CN116361543A - Travel strategy recommendation method and device - Google Patents
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Abstract
The application discloses a recommendation method and device for travel strategies. In the recommendation method of the travel attack, the user label information of the user is obtained based on the user information of the user, the travel preference information of the user is obtained based on the user label information and the travel statistical information acquired in advance, and finally the travel attack scheme of the user is obtained based on the travel preference information, the travel target information in the user information and the self-learning model. In the process, the acquired travel preference information of the user can recommend interested travel places for the user, and based on the travel preference information, the travel target information and the self-learning model, the personalized travel attack scheme of the user can be acquired, and the travel attack scheme meeting personalized requirements is provided for the user.
Description
Technical Field
The application relates to the technical field of artificial intelligence, in particular to a recommendation method and device for travel strategies.
Background
With the development of internet economies, many tourists will describe and distribute their own itineraries on the network, and the attack describing tourist attractions and travel experiences is called a travel attack.
At present, most travel strategies are written by tourists according to the conditions of the tourists, and the travel strategies have certain timeliness and limitations, cannot be suitable for various periods, cannot meet personalized travel requirements of users, and cannot be recommended for the users.
Disclosure of Invention
The invention provides a recommendation method and a recommendation device for travel strategies, which can recommend the travel strategies according to the interests of a user, and achieve the purpose of recommending the travel strategies with personalized requirements for the user.
In a first aspect, the present application provides a travel strategy recommendation method, the method comprising:
obtaining user information of a user, wherein the user information comprises basic information and travel target information;
analyzing the basic information to obtain user tag information, wherein the user tag information is used for indicating the type of the behavior characteristics of the user;
acquiring travel preference information of a user based on the user tag information and the acquired travel statistical information in advance;
based on travel preference information, travel target information and a self-learning model, determining a travel attack scheme of a user, wherein the self-learning model is a trained neural network model, and a training sample of the self-learning model comprises a plurality of historical travel statistical information, a plurality of historical user information and travel data sets corresponding to each historical user.
Optionally, analyzing the basic information to obtain user tag information, including:
obtaining a behavior characteristic data set in basic information, wherein the behavior characteristic data set comprises a behavior data set and a characteristic data set, the behavior data set is used for indicating the behavior type of a user, and the characteristic data set is used for indicating the characteristic type of the user;
and carrying out cluster analysis on the label types of the users according to the behavior data set and the characteristic data set to obtain user label information.
Optionally, obtaining the behavior feature data set in the base information includes:
extracting behavior information and characteristic information in the basic information;
matching the behavior information with behavior tags to obtain a behavior data set, wherein the behavior tags are preset tags for describing the behavior of a user;
and matching the characteristic information with a characteristic tag to obtain a characteristic data set, wherein the characteristic tag is a preset tag for describing the characteristics of the user.
Optionally, obtaining travel preference information of the user based on the user tag information and the pre-acquired travel statistics includes:
and carrying out association calculation on the user tag information and the travel statistical information to obtain the travel preference information.
Optionally, determining a travel strategy solution based on the travel preference information, the travel objective information, and the self-learning model includes:
inputting the travel preference information and the travel target information into a self-learning model to obtain a travel data set corresponding to a user;
a travel strategy is determined based on the travel dataset corresponding to the user.
Optionally, the training process of the self-learning model includes:
acquiring a plurality of historical travel statistical information and historical user information of a plurality of users;
respectively carrying out associated calculation on the historical travel statistical information and the historical user information of the plurality of users to obtain a travel data set corresponding to each user in the plurality of users;
and training the initial model by taking the historical travel statistical information of a plurality of users, the historical user information of a plurality of users and the travel data set corresponding to each user as training samples to obtain a self-learning model.
In a second aspect, the present application provides a recommendation device for travel strategies, the device comprising:
the system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring user information of a user, and the user information comprises basic information and travel target information of the user;
the processing unit is used for analyzing the basic information to obtain user tag information, wherein the user tag information is used for indicating the type of the behavior characteristics of the user;
the processing unit is also used for obtaining the travel preference information of the user based on the user tag information and the acquired travel statistical information in advance, wherein the travel preference information comprises the travel information preferred by the user;
the determining unit is used for determining a travel attack scheme based on the travel preference information, the travel target information and a self-learning model, wherein the self-learning model is a trained neural network model, and a training sample of the self-learning model comprises a plurality of historical travel statistical information, a plurality of historical user information and travel data sets corresponding to each historical user.
