CN113111266A - Destination recommendation method and device and computer-readable storage medium - Google Patents
Destination recommendation method and device and computer-readable storage medium Download PDFInfo
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Abstract
The application discloses a destination recommendation method, a destination recommendation device and a computer-readable storage medium. The destination recommendation method provided by the application comprises the following steps: acquiring travel information and personal basic information of a vehicle owner; carrying out data preprocessing on the travel information and the personal basic information to obtain travel characteristics and owner characteristics of the car owner; analyzing and processing the travel characteristics and the vehicle owner characteristics according to a collaborative filtering algorithm based on the user to obtain first destination data; analyzing and processing the travel characteristics according to a collaborative filtering algorithm based on the articles to obtain second destination data; obtaining heat ranking information according to the ranking information; obtaining third destination data according to the heat ranking information; and filtering the first destination data, the second destination data and the third destination data according to the travel characteristics to obtain target destination information. The destination recommendation method provided by the application realizes effective recommendation and guidance for the selection of the tour places of the car owners.
Description
Technical Field
The present application relates to, but not limited to, the field of computers, and in particular, to a destination recommendation method, apparatus, and computer-readable storage medium.
Background
The frequently-used destination recommendation method is to simply count places that a user has gone, sort the places according to the number of times of going each place, and then select the place with the largest number of corresponding times as a recommended destination.
Disclosure of Invention
The present application is directed to solving at least one of the problems in the prior art. Therefore, the destination recommendation method is provided, and the problem that effective recommendation and guidance cannot be obtained for selecting the tour places of the car owners can be solved.
According to a destination recommendation method of an embodiment of a first aspect of the present application, the destination recommendation method includes: acquiring travel information and personal basic information of a vehicle owner; data preprocessing is carried out on the travel information and the personal basic information to obtain travel characteristics and owner characteristics of the owner; analyzing and processing the travel characteristics and the vehicle owner characteristics according to a collaborative filtering algorithm based on a user to obtain first destination data; analyzing and processing the travel characteristics according to a collaborative filtering algorithm based on articles to obtain second destination data; obtaining heat ranking information according to the ranking information; obtaining third destination data according to the heat ranking information; and filtering the first destination data, the second destination data and the third destination data according to the travel characteristics to obtain target destination information.
According to the destination recommendation method of the embodiment of the application, at least the following technical effects are achieved: according to the destination recommending method, the travel characteristics and the owner characteristics are obtained according to the travel information and the personal basic information of the owner, three kinds of destination data are obtained by adopting three recommending methods based on a user, an article and a heat rank, and then the target destination information is filtered out.
According to some embodiments of the present application, the analyzing and processing the travel characteristic and the vehicle owner characteristic according to a collaborative filtering algorithm based on a user to obtain first destination data includes: acquiring the travel characteristic and the owner characteristic of the owner; carrying out big data analysis processing on the travel characteristic and the vehicle owner characteristic according to a collaborative filtering algorithm based on a user to obtain first similarity information of the vehicle owner; and obtaining the first destination data according to the first similarity information.
According to some embodiments of the present application, the analyzing and processing the travel characteristics according to an article-based collaborative filtering algorithm to obtain second destination data includes: acquiring travel characteristics of the vehicle owner; performing big data analysis processing on the travel characteristics according to a collaborative filtering algorithm based on articles to obtain second similarity information of the travel characteristics; and obtaining the second destination data according to the second similarity information.
According to some embodiments of the present application, obtaining the popularity ranking information according to the travel information includes: acquiring the travel information; obtaining the residence address of the vehicle owner according to the travel information; and obtaining heat ranking information according to the residential address.
According to some embodiments of the present application, the filtering the first destination data, the second destination data, and the third destination data according to the travel characteristics to obtain target destination information includes: acquiring the travel characteristics of the vehicle owner; travel location information in the first destination data, the second destination data and the third destination data is obtained; and filtering the travel place information to obtain the target destination data.
According to some embodiments of the application, the destination recommendation method further comprises: acquiring destination selection information of the vehicle owner; and updating the vehicle owner characteristic and the travel characteristic according to the destination selection information.
According to some embodiments of the application, the destination recommendation method further comprises: acquiring target destination data; transmitting the target destination data to a travel agent server; so that the travel agency server generates travel plan information according to the target destination data.
