CN116934389B - Digital television value ticket card management system based on cloud computing and cross-platform technology - Google Patents
Digital television value ticket card management system based on cloud computing and cross-platform technology Download PDFInfo
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Abstract
The invention relates to the technical field of data push management, in particular to a digital television value card management system based on cloud computing and cross-platform technology. The system comprises: the acquisition module is used for acquiring the video watching record data and the consumption record data of the same user with multiple platforms; the processing module is used for obtaining a viewing characteristic coefficient according to the type of the viewing program, the viewing frequency and the total viewing time, determining a drama characteristic coefficient according to the viewing time point of the hot-cast film and the film showing time point, and determining a viewing habit characteristic coefficient of a user according to the viewing characteristic coefficient and the drama characteristic coefficient; and the management module is used for fitting the viewing habit characteristic coefficients according to the linear function and the activating function to obtain a viewing habit fitting function, and carrying out ticket card pushing management on the user according to the viewing habit fitting function and the consumption amount. The invention can improve the reliability of the push management of the valuable card and enhance the adaptability of the push management of the valuable card.
Description
Technical Field
The invention relates to the technical field of data push management, in particular to a digital television value card management system based on cloud computing and cross-platform technology.
Background
The digital television value card is a prepaid card that can be used to purchase digital television services. In the current digital televisions, a lot of programs can be watched after paying, in order to bring more viewing users, service providers usually issue coupons, month cards, year cards and other valuable coupon card services to users, so that users are stimulated to consume, and high-quality content needs to be pushed according to the preference of the users, and the consumption desire of the users is improved. The valuable card can improve sales of merchants and improve consumption experience of customers.
In the related art, through a mode of setting a label for a user, the push management of the valuable card is performed on the user according to the label, in this mode, the label of the user may have conflicts on different platforms, so that the final push management can generate paradoxical push logic, and further the information push of the valuable card cannot effectively fit with the viewing habit and the consumption habit of the user, the reliability of the push management of the valuable card is poor, and the push management adaptability of the valuable card is insufficient.
Disclosure of Invention
In order to solve the technical problems of poor reliability of valuable card pushing management and insufficient adaptability of valuable card pushing management, the invention provides a digital television valuable card management system based on cloud computing and cross-platform technology, which adopts the following technical scheme:
the invention provides a digital television value ticket card management system based on cloud computing and cross-platform technology, which comprises:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring video watching record data and consumption record data of a digital television platform and other video platforms for the same user, the video watching record data comprise video watching program types, video watching total time length, video watching frequency and video watching time points, and the consumption record data comprise consumption amounts on different platforms;
the processing module is used for obtaining a viewing characteristic coefficient according to the viewing program type, the viewing frequency and the viewing total duration, determining a chasing characteristic coefficient according to the viewing time point of the thermally-cast film and the film showing time point, and determining a viewing habit characteristic coefficient of the user according to the viewing characteristic coefficient and the chasing characteristic coefficient;
and the management module is used for fitting the viewing habit characteristic coefficients according to the linear function and the activating function to obtain a viewing habit fitting function, determining the ticket card pushing index of the user according to the viewing habit fitting function and the consumption amount, and managing the ticket card pushing of the user according to the ticket card pushing index.
Further, the obtaining the viewing characteristic coefficient according to the viewing program type, the viewing frequency and the total viewing duration includes:
calculating the ratio of the number of the types of the watching program to the total number of the preset types to obtain a type influence factor;
calculating the ratio of the total video watching time length to the video watching frequency as a single average time length, and carrying out normalization processing on the single average time length to obtain a time length influence factor;
and obtaining a film watching characteristic coefficient according to the type influence factor and the duration influence factor, wherein the type influence factor and the film watching characteristic coefficient form a positive correlation, the duration influence factor and the film watching characteristic coefficient form a positive correlation, and the value of the film watching characteristic coefficient is a normalized value.
Further, the determining the chasing feature coefficient according to the film watching time point and the film showing time point of the thermally-broadcast film includes:
calculating the time interval between the film showing time point and the film watching time point of the same hot-cast film to obtain the drama time difference of the user to the hot-cast film;
and taking the inverse proportion normalized value of the user's chasing time difference mean value of all the thermally-broadcast films at the current moment as the chasing feature coefficient.