Optionally, the obtaining unit is further configured to:
a behavior feature data set in the basic information is obtained, wherein the behavior feature data set comprises a behavior data set and a feature data set, the behavior data set is used for indicating the behavior type of the user, and the feature data set is used for indicating the feature type of the user.
Optionally, the processing unit is further configured to:
and carrying out cluster analysis on the label types of the users according to the behavior data set and the characteristic data set to obtain user label information.
Optionally, the apparatus further comprises:
and the extraction unit is used for extracting the behavior information and the characteristic information in the basic information.
Optionally, the obtaining unit is further configured to:
and matching the behavior information with behavior tags to obtain a behavior data set, wherein the behavior tags are preset tags for describing the behaviors of the user.
And matching the characteristic information with a characteristic tag to obtain a characteristic data set, wherein the characteristic tag is a preset tag for describing the characteristics of the user.
Optionally, the obtaining unit is further configured to:
and carrying out association calculation on the user tag information and the travel statistical information to obtain the travel preference information.
Optionally, the obtaining unit is further configured to:
and inputting the travel preference information and the travel target information into the self-learning model to obtain a travel data set corresponding to the user.
Optionally, the determining unit is further configured to:
a travel strategy is determined based on the travel dataset corresponding to the user.
In a third aspect, the present application provides an electronic device comprising a memory and a processor:
the memory is used for storing a computer program;
the processor is configured to perform the method provided in the first aspect above according to a computer program.
In a fourth aspect, the present application also provides a computer readable storage medium for storing a computer program for performing the method provided in the first aspect.
From this, this application has following beneficial effect:
the application provides a recommendation method of travel attack, which comprises the steps of obtaining user information of a user, wherein the user information comprises basic information and travel target information, analyzing the basic information and obtaining user tag information; then, based on the user tag information and the travel information, obtaining travel preference information of the user; finally, a travel strategy for the user is determined based on the travel preference information, the travel target information, and the self-learning model. In the process, user tag information representing the behavior characteristics of the user is obtained from the basic information of the user, and then the real-time travel information is combined, so that travel preference information recommended to the user can be obtained, personalized requirements of the user are met, and then a travel attack scheme meeting the requirements of the user can be automatically and quickly obtained based on a self-learning model, and the time for the user to make travel attack can be saved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments described in the present application, and other drawings may be obtained according to these drawings for a person having ordinary skill in the art.
FIG. 1 is a flow chart of a recommendation method for travel strategies in an embodiment of the present application;
FIG. 2 is a schematic diagram of a travel attack recommendation system 200 according to an embodiment of the present application;
FIG. 3 is a flow chart of an embodiment of a travel strategy recommendation method according to the present application;
FIG. 4 is a schematic diagram of a travel attack recommendation device 400 according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an electronic device 500 according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but 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.
At present, the known world-known tourist attractions are removed, with the development of the self-media tourist industry, the network red card-punching places are frequently born in all places, so that the demands of people for tourist strategies are increasing, but most of the tourist strategies on the network are fixed places, have smaller coverage areas and have hysteresis, and if the tourist strategies at present are used, the situation that the description is not in line with the actual situation or the personalized demands of the tourist cannot be met can occur.
In the embodiment of the application, the user information of the user is obtained, wherein the user information comprises basic information and travel target information, and the basic information is analyzed to obtain user tag information; then, based on the user tag information and the travel information, obtaining travel preference information of the user; finally, a travel strategy for the user is determined based on the travel preference information, the travel target information, and the self-learning model. Therefore, by the method provided by the implementation of the application, the travel preference information of the user can be obtained by obtaining the user tag information, so that interested travel places are recommended for the user, and based on the travel preference information, the travel target information and the self-learning model, the personalized travel attack scheme of the user can be obtained, the personalized requirements of the user are met, and the travel experience of the user is improved.
In order to facilitate understanding of the specific implementation of the method for making a travel strategy provided in the embodiments of the present application, the following description will be made with reference to the accompanying drawings.
