteristics of the video include, but are not limited to, dimensions such as various classification criteria and specific types under the criteria of the video, such as horror, action, love, and the like. The introduction of the display feature can result in better recommendation.
S104, according to the matrixes A and B, respectively processing the acquired sample video display characteristics and the acquired grading user display characteristics into a sample video display characteristic matrix C and a grading user display characteristic matrix D;
in the embodiment of the application, the obtained sample video display characteristics and the grading user display characteristics are processed into a sample video display characteristic matrix C and a grading user display characteristic matrix D; wherein the sample video display feature matrix C ∈ CI×M(ii) a Wherein I is the dimension number of the display characteristics of the sample video; the scoring user display characteristic matrix D E DN×J(ii) a Where J displays the number of feature dimensions for the scoring user. The application combines the numerical expression of the explicit characteristics with the implicit characteristic matrix decomposed from the MF matrix to improve the recommendation accuracy.
S105, processing the matrix C and the matrix D according to a preset processing strategy to obtain a matrix G;
specifically, according to a preset processing strategy, the processing procedure of the matrix C and the matrix D is as follows: firstly, performing dimensionality reduction treatment on a matrix C and a matrix D respectively by utilizing an LR linear regression algorithm to obtain a matrix E and a matrix F respectively; wherein the matrix E ∈ EH×M，F∈FN ×HIn which H is<I，H<J; and multiplying the matrix E and the matrix F to obtain a matrix G.
And S106, performing grading prediction on all videos according to the matrix G and recommending the videos to the target user according to the grading prediction result.
In the embodiment of the application, the matrix G realizes the preset result of the grading of all video resources in the resource library, so that high-grade videos or movies meeting the type of the videos can be recommended to the user according to the obtained video prediction grading values and the type of the user, and the recommendation rule can accept the user-defined modification of the user.
Based on the video recommendation method shown in fig. 1, another aspect of the present application provides a video recommendation apparatus, as shown in fig. 2, the apparatus may include: 201 an interface module, 202 a processing module, 203 a prediction module and 204 a recommendation module; wherein the content of the first and second substances,
the 201 interface module is used for acquiring sample video evaluation diversity; the sample video scoring set is a scoring data matrix R of a scoring user on the sample video set;
the 202 processing module is configured to process the sample video scoring set to obtain a sample video hidden feature matrix a and a scored user hidden feature matrix B, respectively;
the 201 interface module is further configured to obtain a sample video display feature and a score user display feature respectively;
the 202 processing module is configured to process the acquired sample video display characteristics and the scoring user display characteristics into a sample video display characteristic matrix C and a scoring user display characteristic matrix D, respectively, according to the matrices a and B;
the 202 processing module is further configured to process the matrix C and the matrix D according to a preset processing strategy to obtain a matrix G;
the 203 prediction module is used for performing grading prediction on all videos according to the matrix G;
and the 204 recommendation module is used for recommending videos to the target user according to the grading prediction result.
Optionally, the sample video scoring set acquired by the interface module 201 is a scoring matrix table in a MovieLens 1M dataset.
It is understood that the above-mentioned respective constituent devices of the video recommendation apparatus in the present embodiment have functions of implementing the respective steps of the method in the embodiment shown in fig. 1. The function can be realized by hardware, and can also be realized by executing corresponding software by hardware. The hardware or software includes one or more modules or systems corresponding to the above-described functions. The modules and systems can be software and/or hardware, and the modules and systems can be realized independently or integrated by a plurality of modules and systems. For the functional description of each module and system, reference may be specifically made to the corresponding description of the method in the embodiment shown in fig. 1, and therefore, the beneficial effects that can be achieved by the method may refer to the beneficial effects in the corresponding method provided above, which are not described again here.
It is to be understood that the illustrated structure of the embodiment of the present invention does not specifically limit the specific structure of the video recommendation apparatus. In other embodiments of the present application, the video recommendation device may include more or fewer components than shown, or combine certain components, or split certain components, or a different arrangement of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.
