CN110245286B - travel recommendation method and device based on data mining - Google Patents
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Abstract
The method comprises the steps of obtaining basic data and comment data in a website, establishing a mapping relation among a basic data set, a scenery spot data set and an emotion data set, building a big travel recommendation data analysis environment, cleaning the data set, performing -based processing on concepts, obtaining user browsing history, performing main and scenery spot analysis on a skipped webpage, obtaining emotion analysis assignment scores of the skipped webpage comment data, combining the basic scenery spots obtained by analyzing the basic data set according to the emotion data set, the dwell time of the user webpage and the scenery spots of the skipped webpage, and returning a travel recommendation result after sorting.
Description
Technical Field
The application relates to the field of travel recommendation and data mining, in particular to travel recommendation methods and devices based on data mining.
Background
The tourism recommendation method is characterized in that tourism resources, tourism economy, tourism activities, tourists and other information are integrated according to the actual conditions of the tourists, and tourists are provided with tour routes most suitable for the tourists, so that the tourism experience of the tourists is improved.
Therefore, the travel recommendation method and device based on data mining can be designed by considering the fusion data mining technology.
Disclosure of Invention
In view of this, the present application aims to provide travel recommendation methods and apparatuses based on data mining, so as to improve the accuracy of travel recommendation, and achieve the technical effect of improving the accuracy of travel information recommendation by analyzing the posted sentiment in a website.
In view of the above, the present application provides data mining-based travel recommendation methods, including:
acquiring basic data and comment data in a website, extracting user attribute information in the basic data through a rule matching algorithm to form a basic data set, extracting scenery spot information in the comment data through a named entity recognition algorithm to form a scenery spot data set, extracting emotion information in the comment data through an emotion analysis algorithm to form an emotion data set, establishing a mapping relation among the basic data set, the scenery spot data set and the emotion data set, importing the mapping relation into a data warehouse, and establishing a travel recommendation big data analysis environment;
cleaning the basic data set, the scenery spot data set and the emotion data set, performing grouping processing on concepts in the basic data set and the scenery spot data set, performing travel basic recommendation index on the basic data set, and performing travel concept expansion on the scenery spot data set;
acquiring user browsing history, extracting the stay time and the skip sequence of each webpage of a user, recording anchor text information clicked in the skip process of the user, performing main and scenery spot analysis on the skipped webpages, acquiring emotion analysis assignment scores of review data of the skipped webpages, and importing the travel recommendation big data analysis environment;
and according to the emotion data set, the retention time of the user webpage and the theme scenery spot of the user webpage, combining the basic scenery spot obtained by analyzing the basic data set, and returning a travel recommendation result after sequencing.
In , the extracting the sight information in the comment data by the named entity recognition algorithm to form a sight data set further includes:
and obtaining the gist of each sight spot in the webpage according to the font size, color and position of the sight spot information in the webpage, and determining the gist and sight spots of the webpage after sequencing.
In , the establishing a mapping relationship among the base data set, the sight data set, and the emotion data set includes:
establishing th mapping relation between the basic data set and the sight spot data set;
and establishing a second mapping relation between the sight spot data set and the emotion data set.
In embodiments, the importance of each sight spot in the web page is obtained according to the font size, color, and position of the sight spot information in the web page, and is calculated by the following formula:
D＝∑ωi·Pi，
wherein D is the subject degree of the attraction, ωiWeighting factor, P, for the ith web page attributeiAnd the quantized value of the ith webpage attribute in the webpage is obtained.
In , the indexing the base data set for travel base recommendations and the extending the concepts of the sights data set for travel comprises:
inquiring a preset travel basic recommendation model according to user basic information input during user registration to obtain a basic recommendation result, and establishing an index relation with scenic spots in the basic recommendation result;
and expanding the scenic spots in the geographical areas to which the scenic spots belong according to the names of the scenic spots, and expanding to obtain the scenic spots with the travel characteristics.
In embodiments, the recording anchor text information clicked during the user's jumping process, and performing a subject matter scene analysis on the jumping webpage includes:
extracting the sight spot information in the anchor text, and performing semantic expansion to obtain a th subject concept;
analyzing the main subject sight spot of the jump webpage to obtain a second main subject concept;
and performing intersection operation on the th main concept and the second main concept to obtain the main concept and the sight spot concept concerned by the user.