Further, the determining the viewing habit feature coefficient of the user according to the viewing feature coefficient and the chasing feature coefficient includes:
and taking the sum normalized value of the viewing characteristic coefficient and the chasing characteristic coefficient as a viewing habit characteristic coefficient.
Further, the activation function is a sigmoid function, the fitting is performed on the viewing habit feature coefficients according to a linear function and the activation function to obtain a viewing habit fitting function, and the method comprises the following steps:
taking the characteristic coefficient of the viewing habit as the slope of an independent variable in the linear function to obtain a viewing linear function;
multiplying the viewing habit characteristic coefficient with a sigmoid function to obtain a viewing activation function;
calculating the difference between the viewing linear function and the viewing activation function as a fusion difference;
and taking the fusion difference as a weight, and respectively carrying out weighted average fusion treatment on the viewing linear function and the viewing activation function to obtain a viewing habit fitting function.
Further, the calculating the difference between the viewing linear function and the viewing activation function as a fusion difference includes:
and calculating the fixed integral of the difference between the viewing linear function and the viewing activation function to obtain the fusion difference.
Further, the determining the card pushing index of the user according to the viewing habit fitting function and the consumption amount includes:
calculating the average value of the consumption amounts of all the users as a consumption average value; taking the ratio normalized value of the consumption amount and the consumption average value as a consumption habit characteristic coefficient;
substituting the consumption habit characteristic coefficient as an independent variable into the viewing habit fitting function to obtain a coupon card pushing index.
Further, the managing the card pushing performed on the user according to the card pushing index includes:
presetting at least two card combinations, and pushing the card combinations according to the sizes of the card pushing indexes.
Further, the ticket card combination includes a heavy viewing ticket card combination and a light viewing ticket card combination, the pushing the ticket card combination according to the size of the ticket card pushing index includes:
when the ticket card pushing index is larger than a preset index threshold, selecting the severe movie viewing ticket card combination;
and when the card pushing index is smaller than or equal to a preset index threshold, selecting the light film watching card combination.
The invention has the following beneficial effects:
the invention is applied to the technical field of data push management, and obtains the viewing records and consumption records of the same user on different platforms through a cross-platform technology to obtain the viewing record data and consumption record data, so that the viewing characteristic coefficient is determined according to the viewing program type, the viewing frequency and the total viewing duration in the viewing record data, the viewing behavior of the user can be effectively analyzed, the chasing characteristic coefficient is obtained through the viewing time point and the film showing time point, the chasing behavior of the user is effectively analyzed, the viewing behavior and the chasing behavior can be combined, the viewing habit of the user is objectively analyzed to obtain the viewing habit characteristic coefficient, the viewing habit characteristic coefficient can effectively represent the viewing habit and viewing preference of the user, multiple crowds can be covered when the valuable coupon card push management is carried out according to the habit characteristic coefficient, the application scope is wide, and the result is more accurate; according to the invention, according to the viewing habit fitting function and the consumption amount, the ticket card pushing index is determined, namely, the pushing index of the corresponding valuable ticket card combination is determined, and the adaptive ticket card pushing index is acquired through the viewing habit fitting function and the consumption amount, so that the best pushing management result is obtained, the ticket card pushing index can be used for pushing and managing the valuable ticket card accurately and objectively according to the viewing habit and the consumption habit of a user, the information pushing of the valuable ticket card is more fit with the viewing habit and the consumption behavior of the user, the reliability of the valuable ticket card pushing management is improved, and the adaptability of the valuable ticket card pushing management is enhanced.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a block diagram of a digital television value card management system based on cloud computing and cross-platform technology according to an embodiment of the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention to achieve the preset aim, the following detailed description is given below of a digital television value card management system based on cloud computing and cross-platform technology according to the invention, and the detailed implementation, structure, characteristics and effects thereof are as follows. In the following description, different "one embodiment" or "another embodiment" means that the embodiments are not necessarily the same. Furthermore, the particular features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The invention provides a digital television value card management system based on cloud computing and cross-platform technology.
Referring to fig. 1, a block diagram of a digital television value card management system based on cloud computing and cross-platform technology according to an embodiment of the present invention is shown, where the system includes: an acquisition module 101, a processing module 102 and a management module 103.