According to a second aspect of the present application, a destination recommendation apparatus includes: the information acquisition module is used for acquiring travel information and personal basic information of the vehicle owner; the characteristic acquisition module is used for carrying out data preprocessing on the travel information and the personal basic information to obtain travel characteristics and owner characteristics of the owner; the first destination data analysis module is used for analyzing and processing the travel characteristics and the vehicle owner characteristics according to a collaborative filtering algorithm based on a user to obtain first destination data; the second destination data analysis module is used for analyzing and processing the travel characteristics according to a collaborative filtering algorithm based on articles to obtain second destination data; the heat ranking information generating module is used for obtaining heat ranking information according to the travel information; the third destination data analysis module is used for obtaining third destination data according to the heat ranking information; and the data filtering module is used for filtering the first destination data, the second destination data and the third destination data according to the travel characteristics to obtain target destination information.
According to a destination recommending apparatus of an embodiment of the third aspect of the present application, the destination recommending apparatus includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor when executing the program implementing: the destination recommendation method of the foregoing first aspect embodiment of the present application.
A computer-readable storage medium according to an embodiment of the fourth aspect of the present application, having stored thereon computer-executable instructions for: the destination recommendation method described in the above embodiment of the first aspect is performed.
Additional aspects and advantages of the application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the application.
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The present application is further described with reference to the following figures and examples, in which:
FIG. 1 is a flow chart of a destination recommendation method provided by one embodiment of the present application;
FIG. 2 is a flowchart of step S130 in FIG. 1;
FIG. 3 is a flowchart of step S140 in FIG. 1;
FIG. 4 is a flowchart of step S150 in FIG. 1;
fig. 5 is a flowchart of step S170 in fig. 1;
FIG. 6 is a flow chart of a destination recommendation method provided by another embodiment of the present application;
fig. 7 is a flowchart of a destination recommendation method according to another embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application.
In the description of the present application, if there are first and second described only for the purpose of distinguishing technical features, it is not understood that relative importance is indicated or implied or that the number of indicated technical features or the precedence of the indicated technical features is implicitly indicated or implied.
In the description of the present application, reference to the description of the terms "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
According to the destination recommendation method of the embodiment of the application, the destination recommendation method comprises the following steps: acquiring travel information and personal basic information of a vehicle owner; carrying out data preprocessing on the travel information and the personal basic information to obtain travel characteristics and owner characteristics of the car owner; analyzing and processing the travel characteristics and the vehicle owner characteristics according to a collaborative filtering algorithm based on the user to obtain first destination data; analyzing and processing the travel characteristics according to a collaborative filtering algorithm based on the articles to obtain second destination data; obtaining heat ranking information according to the ranking information; obtaining third destination data according to the heat ranking information; and filtering the first destination data, the second destination data and the third destination data according to the travel characteristics to obtain target destination information.
As shown in fig. 1, fig. 1 is a flowchart of a destination recommendation method provided in some embodiments, where the destination recommendation method includes, but is not limited to, steps S110 to S170, and specifically includes:
s110, acquiring travel information and personal basic information of a vehicle owner;
s120, carrying out data preprocessing on the travel information and the personal basic information to obtain travel characteristics and owner characteristics of the vehicle owner;
s130, analyzing and processing the travel characteristics and the vehicle owner characteristics according to a collaborative filtering algorithm based on the user to obtain first destination data;
s140, analyzing and processing the travel characteristics according to a collaborative filtering algorithm based on the articles to obtain second destination data;
s150, obtaining heat ranking information according to the ranking information;
s160, obtaining third destination data according to the heat ranking information;
and S170, filtering the first destination data, the second destination data and the third destination data according to the travel characteristics to obtain target destination information.
In step S110, the trip information of the vehicle owner includes, but is not limited to, positioning data, a vehicle unlocking and locking state, and data reporting time, and the personal basic information of the vehicle owner includes, but is not limited to, a unique vehicle owner identification code, a vehicle owner gender, a vehicle owner age, and a vehicle owner education level.
In step S120, the data preprocessing includes, but is not limited to, a data acquisition and preprocessing process, a data calculation preparation process, and a data analysis process.
