CN115544358A - Short video live broadcast marketing recommendation system and method fusing multi-platform behavior characteristics - Google Patents
Short video live broadcast marketing recommendation system and method fusing multi-platform behavior characteristics Download PDFInfo
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Abstract
The invention discloses a short video live broadcasting marketing recommendation system and method fusing multi-platform behavior characteristics, and the system comprises a data acquisition module, an acquired data preprocessing module, a recommendation system historical behavior data preprocessing module, a live broadcasting task recall establishing model module, a live broadcasting task database storage module, a live broadcasting task sequencing establishing model module and a live broadcasting task recommendation module, wherein the data acquisition module is in control connection with the acquired data preprocessing module, the acquired data preprocessing module and the recommendation system historical behavior data preprocessing module are in control connection with the live broadcasting task recall establishing model module, and the live broadcasting task recall establishing model module is in control connection with the live broadcasting task database storage module; the method and the system make up the defect that a short video live broadcasting marketing system is lack of a recommendation function, provide a task recommendation function for broadcasting main user selections, assist the main users in quickly selecting live broadcasting tasks, and provide an efficient task selection scheme for short video live broadcasting marketing.
Description
Technical Field
The invention relates to the technical field of recommendation systems, in particular to a short video live broadcast marketing recommendation system and method integrating multi-platform behavior characteristics.
Background
Today, with the rapid development of network technologies, short videos attract more and more people's attention by virtue of the characteristics of low threshold, convenient use and strong social participation. The short video live broadcast marketing is a product combining electronic commerce and short video live broadcast, live broadcast with goods is carried out in a short video live broadcast mode, and the rapid change of broadcast main flow is promoted by utilizing the huge user base number and the convenient live broadcast platform of the short video platform. The short video live broadcast marketing can well make up for short boards with insufficient commodity description and high price in the traditional e-commerce platform, so that a transaction link between brand merchants and consumers is constructed, and the short video live broadcast marketing has important significance for optimizing an industrial structure and realizing the coordinated development of the industry.
However, in the existing live broadcast marketing recommendation system, recommendation is mainly performed according to the historical behavior record of the user, the purchasing characteristics of the consumer are lacked, and the marketing task is difficult to be accurately recommended, so that a certain limit exists between a broadcaster and a brand owner, and the broadcaster cannot select products according to the own shipment style. Therefore, a short video live broadcast marketing recommendation system for collecting multi-platform user characteristics is urgently needed, the commodity data of a user shop and the historical behavior sequence of the user are fused, a credible third party recommendation and transaction platform is provided for a broadcaster and a brand merchant, and electronic commerce selection is assisted.
Disclosure of Invention
The invention aims to provide a short video live broadcast marketing recommendation system and method fusing multi-platform behavior characteristics, and aims to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a short video live broadcast marketing recommendation method integrating multi-platform behavior characteristics comprises the following steps: step one, collecting; step two, collected data are preprocessed; step three, preprocessing historical behavior data; step four, establishing a model; step five, storing; step six, predicting; step seven, recommending;
in the first step, user data of a plurality of platforms are acquired through a set data acquisition module;
in the second step, the collected data is preprocessed and persisted by using a collected data preprocessing module, and the preprocessing of the collected data comprises the preprocessing of commodity data of a shop of a user and demographic information of the user;
in the third step, a recommendation system historical behavior data preprocessing module is used for preprocessing the recommendation system historical behavior data;
in the fourth step, a live broadcast task recall model is established by the live broadcast task recall model establishing module, and then the preprocessed user store commodity data, the preprocessed demographic information and the historical behavior data of the recommendation system are used as the input of the live broadcast task recall model to obtain a live broadcast task sequence to be recommended;
in the fifth step, the live broadcast task sequence to be recommended is stored in the database in a persistent mode through a live broadcast task database storage module;
in the sixth step, the establishment of a live broadcast task ordering model is completed by utilizing a live broadcast task ordering model establishment module, the live broadcast task ordering model takes the preprocessed user shop commodity sequence and each live broadcast task to be recommended as input, and the probability of clicking the live broadcast task by the user is predicted;
and in the seventh step, the set live task recommendation module is used, and when the broadcasting master user logs in the live marketing recommendation system, the task with the highest click rate is taken out from the live task recommendation database and recommended to the broadcasting master user.
