CN109936770B - Program recommendation method based on matrix online completion and program characteristics - Google Patents
Program recommendation method based on matrix online completion and program characteristics Download PDFInfo
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Abstract
The invention discloses a program recommendation method based on matrix online completion and program characteristics, which comprises the following steps: the method comprises the following steps: s01, reading the characteristics of each program; s02, calculating the correlation among the programs; s03, solving the objective function through an iteration method to obtain a completion matrix X; and S04, selecting programs for the target audience according to the completion matrix X to recommend the programs. The scoring matrix can be adjusted in real time after the new program is put in storage or the audience scores, and the pushing pertinence and the accuracy are high. The scheme is suitable for screening and recommending the multimedia program platform.
Description
Technical Field
The invention relates to the field of media program evaluation and pushing, in particular to a program recommendation method based on matrix online completion and program characteristics.
Background
Watching/listening to media programs such as audio and video is one of the common entertainment modes of people, and with the development of science and technology and the improvement of living standard, people listen to and watch programs more on internet-based equipment such as computers, mobile phones and PADs. In order to attract users to watch a plurality of different programs and improve the audience rating, the media provider can actively push the programs to the users. How to accurately push interested programs to users is one of the important topics in the big data era. The existing recommendation algorithm is often low in efficiency and cannot meet the requirement of real-time performance. And the feature information of the program itself is not applied to the recommendation, so the recommendation accuracy is not high. Especially for the program which is just put in storage and lacks enough evaluation and playing amount, how to quickly push the program to the possibly interested group is a problem which is difficult to solve.
Disclosure of Invention
The invention mainly solves the technical problem of insufficient pushing precision in the prior art, and provides a program recommendation method based on matrix online completion and program characteristics with high precision and low evaluation requirements.
The invention mainly solves the technical problems through the following technical scheme: a program recommendation method based on matrix online completion and program characteristics comprises the following steps:
s01, reading the characteristic of each program, and recording the characteristic f of the ith programiThe feature of the jth program is fj；
S02, calculating the correlation between the programs, and marking the correlation between the ith program and the jth program as sim (f)i,fj)；
S03, solving the following objective function through an iterative method:
obtaining a completion matrix X;
s04, selecting programs for the target audience to recommend according to the completion matrix X;
at time t0，t1，...，tkThe observation matrices are M respectively0，M1，…，MkThe observed index sets are respectively omega0，Ω1，…，ΩkWherein
The kth time tkThe newly appeared value of credit is
Represents the time tkThe index set of the program corresponding to the newly appeared credit value;
Preferably, the kernel norm | | X | | luminance of the matrix is employed*Instead of rank (X), the objective function is
||X||*Defined as the sum of all singular values of the matrix.
Preferably, in step S03, the initial value of the iterative solution is the completion matrix X calculated at the previous timek-1。
Preferably, in step S02, the program correlation is determined by the following formula:
sim(fi，fj)＝1-|fi-fj|/(fi+fj)
sim(fi，fj) Is the correlation between the ith program and the jth program.
The method has the substantial effects that the scoring matrix can be adjusted in real time after the new program is put in storage or the audience scores, the pushing pertinence is strong, and the accuracy is high.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
The technical scheme of the invention is further specifically described by the following embodiments and the accompanying drawings.
Example (b): there are a large number of broadcast programs on the system platform, so recommending broadcast programs of interest to audiences is an important function of the system platform. As the audience interest changes every day, the data in the system platform is continuously updated, for example, some programs are added, some programs are scored by the audience, and so on. Therefore, the real-time performance of the recommendation has a very important influence on the accuracy of the broadcast program recommendation. The conventional recommendation algorithm performs poorly in real-time.
If the audience and the broadcast program are viewed as two dimensions, the rating of the program by the audience can be viewed as a matrix, referred to as a rating matrix. Because only a portion of the audience has scored certain programs, the elements of some positions in the scoring matrix are unknown. Recommending broadcast programs for an audience requires estimating audience preferences for the programs, i.e., predicting unknown elements in the scoring matrix, so that estimation of unknown scores can translate into a matrix-filling problem. And (3) complementing the scoring matrix under the assumption of low rank, namely, ensuring that the rank of the scoring matrix is as small as possible under the condition that the predicted scoring matrix is limited to be consistent with the existing scoring matrix on an observation set. The reason for using the low rank assumption is that there is some correlation between programs and between audiences, and therefore, the rows and columns of the scoring matrix exhibit some correlation and thus have a low dimensionality.
