CN111191121A - Smart tourism target matching method based on big data - Google Patents
Smart tourism target matching method based on big data Download PDFInfo
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- CN111191121A CN111191121A CN201911320842.4A CN201911320842A CN111191121A CN 111191121 A CN111191121 A CN 111191121A CN 201911320842 A CN201911320842 A CN 201911320842A CN 111191121 A CN111191121 A CN 111191121A
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Abstract
The invention discloses a big data-based intelligent tourism target matching method, which comprises the following steps of: constructing a travel information database, wherein the travel information database is based on the existing travel data; collecting historical travel data and personal behavior data of a user terminal, associating the historical travel data and the personal behavior data with an identification code of the user terminal, and forming travel associated information; extracting multi-dimensional travel features of the target user based on the travel associated information; matching tour categories from a constructed tour information database based on multi-dimensional characteristics of users, classifying target users, and obtaining historical base figures of the target users; based on the historical base portrait of the target user, pushing preset travel advertisements and introduction information matched with the base portrait to the user, and determining a final portrait; and pushing final travel recommendation information matched with the final portrait based on the final portrait. The method has high efficiency of matching and processing the target and good matching effect, and makes the travel information matching more targeted.
Description
Technical Field
The invention relates to the technical field of intelligent tourism, in particular to a big data-based intelligent tourism target matching method.
Background
"travel" is travel, go out, i.e. the process of spatially traveling from first place to second place for the purpose of achieving a certain purpose; "tour" is going out for touring, sightseeing, entertainment, i.e. the travel for achieving these purposes. The two are combined together to make the tour. Therefore, the travel is more focused on traveling, and the travel not only has 'traveling', but also has sightseeing and entertainment meanings. Travel refers to activities that leave their usual environment, go to some place and stay there for leisure, business or other purposes, but continue for no more than one year. Travel purposes include six categories: leisure, entertainment, vacation, dating to and from friends, business, professional visits, health care, religion/worship, and the like. With the continuous improvement of the living standard of the whole people, the interest of the public in tourism is continuously strengthened, and the times of the tourism of the whole people are quietly stepping into our lives. In the face of such huge tourists and markets, how to better serve the masses becomes a central topic. Meanwhile, the requirements of the public tourism service of the people are rapidly improved, and particularly, the demands of self-service tourists and tourists with large area are different.
In the traditional intelligent travel target matching process, the matching is generally carried out only through the single-dimensional characteristics of the tourists, and the relevance of the individual characteristics of the tourists and other multi-dimensional travel characteristics such as travel and interest is not considered, so that the matching accuracy is not high, and the target matching efficiency is low.
Disclosure of Invention
The invention aims to provide a big data-based intelligent tourism target matching method to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: a big data-based intelligent tourism target matching method comprises the following steps:
the method comprises the following steps: constructing a tourism information database which is based on the existing tourism data and comprises a team tourism class and a free self-service tourism class;
step two: collecting historical travel data and personal behavior data of a user terminal, associating the historical travel data and the personal behavior data with an identification code of the user terminal, and forming travel associated information;
step three: extracting multi-dimensional travel features of the target user based on the travel associated information;
step four: matching tour categories from a constructed tour information database based on multi-dimensional characteristics of users, classifying target users, and obtaining historical base figures of the target users;
step five: based on the historical base portrait of the target user, pushing preset travel advertisements and introduction information matched with the base portrait to the user, and determining a final portrait of the target user based on the click rate of the introduction information;
step six: and pushing final travel recommendation information and advertisement information matched with the final portrait based on the final portrait of the target user.
Preferably, the user terminal is provided with a social client, a positioning client and a payment client.
Preferably, the personal behavior data comprises user basic characteristics, network access characteristics and consumption characteristics, wherein the user basic characteristics comprise age, gender, geographic position and professional information, the network access characteristics comprise historical tourism browsing behavior data and public tourism data of sharing software such as a friend circle and a microblog, and the consumption characteristics comprise user consumption behavior data, payment behavior data and life track behavior information.
Preferably, the group tourism category comprises a leisure entertainment category, and the free tourism category comprises a visiting relatives category, a visiting friends category, a business category and a professional visiting category.