The obtaining module 101 is configured to obtain, for the same user, viewing record data and consumption record data of the digital television platform and other video platforms, where the viewing record data includes a viewing program type, a viewing total duration, a viewing frequency, and a viewing time point, and the consumption record data includes consumption amounts on different platforms.
It can be understood that, because the value ticket card of the digital television is pushed according to the data such as viewing preference and consumption behavior of the user, the method mainly analyzes the program types that the user likes to watch and whether the user has the habit of viewing the video in a payment manner.
In the embodiment of the invention, the video watching record data and consumption record data of the same user can be obtained based on the network record, and it is to be noted that in the embodiment of the invention, the collection and the obtaining of the video watching record, the consumption record and other data of the user are agreed by the user, the obtaining process complies with the relevant legal regulations, and the public order harmony is not violated.
In the embodiment of the invention, the video recording data is recording data for representing the video condition of the user, and the video recording data comprises video program types, video total duration, video frequency and video time points, wherein the video program types are type labels of films watched by the user, for example, the video program types can be specifically various labels such as 'ancient wind', 'suspense', 'inference', 'even image' and the like, corresponding labels can be configured for different video dramas in advance by a digital television platform or a video platform, and then, the video program types of the user are added according to the video dramas watched by the user.
The total film watching time length is the total film watching time length of the user, the film watching frequency is the clicking times of the user watching the film, and the film watching time point is the time point of the user watching the film and television drama. That is, the type of the video watching program, the total video watching time, the video watching frequency and the video watching time point are all used for representing habit features of the user in the video watching process, and then the video watching habit of the user can be analyzed by acquiring related video watching record data.
Of course, in other embodiments of the present invention, the video recording data may also include a plurality of other possible recording data, such as a single time period for watching a movie, a concentrated time period for watching a movie, etc., which is not limited thereto.
The consumption record data comprises consumption amounts on different platforms, namely the consumption record data is data corresponding to the consumption habit of the user.
After the video watching record data and the consumption record data of the same user are obtained by the multi-platform, the video watching habit, the consumption habit and the like of the user can be analyzed according to the video watching record data and the consumption record data, and then the digital television valuable coupon card is adaptively pushed based on the video watching habit and the consumption habit, and the follow-up embodiment is particularly referred to.
The processing module 102 is configured to obtain a viewing characteristic coefficient according to a viewing program type, a viewing frequency and a total viewing duration, determine a drama characteristic coefficient according to a viewing time point of a thermally-cast film and a film showing time point, and determine a viewing habit characteristic coefficient of a user according to the viewing characteristic coefficient and the drama characteristic coefficient.
Further, in some embodiments of the present invention, obtaining the viewing characteristic coefficients according to the type of the viewing program, the viewing frequency and the total viewing duration includes: calculating the ratio of the number of the types of the watching program to the total number of the preset types to obtain a type influence factor; calculating the ratio of the total video watching time length to the video watching frequency as a single average time length, and carrying out normalization processing on the single average time length to obtain a time length influence factor; and obtaining a video watching characteristic coefficient according to the type influence factor and the duration influence factor, wherein the type influence factor and the video watching characteristic coefficient have positive correlation, the duration influence factor and the video watching characteristic coefficient have positive correlation, and the value of the video watching characteristic coefficient is a normalized value.
The positive correlation relationship indicates that the dependent variable increases along with the increase of the independent variable, the dependent variable decreases along with the decrease of the independent variable, and the specific relationship can be multiplication relationship, addition relationship, idempotent of an exponential function and is determined by practical application; the negative correlation indicates that the dependent variable decreases with increasing independent variable, and the dependent variable increases with decreasing independent variable, which may be a subtraction relationship, a division relationship, or the like, and is determined by the actual application.
The total number of preset types is the number of preset movie program types, and the total number of preset types can be determined according to movie and television labels in each actual platform, that is, the total number of different types of movie and television labels in all platforms is taken as the total number of preset types.