In , the obtaining of sentiment analysis score of comment data of the jumped webpage includes:
when comment information of a login user exists in the skipped webpage, emotion analysis is directly performed on the comment information, and emotion analysis assignment scores of the skipped webpage are determined;
and when the comment information of the login user does not exist in the skipped webpage, inquiring the emotion data set, and determining the emotion analysis assignment score of the skipped webpage.
In embodiments, the step of returning a travel recommendation result after sorting according to the emotion data set, the dwell time of the user webpage, and the theme spot of the user-skipped webpage by combining the basic scenery spot obtained by analyzing the basic data set includes:
and when no intersection exists between the main scenic spot and the basic scenic spot, returning all the main scenic spots and the basic scenic spots as recommendation results.
In view of the above, the present application also proposes data mining-based travel recommendation devices, including:
the system comprises a building module, a database module and a database module, wherein the building module is used for acquiring basic data and comment data in a website, extracting user attribute information in the basic data through a rule matching algorithm to form a basic data set, extracting scenery spot information in the comment data through a named entity recognition algorithm to form a scenery spot data set, extracting emotion information in the comment data through an emotion analysis algorithm to form an emotion data set, establishing a mapping relation among the basic data set, the scenery spot data set and the emotion data set, importing the mapping relation into a data warehouse, and building a travel recommendation big data analysis environment;
the arrangement module is used for cleaning the basic data set, the scenery spot data set and the emotion data set, performing grouping processing on concepts in the basic data set and the scenery spot data set, performing travel basic recommendation index on the basic data set, and performing travel concept expansion on the scenery spot data set;
the skip module is used for acquiring the browsing history of a user, extracting the staying time and the skip sequence of the user in each webpage, recording the anchor text information clicked in the skip process of the user, performing main and scenery spot analysis on the skipped webpage, acquiring emotion analysis assignment scores of the review data of the skipped webpage, and importing the travel recommendation big data analysis environment;
and the return module is used for combining the basic scenic spots obtained by analyzing the basic data set according to the emotion data set, the stay time of the user webpage and the theme scenic spots of the user webpage, and returning the travel recommendation result after sequencing.
In , the building module includes:
the mapping unit is used for controlling the distribution and resource allocation of tasks, establishing mapping relation between the basic data set and the scenery spot data set;
and the second mapping unit is used for establishing a second mapping relation between the sight spot data set and the emotion data set.
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In the drawings, like numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified, and in which not are drawn to scale, it should be understood that these drawings depict only embodiments of in accordance with the present disclosure and are not to be considered limiting of the scope of the disclosure.
FIG. 1 shows a flow diagram of a data mining based travel recommendation method according to an embodiment of the invention.
Fig. 2 is a block diagram illustrating a travel recommendation apparatus based on data mining according to an embodiment of the present invention.
Fig. 3 shows a constitutional diagram of a building block according to an embodiment of the present invention.
Detailed Description
The present application is described in further detail in with reference to the drawings and the examples, it being understood that the specific examples are set forth herein for the purpose of illustration and not as a definition of the limits of the invention.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict. The present application will be described in detail below with reference to the embodiments with reference to the attached drawings.
FIG. 1 shows a flow diagram of a data mining based travel recommendation method according to an embodiment of the invention. As shown in fig. 1, the data mining-based travel recommendation method includes:
s11, obtaining basic data and comment data in a website, extracting user attribute information in the basic data through a rule matching algorithm to form a basic data set, extracting scenery spot information in the comment data through a named entity recognition algorithm to form a scenery spot data set, extracting emotion information in the comment data through an emotion analysis algorithm to form an emotion data set, establishing a mapping relation among the basic data set, the scenery spot data set and the emotion data set, importing the mapping relation into a data warehouse, and building a travel recommendation big data analysis environment.
In , the extracting the sight information in the comment data by the named entity recognition algorithm to form a sight data set further includes:
and obtaining the gist of each sight spot in the webpage according to the font size, color and position of the sight spot information in the webpage, and determining the gist and sight spots of the webpage after sequencing.
In , the establishing a mapping relationship among the base data set, the sight data set, and the emotion data set includes:
establishing th mapping relation between the basic data set and the sight spot data set;
and establishing a second mapping relation between the sight spot data set and the emotion data set.
