CN111859130A - Tourist attraction recommendation method and device based on big data analysis - Google Patents
Tourist attraction recommendation method and device based on big data analysis Download PDFInfo
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Abstract
The invention discloses a tourist attraction recommendation method and device based on big data analysis, and relates to the technical field of internet, wherein the method comprises the following steps: public opinion information related to tourist attractions is obtained, the public opinion information is classified and analyzed, and a network attention database of the tourist attractions is established; acquiring the name and the amount of tourists of each tourist spot in the current period and the order number of each tourist spot on a tourist website, and correcting the data in the network attention database by using the current order number and the current amount of tourists of each tourist spot to obtain a comprehensive attention database; obtaining types of tourist attractions concerned by a user; and screening tourist attractions meeting the conditions in the comprehensive attention degree database according to the types of the tourist attractions concerned by the user, and recommending the screened tourist attractions to the user. According to the method, based on the traditional prediction method, the Internet public opinion analysis is combined, information which can reflect the real attention degree is obtained, and the accuracy and the reasonability of recommendation are improved.
Description
Technical Field
The invention relates to the technical field of internet, in particular to a scenic spot recommendation method and device based on big data analysis.
Background
Tourism is the first-selected leisure form that people relaxed at present, and the tourism action takes place in festivals and holidays mostly, but because the operating time of most people all is unanimous with the time of national regulation, lead to festival holiday tourism number blowout formula outbreak, greatly reduced visitor's experience, consequently many people all are willing to select the wrong peak trip now to obtain complete tourism when visitor's number is few and experience.
In order to avoid the influence of travel peaks, many people can choose to travel on non-holidays. However, this method cannot guarantee that there are no many tourists in the scenic spot, and therefore, a method for predicting the passenger flow volume according to some objective factors appears. Most of the passenger flow volume prediction methods are used for predicting the passenger flow volume according to the current same time period, and although the passenger flow volume prediction methods have certain reference significance, the passenger flow volume prediction methods do not conform to the new trend of current travel.
At present, a lot of tourist attractions are creating a 'net red' phenomenon, namely, images of the attractions are publicized vigorously through the Internet, so that young people as tourist main bodies greatly increase the interest in the attractions, and tourists in the attractions in a short time are increased greatly. The propaganda behaviors of the scenic spots can occur at any time, and the connection with festivals and holidays is not very tight, so that the prediction accuracy of the traditional prediction method on the scenic spot passenger flow is not suitable for the society developing at a high speed at present.
Disclosure of Invention
The embodiment of the invention provides a scenic spot recommendation method and device based on big data analysis, which can solve the problems in the prior art.
The invention provides a tourist attraction recommendation method based on big data analysis, which comprises the following steps:
public opinion information about scenic spots on the Internet is obtained, the public opinion information is classified and analyzed, and a network attention database of the scenic spots is established;
acquiring the name and the amount of tourists of each tourist spot in the current period and the order number of each tourist spot on a tourist website, and correcting the data in the network attention database by using the current order number and the current amount of tourists of each tourist spot to obtain a comprehensive attention database;
obtaining types of tourist attractions concerned by a user;
and screening tourist attractions meeting conditions in the comprehensive attention degree database according to the types of the tourist attractions concerned by the user, and recommending the screened tourist attractions to the user.
The invention also provides a tourist attraction recommendation device based on big data analysis, which comprises:
the system comprises a network server, a database and a database, wherein the network server is used for acquiring public opinion information about scenic spots on the Internet, classifying and analyzing the public opinion information and establishing a network attention database of the scenic spots;
The network server is also used for acquiring the names and the tourist volumes of tourist attractions in the current period and the ordering number of each tourist attraction on the tourist website, and correcting the data in the network attention database by using the current ordering number and the current tourist volumes of each tourist attraction to obtain a comprehensive attention database;
and the mobile terminal is used for acquiring the types of the tourist attractions concerned by the user, screening the tourist attractions meeting the conditions in the comprehensive attention degree database according to the types of the tourist attractions concerned by the user, and recommending the screened tourist attractions to the user.
