CN116955834A - Intelligent travel market prediction and recommendation system and method - Google Patents

Intelligent travel market prediction and recommendation system and method Download PDF

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CN116955834A
CN116955834A CN202311218926.3A CN202311218926A CN116955834A CN 116955834 A CN116955834 A CN 116955834A CN 202311218926 A CN202311218926 A CN 202311218926A CN 116955834 A CN116955834 A CN 116955834A
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tourist attraction
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张吉英
董阳
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Beijing Zhongjing Hetian Technology Co ltd
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Abstract

The invention relates to the technical field of intelligent travel recommendation, in particular to an intelligent travel market prediction and recommendation system and method. The system comprises an acquisition unit, a processing unit and a processing unit, wherein the acquisition unit is used for acquiring the position information of a user based on GPS positioning and acquiring the travel information of the user; the processing unit is used for calculating the average travel distance of the user and calculating the average travel days of the user; the establishing unit is used for establishing a travel area model of the user according to the position information and the average travel distance of the user; a calculation unit for calculating the number of tourist attractions in the area; and the recommending unit is used for calculating the number of tourist attractions recommended to the user according to the average travel days, acquiring the scores of the tourist attractions, and recommending in sequence according to the number of the tourist attractions and the scores of the tourist attractions from high to low. According to the invention, the tourist attraction is recommended according to the personalized requirements of the user, so that the accuracy of the recommendation is improved, the requirements of the user are better met, and the experience of the user is improved.

Description

Intelligent travel market prediction and recommendation system and method
Technical Field
The invention relates to the technical field of intelligent travel recommendation, in particular to an intelligent travel market prediction and recommendation system and method.
Background
With the continuous expansion of the scale of the tourism industry in China, the tourism information is explosively increased, so that tourists are difficult to acquire valuable information, and the tourism service starts to change from traditional informatization to intelligent. The intelligent travel recommendation system discovers information interested by tourists from massive travel information resources and recommends personalized demand information to corresponding tourist users, so that research and development of the recommendation system has important significance for development of intelligent travel.
However, in the prior art, since the situation that the user goes out and plays only a single scenic spot is not enough for the scenic spot recommendation when the user goes out exists, but the multi-scenic spot play is performed according to the travel route, the traditional recommendation result based on big data cannot well meet the targeted requirement of each user, the recommended scenic spot cannot meet the user play requirement, the requirements of users with different age segments are different, the requirements of tourist scenic spots are different, the recommendation mode of the users in the prior art is based on scenic spot score to conduct a large number of scenic spot recommendation, however, the influence of personalized travel habits of the users on the play requirement is omitted, and further, the user cannot be accurately recommended, and the user experience is reduced.
Disclosure of Invention
The invention aims to provide an intelligent travel market prediction and recommendation system and method.
In order to achieve the above object, the present invention provides the following technical solutions:
an intelligent travel market prediction and recommendation system comprising:
the system comprises an acquisition unit, a storage unit and a storage unit, wherein the acquisition unit is used for acquiring position information of a user based on GPS positioning and acquiring travel information of the user, and the travel information comprises a plurality of historical travel distances, a plurality of historical travel days and a user age i;
the processing unit is used for calculating the average travel distance S of the user according to a plurality of the historical travel distances and calculating the average travel number d of the user according to a plurality of the historical travel numbers; wherein,,
the average travel distance S is obtained by calculating the average value of a plurality of historical travel distances, and the average travel number d is obtained by calculating the average value of a plurality of historical travel numbers;
the establishing unit is used for establishing a travel area model of the user according to the position information of the user and the average travel distance S; wherein,,
The travel area model is a circular area established by taking the position information of the user as an origin and taking the average travel distance S of the user as a radius;
the calculating unit is used for calculating the number of tourist attractions in the area according to the travel area model;
and the recommending unit is used for calculating the number of the tourist attractions recommended to the user according to the average travel days d, acquiring the scores of the tourist attractions, and sequentially recommending according to the number of the tourist attractions recommended to the user and the scores of the tourist attractions from high to low.