Preferably, the step five specifically comprises: step a: counting the click rate of the introduction information and the retention time of a user on an introduction information page; step b: judging the attention index of the user to the information based on the click rate and the retention time; step c: and determining a final portrait of the target user based on the size of the attention index, wherein the final portrait comprises the identity characteristic of the user and the attention preference characteristic of the user.
Preferably, the target user history base image comprises the identity characteristic of the user, the historical preference characteristic of the user and the historical consumption characteristic of the user.
Preferably, the sixth step further includes matching other target users matched with the final portrait based on the final portrait of the target, and establishing a chat room for the target user and the other target users, wherein the target user and the other target users can selectively enter the chat room for communication of the travel-related information.
Compared with the prior art, the invention has the beneficial effects that:
the invention has high target matching processing efficiency and good matching effect, and can accurately target the target population by matching a plurality of dimensions based on the user basic characteristics, the network access characteristics, the consumption characteristics and the historical tourism data, so that the matching of the tourism information is more targeted, the requirements of different populations are met, the tourism experience effect is comprehensively improved, and the personalized tourism development is promoted. And moreover, friends and the like can be matched based on the final images of similar users, a chat room is established for the target user and other target users, and the target user and other target users can select to enter the chat room for communication of tourism related information, so that the experience of the user is optimized, and the method has good market prospect and wide applicability.
Drawings
FIG. 1 is an overall flow chart of the present invention;
FIG. 2 is a flow chart of step five of 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. 1-2, the present invention provides a technical solution: a big data-based intelligent tourism target matching method comprises the following steps:
the method comprises the following steps: constructing a tourism information database which is based on the existing tourism data and comprises a team tourism class and a free self-service tourism class;
step two: collecting historical travel data and personal behavior data of a user terminal, associating the historical travel data and the personal behavior data with an identification code of the user terminal, and forming travel associated information;
step three: extracting multi-dimensional travel features of the target user based on the travel associated information;
step four: matching tour categories from a constructed tour information database based on multi-dimensional characteristics of users, classifying target users, and obtaining historical base figures of the target users;
step five: based on the historical base portrait of the target user, pushing preset travel advertisements and introduction information matched with the base portrait to the user, and determining a final portrait of the target user based on the click rate of the introduction information;
step six: and pushing final travel recommendation information and advertisement information matched with the final portrait based on the final portrait of the target user.
Further, a social client, a positioning client and a payment client are installed on the user terminal.
Further, the personal behavior data comprises user basic characteristics, network access characteristics and consumption characteristics, wherein the user basic characteristics comprise age, gender, geographic position and occupation information, the network access characteristics comprise historical tourism browsing behavior data and public tourism data of sharing software such as a friend circle and a microblog, and the consumption characteristics comprise user consumption behavior data, payment behavior data and life track behavior information.
Further, the team tourism category comprises a leisure entertainment category, and the free tourism category comprises a visiting friend category, a business category and a professional visiting friend category.
Further, the fifth step specifically comprises: step a: counting the click rate of the introduction information and the retention time of a user on an introduction information page; step b: judging the attention index of the user to the information based on the click rate and the retention time; step c: and determining a final portrait of the target user based on the size of the attention index, wherein the final portrait comprises the identity characteristic of the user and the attention preference characteristic of the user.
Further, the target user history base image comprises the identity characteristics of the user, the historical preference characteristics of the user and the historical consumption characteristics of the user.
Further, matching other target users matched with the final portrait based on the final portrait of the target, and establishing chat rooms for the target users and the other target users, wherein the target users and the other target users can selectively enter the chat rooms to communicate travel-related information, so that users with consistent travel interests can chat smoothly, and the purpose of optimizing user experience is achieved.