The invention discloses a tourist attraction recommendation method and device based on big data analysis, wherein the method comprises the following steps: public opinion information related to tourist attractions is obtained, the public opinion information is classified and analyzed, and a network attention database of the tourist attractions is established; acquiring the name and the amount of tourists of each tourist spot in the current period and the order number of each tourist spot on a tourist website, and correcting the data in the network attention database by using the current order number and the current amount of tourists of each tourist spot to obtain a comprehensive attention database; obtaining types of tourist attractions concerned by a user; and screening tourist attractions meeting the conditions in the comprehensive attention degree database according to the types of the tourist attractions concerned by the user, and recommending the screened tourist attractions to the user. According to the method, based on the traditional prediction method, the Internet public opinion analysis is combined, information which can reflect the real attention degree is obtained, and the accuracy and the reasonability of recommendation are improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flowchart of a scenic spot recommendation method based on big data analysis according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, the invention provides a tourist attraction recommendation method based on big data analysis, which mainly comprises the following steps:
Public opinion information about scenic spots on the Internet is obtained, the public opinion information is classified and analyzed, and a network attention database of the scenic spots is established;
acquiring the name and the amount of tourists of each tourist spot in the current period and the order number of each tourist spot on a tourist website, and correcting the data in the network attention database by using the current order number and the current amount of tourists of each tourist spot to obtain a comprehensive attention database;
obtaining types of tourist attractions concerned by a user;
and screening tourist attractions meeting conditions in the comprehensive attention degree database according to the types of the tourist attractions concerned by the user, and recommending the screened tourist attractions to the user.
In the above steps, the public opinion information is obtained by crawling all information in a period of time from databases of various network platforms, and then information about tourist attractions is obtained from the crawled information in a keyword screening manner. Because the network platforms such as the microblog, the WeChat, the self-media and the like are widely used in the young people, and the audiences are main participants of the tourism behaviors, the attention degree of the related tourist attractions on the network platform is obtained, and real data reference can be provided for the tourist flow prediction of the tourist attractions.
The obtained public opinion information includes all information related to each tourist attraction, and the information is often disordered and needs to be classified and analyzed. In the classification process, a main label is formulated for each piece of public opinion information by using a keyword extraction mode, and then the public opinion information with the same main label is classified into the same category. In an embodiment of the invention, the primary label is the name of a tourist attraction.
In the analysis process, each type of public opinion information is analyzed independently, and the analysis method of each type of public opinion information is the same. During specific analysis, the information of the initiating main body, the initiating content and the participating content in the public sentiment information is respectively extracted, and then the emotional tendency of the initiating content, namely the tendency of recommending tourist attractions, is analyzed by adopting an emotional word analysis method. And then analyzing the emotional tendency of all the participated contents in the initiating content, and counting the number of the participated contents with the same emotional tendency as the initiating content to be used as a first sub-label of the public opinion information.
Then, the concerned amount of the initiating main body and the forwarded amount of the initiating content are analyzed, and the two data are comprehensively calculated to obtain a second sub-label of the public opinion information.
And finally, comprehensively calculating the first sub-label and the second sub-label to obtain the sub-label of the public opinion information. And combining the sub-label and the main label to obtain a comprehensive label of the tourist attraction.
For example, a certain microblog user has 50 ten thousand concerned users, and after the certain microblog user issues a blog article recommending the beijing Imperial palace, 8000 replies are obtained, wherein 5000 express a clear emotion that likes the beijing Imperial palace, and the blog article obtains a forwarding amount of 10000. The main label of the piece of public sentiment information is Beijing Imperial palace, the first sub-label is participation amount 5000, the second sub-label is comprehensive amount 500000/10000 × 10000 ═ 500000, and the final sub-label is attention amount 500000/10000 × 5000 ═ 250000. After the main label and the sub-label are combined, the obtained comprehensive label is 250000 of Beijing Imperial palace attention.