In some embodiments of the present application, a preset average travel day matrix T0 and a preset recommended tourist attraction number matrix a are preset in the recommendation unit, and for the preset recommended tourist attraction number matrix a, a (A1, A2, A3, A4) is set, wherein A1 is a first preset recommended tourist attraction number, A2 is a second preset recommended tourist attraction number, A3 is a third preset recommended tourist attraction number, and A4 is a fourth preset recommended tourist attraction number;
setting T0 (T01, T02, T03 and T04) for the preset average travel day matrix T0, wherein T01 is a first preset average travel day, T02 is a second preset average travel day, T03 is a third preset average travel day, T04 is a fourth preset average travel day, and T01 is less than T02 and T03 is less than T04;
The recommending unit is used for selecting the corresponding recommended tourist attraction quantity as the tourist attraction quantity recommended to the user according to the relation between d and the preset average travel days matrix T0;
when d < T01, selecting the first preset recommended tourist attraction number A1 as the number of tourist attractions recommended to the user;
when T01 is less than or equal to K and less than T02, selecting the second preset recommended tourist attraction quantity A2 as the quantity of the tourist attraction recommended to the user;
when T02 is less than or equal to K and less than T03, selecting the third preset recommended tourist attraction quantity A3 as the quantity of the tourist attraction recommended to the user;
and when T03 is less than or equal to K and less than T04, selecting the fourth preset recommended tourist attraction quantity A4 as the quantity of the tourist attraction recommended to the user.
In some embodiments of the present application, a preset user age matrix R0 and a preset tourist attraction number correction coefficient matrix B are further set in the recommendation unit, for the preset tourist attraction number correction coefficient matrix B, B (B1, B2, B3, B4) is set, wherein B1 is a first preset tourist attraction number correction coefficient, B2 is a second preset tourist attraction number correction coefficient, B3 is a third preset tourist attraction number correction coefficient, B4 is a fourth preset tourist attraction number correction coefficient, and B1 is more than 1 and less than 2 and B3 is more than 3 and less than 1.5;
Setting R0 (R01, R02, R03, R04) for the preset user age matrix R0, wherein R01 is a first preset user age, R02 is a second preset user age, R03 is a third preset user age, R04 is a fourth preset user age, and R01 is less than R02 and less than R03 is less than R04;
the recommending unit is also used for selecting corresponding correcting coefficients of the number of the recommended tourist attractions according to the relation between i and the preset user age matrix R0 so as to correct the number of each preset recommended tourist attraction, and when the number of the corrected recommended tourist attraction is not an integer, the recommended tourist attraction is rounded upwards and used as the number of the recommended tourist attraction;
when i is less than R01, selecting the fourth preset recommended tourist attraction quantity correction coefficient B4 to correct the first preset recommended tourist attraction quantity A1, wherein the corrected recommended tourist attraction quantity is A1 x B4;
when R01 is less than or equal to i and less than R02, selecting the third preset recommended tourist attraction quantity correction coefficient B3 to correct the second preset recommended tourist attraction quantity A2, wherein the corrected recommended tourist attraction quantity is A2 x B3;
when R02 is less than or equal to i and less than R03, selecting the second preset recommended tourist attraction quantity correction coefficient B2 to correct the third preset recommended tourist attraction quantity A3, wherein the corrected recommended tourist attraction quantity is A3 x B2;
When R03 is less than or equal to i and less than R04, the first preset recommended tourist attraction quantity correction coefficient B1 is selected to correct the fourth preset recommended tourist attraction quantity A4, and the corrected recommended tourist attraction quantity is A1 x B4.