The invention has high target matching processing efficiency and good matching effect, and can accurately target the target population by matching a plurality of dimensions based on the user basic characteristics, the network access characteristics, the consumption characteristics and the historical tourism data, so that the matching of the tourism information is more targeted, the requirements of different populations are met, the tourism experience effect is comprehensively improved, and the personalized tourism development is promoted. And moreover, friends and the like can be matched based on the final images of similar users, a chat room is established for the target user and other target users, and the target user and other target users can select to enter the chat room for communication of tourism related information, so that the experience of the user is optimized, and the method has good market prospect and wide applicability.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. A big data-based intelligent travel target matching method is characterized by comprising the following steps:
the method comprises the following steps: constructing a tourism information database which is based on the existing tourism data and comprises a team tourism class and a free self-service tourism class;
step two: collecting historical travel data and personal behavior data of a user terminal, associating the historical travel data and the personal behavior data with an identification code of the user terminal, and forming travel associated information;
step three: extracting multi-dimensional travel features of the target user based on the travel associated information;
step four: matching tour categories from a constructed tour information database based on multi-dimensional characteristics of users, classifying target users, and obtaining historical base figures of the target users;
step five: based on the historical base portrait of the target user, pushing preset travel advertisements and introduction information matched with the base portrait to the user, and determining a final portrait of the target user based on the click rate of the introduction information;
step six: and pushing final travel recommendation information and advertisement information matched with the final portrait based on the final portrait of the target user.
2. The big data-based intelligent travel target matching method as claimed in claim 1, wherein the user terminal is installed with a social client, a location client and a payment client.
3. The big data-based intelligent travel target matching method as claimed in claim 1, wherein the personal behavior data includes user basic characteristics, network access characteristics and consumption characteristics, wherein the user basic characteristics include age, gender, geographic location and occupation information, the network access characteristics include historical travel browsing behavior data and public travel data of sharing software such as a circle of friends and microblogs, and the consumption characteristics include user consumption behavior data, payment behavior data and life track behavior information.
4. The big-data based intelligent travel target matching method as claimed in claim 1, wherein the team travel category comprises a recreation category, and the free travel category comprises a visiting friend category, a business category, and a professional visiting friend category.
5. The big data based intelligent travel target matching method as claimed in claim 1, wherein the target user historical base image comprises the identity of the user, the historical preference of the user and the historical consumption of the user.
6. The big data-based intelligent travel target matching method as claimed in claim 1, wherein the fifth step specifically comprises: step a: counting the click rate of the introduction information and the retention time of a user on an introduction information page; step b: judging the attention index of the user to the information based on the click rate and the retention time; step c: and determining a final portrait of the target user based on the size of the attention index, wherein the final portrait comprises the identity characteristic of the user and the attention preference characteristic of the user.
7. The method as claimed in claim 1, wherein the sixth step further comprises matching other target users matched with the final portrait based on the final portrait of the target, and establishing a chat room between the target user and other target users, wherein the target user and other target users can optionally enter the chat room for communication of information related to travel.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911320842.4A CN111191121A (en) | 2019-12-19 | 2019-12-19 | Smart tourism target matching method based on big data |
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| Application Number | Priority Date | Filing Date | Title |
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| CN201911320842.4A CN111191121A (en) | 2019-12-19 | 2019-12-19 | Smart tourism target matching method based on big data |
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| CN111191121A true CN111191121A (en) | 2020-05-22 |
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| CN201911320842.4A Withdrawn CN111191121A (en) | 2019-12-19 | 2019-12-19 | Smart tourism target matching method based on big data |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112287248A (en) * | 2020-11-10 | 2021-01-29 | 桂林旅游学院 | Method for matching travel destination based on travel big data by utilizing probability statistics |
| WO2022041982A1 (en) * | 2020-08-28 | 2022-03-03 | 腾讯科技（深圳）有限公司 | Data recommendation method and apparatus, computer device, and storage medium |
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2019
- 2019-12-19 CN CN201911320842.4A patent/CN111191121A/en not_active Withdrawn
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2022041982A1 (en) * | 2020-08-28 | 2022-03-03 | 腾讯科技（深圳）有限公司 | Data recommendation method and apparatus, computer device, and storage medium |
| CN112287248A (en) * | 2020-11-10 | 2021-01-29 | 桂林旅游学院 | Method for matching travel destination based on travel big data by utilizing probability statistics |
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Application publication date: 20200522 |
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