Specifically, the basic data information of the scenic spot can be inquired through the th mapping relation, the emotion data information of the scenic spot and the user for the scenic spot can be inquired through the second mapping relation of the same country, and the mapping process can realize the quick search of data through the modes of establishing an index and the like.
In embodiments, the importance of each sight spot in the web page is obtained according to the font size, color, and position of the sight spot information in the web page, and is calculated by the following formula:
D＝∑ωi·Pi，
wherein D is the subject degree of the attraction, ωiWeighting factor, P, for the ith web page attributeiAnd the quantized value of the ith webpage attribute in the webpage is obtained.
And S12, cleaning the basic data set, the scenery spot data set and the emotion data set, performing -based treatment on concepts in the basic data set and the scenery spot data set, performing travel basic recommendation index on the basic data set, and performing travel concept expansion on the scenery spot data set.
In real-time modes, according to user basic information input during user registration, a preset travel basic recommendation model is inquired to obtain a basic recommendation result, and an index relation with a scenic spot in the basic recommendation result is established;
in embodiments, the sights in the geographical area of the sight are expanded according to the sight name, and the sight with the travel characteristic is expanded.
Specifically, concept expansion may be performed by looking up public geographic information databases and travel databases.
And step S13, acquiring user browsing history, extracting the stay time and the skip sequence of each webpage of the user, recording anchor text information clicked in the skip process of the user, performing main and scenery spot analysis on the skipped webpages, acquiring emotion analysis assignment scores of the review data of the skipped webpages, and importing the travel recommendation big data analysis environment.
For example, in a travel website, - web pages introduce sights or geographic areas that may be understood as the primary sights of the web page.
In embodiments, the recording anchor text information clicked during the user's jumping process, and performing a subject matter scene analysis on the jumping webpage includes:
extracting the sight spot information in the anchor text, and performing semantic expansion to obtain a th subject concept;
analyzing the main subject sight spot of the jump webpage to obtain a second main subject concept;
and performing intersection operation on the th main concept and the second main concept to obtain the main concept and the sight spot concept concerned by the user.
For example, when the anchor text information has the 'Laogong' word, the anchor text information can be expanded into concepts related to the 'Laogong' such as 'Beijing', 'Tianan field', 'great wall', 'Saxiong' and the like through the ontology concept.
In , the obtaining of sentiment analysis assigned scores of the comment data of the jumped web page includes:
when comment information of a login user exists in the skipped webpage, emotion analysis is directly performed on the comment information, and emotion analysis assignment scores of the skipped webpage are determined;
and when the comment information of the login user does not exist in the skipped webpage, inquiring the emotion data set, and determining the emotion analysis assignment score of the skipped webpage.
And step S14, according to the emotion data set, the stay time of the user webpage and the theme spot of the user webpage, combining the basic scenery spot obtained by analyzing the basic data set, and returning a travel recommendation result after sequencing.
For example, the longer the user stays in the webpage, the more detailed the user pays attention to the main scenery point pointed by the webpage, and the higher the coincidence degree between the main scenery point of the user jumping to the next webpage through the webpage and the main scenery point of the webpage is, which indicates that the user expects the detailed information of the main scenery point and the more attention.
In embodiments, the sorting and returning a travel recommendation result according to the emotion data set, the dwell time of the user webpage, and the theme spot of the user webpage, in combination with the basic spot obtained by analyzing the basic data set, includes:
and when no intersection exists between the main scenic spot and the basic scenic spot, returning all the main scenic spots and the basic scenic spots as recommendation results.