There are m programs, n audiences, and the observation matrix is
The required completion matrix is
The index of the element observed in M is
Under the condition of low rank limitation, the objective function of matrix completion is as follows:
wherein
Is the projection operator, and the projection operator,
||·||Fis the Frobenious norm of the matrix, λ is the regularization parameter, rank (X) is the rank of X. Because rank (X) is very difficult to optimize, the academic community generally employs the kernel norm of the matrix | | | X | | luminance*Instead of rank, where | | X | | non-calculation*Defined as the sum of all singular values of the matrix. Thus, the objective function of the matrix completion in practice is as follows:
the existing scoring prediction model based on low-rank matrix completion has the following defects:
(1) in practice, because the scores of audiences for programs are continuously generated, when a new group of audiences score a part of programs, the system needs to update and adjust the whole scoring matrix immediately according to the new information, so as to predict the scores of all the audiences for all the programs more accurately. Therefore, what we need to solve is actually a problem of online matrix completion.
(2) The above model does not take into account the characteristic information of the program, and only evaluates and recommends based on the audience rating. Therefore, the recommendation accuracy is not high.
To this end, consideration needs to be given to designing program recommendations based on online matrix completion and program characteristics. First, the stronger the correlation between the two programs, i.e., sim (f)i，fj) The larger the audience scores the closer the two programs. By mixing
The program recommendation method is introduced into a previous objective function as a regular term, so that the characteristics and the relevance of the program are applied to the recommendation model to improve the recommendation accuracy. The objective function thus becomes:
then, an online solving algorithm is designed for the objective function so as to meet the requirement of real-time recommendation. The main mode is that the calculation result of the last iteration is used as the initial value of the iteration in the iteration process, so that the calculation efficiency is improved.
To facilitate the description of the specific flow, we first give the following notations: set at time t0，t1，...，tkThe observation matrices are M respectively0，M1，…，MkThe observed index sets are respectively omega0，Ω1，…，ΩkWherein
The kth time tkThe newly emerging score value is indexed by Ωk-Ωk-1. Is provided with
Represents the time tkAnd the newly appeared score value corresponds to the index set of the program.
The process is shown in figure 1:
(1) the correlation between the programs is calculated. The characteristics of each program can be obtained by using a cross-media data characteristic extraction model based on deep learning and the like, or the self-contained characteristics of the program can be directly read. Let the characteristics of the ith and jth programs be fiAnd fj。
(2) Calculating program similarity, similarity between fi and fj using sim (f)i，fj) It is shown that,
sim(fi，fj)＝1-|fi-fj|/(fi+fj)
sim(fi，fj) Larger indicates that the ith and jth programs are more relevant.
(3) And solving the current subproblem. At time tkSolving the following sub-problem：
(4) By using the model, the characteristic information of the program is applied to program recommendation, so that the recommendation accuracy is improved.
In addition, by using an online optimization algorithm, the calculation cost can be obviously reduced, and the real-time prediction of the scoring matrix is realized, so that the purpose of real-time recommendation is achieved.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.
Although the terms scoring matrix, completion matrix, nuclear norm, etc. are used more herein, the possibility of using other terms is not excluded. These terms are used merely to more conveniently describe and explain the nature of the present invention; they are to be construed as being without limitation to any additional limitations that may be imposed by the spirit of the present invention.
Claims (4)
1. A program recommendation method based on matrix online completion and program characteristics is characterized by comprising the following steps:
s01, reading the characteristic of each program, and recording the characteristic f of the ith programiThe feature of the jth program is fj；
S02, calculating the correlation between the programs, i-th program and j-th programThe correlation is noted as sim (f)i，fj)；
S03, solving the following objective function through an iterative method:
obtaining a completion matrix X;
s04, selecting programs for the target audience to recommend according to the completion matrix X;
at time t0，t1，…，tkThe observation matrices are M respectively0，M1，…，MkThe observed index sets are respectively omega0，Ω1，…，ΩkWherein
The kth time tkThe newly emerging score value is indexed by Ωk-Ωk-1；
Represents the time tkThe index set of the program corresponding to the newly appeared credit value;
PΩ：
is a projection operator, m is the total number of programs, n is the total number of viewers,
2. The matrix-based online completion and programming system of claim 1The characteristic program recommendation method is characterized in that the kernel norm of the matrix is adopted | | X | | ventilated voice*Instead of rank (X), the objective function is
||X||*Defined as the sum of all singular values of the matrix.
3. The method for recommending programs based on matrix online completion and program characteristics as claimed in claim 1 or 2, wherein in step S03, the initial value of the iterative solution is the completion matrix X calculated at the previous timek-1。
4. The method for recommending programs based on matrix online completion and program characteristics as claimed in claim 1, wherein in said step S02, the program correlation is determined by the following formula:
sim(fi，fj)＝1-|fi-fj|/(fi+fj)
sim(fi，fj) Is the correlation between the ith program and the jth program.
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| CN104965896A (en) * | 2015-06-23 | 2015-10-07 | 中国船舶重工集团公司第七0九研究所 | Recommendation method and system for representing user preference based on quadratic function |
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