CN116561415A - Travel recommendation system and method based on big data - Google Patents
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/95—Retrieval from the web
- G06F16/953—Querying, e.g. by the use of web search engines
- G06F16/9535—Search customisation based on user profiles and personalisation
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/906—Clustering; Classification
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06Q30/00—Commerce
- G06Q30/06—Buying, selling or leasing transactions
- G06Q30/0601—Electronic shopping [e-shopping]
- G06Q30/0631—Item recommendations
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- G—PHYSICS
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Abstract
The invention discloses a travel recommendation system and a travel recommendation method based on big data, wherein the operation method of the system comprises the following steps: step one: the multisource data is collected, and accuracy in data analysis is guaranteed; step two: preprocessing data and extracting features; step three: by constructing the user portrait, the system can accurately recommend new users; step four: generating a personalized recommendation list based on the user portrait and the historical behavior data, and reducing information overload, wherein the multi-source data module is used for collecting user behavior data and travel related data; the data processing module is used for processing the acquired data, including data cleaning and data integration; the user portrait module is used for constructing user portraits to realize accurate recommendation; the group dividing module is used for dividing the same hobby groups and knowing the characteristics and preferences of users in different groups, and has the characteristics of accurately and effectively recommending travel information and reducing information overload.
Description
Technical Field
The invention relates to the technical field of travel recommendation, in particular to a travel recommendation system and method based on big data.
Background
Along with popularization of the internet and mobile equipment, the travel industry is also gradually digitalized, and at present, travel recommendation systems based on big data technology are gradually becoming hot spots for research and application, and the systems can utilize various data sources such as historical behavior data, social network data, geographic position data and the like of users to realize more accurate personalized recommendation through big data analysis and machine learning algorithms. For example, a travel recommendation system based on user location data may recommend surrounding attractions and activities based on the user's current geographic location, and a travel recommendation system based on user historical behavioral data may recommend travel routes and services that meet the user's interests and needs based on the user's historical search, browsing, and purchase records.
However, current big data travel recommendation systems still have some problems. Firstly, the data source is single, various types of data sources cannot be fully utilized to influence the recommendation accuracy, and secondly, the problem of information overload in a recommendation system is solved, namely, the recommendation result is too much, so that a user is difficult to select, and when a new user logs in, the system cannot accurately conduct travel recommendation. Therefore, there is a need for a big data based travel recommendation system and method that provides accurate and efficient information overload reduction.
Disclosure of Invention
The invention aims to provide a travel recommendation system and a travel recommendation method based on big data, so as to solve the problems in the background technology.
In order to solve the technical problems, the invention provides the following technical scheme: travel recommendation system and method based on big data, the operation method of the system comprises the following steps:
step one: the multisource data is collected, and accuracy in data analysis is guaranteed;
step two: preprocessing data and extracting features;
step three: by constructing the user portrait, the system can accurately recommend new users;
step four: a personalized recommendation list is generated based on the user portraits and the historical behavior data, and information overload is reduced.
According to the above technical scheme, the step of collecting the multi-source data and ensuring the accuracy in data analysis comprises the following steps:
collecting user behavior data;
the system acquires tourist product information data of a tourist website and a tourist APP platform.
According to the above technical solution, the step of collecting user behavior data includes:
the system collects search, browse, click, purchase and comment data of a user on a platform in a mode of embedding codes on the platform such as a travel website and a travel APP, when the user searches travel products of a certain city on the platform, the system records search keywords, search time and search result page information of the user, when the user browses a certain travel product on the platform, the system records browsing time, browsing pages and stay time of the user, when the user purchases a certain travel product on the platform, the system records purchase time, purchase amount and commodity purchasing information of the user, when the user reviews or evaluates a certain travel product on the platform, the system records comment content and evaluation score information of the user, and interests and preferences of the user can be known through analysis and processing of the data, so that personalized travel recommendation service is provided for the user.
According to the technical scheme, the system obtains travel product information data of a travel website and a travel APP platform, and the method comprises the following steps:
the system obtains tourist product information data of tourist website, tourist APP, including name, price, place, label of tourist product, the system climbs the tourist product information data of a certain city on the tourist website through the crawler program, including scenic spot name, ticket price, open time, geographical position, comment information, the system can also know the evaluation and opinion of a user to a certain tourist product through crawling the user evaluation data on the tourist APP, thereby better know the quality and the characteristics of the tourist product, compare traditional tourist recommendation system, this system fully excavates user's interest and demand through gathering the data of multiple data source, improve the individuation and the precision of tourist recommendation.
According to the above technical solution, the steps of preprocessing the data and extracting the features include:
adopting data cleaning to remove repeated data, and processing missing data and abnormal data;
integrating the data from different sources by adopting data integration;
and extracting the characteristics of the data by adopting two methods of text mining and user behavior analysis.