The data calculation preparation process specifically comprises the following steps: according to the trip information and the owner information in the step S110, a parking record of each unlocking time that the last locking time interval exceeds 2 hours is calculated, a parking place is identified, and a parking date, a last locking time, a legal working day holiday sign, a parking duration, a parking place name and parking place properties are output, so that the information obtained in the data calculation preparation process is used as new trip information, the trip information is expanded, and subsequent data analysis, processing and destination recommendation are realized. Wherein, the last locking time is the parking time; the parking time length is equal to the current unlocking time minus the last locking time; parking spot properties include, but are not limited to, office space, vacation, entertainment space, residential space.
The data analysis process specifically comprises the following steps: and (3) according to the information of all parking places, parking time and the like of the car owner obtained in the data calculation preparation process, determining the place with the most positioned parking records exceeding 2 hours as the living place of the car owner, analyzing the properties of other parking places of the car owner, and analyzing whether the leisure and vacation places and the entertainment places of the car owner have the time characteristics of fixed period and specific frequency. And then the information obtained in the data analysis process is used as new travel information, so that the travel information is expanded.
According to the destination recommending method, the travel characteristics and the owner characteristics are obtained according to the travel information and the personal basic information of the owner, three kinds of destination data are obtained by adopting three recommending methods based on a user, an article and a heat rank, and then the target destination information is filtered out.
According to some embodiments of the present application, the analyzing and processing the travel characteristics and the car owner characteristics according to the collaborative filtering algorithm based on the user to obtain the first destination data includes: acquiring travel characteristics and owner characteristics of an owner; carrying out big data analysis processing on the travel characteristics and the vehicle owner characteristics according to a collaborative filtering algorithm based on a user to obtain first similarity information of a vehicle owner; and obtaining first destination data according to the first similarity information.
Fig. 2 is a flow chart of step S130 in some embodiments, and step S130 illustrated in fig. 2 includes, but is not limited to, step S210 to step S230:
s210, acquiring travel characteristics and owner characteristics of an owner;
s220, carrying out big data analysis processing on the travel characteristics and the vehicle owner characteristics according to a collaborative filtering algorithm based on the user to obtain first similarity information of the vehicle owner;
and S230, obtaining first destination data according to the first similarity information.
In steps S210 to S230, the travel characteristics of the owner include, but are not limited to, historical travel destinations; the owner characteristics include but are not limited to travel time, age and education degree, first similarity information of the owner and other owners is obtained through calculation by using a collaborative filtering algorithm based on a user, a first similarity threshold value is set, owners similar to the owner characteristics are selected according to the first similarity threshold value, outgoing destination data of the similar owners are used as first destination data, and the first destination data are recommendation lists obtained according to the collaborative filtering algorithm based on the user.
According to some embodiments of the present application, the analyzing and processing the travel characteristics according to the collaborative filtering algorithm based on the article to obtain the second destination data includes: acquiring the travel characteristics of the vehicle owner; carrying out big data analysis processing on the travel characteristics according to a collaborative filtering algorithm based on articles to obtain second similarity information of the travel characteristics; and obtaining second destination data according to the second similarity information.
Fig. 3 is a flowchart of step S140 in some embodiments, and step S140 illustrated in fig. 3 includes, but is not limited to, step S310 to step S330:
s310, acquiring the travel characteristics of the vehicle owner;
s320, performing big data analysis processing on the travel characteristics according to the collaborative filtering algorithm based on the articles to obtain second similarity information of the travel characteristics;
s330, second destination data is obtained according to the second similarity information.
In steps S310 to S330, the travel characteristics of the vehicle owner include, but are not limited to, the nature of the historical travel destination, and the travel destination that the vehicle owner has traveled for the last three months, and further, using a collaborative filtering algorithm based on the article, calculating to obtain second similarity information between the travel destination and other destinations, setting a second similarity threshold, selecting other travel destinations similar to the vehicle owner' S destination according to the second similarity threshold, and using the similar other travel destination data as second destination data, where the second destination data is the obtained recommendation list according to the collaborative filtering algorithm based on the article.
According to some embodiments of the present application, obtaining the popularity ranking information according to the travel information includes: acquiring travel information; obtaining the residence address of the vehicle owner according to the travel information; and obtaining heat ranking information according to the residential address.
Fig. 4 is a flow chart of step S150 in some embodiments, and step S150 illustrated in fig. 4 includes, but is not limited to, step S410 to step S430:
s410, obtaining travel information;
s420, obtaining the residential address of the vehicle owner according to the travel information;
and S430, obtaining the heat ranking information according to the living address.