In some embodiments of the application, the obtaining unit is further configured to obtain a month average consumption level value g of the user;
the recommendation unit is also internally provided with a preset user month average consumption level value matrix W0 and a preset recommended tourist attraction number secondary correction coefficient matrix C, and C (C1, C2, C3 and C4) is set for the preset recommended tourist attraction number secondary correction coefficient matrix C, wherein C1 is a first preset recommended tourist attraction number secondary correction coefficient, C2 is a second preset recommended tourist attraction number secondary correction coefficient, C3 is a third preset recommended tourist attraction number secondary correction coefficient, C4 is a fourth preset recommended tourist attraction number secondary correction coefficient, and C1 is more than 1 and less than C3 and less than 1.2; setting W0 (W01, W02, W03, W04) in the preset user month average consumption level matrix W0, wherein W01 is a first preset user month average consumption level value, W02 is a second preset user month average consumption level value, W03 is a third preset user month average consumption level value, W04 is a fourth preset user month average consumption level value, and W01 is less than W02 and less than W03 is less than W04;
The recommending unit is also used for selecting a corresponding secondary correction coefficient of the number of recommended tourist attractions according to the relation between g and the average consumption level value matrix W0 of the preset user month so as to carry out secondary correction on the number of each preset tourist attraction after correction, and when the number of the recommended tourist attraction after secondary correction is not an integer, the recommended tourist attraction is rounded upwards and used as the number of the recommended tourist attraction;
when g is smaller than W01, selecting a secondary correction coefficient C1 of the first preset recommended tourist attraction number to carry out secondary correction on the corrected first preset recommended tourist attraction number A1, wherein the corrected recommended tourist attraction number is A1 x B4 x C1;
when W01 is less than or equal to g and less than W02, selecting a secondary correction coefficient C2 of the number of the second preset recommended tourist attractions to secondarily correct the corrected number A2 of the second preset recommended tourist attractions, wherein the corrected number of the recommended tourist attractions is A2 x B3 x C2;
when W02 is less than or equal to g and less than W03, selecting a secondary correction coefficient C3 of the number of the third preset recommended tourist attraction to carry out secondary correction on the corrected number A3 of the third preset recommended tourist attraction, wherein the corrected number of the recommended tourist attraction is A3 x B2 x C3;
when W03 is less than or equal to g and less than W04, selecting a secondary correction coefficient C4 of the fourth preset recommended tourist attraction number to carry out secondary correction on the corrected fourth preset recommended tourist attraction number A4, wherein the corrected recommended tourist attraction number is A4 x B1 x C4.
In some embodiments of the present application, the recommendation unit is further configured to obtain a score increase value of each tourist attraction within a preset time, and when the number of tourist attractions and the score of each tourist attraction are recommended to the user, and the scores of the tourist attractions are the same, sequentially recommend each tourist attraction with the same score according to the score increase value of each tourist attraction within the preset time from high to low.
In order to achieve the above objective, the present application further provides a method for predicting and recommending an intelligent travel market, which is applied to the system for predicting and recommending an intelligent travel market, and includes:
acquiring position information of a user based on GPS positioning, and acquiring travel information of the user, wherein the travel information comprises a plurality of historical travel distances, a plurality of historical travel days and a user age i;
calculating an average travel distance S of the user according to the plurality of historical travel distances, and calculating an average travel number d of the user according to the plurality of historical travel numbers; wherein,,
the average travel distance S is obtained by calculating the average value of a plurality of historical travel distances, and the average travel number d is obtained by calculating the average value of a plurality of historical travel numbers;
Establishing a travel area model of the user according to the position information of the user and the average travel distance S; wherein,,
the travel area model is a circular area established by taking the position information of the user as an origin and taking the average travel distance S of the user as a radius;
calculating the number of tourist attractions in the area according to the travel area model;
calculating the number of tourist attractions recommended to the user according to the average travel days d, acquiring the scores of the tourist attractions, and recommending in sequence according to the number of the tourist attractions recommended to the user and the scores of the tourist attractions from high to low.
In some embodiments of the present application, a preset average travel day matrix T0 and a preset recommended tourist attraction number matrix a are preset, for which a (A1, A2, A3, A4) is set, wherein A1 is a first preset recommended tourist attraction number, A2 is a second preset recommended tourist attraction number, A3 is a third preset recommended tourist attraction number, and A4 is a fourth preset recommended tourist attraction number;
setting T0 (T01, T02, T03 and T04) for the preset average travel day matrix T0, wherein T01 is a first preset average travel day, T02 is a second preset average travel day, T03 is a third preset average travel day, T04 is a fourth preset average travel day, and T01 is less than T02 and T03 is less than T04;
Selecting the corresponding recommended tourist attraction quantity as the tourist attraction quantity recommended to the user according to the relation between d and the preset average travel days matrix T0;
when d < T01, selecting the first preset recommended tourist attraction number A1 as the number of tourist attractions recommended to the user;
when T01 is less than or equal to K and less than T02, selecting the second preset recommended tourist attraction quantity A2 as the quantity of the tourist attraction recommended to the user;
when T02 is less than or equal to K and less than T03, selecting the third preset recommended tourist attraction quantity A3 as the quantity of the tourist attraction recommended to the user;
and when T03 is less than or equal to K and less than T04, selecting the fourth preset recommended tourist attraction quantity A4 as the quantity of the tourist attraction recommended to the user.
