CN111966897B - Method, device, terminal and storage medium for sensing travel willingness - Google Patents
Method, device, terminal and storage medium for sensing travel willingness Download PDFInfo
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Abstract
The embodiment of the invention discloses a method, a device, a terminal and a storage medium for sensing travel willingness. The method comprises the following steps: acquiring taxi data in a first preset time and behavior data in a second preset time of a target user, and judging whether the user has first-direction behaviors or not by utilizing a trained behavior judging model based on the taxi data and the behavior data; when the user has the first-direction behavior, acquiring vehicle browsing data in a third preset time of the user, and predicting the behavior intensity of the user by utilizing the trained behavior intensity perception model and the vehicle browsing data; and acquiring vehicle interaction sequence data of the user in fourth preset time, and sensing travel willingness of the user by utilizing a trained behavior demand model based on the vehicle interaction sequence data and the behavior intensity. The vehicle type meeting travel will can be recommended to the user, the user experience is enhanced, the platform can optimize the whole marketing strategy according to the vehicle type, and the matching efficiency of the sharing platform to the vehicle renting requirement of the user is improved.
Description
Technical Field
The embodiment of the invention relates to the field of deep learning, in particular to a method, a device, a terminal and a storage medium for sensing travel will.
Background
With the increase of the demand of renting cars of users, various car renting transaction platforms for providing different services are continuously appeared.
In the prior art, conventional trading-type platforms typically conduct regression prediction on the probability of subsequent transactions of individual users and the size of transactions of a part or the whole platform based on machine learning or deep learning models through historical transaction information. However, such a method is mostly suitable for trading platform scenarios of high-frequency massive users, and relevant predictions are made based on the assumption that user trading preferences cannot be easily changed in a short period of time. However, the vehicle renting trip is a low-frequency high-unit-price heavy decision chain transaction behavior, most of the vehicle renting frequency of users is once in a few months, and the specific vehicle renting behavior will have great relation with vehicle scenes in different periods.
Problems with the prior art include at least: if the taxi travel willingness of the user can not be well identified each time, the vehicle is matched and recommended only according to the historical transaction information, the taxi willingness of the user can not be accurately perceived, the user experience can be greatly reduced, and the demand matching efficiency of the sharing platform for the taxi of the user is reduced.
Disclosure of Invention
In view of the above, the embodiment of the invention provides a perception method, a device, a terminal and a storage medium for travel willingness, which are used for recommending a vehicle type meeting the travel and renting willingness to a user with stronger willingness, so that the user experience is enhanced, the platform can optimize the integral marketing strategy according to the experience, and the matching efficiency of the sharing platform to the user renting requirements is improved.
In a first aspect, an embodiment of the present invention provides a method for sensing travel willingness, including:
acquiring taxi data in a first preset time and behavior data in a second preset time of a target user, and judging whether the target user has a first direction behavior or not by using a pre-trained behavior judging model based on the taxi data in the first preset time and the behavior data in the second preset time;
when the target user has the first-direction behavior, acquiring vehicle browsing data in a third preset time of the target user, and predicting the behavior intensity of the target user by utilizing a pre-trained behavior intensity perception model and the vehicle browsing data in the third preset time;
and acquiring vehicle interaction sequence data of the target user in a fourth preset time, and sensing travel willingness of the target user by utilizing a pre-trained behavior demand model based on the vehicle interaction sequence data and the behavior intensity in the fourth preset time.
Optionally, the behavior discrimination model training process includes:
obtaining behavior discrimination model training data of a sample user, wherein the behavior discrimination model training data comprises at least one of the following: historical car renting data, historical chat data and historical page series access data;
extracting historical order features of the historical taxi data;
extracting vehicle keywords in the historical chat data, and determining fully-connected word vectors based on the vehicle keywords;
the historical page series access data are subjected to sentence processing to obtain page access sentences, and behavior vectors are determined based on the page access sentences;
training by using the historical order features, the word vectors and the behavior vectors to obtain two classifiers serving as the behavior discrimination model.
Optionally, the behavioral intensity perception model training process includes:
obtaining intensity perception model training data of a sample user, wherein the intensity perception model training data comprises at least one of the following: historical stay time data, historical vehicle access data and historical page access data;
training by using the intensity perception model training data to obtain two classifiers serving as a behavior intensity perception model.
Preferably, the training process of the behavior demand model includes:
acquiring behavior demand model training data of a sample user, wherein the behavior demand model training data comprises historical vehicle renting data and historical vehicle interaction sequence data;
the historical vehicle interaction sequence data is subjected to sentence formation to obtain historical vehicle interaction sentences, and the historical vehicle interaction sentences are utilized to determine historical interaction vectors;
and training by using the historical vehicle renting data and the historical interaction vector to obtain a multi-classifier as the behavior demand model.
Preferably, the acquiring the behavior data of the target user includes:
the chat data of the target user in a second preset time is obtained; and/or the number of the groups of groups,
and acquiring page access data of the target user in a second preset time.