According to the technical scheme, the step of extracting the characteristics of the data by adopting two methods of text mining and user behavior analysis comprises the following steps:
text mining mainly aims at the name, description and label information of travel products, and text information is converted into numerical characteristics by a TF-IDF method so as to facilitate subsequent model training and prediction; the user behavior analysis is to process searching, browsing, purchasing and commenting of the user, extract interests and preferences of the user so as to recommend travel products meeting the requirements of the user, and obtain an extracted data set through data preprocessing and feature extraction, thereby providing a basis for subsequent modeling and recommendation.
According to the technical scheme, the method for realizing accurate recommendation of the new user by the system by constructing the user portrait comprises the following steps:
user group division is achieved through cluster analysis;
adopting a basic statistical method and cluster analysis to realize user portrait construction;
when a new user logs into the system, accurate recommendation is made using the user portraits.
According to the technical scheme, the step of realizing user portrait construction by adopting a basic statistical method and combining cluster analysis comprises the following steps:
after the user group is divided by using cluster analysis, group identification is carried out on the user group, such as a cultural travel group, a beach travel group and a showplace travel group, then a basic statistical method is adopted to carry out statistics on the age, the sex and the price of the purchased travel product of each group, so that a user portrait is constructed, for example, in each travel group, the price of the purchased travel product is counted, the variance of the prices is calculated, if the variance is smaller, the price of the group relative to the travel product is relatively stable, otherwise, the price of the group relative to the travel product is relatively sensitive, and the characteristics of the price of the travel product purchased by each user group can be better known through the method.
According to the above technical scheme, the step of generating the personalized recommendation list based on the user portrait and the historical behavior data and reducing information overload comprises the following steps:
adopting a recommendation algorithm based on content;
the user's preferences and needs for different travel products are analyzed to recommend similar travel products to the user.
According to the above technical solution, the system comprises:
the multi-source data module is used for collecting user behavior data and travel related data;
the data processing module is used for processing the acquired data, including data cleaning and data integration;
and the user portrait module is used for constructing user portraits to realize accurate recommendation.
Compared with the prior art, the invention has the following beneficial effects: the invention realizes travel recommendation by arranging a multi-source data module, a data processing module and a user portrait module, firstly collects user behavior data and travel product information data of each travel website and a travel APP platform, then processes the collected data, and the processing method comprises cleaning, integrating and extracting characteristic values, carrying out cluster analysis according to the extracted characteristic values, then carrying out user portrait construction according to a basic statistical method, and carrying out travel information recommendation on new users according to the constructed user images.
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The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flowchart of a travel recommendation method based on big data according to an embodiment of the present invention;
fig. 2 is a schematic diagram of module composition of a travel recommendation system based on big data according to a second embodiment of the present invention.
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.
Embodiment one: fig. 1 is a flowchart of a big data based travel recommendation method according to an embodiment of the present invention, where the method may be executed by the big data based travel recommendation system according to the present invention, and as shown in fig. 1, the method specifically includes the following steps:
step one: the multisource data is collected, and accuracy in data analysis is guaranteed;
in the embodiment of the invention, the personalized pushing and the accuracy of the travel recommendation system are improved by collecting the data of various data sources, including the user behavior data and the travel product information data;
by way of example, the system collects behavior data such as searching, browsing, clicking, purchasing, commenting and evaluating of the user on the platform in a mode of embedding codes on the platform such as a travel website and a travel APP, for example, when the user searches for a travel product of a certain city on the platform, the system records information such as search keywords, search time and search result pages of the user, when the user browses a certain travel product on the platform, the system records information such as browsing time, browsing pages and stay time of the user, when the user purchases a certain travel product on the platform, the system records information such as purchasing time, purchasing amount and purchasing commodity of the user, when the user reviews or evaluates a certain travel product on the platform, the system records information such as comment content and evaluation score of the user, and through analyzing and processing the data, the interest and preference of the user can be known, so that personalized travel recommendation service is provided for the user;
the system can acquire tourist product information data of a tourist website, a tourist APP and other platforms, the information data comprises information data such as names, prices, places, labels and the like of the tourist products, the system can crawl the tourist product information data of a certain city on the tourist website through a crawler program, the information comprises information such as scenic spot names, ticket prices, open time, geographic positions, comments and the like, the system can also learn the evaluation and comments of a user on a certain tourist product through crawling user evaluation data on the tourist APP, so that the quality and characteristics of the tourist product are better known.