In steps S410 to S430, the place where the parking record is most located for more than 2 hours is determined as the home address of the vehicle owner, and then the heat ranking information is obtained according to the travel history of the vehicle owners close to the home address of the vehicle owner, and further the third destination data is obtained according to the heat ranking information.
According to some embodiments of the present application, the filtering the first destination data, the second destination data, and the third destination data according to the travel characteristics to obtain the target destination information includes: acquiring the travel characteristics of the vehicle owner; travel place information in first destination data, second destination data and third destination data is obtained; and filtering the travel place information to obtain target destination data.
Fig. 5 is a flowchart of step S170 in some embodiments, and step S170 illustrated in fig. 5 includes, but is not limited to, step S510 to step S530:
s510, acquiring travel characteristics of the vehicle owner;
s520, travel place information in the first destination data, the second destination data and the third destination data is obtained;
s530, filtering the travel place information to obtain target destination data.
In steps S510 to S530, the travel characteristics include, but are not limited to, destinations that the owner has gone to in the last year, then the destinations that the owner has gone to in the last year are filtered out according to the first destination data, the second destination data, and the third destination data, and the obtained destinations are sorted according to the popularity information to obtain the target destination data.
According to some embodiments of the application, the destination recommendation method further comprises: acquiring destination selection information of a vehicle owner; and updating the vehicle owner characteristic and the travel characteristic according to the destination selection information.
As shown in fig. 6, fig. 6 is a flowchart of a destination recommendation method according to another embodiment, where the destination recommendation method further includes:
s610, acquiring destination selection information of a vehicle owner;
and S620, updating the owner characteristic and the travel characteristic according to the destination selection information.
In steps S610 to S620, after the target destination data is generated, the target destination data is pushed to the vehicle owner to guide the customer to make a line selection, at this time, the destination selection information of the vehicle owner is recorded, and the recommendation model applied in the recommendation method is updated and iterated according to the destination selection information of the vehicle owner, so as to ensure the accuracy and the practicability of the recommendation model.
According to some embodiments of the application, the destination recommendation method further comprises: acquiring target destination data; transmitting the target destination data to the travel agent server; so that the travel agency server generates travel plan information according to the target destination data.
As shown in fig. 7, fig. 7 is a flowchart of a destination recommendation method according to further embodiments, where the destination recommendation method further includes:
s710, acquiring target destination data;
s720, sending the target destination data to the travel agency server;
in steps S710 to S720, the target destination data is transmitted to the travel agency server to enable the travel agency server to generate travel plan information according to the target destination data, thereby implementing interface intercommunication with the travel agency and implementing the one-touch travel plan.
The destination recommending method provided by the application guides and recommends the travel destination of the car owner in leisure time by analyzing the travel habit of the car owner, solves the problem of which the car owner plays, increases the customer viscosity of the car owner to the car enterprise app, and provides a new channel for the car enterprise app to get through with a travel business agent.
According to a destination recommendation device of an embodiment of the present application, the destination recommendation device includes: the information acquisition module is used for acquiring travel information and personal basic information of the vehicle owner; the characteristic acquisition module is used for carrying out data preprocessing on the travel information and the personal basic information to obtain travel characteristics and owner characteristics of the car owner; the first destination data analysis module is used for analyzing and processing travel characteristics and vehicle owner characteristics according to a user-based collaborative filtering algorithm to obtain first destination data; the second destination data analysis module is used for analyzing and processing the travel characteristics according to a collaborative filtering algorithm based on articles to obtain second destination data; the heat ranking information generating module is used for obtaining heat ranking information according to the travel information; the third destination data analysis module is used for obtaining third destination data according to the heat ranking information; and the data filtering module is used for filtering the first destination data, the second destination data and the third destination data according to the travel characteristics to obtain target destination information.
The destination recommending device achieves a destination recommending method, obtains travel characteristics and owner characteristics according to travel information and personal basic information of a vehicle owner, further obtains three kinds of destination data by adopting three recommending methods based on a user, articles and hot rows, and filters out target destination information.
According to a destination recommendation device of an embodiment of the present application, the destination recommendation device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor when executing the program implementing: the destination recommendation method of any of the above embodiments of the present application.