Preferably, the acquiring vehicle browsing data of the target user includes:
acquiring residence time length data of the target user in a third preset time; and/or the number of the groups of groups,
acquiring vehicle access data of the target user in a third preset time; and/or the number of the groups of groups,
and acquiring page access data of the target user in a third preset time.
Further, the determining, by using a pre-trained behavior discrimination model, whether the target user has the first direction behavior includes:
and when the target user does not have the first-direction behavior, storing the taxi data in the first preset time and/or the behavior data in the second preset time, so as to be used for updating training of the behavior discrimination model.
In a second aspect, an embodiment of the present invention further provides a device for sensing travel willingness, including:
a first direction behavior judging module, configured to obtain taxi data in a first preset time and behavior data in a second preset time of a target user, and judge whether the target user has a first direction behavior by using a pre-trained behavior judging model based on the taxi data in the first preset time and the behavior data in the second preset time
The behavior intensity prediction module is used for acquiring vehicle browsing data in a third preset time of the target user when the target user has the first direction behavior, and predicting the behavior intensity of the target user by utilizing a pre-trained behavior intensity perception model and the vehicle browsing data in the third preset time;
The willingness demand sensing module is used for acquiring vehicle interaction sequence data in a fourth preset time of the target user, and sensing traveling willingness of the target user by utilizing a pre-trained behavior demand model based on the vehicle interaction sequence data in the fourth preset time and the behavior intensity.
In a third aspect, an embodiment of the present invention further provides a terminal, including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, where the processor implements a method for sensing travel willingness as provided in any embodiment of the present application when executing the program.
In a fourth aspect, an embodiment of the present invention further provides a computer readable storage medium, on which a computer program is stored, where the program, when executed by a processor, implements a method for sensing travel willingness as provided in any embodiment of the present application.
According to the perception method, the device, the terminal and the storage medium for travel willingness, which are provided by the embodiment of the invention, firstly, taxi data in a first preset time and behavior data in a second preset time of a target user are obtained, and whether the target user has first-direction behavior is judged by utilizing a pre-trained behavior judging model based on the taxi data in the first preset time and the behavior data in the second preset time; when the target user has the first-direction behavior, acquiring vehicle browsing data in a third preset time of the target user, and predicting the behavior intensity of the target user by utilizing a pre-trained behavior intensity perception model and the vehicle browsing data in the third preset time; and acquiring vehicle interaction sequence data of the target user in fourth preset time, and predicting a willingness vehicle type of the target user by sensing the travel willingness of the user by using a pre-trained behavior demand model based on the vehicle interaction sequence data and the behavior intensity in the fourth preset time. The vehicle type meeting the willingness of renting the vehicle on going can be recommended to the user with stronger willingness, the user experience is enhanced, the platform can optimize the integral marketing strategy according to the vehicle type, and the matching efficiency of the sharing platform to the renting requirements of the user is improved.
Drawings
Fig. 1 is a flow chart of a method for sensing travel willingness according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a sensing flow of user travel willingness according to an embodiment of the present invention;
fig. 3 is a flow chart of a method for sensing travel willingness according to a second embodiment of the present invention;
fig. 4 is a block diagram of a device for sensing travel willingness according to the third embodiment of the present invention;
fig. 5 is a schematic structural diagram of a terminal according to a fourth embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described by means of implementation examples with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention. In the following embodiments, optional features and examples are provided in each embodiment at the same time, and the features described in the embodiments may be combined to form multiple alternatives, and each numbered embodiment should not be considered as only one technical solution.
Example 1
Fig. 1 is a flow chart of a method for sensing travel willingness provided in an embodiment of the present invention, where the embodiment is applicable to a situation where a user senses travel willingness of a target user when renting a platform taxi. The method can be executed by the sensing device for travel willingness provided by the embodiment of the invention, and the sensing device can be configured in the terminal provided by the embodiment of the invention, for example, can be configured in computer equipment or a mobile phone, and is not particularly limited.
As shown in fig. 1, the method for sensing travel willingness specifically includes the following steps:
s110, obtaining taxi data in a first preset time and behavior data in a second preset time of a target user, and judging whether the target user has first-direction behaviors or not by using a pre-trained behavior judging model based on the taxi data in the first preset time and the behavior data in the second preset time.
The rental data may be a model number, a rental time, a rental fee, etc. of the vehicle that the user rents in the first preset time, and preferably, the first preset time may be one year, and by way of example, the rental data is what time the user rents in the last year, how long the user rents, how much the rental fee is, etc., respectively; the behavior data refers to data obtained from a behavior activity performed by a user on the platform application within a second preset time, preferably, the second preset time may be three months, and exemplary, the behavior data may be data obtained from the behavior activity performed by the user when entering the platform every time in three months.