In the embodiment of the present invention, the calculation formula of the film-viewing characteristic coefficient may specifically be, for example:
in the embodiment of the invention, K a A represents the viewing characteristic coefficient of the a-th user, a represents the index of the user, n a Representing the number of types of viewing programs for the a-th user, n all Representing the total number of preset types, t a Representing the total time length of watching a-th user, f a Representing the viewing frequency of the a-th user,a type influencing factor representing the a-th user, < ->Represents the single average duration of the a-th user, < >>Representing the duration impact factor of the a-th user, G () represents the normalization process, which in one embodiment of the present invention may be specifically, for example, the maximum-minimum normalization process, and normalization in the subsequent steps may each employ the maximum-minimum normalization processIn other embodiments of the present invention, other normalization methods may be selected according to a specific numerical range, which will not be described herein.
It will be appreciated that when the user views the film, the corresponding video recording data is generated, and the user must click on the film to view the film, the corresponding video frequency is 1, that is, the minimum value of the video frequency is 1, and the situation of 0 cannot occur.
In the embodiment of the invention, the type influence factors can represent the comprehensiveness of the user in watching the movie, namely, the more types of the movie watched by the user, the larger the corresponding type influence factors, and further, it can be understood that when the type influence factors are larger, the more likely the corresponding users are severe movie viewing fans, and the larger the movie feature coefficients are. The length influence factor characterizes the average film watching length of the user, the ratio of the total film watching length to the film watching frequency is taken as single average length, when the single average length is larger, the user can be more characterized as the real audience of the film and television drama, when the single average length is smaller, the user can only click in, or watch for a few minutes, the watching is stopped, that is, the watching dependence of the user on the film and television drama is not high, the corresponding film watching fan which can characterize the user as mild is shown, and the film watching characteristic coefficient is smaller, so that the type influence factor and the film watching characteristic coefficient are in positive correlation, and the length influence factor and the film watching characteristic coefficient are in positive correlation.
Further, in some embodiments of the present invention, in order to more accurately and comprehensively determine viewing habits of users, the present invention further analyzes chasing features of users, and determines chasing feature coefficients according to a viewing time point of a thermally-cast film and a film showing time point, including: calculating the time interval between the film showing time point and the film watching time point of the same hot-cast film to obtain the drama time difference of the user to the hot-cast film; and taking the inverse proportion normalized value of the user's chasing time difference mean value of all the thermally-broadcast films at the current moment as the chasing feature coefficient.
It can be understood that most chasing dramas begin to watch at the beginning of the movie and more chasing the movie, and the platform limits at least one heat-collecting broadcast film for charging and guiding broadcasting, so that users watching the heat-collecting broadcast film immediately just come out, and the corresponding users prefer to pay to watch, and the invention can count the time of the heat-collecting broadcast film of all platforms in different time periods as a film showing time point, and the time interval between the time of the user watching the heat-collecting broadcast film and the time point of the heat-collecting broadcast film as the time difference of the user chasing the heat-collecting broadcast film. Therefore, all the hot-broadcast films watched by the user are counted, and inverse proportion normalization values are carried out according to the average value of the chasing time differences of all the hot-broadcast films, so that the chasing characteristic coefficients are obtained.
In the embodiment of the invention, the chasing feature coefficient can represent the chasing degree of the corresponding user, and the larger the chasing feature coefficient is, the larger the chasing degree of the user is, that is, the more the corresponding user has the willingness to consume for chasing.
Further, in some embodiments of the present invention, determining the viewing habit feature coefficient of the user according to the viewing feature coefficient and the chasing feature coefficient includes: and taking the sum normalized value of the viewing characteristic coefficient and the chasing characteristic coefficient as the viewing habit characteristic coefficient.
The video watching depth of the user is represented by the video watching characteristic coefficient, the video watching characteristic coefficient is larger, the video watching time of the user is longer, the video watching type is wider, the attention tracking characteristic coefficient represents the updating attention of the user to the thermally-broadcast video, the attention tracking characteristic coefficient is larger, the current user is willing to pay more, therefore, the sum normalized value of the video watching characteristic coefficient and the attention tracking characteristic coefficient is calculated as the video watching habit characteristic coefficient, and the video watching habit characteristic coefficient can be further combined with the video watching characteristic coefficient and the attention tracking characteristic coefficient to represent the video watching habit of the user.
And the management module 103 is used for fitting the viewing habit characteristic coefficients according to the linear function and the activation function to obtain a viewing habit fitting function, determining the coupon pushing index of the user according to the viewing habit fitting function and the consumption amount, and managing the coupon pushing of the user according to the coupon pushing index.