The embodiment of the application provides an electronic device, which comprises a processor and a memory;
a memory for storing operating instructions;
and the processor is used for executing the video recommendation method provided by any embodiment of the application by calling the operation instruction.
As an example, fig. 3 shows a schematic structural diagram of an electronic device to which the embodiment of the present application is applied, and as shown in fig. 3, the electronic device 300 includes: a processor 301 and a memory 303. Wherein processor 301 is coupled to memory 303, such as via bus 302. Optionally, the electronic device 300 may further include a transceiver 304. It should be noted that the practical application of the transceiver 304 is not limited to one. It is to be understood that the illustrated structure of the embodiment of the present invention does not constitute a specific limitation to the specific structure of the electronic device 300. In other embodiments of the present application, electronic device 300 may include more or fewer components than shown, or some components may be combined, some components may be split, or a different arrangement of components. The illustrated components may be implemented in hardware, software, or a combination of software and hardware. Optionally, the electronic device may further include a display screen 305 for displaying images or receiving operation instructions of a user as needed.
The processor 301 is applied to the embodiment of the present application, and is configured to implement the method shown in the foregoing method embodiment. The transceiver 304 may include a receiver and a transmitter, and the transceiver 304 is applied in the embodiment of the present application and is used for implementing the function of the electronic device of the embodiment of the present application to communicate with other devices when executed.
The Processor 301 may be a CPU (Central Processing Unit), a general-purpose Processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other Programmable logic device, a transistor logic device, a hardware component, or any combination thereof. Which may implement or perform the various illustrative logical blocks, modules, and circuits described in connection with the disclosure. The processor 301 may also be a combination of computing functions, e.g., comprising one or more microprocessors, a combination of a DSP and a microprocessor, or the like.
The processor 301 may operate the video recommendation method provided in the embodiment of the present application, so as to reduce the operation complexity of the user, improve the intelligent degree of the terminal device, and improve the user experience. The processor 301 may include different devices, for example, when the CPU and the GPU are integrated, the CPU and the GPU may cooperate to execute the video recommendation method provided in the embodiment of the present application, for example, part of the algorithm in the video recommendation method is executed by the CPU, and another part of the algorithm is executed by the GPU, so as to obtain faster processing efficiency.
The Memory 303 may be a ROM (Read Only Memory) or other type of static storage device that can store static information and instructions, a RAM (Random Access Memory) or other type of dynamic storage device that can store information and instructions, an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact disk), a high speed Random Access Memory, a non-volatile Memory such as at least one magnetic disk storage device, a flash Memory device, a universal flash Memory (UFS), or other optical disk storage, optical disk storage (including Compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, a magnetic disk storage medium, or other magnetic storage device, Or any other medium which can be used to carry or store desired program code in the form of instructions or data structures and which can be accessed by a computer, but is not limited to such.
Optionally, the memory 303 is used for storing application program codes for executing the scheme of the present application, and is controlled by the processor 301 to execute. The processor 301 is configured to execute the application program code stored in the memory 303 to implement the video recommendation method provided in any embodiment of the present application.
The memory 303 may be used to store computer-executable program code, which includes instructions. The processor 301 executes various functional applications of the electronic device 300 and data processing by executing instructions stored in the memory 303. The memory 303 may include a program storage area and a data storage area. Wherein, the storage program area can store the codes of the operating system and the application program, etc. The storage data area may store data created during use of the electronic device 300 (e.g., images, video, etc. captured by a camera application), and the like.
The memory 303 may further store one or more computer programs corresponding to the video recommendation method provided in the embodiment of the present application. The one or more computer programs stored in the memory 303 and configured to be executed by the one or more processors 301 include instructions that may be used to perform the various steps in the respective embodiments described above.
Of course, the code of the video recommendation method provided in the embodiment of the present application may also be stored in the external memory. In this case, the processor 301 may execute the code of the video recommendation method stored in the external memory through the external memory interface, and the processor 301 may control the execution of the video recommendation process.