The first direction behavior may be positive feedback of travel willingness, i.e. the user has a willingness to rent a car for travel. And respectively extracting features of the taxi data in the first preset time and the behavior data in the second preset time, inputting the extracted features into a pre-trained behavior discrimination model, and judging whether a user has willingness to rent and travel according to an output result. Through obtaining the multidimensional data of the user, the abnormal change of the renting willingness of the user can be more accurately identified by utilizing the neural network, and the abnormal willingness of the renting can be, for example, the renting again in a short time after renting the vehicle for a plurality of times at a fixed frequency, or renting the vehicle for a plurality of times after renting the vehicle for a B type, and the like.
Optionally, the acquiring the behavior data of the target user includes:
the chat data of the target user in a second preset time is obtained; and/or the number of the groups of groups,
and acquiring page access data of the target user in a second preset time.
The target user's behavior data may be chat records of the user in a second preset time, or may be page access data of the user, that is, a series of behavior data of the user's access page generated according to a time line when the user enters the taxi platform every time in the second preset time, preferably, the user page access data includes all access data of the user to different pages every time the user enters the platform application in the second preset time, the second preset time may be three months, and exemplary, the chat records of the user may be data obtained by the user according to communication records of the user and the taxi platform customer service in three months, or may be data obtained by communication records of the user and the owner, etc., the user page access data may include that the user opens the taxi application in a certain time in three months, first enters the application first page, then searches for a certain vehicle, clicks a certain vehicle, looks for a detail page of the vehicle, then returns to the first page, and finally exits the platform application.
And acquiring behavior data of the target user in a preset time, so that whether the user has a specific trip intention recently or not can be judged conveniently.
S120, when the target user has the first-direction behavior, acquiring vehicle browsing data in a third preset time of the target user, and predicting the behavior intensity of the target user by utilizing the pre-trained behavior intensity perception model and the vehicle browsing data in the third preset time.
Vehicle browsing data refers to data determined according to behavior activities related to renting vehicles performed by a target user on an application platform. When the travel intention judgment result of the user is that the user has a first-direction behavior by utilizing the behavior judgment model, namely, the target user has travel intention to rent a vehicle, vehicle browsing data of the user in a third preset time of platform application are acquired, data features extracted from the vehicle browsing data are input into the behavior intensity perception model, the travel intention intensity of the user is predicted, and the output result is utilized to judge the strength of the travel intention of the user. Preferably, the output of the behavior intensity perception model can be an intensity value of 0-1, a threshold value for judging that the range of the travel intention of the user is in [0,1] is set, and the travel intention is strong when the threshold value is larger than or equal to the threshold value and weak when the threshold value is smaller than the threshold value.
Optionally, the acquiring vehicle browsing data of the target user includes:
acquiring residence time length data of the target user in a third preset time; and/or the number of the groups of groups,
acquiring vehicle access data of the target user in a third preset time; and/or the number of the groups of groups,
and acquiring page access data of the target user in a third preset time.
The vehicle browsing data of the target user comprise stay time data of the user in a third preset time, wherein the stay time data can be the stay time of displaying a plurality of vehicles on a certain page, the stay time of opening a homepage of a certain vehicle, and the stay time from entering an application platform to exiting the application platform; the vehicle browsing data can also comprise vehicle access data of a user in a third preset time, which can be what model of vehicle the user accesses, which vehicle pictures, information and the like are seen; the vehicle browsing data may further include page access data of the user in a third preset time, which may be the number of times and time of access of a certain vehicle type, or may be access time intervals of access to the platform application for multiple times, and preferably, the third preset time may be 3-5 days.
And acquiring vehicle browsing data in different aspects, and conveniently inputting the vehicle browsing data of the target user in a third preset time into a behavior intensity perception model, so as to predict the willingness of the target user to rent the vehicle.
Optionally, the determining, by using a pre-trained behavior discrimination model, whether the target user has the first direction behavior includes:
and when the target user does not have the first-direction behavior, storing the taxi data in the first preset time and/or the behavior data in the second preset time, so as to be used for updating training of the behavior discrimination model.
When the travel intention judgment result of the user is that the travel intention judgment result does not have the first direction behavior by using the behavior judgment model, namely, when the travel intention is in negative feedback, the user can only browse and see at will at the moment, or can be in the process of renting the vehicle, the user enters the platform application to see the vehicle renting information such as the time of returning, and the like, at the moment, the acquired vehicle renting data of the user at the first preset time and the acquired behavior data in the second preset time can be stored so as to update a sample user data set for training the behavior judgment model, and the sample user data set for updating and training the behavior judgment model is used for updating and training the behavior judgment model.
And the vehicle renting data and the behavior data of the user without the vehicle renting will are stored, so that the sample user data set for training the behavior discrimination model is expanded, and a data base is provided for updating and training the behavior discrimination model.
S130, acquiring vehicle interaction sequence data of the target user in fourth preset time, and sensing travel willingness of the target user by using a pre-trained behavior demand model based on the vehicle interaction sequence data and the behavior intensity in the fourth preset time.