It should be noted that, the main body implementing the recommendation method of the travel attack may be the recommendation system of the travel attack provided by the embodiment of the application, or may be the recommendation device of the travel attack provided by the embodiment of the application, where the recommendation device of the travel attack may be carried in an electronic device or a functional module of the electronic device. The electronic device in the embodiment of the present application may be any device capable of implementing the recommendation method of travel attack in the embodiment of the present application, for example, may be an internet of things (Internet ofThings, ioT) device.
Fig. 1 is a flow chart of a travel attack recommendation method according to an embodiment of the present application. The method may be applied to a travel attack recommender system, such as travel attack recommender system 200 shown in fig. 2, a travel attack recommender, such as travel attack recommender 400 shown in fig. 4, or a functional module integrated in electronic device 500 shown in fig. 5.
As shown in fig. 1, the method includes the following S101 to S104:
s101: user information of a user is obtained, wherein the user information comprises basic information and travel target information.
In order to obtain the travel attack scheme, firstly, user information of a user is required to be obtained, then user tag information is obtained based on basic information in the user information, then travel preference information is obtained based on the user tag information and travel statistical information acquired in advance, and finally the travel attack scheme is determined based on the travel preference information, travel target information in the user information and a self-learning model. Therefore, the embodiment of the application provides a precondition for obtaining the user tag information by obtaining the user information of the user through S101.
As one example, S101 may include: user information of a user is obtained, wherein the user information comprises basic information and travel target information, the basic information comprises information such as basic identity information, hobbies and interests of the user, daily habits and the like, and the travel target information comprises information such as travel places, travel time, accommodation choices and the like of the user for travel.
S102: and analyzing the basic information to obtain user tag information, wherein the user tag information is used for indicating the type of the behavior characteristics of the user.
As one example, S102 may include: s1021, a behavior feature data set in the basic information is obtained, wherein the behavior feature data set comprises a behavior data set and a feature data set, the behavior data set is used for indicating the behavior type of the user, and the feature data set is used for indicating the feature type of the user. And S1022, performing cluster analysis on the label types of the user according to the behavior data set and the characteristic data set to obtain the user label information. The label type of the user is subjected to cluster analysis according to the behavior data set and the characteristic data set obtained in S1021, so that user label information of the user can be better obtained from two aspects, preference information of the user can be obtained, and pre-preparation is provided for recommending preferred travel information for the user.
As an example, S1021 described above may include: s10211, extracting behavior information and characteristic information in the basic information; s10212, matching behavior information with behavior labels to obtain a behavior data set, wherein the behavior labels are preset labels for describing user behaviors; s10213, matching the feature information with feature labels to obtain a feature data set, wherein the feature labels are preset labels for describing user features. By analyzing the behavior information and the characteristic information in the basic information, the behavior label and the characteristic label of the user can be obtained, and a pre-preparation is provided for obtaining the specific label of the user.
S103: and obtaining the travel preference information of the user based on the user tag information and the travel statistical information acquired in advance.
As one example, S103 may include: and carrying out association calculation on the user tag information and the travel statistical information to obtain the travel preference information. The travel preference information of the user can be obtained according to the interest preference of the user, so that tourist attractions interested by the user can be recommended for the user according to the travel preference information. The travel statistical information can be obtained from the current world travel scenic spots, the network red card punching places and the network red snack shops, and the statistical information can comprise the longitude and latitude of places, the peak time distribution of people flow, business hours and other information, can be updated in real time, and has timeliness.
The user tag information and the travel statistics information in the above example may be stored in a database, so as to directly call the data in the database to perform association calculation, or may be associated calculation, so as to obtain the travel preference information of the user.
S104: based on travel preference information, travel target information and a self-learning model, determining a travel attack scheme of a user, wherein the self-learning model is a trained neural network model, and a training sample of the self-learning model comprises a plurality of historical travel information, a plurality of historical user information and travel data sets corresponding to each historical user.
As one example, S104 may include: the travel preference information and the travel target information are input into the self-learning model, a travel data set corresponding to the user is obtained, and a travel attack scheme of the user can be determined based on the travel data set corresponding to the user. The travel data set corresponding to the user indicates the target travel statistical information of the user traveling at this time, and a corresponding travel strategy scheme can be determined for the user according to the target travel statistical information and a preset information template, so that the time for the user to make a travel strategy is saved.