Preferably, in the step one, the data acquisition module includes:
1) Collecting user shop commodity characteristics and collected user shop commodity data by using Scapy crawler frame
Wherein x is s The data of the commercial products of the shop on behalf of the user,
indicates the kth user store commodity data,
the price of the goods is expressed and,
which indicates the time at which the goods are released,
which is indicative of the type of the article,
indicating commodity sales;
2) Collects the K recently issued by the user r Individual item information defining a user store item sequence
3) Collecting demographic information X of broadcasting main users P ＝[p sex ,p age ,p position ] T ，p sex Representing the gender, p, of the user age Indicates the age, p, of the user position Representing geographical location information.
Preferably, in the second step, the collected data preprocessing module specifically works as follows:
1) The pre-processing of the store commodity data comprises the processing of commodity price, commodity release time, commodity type and commodity sales volume, and the user store commodity data is preprocessed into
The pre-processed user shop commodity sequence is
2) The preprocessing of the acquired data also includes preprocessing demographic information of the broadcasting master user to obtain processed demographic characteristics
3) The latest K of the processed user shop commodity is processed by using a MySQL relational database in the persistence processing of the acquired data r A sequence
And demographic information
And performing persistent storage.
Preferably, in the third step, the recommendation system historical behavior data preprocessing module specifically works as follows:
1) Defining a single historical behavioral data of a live marketing recommendation system as
Wherein x is b A single historical behavioral data representing the broadcasting master user,
denotes the kth b The historical data of the behavior of the user is stored,
representing a task budget, representing
The time at which the task was issued,
indicating that the user's browsing duration is played,
the status of the task is represented and,
indicating the number of the task-associated loaded commodities;
2) Selecting the nearest K of the user r Historical behavioral data, deficiency of K r The part of the user data is filled by using a default value to obtain the latest K of the broadcast main user r A historical behavior data sequence K b Is defined as
3) Preprocessing broadcast master user recent K r A historical behavior data sequence X b The single historical behavior characteristic after the preprocessing is obtained as
Historical behavior sequencesIs composed of
K for processed historical behavioral data sequence using MySQL relational database r A sequence
Performing persistent storage of sequences
And performing persistent storage.
Preferably, in the fourth step, the specific work of establishing the live task recall model module is as follows:
1) Defining live tasks as
t budget For task budgeting, t pos For task release time, t state Is the task state, t telate Associating the number of items in stock for the task, t model A task mode is adopted;
2) Input of live task recall model is user store commodity sequence after preprocessing
Demographic information
And historical behavior sequence of live marketing recommendation system
Outputting a sequence of central tasks represented by a marketing task type of interest to a user
3) The direct broadcast task recall model calculates the middle of the marketing task type represented by the marketing task type by using a k nearest neighbor methodClosest N in the heart task f Individual tasks forming a set of recalled tasks
N h The number of tasks recalled.
Preferably, in the fifth step, the specific work of the live task database storage module is to collect the task set T obtained in the fourth step h And persisting to the live task database.
Preferably, in the sixth step, the specific work of establishing the live task sequencing model module is as follows:
1) The input of the live broadcast task sequencing model is a user shop commodity sequence after preprocessing
And recall tasks
2) The live broadcast task sequencing model obtains a predicted click rate, and the tasks are sequenced from large to small according to the click rate to obtain a final recommended task sequence
Preferably, in the seventh step, after the live main user logs in the live marketing recommendation system, the N with the highest click rate is taken out from the live task recommendation database top And recommending the tasks to the broadcasting master user as final marketing recommendation tasks and recommending the tasks to the user on the page.
A short video live broadcasting marketing recommendation system fusing multi-platform behavior characteristics comprises a data acquisition module, a collected data preprocessing module, a recommendation system historical behavior data preprocessing module, a live broadcasting task recall model establishing module, a live broadcasting task database storage module, a live broadcasting task sequencing model establishing module and a live broadcasting task recommendation module, wherein the data acquisition module is in control connection with the collected data preprocessing module, the collected data preprocessing module and the recommendation system historical behavior data preprocessing module are in control connection with the live broadcasting task recall model establishing module, the live broadcasting task recall model establishing module is in control connection with the live broadcasting task database storage module, the live broadcasting task database storage module is in control connection with the live broadcasting task sequencing model establishing module, and the live broadcasting task sequencing model establishing module is in control connection with the live broadcasting task recommendation module.