The vehicle interaction sequence data is data obtained according to a series of actions related to the vehicle, which are performed by the target user in a fourth preset time, preferably, the fourth preset time may be three days, the vehicle interaction sequence data may be total data of interaction sequence data of different vehicles, which enter the platform application in three days, and the interaction sequence data may be, for example, actions according to the following actions of the user: the user firstly enters all the vehicle display pages, then clicks the detail page of a certain vehicle, shares the information of the vehicle or collects the data acquired by the vehicle information and the like, and the content of the vehicle interaction sequence data is not particularly limited in the embodiment of the invention.
And (3) carrying out feature vectorization on the vehicle interaction sequence data of the target user in the fourth preset time, inputting a feature vectorization result and the user renting intention intensity value predicted in the step (120) into a pre-trained behavior demand model, and outputting to obtain a vehicle model which meets the user renting intention, namely realizing perception of the target user on the traveling intention.
All operation behaviors of a user are converted into vectors, and the final vehicle renting willingness requirement judgment is carried out through a two-way serialization method similar to language texts, so that the method has obvious advantages in a vehicle renting repeated comparison re-decision scene compared with the traditional one-way sequence structure model (such as a Markov chain model and an RNN model).
Referring to the schematic diagram of the perception flow of the user travel willingness shown in fig. 2, the last year of the taxi renting data and the last 3 months of the behavior data of the target user are obtained, the behavior data comprise the IM chatting data and the page access data, and whether the target user has the taxi travel willingness or not is judged by utilizing the pre-trained behavior judging model based on the last year of the taxi renting data, the last 3 months of the IM chatting data and the last 3 months of the page access data. If the user has a willingness to rent the vehicle, vehicle browsing data of the user in the near 3-5 days are obtained, wherein the vehicle browsing data comprise the data of the stopping time of the user in the near 3-5 days, the vehicle access data of the user in the near 3-5 days and the page access data of the user in the near 3-5 days, and the behavior intensity of the renting and the traveling of the user is predicted by utilizing a pre-trained behavior intensity perception model and the vehicle browsing data of the user in the near 3-5 days. And acquiring vehicle interaction sequence data of the user in the last 3 days, and recommending the vehicle type meeting the travel wish to the user by utilizing a pre-trained behavior demand model based on the vehicle interaction sequence data in the last 3 days and the predicted behavior intensity of the user.
If the behavior discrimination model predicts that the user does not have willingness to rent the vehicle for traveling, the judgment is that the user can just look at the vehicle at random or the vehicle is renting, and the user is visiting in the journey.
According to the perception method of travel willingness, firstly, taxi data in a first preset time, chat data in a second preset time and page access data of a target user are obtained, and whether the target user has first-direction behaviors or not is judged by utilizing a pre-trained behavior judging model based on the taxi data in the first preset time and the chat data and page access data in the second preset time; when the target user has the first-direction behavior, acquiring stay time length data, vehicle access data and page access data in a third preset time of the target user, and predicting the behavior intensity of the target user by utilizing a pre-trained behavior intensity perception model and the stay time length data, the vehicle access data and the page access data in the third preset time; and acquiring vehicle interaction sequence data of the target user in fourth preset time, and predicting the willingness vehicle type of the target user by utilizing a pre-trained behavior demand model to perceive the travel willingness of the user based on the vehicle interaction sequence data in the fourth preset time and the predicted behavior intensity value. The vehicle type meeting the willingness of renting the vehicle on going can be recommended to the user with stronger willingness, the user experience is enhanced, the platform can optimize the integral marketing strategy according to the vehicle type, and the matching efficiency of the sharing platform to the renting requirements of the user is improved.
Example two
Fig. 3 is a flow chart of a method for sensing travel willingness according to a second embodiment of the present invention, and the training process of the behavior discrimination model, the behavior intensity perception model and the behavior demand model is specifically described based on the above embodiment. The embodiment of the invention and the method for sensing travel will provided by the embodiment belong to the same invention conception, and technical details which are not described in detail can be seen from the embodiment, and the method has the same technical effects.
As shown in fig. 3, the recommendation method of the vehicle data specifically includes the following steps:
s211, acquiring behavior discrimination model training data of a sample user, wherein the behavior discrimination model training data comprises at least one of the following: historical car rental data, historical chat data, and historical page series access data.
The method comprises the steps that stored historical data of all sample users are obtained to serve as training data of a behavior discrimination model, the historical data of the sample users can comprise historical taxi data, historical chat data and historical page series access data of the sample users, the historical taxi data can be total data of vehicle types, taxi time, taxi duration, rent and the like which are used for historical taxi of the sample users, the historical chat data can be data obtained from communication records of the sample users and customer service or data obtained from communication records of the sample users and vehicle owners, the historical page access data can be data which are respectively determined according to series behaviors of each sample user in a sample user group for each time of accessing pages, and the content of the historical taxi data, the historical chat data and the historical page series access data of the sample users used for training of the behavior discrimination model is not particularly limited.
S221, extracting historical order characteristics of historical taxi data.