In some embodiments of the present application, a preset user age matrix R0 and a preset tourist attraction number correction coefficient matrix B are preset, for which B (B1, B2, B3, B4) is set, wherein B1 is a first preset tourist attraction number correction coefficient, B2 is a second preset tourist attraction number correction coefficient, B3 is a third preset tourist attraction number correction coefficient, B4 is a fourth preset tourist attraction number correction coefficient, and B1 is more than 1 and less than 2 and less than 3 and less than 4 and less than 1.5;
Setting R0 (R01, R02, R03, R04) for the preset user age matrix R0, wherein R01 is a first preset user age, R02 is a second preset user age, R03 is a third preset user age, R04 is a fourth preset user age, and R01 is less than R02 and less than R03 is less than R04;
selecting a corresponding correction coefficient of the number of recommended tourist attractions according to the relation between i and the preset user age matrix R0 so as to correct the number of each preset recommended tourist attraction, and when the number of the corrected recommended tourist attraction is not an integer, rounding up and taking the number as the number of the recommended tourist attraction;
when i is less than R01, selecting the fourth preset recommended tourist attraction quantity correction coefficient B4 to correct the first preset recommended tourist attraction quantity A1, wherein the corrected recommended tourist attraction quantity is A1 x B4;
when R01 is less than or equal to i and less than R02, selecting the third preset recommended tourist attraction quantity correction coefficient B3 to correct the second preset recommended tourist attraction quantity A2, wherein the corrected recommended tourist attraction quantity is A2 x B3;
when R02 is less than or equal to i and less than R03, selecting the second preset recommended tourist attraction quantity correction coefficient B2 to correct the third preset recommended tourist attraction quantity A3, wherein the corrected recommended tourist attraction quantity is A3 x B2;
When R03 is less than or equal to i and less than R04, the first preset recommended tourist attraction quantity correction coefficient B1 is selected to correct the fourth preset recommended tourist attraction quantity A4, and the corrected recommended tourist attraction quantity is A1 x B4.
In some embodiments of the application, further comprising:
acquiring a month average consumption level value g of the user;
presetting a preset user month average consumption level value matrix W0 and a preset recommended tourist attraction number secondary correction coefficient matrix C, and setting C (C1, C2, C3 and C4) for the preset recommended tourist attraction number secondary correction coefficient matrix C, wherein C1 is a first preset recommended tourist attraction number secondary correction coefficient, C2 is a second preset recommended tourist attraction number secondary correction coefficient, C3 is a third preset recommended tourist attraction number secondary correction coefficient, C4 is a fourth preset recommended tourist attraction number secondary correction coefficient, and C1 is more than 1 and less than C2 and C3 is more than 1.2; setting W0 (W01, W02, W03, W04) for the preset user month average consumption level matrix W0, wherein W01 is a first preset user month average consumption level value, W02 is a second preset user month average consumption level value, W03 is a third preset user month average consumption level value, W04 is a fourth preset user month average consumption level value, and W01 is less than W02 and less than W03 is less than W04;
Selecting a corresponding secondary correction coefficient of the number of recommended tourist attractions according to the relation between g and the preset average consumption level value matrix W0 of the user month so as to secondarily correct the number of each preset tourist attraction after correction, and when the number of the recommended tourist attractions after secondary correction is not an integer, rounding upwards to be used as the number of the recommended tourist attraction;
when g is smaller than W01, selecting a secondary correction coefficient C1 of the first preset recommended tourist attraction number to carry out secondary correction on the corrected first preset recommended tourist attraction number A1, wherein the corrected recommended tourist attraction number is A1 x B4 x C1;
when W01 is less than or equal to g and less than W02, selecting a secondary correction coefficient C2 of the number of the second preset recommended tourist attractions to secondarily correct the corrected number A2 of the second preset recommended tourist attractions, wherein the corrected number of the recommended tourist attractions is A2 x B3 x C2;
when W02 is less than or equal to g and less than W03, selecting a secondary correction coefficient C3 of the number of the third preset recommended tourist attraction to carry out secondary correction on the corrected number A3 of the third preset recommended tourist attraction, wherein the corrected number of the recommended tourist attraction is A3 x B2 x C3;
when W03 is less than or equal to g and less than W04, selecting a secondary correction coefficient C4 of the fourth preset recommended tourist attraction number to carry out secondary correction on the corrected fourth preset recommended tourist attraction number A4, wherein the corrected recommended tourist attraction number is A4 x B1 x C4.