A computer-readable storage medium according to an embodiment of the present application stores computer-executable instructions for: the destination recommendation method of any of the above embodiments is performed.
The above-described embodiments of the apparatus are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may also be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
It will be understood by those of ordinary skill in the art that all or some of the steps, means, and methods disclosed above may be implemented as software, firmware, hardware, or suitable combinations thereof. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
The embodiments of the present application have been described in detail with reference to the drawings, but the present application is not limited to the embodiments, and various changes can be made within the knowledge of those skilled in the art without departing from the gist of the present application. Furthermore, the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
Claims (10)
1. The destination recommendation method is characterized by comprising the following steps:
acquiring travel information and personal basic information of a vehicle owner;
data preprocessing is carried out on the travel information and the personal basic information to obtain travel characteristics and owner characteristics of the owner;
analyzing and processing the travel characteristics and the vehicle owner characteristics according to a collaborative filtering algorithm based on a user to obtain first destination data;
analyzing and processing the travel characteristics according to a collaborative filtering algorithm based on articles to obtain second destination data;
obtaining heat ranking information according to the ranking information;
obtaining third destination data according to the heat ranking information;
and filtering the first destination data, the second destination data and the third destination data according to the travel characteristics to obtain target destination information.
2. The destination recommendation method according to claim 1, wherein the analyzing and processing the travel characteristic and the owner characteristic according to a collaborative filtering algorithm based on a user to obtain first destination data comprises:
acquiring the travel characteristic and the owner characteristic of the owner;
carrying out big data analysis processing on the travel characteristic and the vehicle owner characteristic according to a collaborative filtering algorithm based on a user to obtain first similarity information of the vehicle owner;
and obtaining the first destination data according to the first similarity information.
3. The destination recommendation method according to claim 1, wherein the analyzing and processing the travel characteristics according to the article-based collaborative filtering algorithm to obtain second destination data comprises:
acquiring travel characteristics of the vehicle owner;
performing big data analysis processing on the travel characteristics according to a collaborative filtering algorithm based on articles to obtain second similarity information of the travel characteristics;
and obtaining the second destination data according to the second similarity information.
4. The destination recommendation method according to claim 1, wherein said obtaining heat ranking information according to said travel information comprises:
acquiring the travel information;
obtaining the residence address of the vehicle owner according to the travel information;
and obtaining heat ranking information according to the residential address.
5. The destination recommendation method according to claim 1, wherein the filtering the first destination data, the second destination data, and the third destination data according to the travel characteristics to obtain target destination information comprises:
acquiring the travel characteristics of the vehicle owner;
travel location information in the first destination data, the second destination data and the third destination data is obtained;
and filtering the travel place information to obtain the target destination data.
6. The destination recommendation method according to any one of claims 1 to 5, characterized by further comprising:
acquiring destination selection information of the vehicle owner;
and updating the vehicle owner characteristic and the travel characteristic according to the destination selection information.
7. The destination recommendation method according to any one of claims 1 to 5, characterized by further comprising:
acquiring target destination data;
transmitting the target destination data to a travel agent server; so that the travel agency server generates travel plan information according to the target destination data.
8. A destination recommendation apparatus, characterized in that the destination recommendation apparatus comprises:
the information acquisition module is used for acquiring travel information and personal basic information of the vehicle owner;
the characteristic acquisition module is used for carrying out data preprocessing on the travel information and the personal basic information to obtain travel characteristics and owner characteristics of the owner;
the first destination data analysis module is used for analyzing and processing the travel characteristics and the vehicle owner characteristics according to a collaborative filtering algorithm based on a user to obtain first destination data;
the second destination data analysis module is used for analyzing and processing the travel characteristics according to a collaborative filtering algorithm based on articles to obtain second destination data;
the heat ranking information generating module is used for obtaining heat ranking information according to the travel information;
the third destination data analysis module is used for obtaining third destination data according to the heat ranking information;
and the data filtering module is used for filtering the first destination data, the second destination data and the third destination data according to the travel characteristics to obtain target destination information.
9. A destination recommendation apparatus, characterized in that the destination recommendation apparatus comprises: a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor when executing the program implementing:
the destination recommendation method of any one of claims 1-7.
10. A computer-readable storage medium having stored thereon computer-executable instructions for:
performing the destination recommendation method of any of claims 1 to 7.
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