Step two: data preprocessing and feature extraction;
in the embodiment of the invention, in order to improve the effect of the travel recommendation system, the acquired data is required to be preprocessed and extracted in characteristics;
the data is preprocessed by two methods of data cleaning and data integration, wherein the data cleaning comprises the steps of removing repeated data, processing missing data, abnormal data and the like, so that the accuracy and the reliability of the data are ensured; the data integration is to integrate the data from different sources together, remove repeated data and unify data formats so as to facilitate subsequent data analysis and mining, in the aspect of feature extraction, two methods of text mining and user behavior analysis are adopted, the text mining is mainly used for processing text information such as names, descriptions, labels and the like of travel products, and the text information is converted into numerical features through a TF-IDF method so as to facilitate subsequent model training and prediction; the user behavior analysis is to process behavior data such as searching, browsing, purchasing and commenting of the user, extract interests and preferences of the user so as to recommend travel products meeting the requirements of the user, and obtain an extracted data set through data preprocessing and feature extraction so as to provide a basis for subsequent modeling and recommendation.
Step three: analyzing and summarizing the extracted features, constructing a user portrait, and accurately pushing a new user;
in the embodiment of the invention, the characteristics extracted from the multi-source data are analyzed and generalized to construct the user portrait, thereby providing personalized recommendation service for the travel recommendation system;
the method comprises the steps of using a K-means clustering algorithm to group users, dividing a data set into K clusters, enabling the similarity of data points in each cluster to be higher, enabling the similarity between different clusters to be lower, enabling K to refer to the number of groups to be divided, searching the optimal number of groups by testing different K values, and in particular implementation, loading processed user characteristic data to construct characteristic vectors, setting K values, namely the number of clusters to be divided, finally carrying out clustering analysis on the user characteristic vectors by adopting a KMeans algorithm, obtaining labels of different clusters, dividing the users into different groups, and knowing characteristics and preferences of the users in the different groups;
the users are divided according to the travel preference, for example, the users are divided into groups such as a cultural travel group, a beach travel group, a showplace travel group and the like, then a basic statistical method is adopted to count the age, the sex, the price of the purchased travel products and the like of each group to form a user image, for example, in each travel group, the price of the purchased travel products is counted, the variance of the prices is calculated, if the variance is smaller, the price of the group for the travel products is relatively stable, otherwise, the price of the group for the travel products is relatively sensitive, and the characteristics of the user group for the price of the travel products can be better known through the method;
when a new user logs in the system, the system can match basic information and interest provided by the user in combination with the prior user data, sequentially match information such as age, gender, travel budget, interest and the like, and classify the information into a proper user group, and then the system generates a recommendation list for the new user according to group characteristics and historical behavior data of the user.
Step four: a personalized recommendation list is generated based on the user portraits and the historical behavior data, and information overload is reduced.
In the embodiment of the invention, based on user portrait and historical behavior data, a recommendation algorithm is adopted to generate a personalized recommendation list for each user;
by adopting a content-based recommendation algorithm, analyzing the preference and the demand of users for different travel products based on the information such as historical browsing records, praise records and comment records of the users, so as to recommend similar travel products for the users, based on the recommendation algorithm, the personalized recommendation service can be realized.
Embodiment two: fig. 2 is a schematic diagram of module composition of the big data based travel recommendation system according to the second embodiment of the present invention, as shown in fig. 2, and the system includes:
the multi-source data module is used for collecting user behavior data and travel related data;
the data processing module is used for processing the acquired data, including data cleaning and data integration;
the user portrait module is used for constructing user portraits to realize accurate recommendation;
in some embodiments of the invention, the multi-source data module comprises:
the user behavior data acquisition module is used for acquiring behavior data such as searching, browsing, clicking, purchasing, commenting and evaluating of a user on the platform;
the website data acquisition module is used for acquiring tourist product information data of tourist websites, tourist APP and other platforms;
in some embodiments of the invention, the data processing module comprises:
the data cleaning module is used for removing repeated data, processing missing data, abnormal data and the like so as to ensure the accuracy and the reliability of the data;
the data integration module is used for integrating the data from different sources together so as to facilitate subsequent data analysis and mining;
the feature extraction module is used for subsequent model training and prediction;
in some embodiments of the invention, the user portrayal module comprises:
the group dividing module is used for dividing the same hobby groups and knowing the characteristics and preferences of users in different groups;
the new user recommending module is used for recommending accurate travel information for the new user;
and the personalized recommendation module is used for generating a personalized recommendation list based on the user portrait and the historical behavior data.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: the foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited thereto, but it is to be understood that modifications and equivalents of some of the technical features described in the foregoing embodiments may be made by those skilled in the art, although the present invention has been described in detail with reference to the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. The travel recommendation method based on big data is characterized by comprising the following steps of: the method comprises the following steps:
step one: the multisource data is collected, and accuracy in data analysis is guaranteed;
step two: preprocessing data and extracting features;
step three: by constructing the user portrait, the system can accurately recommend new users;
step four: a personalized recommendation list is generated based on the user portraits and the historical behavior data, and information overload is reduced.