In some embodiments of the application, further comprising:
and acquiring a score increasing value of each tourist attraction in a preset time, and when the tourist attraction is recommended to the user according to the number of the tourist attraction and the score of each tourist attraction and the scores of a plurality of tourist attraction are the same, recommending the tourist attraction with the same score in sequence from high to low according to the score increasing value of each tourist attraction in the preset time.
The application provides an intelligent travel market prediction and recommendation system and method, which have the beneficial effects that compared with the prior art:
according to the application, the recommended number of tourist attractions in the area is determined by combining the personalized information of the user and the travel days of the user, and the recommended number is subjected to targeted effective correction by taking the age and consumption level of the user as correction parameters, so that the most suitable recommended number of the scenic spots is ensured, the method is suitable for different crowds, the problem of redundancy of the traditional recommended number is solved, the recommending accuracy is improved, and the experience of the user is ensured.
Drawings
FIG. 1 is a functional block diagram of a smart travel market prediction and recommendation system in accordance with an embodiment of the present application;
FIG. 2 is a flow chart of a method for predicting and recommending intelligent travel markets in accordance with an embodiment of the present application.
Detailed Description
The following describes in further detail the embodiments of the present application with reference to the drawings and examples. The following examples are illustrative of the application and are not intended to limit the scope of the application.
In the description of the present application, it should be understood that the terms "center," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present application and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present application.
The terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, unless otherwise indicated, the meaning of "a plurality" is two or more.
In the description of the present application, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be the communication between the inner sides of the two elements. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art.
In the prior art, since the situation that users go out and only play a single scenic spot exists, but multi-scenic spot play is carried out according to travel lines, the traditional recommendation result based on big data cannot meet the targeted requirement of each user well, the recommended scenic spots cannot meet the user play requirement frequently, the requirements of users with different age sections are different, the requirements of tourist scenic spots are different, the recommendation mode of the users in the prior art is based on scenic spot scoring to carry out a large number of scenic spot recommendation, however, the influence of personalized travel habits of the users on the play requirement is ignored, and further the users cannot be recommended accurately, so that the problems of user experience feeling and the like are reduced. Therefore, the intelligent travel market prediction and recommendation system provided by the application improves the accuracy of recommendation by recommending the travel scenic spot according to the personalized demand of the user, overcomes the technical problems, better meets the demand of the user and improves the experience of the user.
Referring to FIG. 1, an embodiment of the present disclosure provides an intelligent travel market prediction and recommendation system, comprising:
the acquisition unit is used for acquiring the position information of the user based on GPS positioning and acquiring the travel information of the user, wherein the travel information comprises a plurality of historical travel distances, a plurality of historical travel days and a user age i;
the processing unit is used for calculating the average travel distance S of the user according to the plurality of historical travel distances and calculating the average travel days d of the user according to the plurality of historical travel days; wherein,,
the average travel distance S is obtained by calculating the average value of a plurality of historical travel distances, and the average travel days d is obtained by calculating the average value of a plurality of historical travel days;
the establishing unit is used for establishing a travel area model of the user according to the position information of the user and the average travel distance S; wherein,,
the travel area model is a circular area established by taking the position information of the user as an origin and taking the average travel distance S of the user as a radius;
the calculation unit is used for calculating the number of tourist attractions in the area according to the travel area model;
and the recommending unit is used for calculating the number of tourist attractions recommended to the user according to the average travel days d, acquiring the scores of the tourist attractions, and sequentially recommending according to the number of the tourist attractions recommended to the user and the scores of the tourist attractions from high to low.