As one example, the training process of the self-learning model in S104 may include: firstly, obtaining a plurality of historical travel statistical information and historical user information of a plurality of users; then, respectively carrying out associated calculation on the historical travel statistical information and the historical user information of the plurality of users to obtain a travel data set corresponding to each user in the plurality of users; and finally, training the initial model by taking a plurality of historical travel statistical information, historical user information of a plurality of users and travel data sets corresponding to each user as training samples to obtain a self-learning model.
The specific process of training the initial model based on the training samples to obtain the self-learning model may include, for example: inputting the historical travel statistical information 1 and the historical user information 1 in the training sample 1 into an initial model, comparing an output data set 1 of the initial model with the travel data set 1 of the training sample 1 to obtain a comparison result 0, and adjusting the initial model based on the comparison result 0 to obtain a self-learning model 1; the method comprises the steps of inputting historical travel statistical information 2 and historical user information 2 in a training sample 2 into a self-learning model 1, comparing an output data set 2 of the self-learning model 1 with a travel data set 2 of the training sample 2 to obtain a comparison result 1, and adjusting the self-learning model 1 based on the comparison result 1 to obtain the self-learning model 2; and so on, until the obtained self-learning model meets the preset condition, the self-learning model meeting the preset condition is recorded as a trained self-learning model (namely, the self-learning model in S102).
It can be seen that, according to the method of the embodiment of the application, firstly, user information of a user is obtained, then basic information in the user information is analyzed to obtain user tag information, then, travel preference information of the user is obtained based on the user tag information and travel statistical information acquired in advance, and finally, a travel attack scheme of the user is determined based on the travel preference information, travel target information and a self-learning model. In the process, the acquired travel preference information of the user can recommend interested tourist attractions for the user, and based on the self-learning model, a travel attack scheme can be automatically and rapidly acquired, and a travel attack based on individual requirements of the user is recommended for the user.
The system in the embodiment of the present application may refer to, for example, a travel attack recommendation system 200 shown in fig. 2, and may include, for example:
the data acquisition module 201 is configured to acquire travel statistics information of the present world travel scenic spots, the online red punching card places and the online red snack bars, where the statistics information may include, for example, longitude and latitude of a place, peak time distribution of people flow, business hours, and the like;
a registered user center module 202, configured to obtain basic information of a user, including basic identity information, interests, daily habits, and other information;
the behavior analysis module 203 is configured to analyze the basic information of the user to obtain a behavior data set;
the feature analysis module 204 is configured to analyze the basic information of the user to obtain a feature data set;
the clustering module 205 is configured to cluster the behavior data set and the feature data set of the user to the tag type of the user, so as to obtain user tag information of the user;
a storage module 206 for storing travel statistics and user tag information;
the travel information and user information joint calculation module 207 is configured to perform a joint calculation on the travel statistics information and the user tag information to obtain travel preference information of the user;
a user input module 208 for obtaining travel target information for a user;
the self-learning module 209 is configured to input travel target information and travel preference information of a user into the self-learning model, and obtain a travel attack scenario of the user;
the output module 210 is configured to output the travel scenario of the self-learning module 209.
In order to make the method provided by the embodiment of the present application clearer and easier to understand, a specific example of the method based on the construction diagram shown in fig. 2 will be described below with reference to fig. 3.
As shown in fig. 3, this embodiment may include:
s301: the data acquisition module 201 acquires travel statistics on the internet and transmits the travel statistics to the storage module 206 for storage.
The data collection module 201 can collect the latest travel statistics information from the internet, and the statistics information can include longitude and latitude of a place, peak time distribution of people stream, business hours and the like, so that timeliness and universality of the travel statistics information can be guaranteed, and the travel statistics information can be collected whether the world-known tourist attractions or new network card punching points are known.
S302: the registered user center module 202 collects basic information of the user, transmits behavior information in the basic information to the behavior analysis module 203, and transmits feature information in the basic information to the feature analysis module 204.