The display screen 305 includes a display panel. The display panel may be a Liquid Crystal Display (LCD), an organic light-emitting diode (OLED), an active-matrix organic light-emitting diode (active-matrix organic light-emitting diode, AMOLED), a flexible light-emitting diode (FLED), a miniature, a Micro-oeld, a quantum dot light-emitting diode (QLED), or the like. In some embodiments, the electronic device 300 may include 1 or N display screens 305, N being a positive integer greater than 1. The display screen 305 may be used to display information input by or provided to the user as well as various Graphical User Interfaces (GUIs). For example, the display screen 305 may display a photograph, video, web page, or file, etc.
The electronic device provided by the embodiment of the present application is applicable to any embodiment of the above method, and therefore, the beneficial effects that can be achieved by the electronic device can refer to the beneficial effects in the corresponding method provided above, and are not described again here.
The embodiment of the application provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the video recommendation method shown in the above method embodiment is implemented.
The computer-readable storage medium provided in the embodiments of the present application is applicable to any embodiment of the foregoing method, and therefore, the beneficial effects that can be achieved by the computer-readable storage medium can refer to the beneficial effects in the corresponding method provided above, and are not described herein again.
The embodiment of the present application further provides a computer program product, which when running on a computer, causes the computer to execute the above related steps to implement the method in the above embodiment. The computer program product provided in the embodiments of the present application is applicable to any of the embodiments of the method described above, and therefore, the beneficial effects that can be achieved by the computer program product can refer to the beneficial effects in the corresponding method provided above, and are not described herein again.
The video recommendation scheme disclosed by the embodiment of the application acquires sample video evaluation diversity; the sample video scoring set is a scoring data matrix R of a scoring user on the sample video set; processing the sample video scoring set to respectively obtain a sample video hidden feature matrix A and a scoring user hidden feature matrix B; respectively acquiring a sample video display characteristic and a grading user display characteristic; according to the matrixes A and B, respectively processing the obtained sample video display characteristics and the grading user display characteristics into a sample video display characteristic matrix C and a grading user display characteristic matrix D; processing the matrix C and the matrix D according to a preset processing strategy to obtain a matrix G; and performing score prediction on all videos according to the matrix G and recommending the videos to the target user according to the score prediction result. The method is based on an optimization dimension reduction algorithm, specifically, two dimension reduction methods are used for carrying out secondary dimension reduction on an acquired sample video evaluation diversity matrix, and explicit characteristics of a user are quantitatively added to a recommendation process, so that the aims of better coping with data sparseness, fully utilizing user information and improving recommendation accuracy are fulfilled.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, a module or a unit may be divided into only one logical function, and may be implemented in other ways, for example, a plurality of units or components may be combined or integrated into another apparatus, or some features may be discarded or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may be one physical unit or a plurality of physical units, may be located in one place, or may be distributed to a plurality of different places. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially or partially contributed to by the prior art, or all or part of the technical solutions may be embodied in the form of a software product, where the software product is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present application, and can make several modifications and decorations, and these changes, substitutions, improvements and decorations should also be considered to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method for video recommendation, the method comprising:
acquiring sample video evaluation set; the sample video scoring set is a scoring data matrix R of a scoring user on the sample video set;
processing the sample video scoring set to respectively obtain a sample video hidden feature matrix A and a scoring user hidden feature matrix B;
respectively acquiring a sample video display characteristic and a grading user display characteristic;
according to the matrixes A and B, respectively processing the obtained sample video display characteristics and the grading user display characteristics into a sample video display characteristic matrix C and a grading user display characteristic matrix D;
processing the matrix C and the matrix D according to a preset processing strategy to obtain a matrix G;
and performing score prediction on all videos according to the matrix G and recommending the videos to the target user according to the score prediction result.
2. The video recommendation method according to claim 1, wherein the sample video scoring sets are scoring matrix tables in a MovieLens 1M dataset.