The historical order feature refers to continuous and/or discrete variables which are extracted from historical taxi data of a sample user and can represent the historical taxi information, the variables can be directly obtained from the historical taxi data, and the variables can be obtained by simple calculation and/or transformation according to the historical taxi data.
And S231, extracting vehicle keywords in the historical chat data, and determining fully-connected word vectors based on the vehicle keywords.
The historical chat data may be one or more sentences spoken by both chat parties, but the sentences include other words as the vehicle renting components of the sentences in addition to the information about the vehicle renting, so that text preprocessing is required for the historical chat data, vehicle keywords related to the vehicle renting are extracted, the vehicle keywords representing the vehicle information are represented as word vectors, and then the word vectors of all the keywords are fully connected.
S241, the historical page series access data is subjected to sentence processing to obtain page access sentences, and behavior vectors are determined based on the page access sentences.
The historical page series access data is determined by a sample user historical page series access record, each step in the historical page series access process is subjected to sentence processing to obtain a plurality of page access sentences, and each sentence is subjected to text vectorization to determine the behavior vector of the historical page access data. For example, the behavior vector may be determined by using a vector space model VSM, or may be determined by using a distributed representation method of text, and the method for determining the behavior vector of the history page series access data according to the embodiment of the present invention is not particularly limited.
S251, training by using the historical order features, word vectors and behavior vectors to obtain two classifiers as a behavior discrimination model.
And inputting the historical order features corresponding to the historical taxi data of all sample users, word vectors expressed by vehicle keywords in the historical chat data and behavior vectors expressed by the historical page access data into a convolutional neural network for training to obtain a two-classifier, and taking the two-classifier as a behavior judging model for judging whether the users have taxi willingness.
S212, acquiring intensity perception model training data of a sample user, wherein the intensity perception model training data comprises at least one of the following: historical stay time data, historical vehicle access data, and historical page access data.
The historical stay time data, the historical vehicle access data and the historical page access data of each access platform application of all sample users are obtained as the intensity perception model training data set, and the stay time data, the historical vehicle access data and the historical page access data of the sample users can be the same as or different from the content included in the vehicle browsing data of the target users in the third preset time in the step S120.
S222, training by using training data of the intensity perception model to obtain two classifiers serving as a behavior intensity perception model.
And inputting the historical stay time data, the historical vehicle access data and the historical page access data of all the sample users into a classification model for classification training, and predicting the vehicle renting behavior intensity of the target user by using the obtained classifier as a behavior intensity perception model. For example, the behavioral intensity perception model may be trained using an XGBoost classification model.
Since the car renting is a transaction behavior of a decision making process in the middle and long periods, decision periods of 3-5 days can be respectively generated according to the difference of weekdays and holidays, and due to the fact that the decision making can enable part of users to continuously switch different car renting platforms to conduct price comparison, corresponding access times and stay time of the users can be relatively fragmented, and therefore the two-classification behavior intensity perception model constructed through the characteristics can well represent the intensity of willingness of the users to rent the cars.
S232, acquiring behavior demand model training data of a sample user, wherein the behavior demand model training data comprises historical vehicle renting data and historical vehicle interaction sequence data.
The historical taxi data and the historical vehicle interaction sequence data of all the sample users on the application platform are obtained as the behavior demand model training data set, the historical taxi data of the sample users can be that the sample users rent one assorted vehicle at a certain time, the content contained in the historical vehicle interaction sequence data can be the same as or different from the content contained in the vehicle interaction sequence data of the target users in the fourth preset time in the step S130, and the content of the historical taxi data and the content of the historical vehicle interaction sequence data of the sample users are not particularly limited.
S242, the historical vehicle interaction sequence data is subjected to sentence processing to obtain historical vehicle interaction sentences, and the historical vehicle interaction sentences are utilized to determine the historical interaction vectors.
The historical vehicle interaction sequence data is determined by the historical vehicle interaction records of the sample users, each step in each historical vehicle interaction record of each sample user is respectively subjected to sentence formation, and the obtained interactive sentences of all the historical vehicles can be used for obtaining the historical interaction vectors by using the text vectorization method described in the step S241.
S252, training by using the historical car renting data and the historical interaction vector to obtain a multi-classifier as a behavior demand model.
The historical car renting data of the sample user and the historical interaction vector obtained in the step S242 are input into a deep learning model for training, the obtained multi-classifier is used as a behavior demand model, in fact, the historical car renting data of the sample user contains the car renting intention strength of the sample user, if the car renting is finished, the intention strength is 1, the car renting intention strength is 0, and the deep learning model can be a BERT deep learning model.
S260, obtaining taxi data in a first preset time and behavior data in a second preset time of the target user, and judging whether the target user has the first direction behavior or not by utilizing a pre-trained behavior judging model based on the taxi data in the first preset time and the behavior data in the second preset time.
S270, when the target user has the first-direction behavior, acquiring vehicle browsing data in a third preset time of the target user, and predicting the behavior intensity of the target user by utilizing the pre-trained behavior intensity perception model and the vehicle browsing data in the third preset time.