Compared with the prior art, the invention has the beneficial effects that: the short-video live broadcasting marketing system overcomes the defect of lack of a recommendation function in the short-video live broadcasting marketing system, provides a task recommendation function for broadcasting the selection of the main user, facilitates the broadcasting of the task which is selected by the main user and accords with the own live broadcasting style, improves the user experience of the system, and enables the short-video live broadcasting marketing to increase.
Drawings
FIG. 1 is a schematic diagram of the operation of the present invention;
FIG. 2 is a flow chart of the method of the present invention;
FIG. 3 is a schematic view of the overall module structure of the present invention;
in the figure: 1. a data acquisition module; 2. a collected data preprocessing module; 3. a recommendation system historical behavior data preprocessing module; 4. establishing a live broadcast task recall model module; 5. a live broadcast task database storage module; 6. establishing a live broadcast task sequencing model module; 7. and a live broadcast task recommendation module.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, an embodiment of the present invention is shown: a short video live broadcast marketing recommendation method integrating multi-platform behavior characteristics comprises the following steps: step one, collecting; step two, collected data are preprocessed; step three, preprocessing historical behavior data; step four, establishing a model; step five, storing; step six, predicting; step seven, recommending;
in the first step, user data of a plurality of platforms are acquired through the set data acquisition module 1; the data acquisition module 1 mainly uses a script crawler frame to acquire the store commodity characteristics of the user, and the acquired user store commodity data is defined as follows:
wherein x is s The data of the commercial products of the shop on behalf of the user,
indicates the kth user store commodity data,
the price of the goods is expressed and,
which indicates the time at which the goods are released,
which is indicative of the type of the article,
indicating commodity sales; collects the K recently issued by the user r The individual item information, which defines the user store item sequence, is defined as follows:
gathering demographic information for the broadcasting user is defined as follows:
X P ＝[p sex ,p age ,p position ] T
wherein p is sex Representing the gender, p, of the user age Indicates the age, p, of the user position To representGeographic location information;
in the second step, the preprocessing of the store commodity data in the collected data preprocessing comprises the processing of commodity price, commodity release time, commodity type and commodity sales volume, and the processing of the kth commodity data
For its commodity price
The normalization method is adopted, and the treatment process is as follows:
wherein,
for the normalized commodity price, e max Maximum number of prices of goods, e min Is the minimum number of commodity prices; commodity release time in data acquisition preprocessing
The preprocessing of (1) converting the commodity release time into a time stamp
Processing difference value with current time stamp to obtain time difference
The treatment process is as follows:
wherein, T c Is the current timestamp; normalizing the time difference by using an arc tangent function to calculate the commodity release time after preprocessing
The process is as follows:
for commodity type in data collection preprocessing
The data preprocessing part adopts one-hot coding to expand discrete commodity type characteristics to European space to obtain the processed commodity type
Sales volume for goods in data acquisition pre-processing
The preprocessing of (3) adopts z-score standardization processing, and firstly calculates the type of the commodity to which the commodity belongs
The standard deviation of commodity sales of (1) is calculated as follows:
wherein N is t The number of the same type goods under the type of the goods,
for the sales volume of the ith commodity, mu represents the mean value of the sales volumes of the commodities under the unified type, and sigma represents the calculated standard deviation; sales volume of commodities
Performing z-score standardization, and calculating commodity sales after pretreatment
The user store commodity data after being preprocessed are as follows:
user store commodity nearest K r The sequences are:
preprocessing demographic information of the broadcasting main user, and broadcasting gender P of the main user sex And geographical location information P position One-hot was used to obtain the processed gender characteristics
And geographic location information features
For user age P age The normalization process is performed in the following manner:
wherein,
is the minimum value of the age of the patient,
is the maximum value of age;
people who broadcast main users after data preprocessingOral statistics information
Is composed of
The latest K of the processed user shop commodity is compared with the MySQL relational database in the persistence operation of the collected data r A sequence
And demographic information
Carrying out persistent storage;
in the third step, the historical behavior data of the recommendation system is preprocessed by using a historical behavior data preprocessing module 3 of the recommendation system; preprocessing historical behavior data of a recommendation system; the recommendation system historical behavior data comprises historical behavior data generated by a broadcasting master user when the broadcasting master user browses the short video live broadcast marketing recommendation system, and the single historical behavior data is defined as follows:
wherein x is b A single historical behavior data representing the broadcasting of the primary user,
denotes the kth b The historical behavior data of the user is stored in a memory,
representing a task budget, representing
The time at which the task was issued,
indicating that the browsing time of the main user is played,
the status of the task is represented and,