The method and the system have the beneficial effects that the accuracy of recommendation is improved by traveling scenic spot recommendation according to the personalized requirements of the user, the requirements of the user are better met, and the experience of the user is improved.
In a specific embodiment of the present application, a preset average travel day matrix T0 and a preset recommended tourist attraction number matrix a are preset in the recommendation unit, and for the preset recommended tourist attraction number matrix a, a (A1, A2, A3, A4) is set, wherein A1 is a first preset recommended tourist attraction number, A2 is a second preset recommended tourist attraction number, A3 is a third preset recommended tourist attraction number, and A4 is a fourth preset recommended tourist attraction number;
setting T0 (T01, T02, T03 and T04) for a preset average travel day matrix T0, wherein T01 is a first preset average travel day, T02 is a second preset average travel day, T03 is a third preset average travel day, T04 is a fourth preset average travel day, and T01 is more than T02 and less than T03 is less than T04;
the recommending unit is used for selecting the corresponding recommended tourist attraction quantity as the tourist attraction quantity recommended to the user according to the relation between d and a preset average travel day matrix T0;
when d is less than T01, selecting a first preset recommended tourist attraction quantity A1 as the quantity of tourist attractions recommended to the user;
When T01 is less than or equal to K and less than T02, selecting a second preset recommended tourist attraction quantity A2 as the quantity of tourist attractions recommended to the user;
when T02 is less than or equal to K and less than T03, selecting a third preset recommended tourist attraction quantity A3 as the quantity of tourist attractions recommended to the user;
when T03 is less than or equal to K and less than T04, selecting a fourth preset recommended tourist attraction quantity A4 as the tourist attraction quantity recommended to the user.
The method has the advantages that the method is suitable for reasonable requirements among different users according to the number of scenic spots in the preset area and the personalized information of the users, and can intuitively and effectively determine the travel range of the users by combining the travel days of the users as parameter standards aiming at different user groups, and can quickly and accurately recommend the scenic spots to the users according to the travel days of the users as the basis for measuring the number of recommended scenic spots, thereby improving the acquisition efficiency and greatly improving the experience of the users.
In a specific embodiment of the present application, a preset user age matrix R0 and a preset tourist attraction number correction coefficient matrix B are further set in the recommendation unit, and for the preset tourist attraction number correction coefficient matrix B, B (B1, B2, B3, B4) is set, wherein B1 is a first preset tourist attraction number correction coefficient, B2 is a second preset tourist attraction number correction coefficient, B3 is a third preset tourist attraction number correction coefficient, B4 is a fourth preset tourist attraction number correction coefficient, and B1 is more than 1 and less than 2 and less than B3 and less than 1.5;
Setting R0 (R01, R02, R03, R04) for a preset user age matrix R0, wherein R01 is a first preset user age, R02 is a second preset user age, R03 is a third preset user age, R04 is a fourth preset user age, and R01 is less than R02 and less than R03 is less than R04;
the recommending unit is also used for selecting corresponding correcting coefficients of the number of the recommended tourist attractions according to the relation between i and the preset user age matrix R0 so as to correct the number of each preset recommended tourist attraction, and when the number of the corrected recommended tourist attraction is not an integer, the number of the recommended tourist attraction is rounded upwards and used as the number of the recommended tourist attraction;
when i is less than R01, a fourth preset recommended tourist attraction quantity correction coefficient B4 is selected to correct the first preset recommended tourist attraction quantity A1, and the corrected recommended tourist attraction quantity is A1 x B4;
when R01 is less than or equal to i and less than R02, selecting a third preset recommended tourist attraction quantity correction coefficient B3 to correct the second preset recommended tourist attraction quantity A2, wherein the corrected recommended tourist attraction quantity is A2 x B3;
when R02 is less than or equal to i and less than R03, selecting a second preset recommended tourist attraction quantity correction coefficient B2 to correct the third preset recommended tourist attraction quantity A3, wherein the corrected recommended tourist attraction quantity is A3;
When R03 is less than or equal to i and less than R04, a first preset recommended tourist attraction quantity correction coefficient B1 is selected to correct the fourth preset recommended tourist attraction quantity A4, and the corrected recommended tourist attraction quantity is A1 and B4.