S280, acquiring vehicle interaction sequence data of the target user in fourth preset time, and sensing travel willingness of the target user by using a pre-trained behavior demand model based on the vehicle interaction sequence data and the behavior intensity in the fourth preset time.
The embodiment of the invention does not limit the training time sequence of the three models of the judging model, the intensity sensing model and the demand model.
According to the perception method of travel willingness, provided by the embodiment of the invention, the historical taxi data, the historical chat data and the historical page series access data of a sample user are obtained to serve as behavior discrimination model training data; extracting historical order features of historical taxi data; extracting vehicle keywords in the historical chat data, and determining fully-connected word vectors based on the vehicle keywords; sentence making is carried out on the historical page series access data to obtain page access sentences, and behavior vectors are determined based on the page access sentences; training by using the historical order features, the word vectors and the behavior vectors to obtain two classifiers as a behavior discrimination model; acquiring historical stay time data, historical vehicle access data and historical page access data of a sample user as intensity perception model training data; training by using training data of the intensity perception model to obtain two classifiers serving as a behavior intensity perception model; acquiring historical vehicle renting data and historical vehicle interaction sequence data of a sample user as behavior demand model training data; sentence making is carried out on the historical vehicle interaction sequence data to obtain historical vehicle interaction sentences, and the historical vehicle interaction sentences are utilized to determine historical interaction vectors; and training by using the historical car renting data and the historical interaction vector to obtain a multi-classifier as the behavior demand model. The training processes of the behavior discrimination model, the behavior intensity perception model and the behavior demand model are respectively described.
Example III
Fig. 4 is a block diagram of a trip willingness sensing device according to a third embodiment of the present invention, where the present embodiment is applicable to a case where a user senses a trip willingness of a target user when renting a platform taxi. The travel willingness sensing device can be used for realizing the travel willingness sensing method provided by any embodiment of the invention. As shown in fig. 4, the trip willingness sensing device includes:
a first direction behavior judging module 310, configured to obtain taxi data in a first preset time and behavior data in a second preset time of a target user, and judge whether the target user has a first direction behavior by using a pre-trained behavior judging model based on the taxi data in the first preset time and the behavior data in the second preset time;
the behavior intensity prediction module 320 is configured to obtain vehicle browsing data in a third preset time of the target user when the target user has the first direction behavior, and predict the behavior intensity of the target user by using a pre-trained behavior intensity perception model and the vehicle browsing data in the third preset time;
The willingness demand sensing module 330 is configured to obtain vehicle interaction sequence data in a fourth preset time of the target user, and perform, based on the vehicle interaction sequence data in the fourth preset time and the behavior intensity, sensing of trip willingness of the target user by using a pre-trained behavior demand model.
Optionally, the acquiring the behavior data of the target user includes:
the chat data of the target user in a second preset time is obtained; and/or the number of the groups of groups,
and acquiring page access data of the target user in a second preset time.
Optionally, the acquiring vehicle browsing data of the target user includes:
acquiring residence time length data of the target user in a third preset time; and/or the number of the groups of groups,
acquiring vehicle access data of the target user in a third preset time; and/or the number of the groups of groups,
and acquiring page access data of the target user in a third preset time.
Optionally, the determining, by using a pre-trained behavior discrimination model, whether the target user has the first direction behavior includes:
and when the target user does not have the first-direction behavior, storing the taxi data in the first preset time and/or the behavior data in the second preset time, so as to be used for updating training of the behavior discrimination model.
Preferably, the behavior discrimination model training process includes:
obtaining behavior discrimination model training data of a sample user, wherein the behavior discrimination model training data comprises at least one of the following: historical car renting data, historical chat data and historical page series access data;
extracting historical order features of the historical taxi data;
extracting vehicle keywords in the historical chat data, and determining fully-connected word vectors based on the vehicle keywords;
the historical page series access data are subjected to sentence processing to obtain page access sentences, and behavior vectors are determined based on the page access sentences;
training by using the historical order features, the word vectors and the behavior vectors to obtain two classifiers serving as the behavior discrimination model.
Preferably, the behavioral intensity perception model training process includes:
obtaining intensity perception model training data of a sample user, wherein the intensity perception model training data comprises at least one of the following: historical stay time data, historical vehicle access data and historical page access data;
training by using the intensity perception model training data to obtain two classifiers serving as a behavior intensity perception model.
Preferably, the training process of the behavior demand model includes:
acquiring behavior demand model training data of a sample user, wherein the behavior demand model training data comprises historical vehicle renting data and historical vehicle interaction sequence data;
the historical vehicle interaction sequence data is subjected to sentence formation to obtain historical vehicle interaction sentences, and the historical vehicle interaction sentences are utilized to determine historical interaction vectors;
and training by using the historical vehicle renting data and the historical interaction vector to obtain a multi-classifier as the behavior demand model.