The registered user center module 202 may collect basic information of the user when the user registers, where the basic information includes basic identity information, interests, daily habits, and other information of the user. As one example, the basic information of the user may include, for example: gender: a male; age: age 25; the academic: a family; school specialty: computer science and technology profession; hobbies: fitness, music and food; daily habit: focusing on information such as efficiency.
S303: the behavior analysis module 203 matches the behavior information with the behavior tags to obtain a behavior data set, and transmits the behavior data set to the clustering module 205.
The behavior module 203 is preset with a tag related to behavior information, and the tag is matched with the behavior information, so that the behavior tag of the user can be obtained, namely, a behavior data set is obtained. As one example, the behavior information of the user may include, for example: music, fitness and food, which are matched with preset behavior tags for listening to music, constant fitness and food loving, the three behavior data sets are obtained.
S304: the feature analysis module 204 matches the behavior information with the feature tags to obtain feature data sets, and transmits the feature data sets to the clustering module 205.
The feature module 204 is preset with a tag related to feature information, and the tag is matched with the feature information, so that a feature tag of the user can be obtained, namely, a feature data set is obtained. As one example, the feature information of the user may include, for example: men, 25 years old, tally professionals and efficiency concerns, which match with preset tally men, young people and efficient feature tags, the feature data sets of these three categories are obtained.
It should be noted that, in the embodiment of the present application, the execution order of S303 and S304 is not limited, and S303 and S304 may be executed first, S303 may be executed second, or S303 and S304 may be executed simultaneously.
S305: the clustering module 205 performs cluster analysis on the label types of the users according to the behavior data set and the characteristic data set to obtain user label information, and transmits the user label information to the storage module 206 for storage.
The clustering module 205 performs cluster analysis on the tag types of the users by using the behavior data set and the feature data set of the users, and considers the two aspects, so as to screen out the user sets conforming to the behavior data set and the feature data set, thereby obtaining the user tag information conforming to the users. Based on the above examples, the user tag information of the user includes information of tags for a reasonable man, a young crowd, high efficiency, listening to music, regular exercise, and loving to eat.
S306: the travel information and user information joint calculation module 207 invokes the travel statistics and user tag information in the storage module 206, performs a joint calculation on the user tag information and the travel statistics, obtains travel preference information, and transmits the travel preference information to the self-learning module 209.
In order to recommend tourist attractions of interest to the user, the tourist information and user information joint calculation module 207 needs to perform association calculation on the user tag information and the tourist statistics information, so that the user can be recommended to tourist attractions with individuation of the user by knowing the daily behavior characteristics of the user.
S307: the user input module 208 obtains travel target information for the user and communicates to the self-learning module 209.
To obtain a particular travel scenario, the user is required to enter travel goal information via the user input module 209, and as one example, the travel goals entered by the user may include, for example: travel location: sand growing; travel time is 1 week; travel budget: 5000 yuan; tourist co-workers: 2 persons.
S308: the self-learning module 209 inputs the user's travel preference information and travel goal information to a self-learning model, obtains a travel dataset, and determines a travel strategy solution based on the travel dataset.
The self-learning module 209 inputs the travel preference information and the travel target information of the user into the self-learning model, and may obtain a travel data set conforming to the travel target, and based on the above example, the obtained travel data set conforms to the travel location as long sand, the travel period as a week, the tourist attraction as a net red food restaurant or a music artistic land, and based on the obtained travel data set and a preset information template, a travel attack plan of the user may be obtained.
S309: the output module 210 outputs the travel strategy scenario in the self-learning module 209.
The embodiment provides a recommendation method of travel attack, which can match corresponding user tag information for a user according to behavior characteristic information of the user; based on the user tag information and the acquired travel statistical information, travel preference information corresponding to the user can be acquired, and interested tourist attractions can be recommended for the user in advance; and then, the travel preference information and the travel target information of the current travel of the user are input into a self-learning model, so that a travel attack scheme for realizing the personalized requirements of the user can be obtained. In addition, due to the fact that the travel attack scheme is automatically obtained by the module, the time for a user to make travel attack can be saved, the travel people flow peak area in the time period can be automatically avoided, and the travel experience of the user is improved.