After the viewing habit characteristic coefficients are determined, a mixed pushing algorithm can be used for constructing a viewing habit model of a user, and it can be understood that the linear relationship can intuitively reflect the corresponding relationship between input and output, but the defect is that the fault tolerance is small, and when the degree of data deviating from a main direction is large, the problem of fitting relationship errors can be caused easily in the data; the nonlinear relation can exactly make up the deficiency in the linear relation and can meet the required data change trend, so that the invention adopts two pushing algorithms, namely a linear pushing algorithm and a nonlinear pushing algorithm, namely the viewing habit of the user is represented by a linear function and a nonlinear function together. The nonlinear function in the embodiment of the present invention may be specifically, for example, a sigmoid activation function, where the sigmoid activation function is an "S" shaped curve, and can properly represent the consumer wish of the user, and the sigmoid activation function is a technology well known in the art and is not described herein.
Further, in some embodiments of the present invention, fitting the viewing habit feature coefficients according to the linear function and the activation function to obtain a viewing habit fitting function includes: taking the characteristic coefficient of the habit of watching video as the slope of the independent variable in the linear function to obtain a linear function of watching video; multiplying the viewing habit characteristic coefficient with a sigmoid function to obtain a viewing activation function; calculating the difference between the viewing linear function and the viewing activation function as a fusion difference; and taking the fusion difference as a weight, and respectively carrying out weighted average fusion treatment on the viewing linear function and the viewing activation function to obtain a viewing habit fitting function.
The formulas corresponding to the linear function and the sigmoid function may specifically be, for example:
wherein y represents a dependent variable, x represents an independent variable, k represents a weight of the independent variable, that is, a viewing habit characteristic coefficient, and e represents a natural constant.
The viewing habit characteristic coefficients are adaptively adjusted to the linear function and the sigmoid function, so that the viewing habit characteristic coefficients are changed, and simultaneously, the corresponding viewing linear function and the viewing activating function are changed correspondingly, for example, when the viewing habit characteristic coefficients are increased, the slope of the corresponding viewing linear function is increased, and the dependent variable is also increased under the condition of homogeneous variable of the viewing activating function curve, namely, the payment intention of a user is represented to be stronger.
Further, the embodiment of the invention calculates the difference between the viewing linear function and the viewing activation function as a fusion difference, and comprises the following steps: the fixed integral of the difference between the viewing linear function and the viewing activation function is calculated to obtain a fusion difference, and the corresponding calculation formula can specifically be as follows:
wherein τ represents the fusion difference, y represents the dependent variable, x represents the independent variable, k represents the weight of the independent variable, namely, the characteristic coefficient of the viewing habit, and e represents the natural constant.
In the embodiment of the invention, the physical meaning of the fixed integral representation is the area of the curved trapezoid of the two function curves in the integral interval. Because the larger the area of the curved trapezoid is when the change relation is fused, the larger the difference between the viewing linear function and the viewing activating function is, and the larger the difference is when the viewing linear function and the viewing activating function represent the consumption wish of a user according to the viewing habit characteristic coefficient of the user respectively, when the data are fused, the larger the fusion weight is required to be, the change relation between the linear change model and the nonlinear change model can be represented, and the obtained curve is further fitted, so that the user's wish of buying the digital television valuable cards can be accurately described, and the more accurate judgment can be realized when the valuable cards are pushed.
In the embodiment of the present invention, the fusion difference is used as a weight, and weighted average fusion processing is performed on the viewing linear function and the viewing activating function to obtain a viewing habit fitting function, where the corresponding viewing habit fitting function may specifically be:
the meaning of each formula symbol in the formula is the same as that of the formula symbol in the fusion difference formula, and the description is omitted.
In the embodiment of the invention, the viewing habit fitting function has a better analysis effect through the viewing linear function and the viewing activating function.
Further, in some embodiments of the present invention, determining a user's ticket push indicator according to a viewing habit fitting function and a consumption amount includes: calculating the average value of the consumption amounts of all the users as a consumption average value; taking the ratio normalized value of the consumption amount and the consumption average value as a consumption habit characteristic coefficient; substituting the consumption habit characteristic coefficient as an independent variable into the viewing habit fitting function to obtain the coupon card pushing index.