Fig. 2 is a block diagram of a travel recommendation apparatus based on data mining according to an embodiment of the present invention. As shown in fig. 2, the data mining-based travel recommendation apparatus may be divided into:
the system comprises a construction module 21, a database and a database, wherein the construction module 21 is used for acquiring basic data and comment data in a website, extracting user attribute information in the basic data through a rule matching algorithm to form a basic data set, extracting scenery spot information in the comment data through a named entity recognition algorithm to form a scenery spot data set, extracting emotion information in the comment data through an emotion analysis algorithm to form an emotion data set, establishing a mapping relation among the basic data set, the scenery spot data set and the emotion data set, importing the mapping relation into a data warehouse, and constructing a travel recommendation big data analysis environment;
the arrangement module 22 is configured to clean the basic data set, the scenery spot data set, and the emotion data set, perform grouping processing on concepts in the basic data set and the scenery spot data set, perform travel basic recommendation indexing on the basic data set, and perform travel concept expansion on the scenery spot data set;
the skip module 23 is configured to obtain a user browsing history, extract a retention time and a skip sequence of each webpage of a user, record anchor text information clicked by the user in a skip process, perform a subject-to-scene analysis on a skipped webpage, obtain an emotion analysis assignment score of review data of the skipped webpage, and import the emotion analysis result into the travel recommendation big data analysis environment;
and the returning module 24 is used for combining the basic scenic spots obtained by analyzing the basic data set according to the emotion data set, the stay time of the user webpage and the main scenic spot of the user webpage, and returning the travel recommendation result after sequencing.
Fig. 3 shows a constitutional diagram of a building block according to an embodiment of the present invention.
As can be seen in fig. 3, the building block 21, comprises:
the mapping unit 211 is used for controlling the distribution and resource allocation of tasks, establishing mapping relation between the basic data set and the attraction data set;
a second mapping unit 212, configured to establish a second mapping relationship between the sight data set and the emotion data set.
In the description herein, reference to the terms " embodiments," " embodiments," "examples," "specific examples," or " examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least embodiments or examples of the invention.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include or more executable instructions for implementing specific logical functions or steps in the process, and the scope of the preferred embodiments of the present invention includes other implementations in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
For the purposes of this description, a "computer-readable medium" can be any medium that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device (e.g., a computer-based system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions).
For example, if implemented in hardware, and in another embodiment , it may be implemented using any item or combination thereof known in the art, a discrete logic circuit having logic circuits for implementing logic functions on data signals, an application specific integrated circuit having appropriate combinational logic circuits, a programmable array (PGA), a field programmable array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware associated with instructions of a program, which may be stored in computer readable storage media, and when executed, the program includes or a combination of the steps of the method embodiments.
In addition, each functional unit in each embodiment of the present invention may be integrated into processing modules, or each unit may exist alone physically, or two or more units are integrated into modules.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various changes or substitutions within the technical scope of the present invention, and these should be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (9)
1, A travel recommendation method based on data mining, comprising:
acquiring basic data and comment data in a website, extracting user attribute information in the basic data through a rule matching algorithm to form a basic data set, extracting scenery spot information in the comment data through a named entity recognition algorithm to form a scenery spot data set, extracting emotion information in the comment data through an emotion analysis algorithm to form an emotion data set, establishing a mapping relation among the basic data set, the scenery spot data set and the emotion data set, importing the mapping relation into a data warehouse, and establishing a travel recommendation big data analysis environment;
cleaning the basic data set, the scenery spot data set and the emotion data set, performing classification processing on concepts in the basic data set and the scenery spot data set, establishing an index after performing travel basic recommendation on the basic data set, and performing travel concept expansion on the scenery spot data set;
acquiring user browsing history, extracting the stay time and the skip sequence of each webpage of a user, recording anchor text information clicked in the skip process of the user, performing main and scenery spot analysis on the skipped webpages, acquiring emotion analysis assignment scores of review data of the skipped webpages, and importing the travel recommendation big data analysis environment;
according to the emotion data set, the retention time of the user webpage and the theme scene point of the user webpage, combining the basic scene point obtained by analyzing the basic data set, and returning a travel recommendation result after sequencing;
the method for analyzing the main and the sight spots of the skipped webpage comprises the following steps of recording anchor text information clicked by a user in the skipping process, and analyzing the main and the sight spots of the skipped webpage, wherein the anchor text information comprises:
extracting the sight spot information in the anchor text, and performing semantic expansion to obtain a th subject concept;
analyzing the main subject sight spot of the jump webpage to obtain a second main subject concept;
and performing intersection operation on the th main concept and the second main concept to obtain the main concept and the sight spot concept concerned by the user.
2. The method of claim 1, wherein the extracting the sight information in the comment data by a named entity recognition algorithm forms a sight data set, further comprising:
and obtaining the gist of each sight spot in the webpage according to the font size, color and position of the sight spot information in the webpage, and determining the gist and sight spots of the webpage after sequencing.