2. The big data based travel recommendation method according to claim 1, wherein: the step of collecting multi-source data and ensuring the accuracy of data analysis comprises the following steps:
collecting user behavior data;
the system acquires tourist product information data of a tourist website and a tourist APP platform.
3. The big data based travel recommendation method according to claim 2, wherein: the step of collecting user behavior data comprises the following steps:
the system collects search, browse, click, purchase and comment data of a user on the platform in a mode of embedding codes in the tourist website and the tourist APP platform, when the user searches for a tourist product of a certain city on the platform, the system records search keywords, search time and search result page information of the user, when the user browses a certain tourist product on the platform, the system records browse time, browse page and stay time of the user, when the user purchases a certain tourist product on the platform, the system records purchase time, purchase amount and commodity purchasing information of the user, when the user reviews or evaluates a certain tourist product on the platform, the system records comment content and evaluation score information of the user, and the user interests and preferences can be known through analysis and processing of the data, so that personalized tourist recommendation services are provided for the user.
4. The big data based travel recommendation method according to claim 2, wherein: the system obtains travel product information data of a travel website and a travel APP platform, and comprises the following steps:
the system obtains tourist product information data of tourist website, tourist APP, including name, price, place, label of tourist product, the system climbs the tourist product information data of a certain city on the tourist website through the crawler program, including scenic spot name, ticket price, open time, geographical position, comment information, the system can also know the evaluation and opinion of a user to a certain tourist product through crawling the user evaluation data on the tourist APP, thereby better know the quality and the characteristics of the tourist product, compare traditional tourist recommendation system, this system fully excavates user's interest and demand through gathering the data of multiple data source, improve the individuation and the precision of tourist recommendation.
5. The big data based travel recommendation method according to claim 1, wherein: the step of preprocessing and extracting the characteristics of the data comprises the following steps:
adopting data cleaning to remove repeated data, and processing missing data and abnormal data;
integrating the data from different sources by adopting data integration;
and extracting the characteristics of the data by adopting two methods of text mining and user behavior analysis.
6. The big data based travel recommendation method according to claim 5, wherein: the step of extracting the characteristics of the data by adopting two methods of text mining and user behavior analysis comprises the following steps:
text mining mainly aims at names, descriptions and labels of travel products, and text information is converted into numerical characteristics through a TF-IDF method so as to facilitate subsequent model training and prediction; the user behavior analysis is to process searching, browsing, purchasing and commenting of the user, extract interests and preferences of the user so as to recommend travel products meeting the requirements of the user, and obtain an extracted data set through data preprocessing and feature extraction, thereby providing a basis for subsequent modeling and recommendation.
7. The big data based travel recommendation method according to claim 1, wherein: the method for realizing accurate recommendation of the new user by the system by constructing the user portrait comprises the following steps:
user group division is achieved through cluster analysis;
adopting a basic statistical method and cluster analysis to realize user portrait construction;
when a new user logs into the system, accurate recommendation is made using the user portraits.
8. The big data based travel recommendation method according to claim 7, wherein: the step of realizing user portrait construction by adopting a basic statistical method and combining cluster analysis comprises the following steps:
after the user group is divided by using cluster analysis, group identification is carried out on the user group, such as a cultural tourism group, a beach tourism group and a tourist group in a showplace, then a basic statistical method is adopted to carry out statistics on the age, the sex and the price of the tourism product purchased by each group, so that a user image is constructed, such as in each tourism group, the price of the tourism product purchased by each group is counted, the variance of the prices is calculated, if the variance is smaller, the price of the group for the tourism product is relatively stable, otherwise, the price of the group for the tourism product is relatively sensitive, and the characteristic of the price of the tourism product purchased by each user group can be better known through the method.
9. The big data based travel recommendation method according to claim 1, wherein: the step of generating a personalized recommendation list based on the user portrait and the historical behavior data and reducing information overload comprises the following steps:
adopting a recommendation algorithm based on content;
the user's preferences and needs for different travel products are analyzed to recommend similar travel products to the user.
10. Travel recommendation system based on big data, its characterized in that: the system comprises:
the multi-source data module is used for collecting user behavior data and travel related data;
the data processing module is used for processing the acquired data, including data cleaning and data integration;
and the user portrait module is used for constructing user portraits to realize accurate recommendation.
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| CN116955834A (en) * | 2023-09-21 | 2023-10-27 | 北京中景合天科技有限公司 | Intelligent travel market prediction and recommendation system and method |
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| CN117557413A (en) * | 2024-01-12 | 2024-02-13 | 华御祥茶科学研究院(深圳)有限公司 | Man-machine interaction method and system for cultural tourism integrated service cloud platform |
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