3. The video recommendation method according to claim 1 or 2, wherein the processing the sample video scale set to obtain a sample video hidden feature matrix a and a scale user hidden feature matrix B respectively comprises:
decomposing the matrix R by using an MF matrix decomposition algorithm to obtain a sample video hidden feature matrix A and a grading user hidden feature matrix B;
wherein R ∈ RN×MWherein N is the number of users and M is the number of movies;
A∈AK×M；B∈BN×K(ii) a Where K represents the number of hidden feature dimensions.
4. The video recommendation method of claim 3, wherein obtaining sample video display characteristics and scored user display characteristics, respectively, comprises:
and respectively analyzing the scoring user display characteristics and the sample video display characteristics from the user information and the video information text of the MovieLens 1M data set.
5. The video recommendation method according to claim 1 or 4, wherein said processing said obtained sample video display characteristics and scored user display characteristics into a sample video display characteristics matrix C and a scored user display characteristics matrix D, respectively, according to said matrices A and B comprises:
processing the obtained sample video display characteristics and the obtained grading user display characteristics into a sample video display characteristic matrix C and a grading user display characteristic matrix D; wherein the content of the first and second substances,
the sample video display characteristic matrix C belongs to CI×M(ii) a Wherein I is the dimension number of the display characteristics of the sample video;
the scoring user display characteristic matrix D E DN×J(ii) a Where J displays the number of feature dimensions for the scoring user.
6. The video recommendation method according to claim 5, wherein said processing the matrix C and the matrix D according to a preset processing strategy to obtain a matrix G comprises:
performing dimensionality reduction treatment on the matrix C and the matrix D respectively by using an LR linear regression algorithm to obtain a matrix E and a matrix F respectively; wherein the matrix E ∈ EH×M，F∈FN×HIn which H is<I，H<J；
And multiplying the matrix E and the matrix F to obtain a matrix G.
7. A video recommendation apparatus, characterized in that the apparatus comprises: the device comprises an interface module, a processing module, a prediction module and a recommendation module; wherein the content of the first and second substances,
the interface module is used for acquiring sample video evaluation sets; the sample video scoring set is a scoring data matrix R of a scoring user on the sample video set;
the processing module is used for processing the sample video scoring set to respectively obtain a sample video hidden feature matrix A and a scoring user hidden feature matrix B;
the interface module is also used for respectively acquiring the display characteristics of the sample video and the display characteristics of the scoring user;
the processing module is used for respectively processing the acquired sample video display characteristics and the grading user display characteristics into a sample video display characteristic matrix C and a grading user display characteristic matrix D according to the matrixes A and B;
the processing module is further configured to process the matrix C and the matrix D according to a preset processing strategy to obtain a matrix G;
the prediction module is used for carrying out grading prediction on all videos according to the matrix G;
and the recommending module is used for recommending videos to the target user according to the grading prediction result.
8. The video recommendation device of claim 7, wherein the sample video scoring sets are scoring matrix tables in a MovieLens 1M dataset.
9. An electronic device comprising a processor and a memory;
the memory is used for storing operation instructions;
the processor is used for executing the method of any one of claims 1-6 by calling the operation instruction.
10. A computer-readable storage medium, characterized in that the storage medium has stored thereon a computer program which, when being executed by a processor, carries out the method of any one of claims 1-6.
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| CN109947983A (en) * | 2017-09-19 | 2019-06-28 | Tcl集团股份有限公司 | Video recommendation method, system, terminal and computer readable storage medium |
| CN111259238A (en) * | 2020-01-13 | 2020-06-09 | 山西大学 | Post-interpretable recommendation method and device based on matrix decomposition |
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| CN109947983A (en) * | 2017-09-19 | 2019-06-28 | Tcl集团股份有限公司 | Video recommendation method, system, terminal and computer readable storage medium |
| CN111259238A (en) * | 2020-01-13 | 2020-06-09 | 山西大学 | Post-interpretable recommendation method and device based on matrix decomposition |
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