And uniformly storing the obtained comprehensive labels to obtain the network attention database. Since there are many repeated data in the public opinion information of the same category in the network attention database, for example, the same user launches multiple pieces of blog articles about the same tourist attraction, or the same user participates in multiple pieces of blog article comments about the same tourist attraction, the comprehensive label in the network attention database needs to be subjected to deduplication processing, and the deduplication processing principle is that only one launch or one participation of the same user is reserved for the same tourist attraction.
After the network attention database is established, data in the network attention database is updated regularly, and real-time performance of the data is guaranteed. When updating, adding, deleting and correcting operations are required according to the occurrence time of the public opinion information, for example, the number of actions of participation, forwarding and the like which are related to Beijing old palace and occur within one month of the occurrence time of the recorded public opinion information is corrected according to the current number; deleting public opinion information of which the occurrence time exceeds one month from the network attention database; public opinion information about Beijing Imperial palace but not recorded occurring within a month is recorded in a network attention database.
And after the current tourist volume of each tourist attraction is obtained, screening out the previous tourist volume in the same period from the current tourist volume data according to the current date, wherein the tourist volume comprises the daily tourist volume in a continuous period of time.
After the amount of tourists of a certain tourist attraction in the past synchronization and the current order number are obtained, mathematical operation is carried out on the two numbers and the data in the network attention degree database to carry out correction, and a comprehensive attention degree database is obtained. For example, if the total amount of public opinion information about the Beijing Imperial palace is 400000, the degree of interest of the Beijing Imperial palace in the integrated interest database is 400000/200000 × 150000, which is the predicted tourist amount of the Beijing Imperial palace in the future period.
Obtaining the types of tourist attractions of interest to the user may be accomplished by the user through information entered on the electronic device. The information input by the user can be various electronic devices with data communication, processing and input functions, such as a mobile phone or a computer, and the information input by the user can be information directly indicating the types of the scenic spots concerned by the user, or can be a record of historical search, and the types of the scenic spots concerned by the user can be analyzed and obtained from the search record.
After the comprehensive attention database is established, corresponding type labels are generated for each tourist attraction, for example, the type label of Beijing old palace is historical ancient writing, and the type label of Yunnan Erhai is natural scene. After the type labels are generated, the tourist attractions can be screened out from the comprehensive attention degree database according to the types of the tourist attractions concerned by the user, and the screened tourist attractions are sorted from high to low according to the comprehensive attention degree. After sorting, the tourist attractions with the comprehensive attention degree exceeding a certain threshold value are deleted from the sorting to obtain a recommended tourist attraction list, and then the recommended tourist attraction list is recommended to the user.
The threshold value can be set by default or by the user. The user can also select to display all the tourist attraction lists processed according to the threshold value, and the user can select tourist attractions needing to go to by himself.
Based on the same inventive concept, the invention also provides a tourist attraction recommendation device based on big data analysis, and the implementation of the device can refer to the implementation of the method, and repeated parts are not repeated. The device comprises:
the system comprises a network server, a database and a database, wherein the network server is used for acquiring public opinion information about scenic spots on the Internet, classifying and analyzing the public opinion information and establishing a network attention database of the scenic spots;
the network server is also used for acquiring the names and the tourist volumes of tourist attractions in the current period and the ordering number of each tourist attraction on the tourist website, and correcting the data in the network attention database by using the current ordering number and the current tourist volumes of each tourist attraction to obtain a comprehensive attention database;
and the mobile terminal is used for acquiring the types of the tourist attractions concerned by the user, screening the tourist attractions meeting the conditions in the comprehensive attention degree database according to the types of the tourist attractions concerned by the user, and recommending the screened tourist attractions to the user.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. A tourist attraction recommendation method based on big data analysis is characterized by comprising the following steps:
public opinion information about scenic spots on the Internet is obtained, the public opinion information is classified and analyzed, and a network attention database of the scenic spots is established;
acquiring the name and the amount of tourists of each tourist spot in the current period and the order number of each tourist spot on a tourist website, and correcting the data in the network attention database by using the current order number and the current amount of tourists of each tourist spot to obtain a comprehensive attention database;
obtaining types of tourist attractions concerned by a user;
and screening tourist attractions meeting conditions in the comprehensive attention degree database according to the types of the tourist attractions concerned by the user, and recommending the screened tourist attractions to the user.