3. The method of claim 1, wherein the establishing a mapping relationship between the base data set, the attraction data set, and the emotion data set comprises:
establishing th mapping relation between the basic data set and the sight spot data set;
and establishing a second mapping relation between the sight spot data set and the emotion data set.
4. The method of claim 2, wherein the degree of gist of each sight spot in the web page is obtained according to the font size, color and position of the sight spot information in the web page, and is calculated by the following formula:
，
wherein D is the degree of the gist of the sight spot,
is the weighting coefficient of the ith web page attribute,
and the quantized value of the ith webpage attribute in the webpage is obtained.
5. The method of claim 1, wherein indexing the base data set after the travel base recommendation and the attraction data set for travel concept augmentation comprises:
inquiring a preset travel basic recommendation model according to user basic information input during user registration to obtain a basic recommendation result, and establishing an index relation with a scenic spot in the basic recommendation result;
and expanding the scenic spots in the geographical areas to which the scenic spots belong according to the names of the scenic spots, and expanding to obtain the scenic spots with the travel characteristics.
6. The method of claim 1, wherein obtaining sentiment analysis assigned scores for the skipped web page comment data comprises:
when comment information of a login user exists in the skipped webpage, emotion analysis is directly performed on the comment information, and emotion analysis assignment scores of the skipped webpage are determined;
and when the comment information of the login user does not exist in the skipped webpage, inquiring the emotion data set, and determining the emotion analysis assignment score of the skipped webpage.
7. The method of claim 1, wherein the step of returning the travel recommendation results after sorting according to the emotion data set, the dwell time of the user webpage, and the subject matter scene of the user jump webpage in combination with the base scene obtained by analyzing the base data set comprises:
and when no intersection exists between the main scenic spot and the basic scenic spot, returning all the main scenic spots and the basic scenic spots as recommendation results.
8, A travel recommendation device based on data mining, comprising:
the system comprises a building module, a database module and a database module, wherein the building module is used for acquiring basic data and comment data in a website, extracting user attribute information in the basic data through a rule matching algorithm to form a basic data set, extracting scenery spot information in the comment data through a named entity recognition algorithm to form a scenery spot data set, extracting emotion information in the comment data through an emotion analysis algorithm to form an emotion data set, establishing a mapping relation among the basic data set, the scenery spot data set and the emotion data set, importing the mapping relation into a data warehouse, and building a travel recommendation big data analysis environment;
the arrangement module is used for cleaning the basic data set, the scenery spot data set and the emotion data set, performing grouping processing on concepts in the basic data set and the scenery spot data set, establishing an index after travel basic recommendation is performed on the basic data set, and performing travel concept expansion on the scenery spot data set;
the skip module is used for acquiring the browsing history of a user, extracting the staying time and the skip sequence of the user in each webpage, recording the anchor text information clicked in the skip process of the user, performing main and scenery spot analysis on the skipped webpage, acquiring emotion analysis assignment scores of the review data of the skipped webpage, and importing the travel recommendation big data analysis environment;
the return module is used for combining the basic scenic spots obtained by analyzing the basic data set according to the emotion data set, the stay time of the user webpage and the theme scenic spots of the user webpage, and returning a travel recommendation result after sequencing;
the method for analyzing the main and the sight spots of the skipped webpage comprises the following steps of recording anchor text information clicked by a user in the skipping process, and analyzing the main and the sight spots of the skipped webpage, wherein the anchor text information comprises:
extracting the sight spot information in the anchor text, and performing semantic expansion to obtain a th subject concept;
analyzing the main subject sight spot of the jump webpage to obtain a second main subject concept;
and performing intersection operation on the th main concept and the second main concept to obtain the main concept and the sight spot concept concerned by the user.
9. The apparatus of claim 8, the build module, comprising:
the mapping unit is used for controlling the distribution and resource allocation of tasks, establishing mapping relation between the basic data set and the scenery spot data set;
and the second mapping unit is used for establishing a second mapping relation between the sight spot data set and the emotion data set.
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| CN111612590A (en) * | 2020-03-19 | 2020-09-01 | 江苏智檬智能科技有限公司 | Scenic spot recommendation method and device based on artificial intelligence big data |
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