The travel willingness sensing device provided by the embodiment of the invention can execute the travel willingness sensing method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. Technical details which are not described in detail can be seen in the method for sensing travel willingness provided by any embodiment of the invention.
Example IV
Fig. 5 is a schematic structural diagram of a terminal according to a fourth embodiment of the present invention. Fig. 5 shows a block diagram of an exemplary terminal 12 suitable for use in implementing any of the embodiments of the invention. The terminal 12 shown in fig. 5 is merely an example, and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention. The terminal 12 is typically a mobile terminal that installs applications.
As shown in fig. 5, the terminal 12 is in the form of a general purpose computing device. The components of the terminal 12 may include, but are not limited to: one or more processors or processing units 16, a memory 28, and a bus 18 connecting the different components, including the memory 28 and the processing unit 16.
Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, and a local bus using any of a variety of bus architectures. By way of example, and not limitation, such architectures include industry standard architecture (Industry Standard Architecture, ISA) bus, micro channel architecture (Micro Channel Architecture, MCA) bus, enhanced ISA bus, video electronics standards association (Video Electronics Standards Association, VESA) local bus, and peripheral component interconnect (Peripheral Component Interconnect, PCI) bus.
Terminal 12 typically includes a variety of computer readable media. Such media can be any available media that is accessible by terminal 12 and includes both volatile and nonvolatile media, removable and non-removable media.
Memory 28 may include computer device readable media in the form of volatile memory, such as random access memory (Random Access Memory, RAM) 30 and/or cache memory 32. The terminal 12 may further include other removable/non-removable, volatile/nonvolatile computer storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (not shown in FIG. 5, commonly referred to as a "hard disk drive"). Although not shown in fig. 5, a disk drive for reading from and writing to a removable nonvolatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from and writing to a removable nonvolatile optical disk (e.g., a Compact Disc-Read Only Memory (CD-ROM), digital versatile Disc (Digital Video Disc-Read Only Memory, DVD-ROM), or other optical media) may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. Memory 28 may include at least one program product 40, with program product 40 having a set of program modules 42 configured to perform the functions of embodiments of the present invention. Program product 40 may be stored, for example, in memory 28, such program modules 42 include, but are not limited to, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods of the embodiments described herein.
The terminal 12 may also communicate with one or more external devices 14 (e.g., keyboard, mouse, camera, etc., and display), one or more devices that enable a user to interact with the terminal 12, and/or any devices (e.g., network card, modem, etc.) that enable the terminal 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Also, the terminal 12 may communicate with one or more networks such as a local area network (Local Area Network, LAN), a wide area network Wide Area Network, a WAN, and/or a public network such as the internet via the network adapter 20. As shown, the network adapter 20 communicates with other modules of the terminal 12 via the bus 18. It should be appreciated that although not shown in fig. 5, other hardware and/or software modules may be used in connection with terminal 12, including, but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, disk array (Redundant Arrays of Independent Disks, RAID) devices, tape drives, data backup storage devices, and the like.
The processor 16 executes various functional applications and data processing by running a program stored in the memory 28, for example, to implement a travel wish sensing method provided by the above-described embodiment of the present invention, the method including:
Acquiring taxi data in a first preset time and behavior data in a second preset time of a target user, and judging whether the target user has a first direction behavior or not by using a pre-trained behavior judging model based on the taxi data in the first preset time and the behavior data in the second preset time;
when the target user has the first-direction behavior, acquiring vehicle browsing data in a third preset time of the target user, and predicting the behavior intensity of the target user by utilizing a pre-trained behavior intensity perception model and the vehicle browsing data in the third preset time;
and acquiring vehicle interaction sequence data of the target user in a fourth preset time, and sensing travel willingness of the target user by utilizing a pre-trained behavior demand model based on the vehicle interaction sequence data and the behavior intensity in the fourth preset time.
Of course, those skilled in the art will understand that the processor may also implement the method for sensing travel willingness provided in any embodiment of the present invention.
Example five
The fifth embodiment of the present invention further provides a computer readable storage medium having a computer program stored thereon, the program when executed by a processor implementing a method for sensing travel willingness as provided in any embodiment of the present application, the method comprising:
Acquiring taxi data in a first preset time and behavior data in a second preset time of a target user, and judging whether the target user has a first direction behavior or not by using a pre-trained behavior judging model based on the taxi data in the first preset time and the behavior data in the second preset time;
when the target user has the first-direction behavior, acquiring vehicle browsing data in a third preset time of the target user, and predicting the behavior intensity of the target user by utilizing a pre-trained behavior intensity perception model and the vehicle browsing data in the third preset time;
and acquiring vehicle interaction sequence data of the target user in a fourth preset time, and sensing travel willingness of the target user by utilizing a pre-trained behavior demand model based on the vehicle interaction sequence data and the behavior intensity in the fourth preset time.
Of course, the computer readable storage medium provided by the embodiments of the present invention, on which the computer program stored is not limited to the above method instructions, may also execute the method for sensing travel willingness provided by any embodiment of the present invention.