Referring to fig. 4, an embodiment of the present application provides a travel attack recommendation device 400, which includes:
an obtaining unit 401, configured to obtain user information of a user, where the user information includes basic information and travel target information of the user; for example, may be consistent with the functionality of the registered user center module 202 and the user input module 208 described above.
A processing unit 402, configured to analyze the basic information to obtain user tag information, where the user tag information is used to indicate a type of a behavior feature of a user; for example, may be consistent with the functionality of the behavior analysis module 203, the feature analysis module 204, and the clustering module 205 described above.
The processing unit 402 is further configured to obtain travel preference information of the user based on the user tag information and the pre-acquired travel statistics, where the travel preference information includes travel information preferred by the user; for example, may be consistent with the functionality of the travel information and user information joint calculation module 207 described above.
A determining unit 403, configured to determine a travel attack scenario based on the travel preference information, the travel target information, and a self-learning model, where the self-learning model is a trained neural network model, and a training sample of the self-learning model includes a plurality of historical travel statistics, a plurality of historical user information, and a travel data set corresponding to each historical user. For example, may be consistent with the functionality of the self-learning module 209 described above.
Optionally, the obtaining unit 401 is further configured to:
a behavior feature data set in the basic information is obtained, wherein the behavior feature data set comprises a behavior data set and a feature data set, the behavior data set is used for indicating the behavior type of the user, and the feature data set is used for indicating the feature type of the user.
Optionally, the processing unit 402 is further configured to:
and carrying out cluster analysis on the label types of the users according to the behavior data set and the characteristic data set to obtain user label information.
Optionally, the apparatus 400 further includes:
and the extraction unit is used for extracting the behavior information and the characteristic information in the basic information.
Optionally, the obtaining unit 401 is further configured to:
and matching the behavior information with behavior tags to obtain a behavior data set, wherein the behavior tags are preset tags for describing the behaviors of the user.
And matching the characteristic information with a characteristic tag to obtain a characteristic data set, wherein the characteristic tag is a preset tag for describing the characteristics of the user.
Optionally, the obtaining unit 401 is further configured to:
and carrying out association calculation on the user tag information and the travel statistical information to obtain the travel preference information.
Optionally, the obtaining unit 401 is further configured to:
and inputting the travel preference information and the travel target information into the self-learning model to obtain a travel data set corresponding to the user.
Optionally, the determining unit 403 is further configured to:
a travel strategy is determined based on the travel dataset corresponding to the user.
It should be noted that, the specific implementation manner and the achieved effect of the apparatus 400 may be referred to the related description in the method provided in fig. 1 or fig. 3, and will not be repeated here.
The embodiment of the present application further provides an electronic device 500, as shown in fig. 5, where the device 500 includes a memory 501 and a processor 502:
the memory 501 is used for storing a computer program;
the processor 502 is configured to perform the methods provided in fig. 1 or 3 described above in accordance with a computer program.
Furthermore, the present application provides a computer readable storage medium for storing a computer program for executing the method provided in fig. 1 or fig. 3.
From the above description of embodiments, it will be apparent to those skilled in the art that all or part of the steps of the above described example methods may be implemented in software plus general hardware platforms. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which may be stored in a storage medium, such as a read-only memory (ROM)/RAM, a magnetic disk, an optical disk, or the like, including several instructions for causing a computer device (which may be a personal computer, a server, or a network communication device such as a router) to perform the methods described in the embodiments or some parts of the embodiments of the present application.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points. The apparatus embodiments described above are merely illustrative, in which the modules illustrated as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, may be located in one place, or may be distributed over multiple network elements. Some or all of the modules may be selected according to actual needs to achieve the objective of the embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
The foregoing is merely exemplary embodiments of the present application and is not intended to limit the scope of the present application.
Claims (10)
1. A method for recommending travel strategies, the method comprising:
obtaining user information of a user, wherein the user information comprises basic information and travel target information;
analyzing the basic information to obtain user tag information, wherein the user tag information is used for indicating the type of the behavior characteristics of the user;
acquiring travel preference information of the user based on the user tag information and the acquired travel statistical information in advance;
determining a travel attack scheme of the user based on the travel preference information, the travel target information and a self-learning model, wherein the self-learning model is a trained neural network model, and a training sample of the self-learning model comprises a plurality of historical travel statistical information, a plurality of historical user information and a travel data set corresponding to each historical user.