According to the invention, the consumption habits of the users can be determined according to the consumption amount of each user on a plurality of different platforms, firstly, the average value of the obtained consumption amounts of all the users is calculated, the consumption condition of all the users can be represented, and then the ratio of the consumption amount to the consumption average value is calculated as the consumption habit characteristic coefficient, that is, the larger the consumption amount is, the larger the corresponding consumption habit characteristic coefficient is.
In the embodiment of the invention, the consumption habit characteristic coefficient can be substituted into the viewing habit fitting function as an independent variable to obtain the coupon card pushing index, that is, the consumption habit characteristic coefficient is substituted into the viewing habit fitting function as an independent variable, the calculated and output dependent variable is recorded as the coupon card pushing index, the coupon card pushing index represents index data corresponding to coupon card pushing, it can be understood that when the coupon card pushing index is larger, the user is more likely to be a serious viewing fan, the user will purchase video service is greater, and when the coupon card pushing index is smaller, the user is more likely to be a slight viewing fan, and therefore, the coupon card pushing management performed on the user according to the coupon card pushing index is performed.
Further, in the embodiment of the present invention, at least two card combinations are preset, and the card combinations are pushed according to the size of the card pushing index.
The ticket card combination is a combination corresponding to the ticket card type, and it can be understood that the platform sets different payment modes for meeting diversified viewing requirements of users, and also sets a plurality of different ticket cards, such as a single movie coupon card, which can be used for purchasing a single movie or designating the viewing rights of the movie, for example, a month card, a week card, a year card, and discount ticket cards with different degrees, without limitation.
The embodiment of the invention can be provided with two corresponding ticket card combinations according to the severe viewing situation and the slight viewing situation, namely the severe viewing ticket card combination and the slight viewing ticket card combination, and of course, in other embodiments of the invention, various ticket card combinations can be prepared according to the actual situation, and the invention is not limited.
Further, in some embodiments of the present invention, when the ticket pushing indicator is greater than a preset indicator threshold, a heavy viewing ticket combination is selected; and when the card pushing index is smaller than or equal to a preset index threshold, selecting a slight viewing card combination.
In the embodiment of the invention, the preset index threshold is specifically, for example, 0.8, that is, when the coupon pushing index is greater than 0.8, the larger the viewing time and viewing range of the user are represented, and the larger the viewing consumption will be, the heavier viewing coupon card combinations such as a push year card and a discount card can be selected at this time, and when the coupon pushing index is less than or equal to 0.8, the viewing will of the user can be represented to be lower, and only the favorite types or movies can be watched, at this time, the lighter viewing coupon card combinations such as a week card, a single movie coupon card and the like can be selected.
The invention is applied to the technical field of data push management, and obtains the viewing records and consumption records of the same user on different platforms through a cross-platform technology to obtain the viewing record data and consumption record data, so that the viewing characteristic coefficient is determined according to the viewing program type, the viewing frequency and the total viewing duration in the viewing record data, the viewing behavior of the user can be effectively analyzed, the chasing characteristic coefficient is obtained through the viewing time point and the film showing time point, the chasing behavior of the user is effectively analyzed, the viewing behavior and the chasing behavior can be combined, the viewing habit of the user is objectively analyzed to obtain the viewing habit characteristic coefficient, the viewing habit characteristic coefficient can effectively represent the viewing habit and viewing preference of the user, multiple crowds can be covered when the valuable coupon card push management is carried out according to the habit characteristic coefficient, the application scope is wide, and the result is more accurate; according to the invention, according to the viewing habit fitting function and the consumption amount, the ticket card pushing index is determined, namely, the pushing index of the corresponding valuable ticket card combination is determined, and the adaptive ticket card pushing index is acquired through the viewing habit fitting function and the consumption amount, so that the best pushing management result is obtained, the ticket card pushing index can be used for pushing and managing the valuable ticket card accurately and objectively according to the viewing habit and the consumption habit of a user, the information pushing of the valuable ticket card is more fit with the viewing habit and the consumption behavior of the user, the reliability of the valuable ticket card pushing management is improved, and the adaptability of the valuable ticket card pushing management is enhanced.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments.