indicating the number of the task-associated loaded commodities; 2. selecting the nearest K of the user r Historical behavioral data, deficiency of K r The part of the user data is filled by using a default value to obtain the latest K of the broadcast main user r A historical behavior data sequence K b The definition is as follows:
budget for tasks
The pretreatment of (2) adopts a normalization method, and the treatment process comprises the following steps:
wherein,
for the purpose of normalizing the task budget after normalization,
for the purpose of the minimum value of the budget,
is the maximum value of the budget;
time to task release in user historical behavior data
The preprocessing of (1) converting task release time into time stamp
Processing the difference value with the current timestamp to obtain the time difference
The treatment process is as follows:
wherein, T c Is the current timestamp; normalizing the processing time difference by using an arc tangent function, and calculating the task release time after preprocessing
The process is as follows:
browsing duration of on-air main user in user historical behavior data
The preprocessing of (2) first converts the browsing duration into a timestamp
Calculating and processing browsing duration by using arc tangent normalization function
The treatment process is as follows:
task state in user historical behavior data
The one-hot coding is adopted for preprocessing, and the processed task state is obtained
Associating number of goods in shipment to task in user historical behavior data
Preprocessing, namely using a normalization method, and comprising the following specific steps of:
wherein,
the number of items in stock is associated for the task after normalization,
associating a minimum number of shipped items for the task,
associating the maximum number of the goods with the task; after pretreatment, the expression K b Individual historical behavioral data
Is shown as
Historical behavioral data sequence
Is composed of
K for recommending historical behavior data sequence completed by processing by using MySQL relational database in system historical behavior data preprocessing operation r A sequence
Performing persistent storage;
in the fourth step, the live broadcast task recall model establishing module 4 establishes a live broadcast task recall model, then preprocessed user store commodity data, demographic information and recommendation system historical behavior data are used as input of the live broadcast task recall model, the live broadcast task recall is used for recalling live broadcast tasks existing in the database, and the live broadcast tasks in the database are defined as follows:
wherein, t budget For task budgeting, t pos For task release time, t state For the task state, t telate Associating the number of loaded items for the task, t model A task mode is adopted; the live broadcast task recall model is a simple neural network classification model, and the input of the live broadcast task recall model is a preprocessed user shop commodity sequence
Demographic information
And historical behavior sequence of live marketing recommendation system
Live broadcast task T in database l Is divided into N b A class, N b ≤N l Selecting the center live broadcasting marketing task represented by each category to form a set T d Expressed as:
wherein,
for the central live marketing task set represented by each class, the output of the live task recall model is whether the live task recall model belongs to the set T d The task of (1), expressed as:
wherein,
represents whether the user is on the kth d Individual task types are of interest; in the live broadcast task recall model, the preprocessed user shop commodity sequence is recalled
Demographic information
And historical behavior sequence of live marketing recommendation system
As input, the final model output is obtained
Get T d And
of intersection of
Is recorded as:
wherein,
center task represented by marketing task type of interest to user, N e For broadcasting ownerThe number of central tasks represented by the marketing task types in which the user is interested; the live broadcast task recall model calculates the most similar N in the central tasks represented by the marketing task types by using a k nearest neighbor method f The calculation process of the task is as follows:
wherein,
x q ∈T d ，
composing a set of recalled tasks
Wherein N is h ≤(N e *N f )，N h Number of tasks recalled;
in the fifth step, the live broadcast task sequence to be recommended is stored in the database in a persistent mode through the live broadcast task database storage module 5; the concrete work of the live broadcast task database storage module 5 is to collect the task set T obtained in the step four h Persisting to a live task database;
in the sixth step, the establishment of a live broadcast task sequencing model is completed by utilizing the establishment of the live broadcast task sequencing model module 6, the live broadcast task sequencing model is a simple regression model, and the input of the live broadcast task sequencing model is a preprocessed user shop commodity sequence
And recall tasks
Output as
Probability P of user clicking the task click The input of the live task ranking model may be expressed as:
the input is collected as
The predicted click rate is obtained by broadcasting a task sequencing model, and the tasks are sequenced from large to small according to the click rate to obtain a final recommended task sequence
Inputting a set input live broadcast task sequencing model to obtain a predicted click rate as follows:
wherein,
denotes the kth p The click probability of each task; the tasks are sorted from large to small according to the click rate to obtain a final recommended task sequence T rec Expressed as:
will T rec Persistently storing the data to a live task recommendation database;
in the seventh step, the set live broadcast task recommendation module 7 is used, and after the live broadcast main user logs in the live broadcast marketing recommendation system, the N with the highest click rate is taken out of the live broadcast task recommendation database top The tasks are recommended to the broadcasting master user, and the broadcasting master user can conveniently select the live marketing tasks meeting the interests of the broadcasting master user.