2. The method of claim 1, wherein analyzing the base information to obtain user tag information comprises:
obtaining a behavior characteristic data set in the basic information, wherein the behavior characteristic data set comprises a behavior data set and a characteristic data set, the behavior data set is used for indicating the behavior type of the user, and the characteristic data set is used for indicating the characteristic type of the user;
and performing cluster analysis on the label type of the user according to the behavior data set and the characteristic data set to obtain the user label information.
3. The method of claim 2, wherein the obtaining the behavioral characteristic dataset in the base information comprises:
extracting behavior information and characteristic information in the basic information;
matching the behavior information with a behavior tag to obtain the behavior data set, wherein the behavior tag is a preset tag for describing the behavior of a user;
and matching the characteristic information with a characteristic label to obtain the characteristic data set, wherein the characteristic label is a preset label for describing the characteristics of the user.
4. The method of claim 1, wherein the obtaining travel preference information for the user based on the user tag information and pre-collected travel statistics comprises:
and carrying out association calculation on the user tag information and the travel statistical information to obtain the travel preference information.
5. The method of claim 1, wherein the determining a travel solution based on the travel preference information, the travel goal information, and a self-learning model comprises:
inputting the travel preference information and the travel target information into the self-learning model to obtain a travel data set corresponding to the user;
and determining the travel attack scheme based on the travel data set corresponding to the user.
6. The method of claim 1, wherein the training process of the self-learning model comprises:
acquiring a plurality of historical travel statistical information and historical user information of a plurality of users;
respectively carrying out associated calculation on the historical travel statistical information and the historical user information of the plurality of users to obtain a travel data set corresponding to each user in the plurality of users;
and training an initial model by taking the historical travel statistical information, the historical user information of the users and the travel data set corresponding to each user as training samples to obtain the self-learning model.
7. A recommendation device for travel strategies, the device comprising:
the information processing device comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring user information of a user, and the user information comprises basic information and travel target information of the user;
the processing unit is used for analyzing the basic information to obtain user tag information, wherein the user tag information is used for indicating the type of the behavior characteristics of the user;
the processing unit is further used for obtaining travel preference information of the user based on the user tag information and the acquired travel statistical information in advance, wherein the travel preference information comprises the travel information preferred by the user;
the determining unit is used for determining a travel attack scheme based on the travel preference information, the travel target information and a self-learning model, wherein the self-learning model is a trained neural network model, and a training sample of the self-learning model comprises a plurality of historical travel statistical information, a plurality of historical user information and travel data sets corresponding to each historical user.
8. The apparatus of claim 7, wherein the device comprises a plurality of sensors,
the obtaining unit is further configured to obtain a behavior feature data set in the basic information, where the behavior feature data set includes a behavior data set and a feature data set, the behavior data set is used to indicate a behavior type of the user, and the feature data set is used to indicate a feature type of the user;
and the processing unit is also used for carrying out cluster analysis on the label type of the user according to the behavior data set and the characteristic data set to obtain the user label information.
9. An electronic device comprising a memory and a processor for executing a program stored in the memory, running the method of any one of claims 1-6.
10. A computer readable storage medium, characterized in that the computer readable storage medium is for storing a computer program for executing the method of any one of claims 1-6.
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| CN202310143278.3A CN116361543A (en) | 2023-02-21 | 2023-02-21 | Travel strategy recommendation method and device |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117874356A (en) * | 2024-03-12 | 2024-04-12 | 江苏信江数字科技有限公司 | Management analysis system based on intelligent text travel big data |
| CN117874356B (en) * | 2024-03-12 | 2024-06-07 | 江苏信江数字科技有限公司 | Management analysis system based on intelligent text travel big data |
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- 2023-02-21 CN CN202310143278.3A patent/CN116361543A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117874356A (en) * | 2024-03-12 | 2024-04-12 | 江苏信江数字科技有限公司 | Management analysis system based on intelligent text travel big data |
| CN117874356B (en) * | 2024-03-12 | 2024-06-07 | 江苏信江数字科技有限公司 | Management analysis system based on intelligent text travel big data |
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