The method has the beneficial effects that age is also an important basis for influencing the travel of the user, travel ranges related to the users in different age stages are different, so that the number of recommended tourist attractions can be effectively determined, the method is suitable for targeted recommendation of people in different age stages, and the accuracy of the number of recommended scenic spots is further improved.
In a specific embodiment of the present application, the obtaining unit is further configured to obtain a monthly average consumption level value g of the user;
the recommendation unit is also internally provided with a preset user month average consumption level value matrix W0 and a preset recommended tourist attraction number secondary correction coefficient matrix C, and C (C1, C2, C3 and C4) is set for the preset recommended tourist attraction number secondary correction coefficient matrix C, wherein C1 is a first preset recommended tourist attraction number secondary correction coefficient, C2 is a second preset recommended tourist attraction number secondary correction coefficient, C3 is a third preset recommended tourist attraction number secondary correction coefficient, C4 is a fourth preset recommended tourist attraction number secondary correction coefficient, and C1 is more than 1 and less than C2 and C3 is more than 1.2; setting W0 (W01, W02, W03 and W04) in a preset user month average consumption level matrix W0, wherein W01 is a first preset user month average consumption level value, W02 is a second preset user month average consumption level value, W03 is a third preset user month average consumption level value, W04 is a fourth preset user month average consumption level value, and W01 is less than W02 and less than W03 is less than W04;
The recommending unit is also used for selecting a corresponding secondary correction coefficient of the number of recommended tourist attractions according to the relation between g and the average consumption level value matrix W0 of the preset user month so as to carry out secondary correction on the number of each preset tourist attraction after correction, and when the number of the recommended tourist attraction after secondary correction is not an integer, the recommended tourist attraction is rounded upwards and used as the number of the recommended tourist attraction;
when g is less than W01, selecting a second correction coefficient C1 of the number of the first preset recommended tourist attractions to carry out second correction on the corrected number A1 of the first preset recommended tourist attractions, wherein the corrected number of the recommended tourist attractions is A1 x B4 x C1;
when W01 is less than or equal to g and less than W02, selecting a second preset recommended tourist attraction quantity secondary correction coefficient C2 to carry out secondary correction on the corrected second preset recommended tourist attraction quantity A2, wherein the corrected recommended tourist attraction quantity is A2 x B3 x C2;
when W02 is less than or equal to g and less than W03, selecting a second correction coefficient C3 of the number of the third preset recommended tourist attraction to carry out second correction on the corrected number A3 of the third preset recommended tourist attraction, wherein the corrected number of the recommended tourist attraction is A3 x B2 x C3;
when W03 is less than or equal to g and less than W04, selecting a second correction coefficient C4 of the number of the fourth preset recommended tourist attraction to carry out second correction on the corrected number A4 of the fourth preset recommended tourist attraction, wherein the corrected number A4 of the recommended tourist attraction is equal to or less than B1 of C4.
The method has the advantages that the consumption interval of the user can be intuitively determined by combining the average consumption level value of the user month, the travel demands of the user and the experience in the travel process can be better determined according to different consumption levels of the user for the travel scenic spot recommendation on the route, the recommendation quantity of the travel scenic spots can be adaptively improved, the experience of the user is met, and meanwhile, effective revenue generation can be carried out for the travel scenic spots.
In a specific embodiment of the present application, the recommendation unit is further configured to obtain a score increment value of each tourist attraction within a preset time, and when the number of tourist attractions and the scores of each tourist attraction are recommended to the user, and the scores of the plurality of tourist attractions are the same, sequentially recommend each tourist attraction with the same score according to the score increment value of each tourist attraction within the preset time from high to low.
It can be understood that when the score of the tourist attraction is increased within a certain time, the attraction can be effectively indicated to be the hot attraction, and the hot attraction can be predicted to meet the demands of more people, so that when a plurality of attraction with the same score appear, priority recommendation is performed according to the score increase value within a preset time, and the demands of more users can be better met.