Referring to fig. 3, an embodiment of the present invention: a short video live broadcasting marketing recommendation system fusing multi-platform behavior characteristics comprises a data acquisition module 1, a data acquisition preprocessing module 2, a recommendation system historical behavior data preprocessing module 3, a live broadcasting task recall establishing model module 4, a live broadcasting task database storage module 5, a live broadcasting task sequencing establishing model module 6 and a live broadcasting task recommendation module 7, wherein the data acquisition module 1 is in control connection with the data acquisition preprocessing module 2, the data acquisition preprocessing module 2 and the recommendation system historical behavior data preprocessing module 3 are in control connection with a live broadcasting task recall establishing model module 4, the live broadcasting task recall establishing model module 4 is in control connection with the live broadcasting task database storage module 5, the live broadcasting task database storage module 5 is in control connection with a live broadcasting task sequencing establishing model module 6, and the live broadcasting task sequencing model module 6 is in control connection with a task recommendation module 7.
Based on the above, the invention has the advantages that: the invention can provide a task recommendation function for the broadcast main user selection, and is convenient for the broadcast main user to select the task according with the own live broadcast style, thereby improving the user experience of the system and enabling the short video live broadcast marketing growth.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (9)
1. A short video live broadcast marketing recommendation method integrating multi-platform behavior characteristics comprises the following steps: step one, collecting; preprocessing collected data; step three, preprocessing historical behavior data; step four, establishing a model; step five, storing; step six, predicting; step seven, recommending; the method is characterized in that:
in the first step, user data of a plurality of platforms are acquired through a set data acquisition module (1);
in the second step, the collected data is preprocessed and persisted by using a collected data preprocessing module (2), and the preprocessing of the collected data comprises the preprocessing of commodity data of a shop of a user and demographic information of the user;
in the third step, a recommendation system historical behavior data preprocessing module (3) is used for preprocessing the recommendation system historical behavior data;
in the fourth step, a live broadcast task recall model is established through the live broadcast task recall model establishing module (4), and then preprocessed user store commodity data, demographic information and recommendation system historical behavior data are used as input of the live broadcast task recall model to obtain a live broadcast task sequence to be recommended;
in the fifth step, the live broadcast task sequence to be recommended is stored into the database in a persistent mode through the live broadcast task database storage module (5);
in the sixth step, the establishment of a live broadcast task sequencing model is completed by utilizing a live broadcast task sequencing model establishment module (6), the live broadcast task sequencing model takes the preprocessed user shop commodity sequence and each live broadcast task to be recommended as input, and the probability of clicking the live broadcast task by the user is predicted;
and in the seventh step, by using the set live task recommendation module (7), after the broadcasting main user logs in the live marketing recommendation system, the task with the highest click rate is taken out from the live task recommendation database and recommended to the broadcasting main user.
2. The short-video live marketing recommendation method integrating multi-platform behavior features according to claim 1, characterized by comprising the following steps of: in the first step, the data acquisition module (1) comprises the following working steps:
1) Collecting user shop commodity characteristics and collected user shop commodity data by using Scapy crawler frame
Wherein x is s The data of the commercial products of the shop on behalf of the user,
indicating the kth user store commodity data,
the price of the goods is represented and,
which indicates the time at which the goods are released,
which is indicative of the type of the article,
indicating commodity sales;
2) Collects the K recently issued by the user r Individual item information defining a user store item sequence
3) Collecting demographic information X of broadcasting main users P ＝[p sex ,p age ,p position ] T ，p sex Representing the gender, p, of the user age Indicates the age, p, of the user position Representing geographical location information.