Claims (5)
1. A digital television value ticket card management system based on cloud computing and cross-platform technology, the system comprising:
the system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring video watching record data and consumption record data of a digital television platform and other video platforms for the same user, the video watching record data comprise video watching program types, video watching total time length, video watching frequency and video watching time points, and the consumption record data comprise consumption amounts on different platforms;
the processing module is used for obtaining a viewing characteristic coefficient according to the viewing program type, the viewing frequency and the viewing total duration, determining a chasing characteristic coefficient according to the viewing time point of the thermally-cast film and the film showing time point, and determining a viewing habit characteristic coefficient of the user according to the viewing characteristic coefficient and the chasing characteristic coefficient;
the management module is used for fitting the viewing habit characteristic coefficients according to a linear function and an activation function to obtain a viewing habit fitting function, determining a ticket card pushing index of the user according to the viewing habit fitting function and the consumption amount, and managing ticket card pushing of the user according to the ticket card pushing index;
the obtaining the viewing characteristic coefficient according to the viewing program type, the viewing frequency and the total viewing duration comprises the following steps:
calculating the ratio of the number of the types of the watching program to the total number of the preset types to obtain a type influence factor;
calculating the ratio of the total video watching time length to the video watching frequency as a single average time length, and carrying out normalization processing on the single average time length to obtain a time length influence factor;
obtaining a film watching characteristic coefficient according to the type influence factor and the duration influence factor, wherein the type influence factor and the film watching characteristic coefficient form a positive correlation, the duration influence factor and the film watching characteristic coefficient form a positive correlation, and the value of the film watching characteristic coefficient is a normalized value;
the step of determining the chasing feature coefficient according to the film watching time point and the film showing time point of the hot-cast film comprises the following steps:
calculating the time interval between the film showing time point and the film watching time point of the same hot-cast film to obtain the drama time difference of the user to the hot-cast film;
taking an inverse proportion normalized value of a user's chasing time difference mean value of all the thermally-broadcast films at the current moment as a chasing feature coefficient;
the determining the viewing habit characteristic coefficient of the user according to the viewing characteristic coefficient and the chasing characteristic coefficient comprises the following steps:
taking the sum normalized value of the viewing characteristic coefficient and the chasing characteristic coefficient as a viewing habit characteristic coefficient;
the activation function is a sigmoid function, the fitting is performed on the viewing habit characteristic coefficients according to a linear function and the activation function to obtain a viewing habit fitting function, and the method comprises the following steps:
taking the characteristic coefficient of the viewing habit as the slope of an independent variable in the linear function to obtain a viewing linear function;
multiplying the viewing habit characteristic coefficient with a sigmoid function to obtain a viewing activation function;
calculating the difference between the viewing linear function and the viewing activation function as a fusion difference;
and taking the fusion difference as a weight, and respectively carrying out weighted average fusion treatment on the viewing linear function and the viewing activation function to obtain a viewing habit fitting function.
2. The digital television value card management system based on cloud computing and cross-platform technology of claim 1, wherein the computing the difference between the viewing linear function and the viewing activation function as a fusion difference comprises:
and calculating the fixed integral of the difference between the viewing linear function and the viewing activation function to obtain the fusion difference.
3. The digital television value card management system based on cloud computing and cross-platform technology of claim 1, wherein said determining the user's card push index according to the viewing habit fitting function and the consumption amount comprises:
calculating the average value of the consumption amounts of all the users as a consumption average value; taking the ratio normalized value of the consumption amount and the consumption average value as a consumption habit characteristic coefficient;
substituting the consumption habit characteristic coefficient as an independent variable into the viewing habit fitting function to obtain a coupon card pushing index.
4. The digital television valuable card management system based on cloud computing and cross-platform technology of claim 1, wherein the card pushing management for the user according to the card pushing index comprises:
presetting at least two card combinations, and pushing the card combinations according to the sizes of the card pushing indexes.
5. The digital television value ticket card management system based on cloud computing and cross-platform technology of claim 4, wherein said ticket card combination comprises a heavy viewing ticket card combination and a light viewing ticket card combination, said pushing said ticket card combination according to the size of said ticket card pushing index comprising:
when the ticket card pushing index is larger than a preset index threshold, selecting the severe movie viewing ticket card combination;
and when the card pushing index is smaller than or equal to a preset index threshold, selecting the light film watching card combination.
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