The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example, but not limited to, an apparatus, device, or means of electronic, magnetic, optical, electromagnetic, infrared, or semiconductor, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution apparatus, device, or apparatus.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution apparatus, device, or apparatus.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out instructions of the present invention may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (10)
1. A method of sensing travel willingness, comprising:
acquiring taxi data in a first preset time and behavior data in a second preset time of a target user, and judging whether the target user has a first direction behavior or not by using a pre-trained behavior judging model based on the taxi data in the first preset time and the behavior data in the second preset time;
when the target user has the first-direction behavior, acquiring vehicle browsing data in a third preset time of the target user, and predicting the behavior intensity of the target user by utilizing a pre-trained behavior intensity perception model and the vehicle browsing data in the third preset time;
acquiring vehicle interaction sequence data of the target user in a fourth preset time, and based on the vehicle interaction sequence data and the behavior intensity in the fourth preset time, sensing the travel willingness of the target user by using a pre-trained behavior demand model, and outputting to obtain a vehicle type which accords with the travel willingness of the user;
the first direction behavior indicates that the line intention is positive feedback;
the behavior intensity refers to the travel willingness intensity of the user;
The vehicle interaction sequence data is data acquired according to a series of behaviors related to the vehicle, which are carried out by the target user in a fourth preset time.
2. The method of claim 1, wherein the behavioral discriminant model training process comprises:
obtaining behavior discrimination model training data of a sample user, wherein the behavior discrimination model training data comprises at least one of the following: historical car renting data, historical chat data and historical page series access data;
extracting historical order features of the historical taxi data;
extracting vehicle keywords in the historical chat data, and determining fully-connected word vectors based on the vehicle keywords;
the historical page series access data are subjected to sentence processing to obtain page access sentences, and behavior vectors are determined based on the page access sentences;
training by using the historical order features, the word vectors and the behavior vectors to obtain two classifiers serving as the behavior discrimination model.
3. The method of claim 1, wherein the behavioral intensity perception model training process comprises:
obtaining intensity perception model training data of a sample user, wherein the intensity perception model training data comprises at least one of the following: historical stay time data, historical vehicle access data and historical page access data;
Training by using the intensity perception model training data to obtain two classifiers serving as a behavior intensity perception model.
4. The method of claim 1, wherein the training process of the behavioral need model comprises:
acquiring behavior demand model training data of a sample user, wherein the behavior demand model training data comprises historical vehicle renting data and historical vehicle interaction sequence data;
the historical vehicle interaction sequence data is subjected to sentence formation to obtain historical vehicle interaction sentences, and the historical vehicle interaction sentences are utilized to determine historical interaction vectors;
and training by using the historical vehicle renting data and the historical interaction vector to obtain a multi-classifier as the behavior demand model.
5. The method according to any one of claims 1, wherein the obtaining behavior data of the target user includes:
the chat data of the target user in a second preset time is obtained; and/or the number of the groups of groups,
and acquiring page access data of the target user in a second preset time.
6. The method of claim 1, wherein the obtaining vehicle browsing data of the target user comprises:
Acquiring residence time length data of the target user in a third preset time; and/or the number of the groups of groups,
acquiring vehicle access data of the target user in a third preset time; and/or the number of the groups of groups,
and acquiring page access data of the target user in a third preset time.
7. The method of claim 1, wherein said determining whether the target user has a first direction behavior using a pre-trained behavior discrimination model comprises:
and when the target user does not have the first-direction behavior, storing the taxi data in the first preset time and/or the behavior data in the second preset time, so as to be used for updating training of the behavior discrimination model.
8. A travel willingness sensing device, comprising:
the first direction behavior judging module is used for acquiring taxi data in a first preset time and behavior data in a second preset time of a target user, and judging whether the target user has first direction behaviors or not by utilizing a pre-trained behavior judging model based on the taxi data in the first preset time and the behavior data in the second preset time;
the behavior intensity prediction module is used for acquiring vehicle browsing data in a third preset time of the target user when the target user has the first direction behavior, and predicting the behavior intensity of the target user by utilizing a pre-trained behavior intensity perception model and the vehicle browsing data in the third preset time;
The willingness demand sensing module is used for acquiring vehicle interaction sequence data of the target user in a fourth preset time, sensing the traveling willingness of the target user by utilizing a pre-trained behavior demand model based on the vehicle interaction sequence data and the behavior intensity in the fourth preset time, and outputting to obtain a vehicle type which accords with the traveling willingness of the user;
the first direction behavior indicates that the line intention is positive feedback;
the behavior intensity refers to the travel willingness intensity of the user;
the vehicle interaction sequence data is data acquired according to a series of behaviors related to the vehicle, which are carried out by the target user in a fourth preset time.
9. A terminal comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of perception of travel willingness according to any one of claims 1-7 when executing the program.
10. A computer readable storage medium having stored thereon a computer program, characterized in that the program, when executed by a processor, implements a method of perception of travel willingness as claimed in any of claims 1-7.
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