3. The short video live broadcasting marketing recommendation method fusing multi-platform behavior features according to claim 1, characterized in that: in the second step, the collected data preprocessing module (2) specifically works as follows:
1) The pre-processing of the store commodity data comprises the processing of commodity price, commodity release time, commodity type and commodity sales volume, and the user store commodity data is preprocessed into
The pre-processed commodity sequence of the user shop is
2) The preprocessing of the collected data also comprises preprocessing the demographic information of the broadcasting main user to obtain the processed demographic characteristics
3) The MySQL relational database is used for processing the latest K of the processed user shop commodities in the persistence processing of the acquired data r A sequence
And demographic information
And performing persistent storage.
4. The short-video live marketing recommendation method integrating multi-platform behavior features according to claim 1, characterized by comprising the following steps of: in the third step, the recommendation system historical behavior data preprocessing module (3) specifically works as follows:
1) Defining a single historical behavioral data of a live marketing recommendation system as
Wherein x is b A single historical behavior data representing the broadcasting of the primary user,
denotes the kth b The historical data of the behavior of the user is stored,
representing a task budget, representing
The time at which the task was issued,
indicating that the browsing time of the main user is played,
the status of the task is represented and,
indicating the number of the task-associated loaded commodities;
2) Selecting the nearest K of the user r Historical behavioral data, deficiency K r The part of the broadcast master user is filled by using a default value to obtain the latest K of the broadcast master user r A historical behavior data sequence K b Is defined as
3) Preprocessing broadcast master user recent K r A historical behavior data sequence X b Obtaining a single historical behavior signature after preprocessing as
The sequence of historical behaviors is
K for processed historical behavioral data sequence using MySQL relational database r A sequence
Performing persistent storage of sequences
And performing persistent storage.
5. The short-video live marketing recommendation method integrating multi-platform behavior features according to claim 1, characterized by comprising the following steps of: in the fourth step, the specific work of establishing the live broadcast task recall model module (4) is as follows:
1) Define live tasks as
t budget For task budgeting, t pos For task release time, t state For the task state, t telate Associating the number of loaded items for the task, t model A task mode is adopted;
2) Input of live task recall model as pre-processed user store merchandise sequence
Demographic information
And historical behavior sequence of live marketing recommendation system
Outputting a sequence of central tasks represented by a marketing task type of interest to a user
3) The direct broadcast task recall model calculates the most similar N in the central tasks represented by the marketing task types by using a k nearest neighbor method f Individual tasks forming a set of recalled tasks
N h The number of tasks recalled.
6. The converged multi-platform behavioral characteristics according to claim 1The short video live broadcast marketing recommendation method is characterized by comprising the following steps: in the fifth step, the specific work of the live broadcast task database storage module (5) is to collect the task set T obtained in the fourth step h Persisted to a live task database.
7. The short video live broadcasting marketing recommendation method fusing multi-platform behavior features according to claim 1, characterized in that: in the sixth step, the specific work of establishing the live task sequencing model module (6) is as follows:
1) The input of the live broadcast task sequencing model is a user shop commodity sequence after preprocessing
And recall tasks
2) The live broadcast task sequencing model obtains a predicted click rate, and the tasks are sequenced from large to small according to the click rate to obtain a final recommended task sequence
8. The short-video live marketing recommendation method integrating multi-platform behavior features according to claim 1, characterized by comprising the following steps of: in the seventh step, after the broadcasting master user logs in the live broadcasting marketing recommendation system, N with the highest click rate is taken out from the live broadcasting task recommendation database top And recommending the tasks to the broadcasting main user as final marketing recommendation tasks to be recommended to the users on the page.
9. The utility model provides a short video live broadcast marketing recommendation system who fuses multi-platform behavioral characteristics, includes data acquisition module (1), data collection preprocessing module (2), recommendation system historical behavior data preprocessing module (3), establishes live broadcast task recall model module (4), live broadcast task database storage module (5), establishes live broadcast task sequencing model module (6) and live broadcast task recommendation module (7), its characterized in that: the system is characterized in that the data acquisition module (1) is in control connection with a data acquisition preprocessing module (2), the data acquisition preprocessing module (2) and the recommendation system historical behavior data preprocessing module (3) are in control connection with a live broadcast task recall model establishing module (4), the live broadcast task recall model establishing module (4) is in control connection with a live broadcast task database storage module (5), the live broadcast task database storage module (5) is in control connection with a live broadcast task sequencing model establishing module (6), and the live broadcast task sequencing model establishing module (6) is in control connection with a live broadcast task recommendation module (7).
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