2. The method as claimed in claim 1, wherein the public opinion information about tourist attractions on the internet is obtained by:
all information in a period of time is crawled from databases of various network platforms, and then public opinion information related to tourist attractions is obtained from the crawled information in a keyword screening mode.
3. The method as claimed in claim 1, wherein when classifying the public opinion information, a main label is created for each piece of public opinion information by means of keyword extraction, and then the public opinion information with the same main label is classified into the same category.
4. The method as claimed in claim 3, wherein when public sentiment information of the same category is analyzed, information of an initiating main body, initiating content and participating content in the public sentiment information is extracted respectively;
analyzing the emotional tendency of the initiating content and the emotional tendency of all the participating contents in the initiating content by adopting an emotional word analysis method, and counting the number of the participating contents with the same emotional tendency as the initiating content to be used as a first sub-label of the public opinion information;
analyzing the concerned amount of the initiating main body and the forwarded amount of the initiating content, and performing comprehensive calculation on the two data to obtain a second sub-label of the public opinion information;
comprehensively calculating the first sub-label and the second sub-label to obtain a sub-label of the public opinion information;
Combining the sub-label and the main label to obtain a comprehensive label of the tourist attraction;
and uniformly storing all the comprehensive labels to obtain the network attention database.
5. The method as claimed in claim 4, wherein after the network attention database is obtained, the comprehensive tags are deduplicated, and only data of one initiation or one participation of the same user to the same tourist attraction is retained.
6. The method of claim 1, wherein the data in the network attention database is updated periodically after the network attention database is established.
7. The method of claim 1, wherein after the comprehensive attention database is obtained, a type tag is generated for each of the scenic spots;
and after the type label is generated, the tourist attractions are screened out from the comprehensive attention database according to the types of the tourist attractions concerned by the user.
8. The method as claimed in claim 7, wherein the selected scenic spots are ranked from high to low according to the comprehensive attention degree;
After sorting, deleting the scenic spots with the comprehensive attention degree exceeding a certain threshold value from the sorting to obtain a recommended scenic spot list;
and recommending the recommended tourist attraction list to the user.
9. The apparatus for implementing the method for tourist attraction recommendation based on big data analysis as claimed in any one of claims 1-8, wherein the apparatus comprises:
the system comprises a network server, a database and a database, wherein the network server is used for acquiring public opinion information about scenic spots on the Internet, classifying and analyzing the public opinion information and establishing a network attention database of the scenic spots;
the network server is also used for acquiring the names and the tourist volumes of tourist attractions in the current period and the ordering number of each tourist attraction on the tourist website, and correcting the data in the network attention database by using the current ordering number and the current tourist volumes of each tourist attraction to obtain a comprehensive attention database;
and the mobile terminal is used for acquiring the types of the tourist attractions concerned by the user, screening the tourist attractions meeting the conditions in the comprehensive attention degree database according to the types of the tourist attractions concerned by the user, and recommending the screened tourist attractions to the user.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113722487A (en) * | 2021-08-31 | 2021-11-30 | 平安普惠企业管理有限公司 | User emotion analysis method, device and equipment and storage medium |
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| CN107657483A (en) * | 2017-09-30 | 2018-02-02 | 四川智胜慧旅科技有限公司 | A kind of universe intelligent tourism system |
| CN111178721A (en) * | 2019-12-20 | 2020-05-19 | 长沙市新时科技发展有限公司 | Intelligent tourism system |
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