Based on the same technical concept, referring to fig. 2, the present invention further provides a method for predicting and recommending an intelligent tourism market, which is applied to an intelligent tourism market predicting and recommending system, and includes:
acquiring position information of a user based on GPS positioning, and acquiring travel information of the user, wherein the travel information comprises a plurality of historical travel distances, a plurality of historical travel days and a user age i;
calculating an average travel distance S of the user according to the plurality of historical travel distances, and calculating an average travel number d of the user according to the plurality of historical travel numbers; wherein,,
the average travel distance S is obtained by calculating the average value of a plurality of historical travel distances, and the average travel days d is obtained by calculating the average value of a plurality of historical travel days;
establishing a travel area model of the user according to the position information of the user and the average travel distance S; wherein,,
the travel area model is a circular area established by taking the position information of the user as an origin and taking the average travel distance S of the user as a radius;
calculating the number of tourist attractions in the area according to the travel area model;
and calculating the number of tourist attractions recommended to the user according to the average travel days d, acquiring the scores of the tourist attractions, and sequentially recommending according to the number of the tourist attractions recommended to the user and the scores of the tourist attractions from high to low.
The tourist attraction recommendation method and device have the advantages that tourist attraction recommendation is performed according to personalized requirements of users, accuracy of recommendation is improved, requirements of the users are met better, and experience of the users is improved.
In a specific embodiment of the present application, a preset average travel day matrix T0 and a preset recommended tourist attraction number matrix a are preset, and for the preset recommended tourist attraction number matrix a, a (A1, A2, A3, A4) is set, wherein A1 is a first preset recommended tourist attraction number, A2 is a second preset recommended tourist attraction number, A3 is a third preset recommended tourist attraction number, and A4 is a fourth preset recommended tourist attraction number;
setting T0 (T01, T02, T03 and T04) for a preset average travel day matrix T0, wherein T01 is a first preset average travel day, T02 is a second preset average travel day, T03 is a third preset average travel day, T04 is a fourth preset average travel day, and T01 is more than T02 and less than T03 is less than T04;
selecting the corresponding recommended tourist attraction quantity as the tourist attraction quantity recommended to the user according to the relation between d and a preset average travel days matrix T0;
when d is less than T01, selecting a first preset recommended tourist attraction quantity A1 as the quantity of tourist attractions recommended to the user;
When T01 is less than or equal to K and less than T02, selecting a second preset recommended tourist attraction quantity A2 as the quantity of tourist attractions recommended to the user;
when T02 is less than or equal to K and less than T03, selecting a third preset recommended tourist attraction quantity A3 as the quantity of tourist attractions recommended to the user;
when T03 is less than or equal to K and less than T04, selecting a fourth preset recommended tourist attraction quantity A4 as the tourist attraction quantity recommended to the user.
The method has the advantages that the method is suitable for reasonable requirements among different users according to the number of scenic spots in the preset area and the personalized information of the users, and can intuitively and effectively determine the travel range of the users by combining the travel days of the users as parameter standards aiming at different user groups, and can quickly and accurately recommend the scenic spots to the users according to the travel days of the users as the basis for measuring the number of recommended scenic spots, thereby improving the acquisition efficiency and greatly improving the experience of the users.
In a specific embodiment of the present application, a preset user age matrix R0 and a preset recommended tourist attraction number correction coefficient matrix B are preset, and for the preset recommended tourist attraction number correction coefficient matrix B, B (B1, B2, B3, B4) is set, wherein B1 is a first preset recommended tourist attraction number correction coefficient, B2 is a second preset recommended tourist attraction number correction coefficient, B3 is a third preset recommended tourist attraction number correction coefficient, B4 is a fourth preset recommended tourist attraction number correction coefficient, and B1 is more than 1 and less than 2 and less than B3 and less than B4 is more than 1.5;
Setting R0 (R01, R02, R03, R04) for a preset user age matrix R0, wherein R01 is a first preset user age, R02 is a second preset user age, R03 is a third preset user age, R04 is a fourth preset user age, and R01 is less than R02 and less than R03 is less than R04;
selecting a corresponding correction coefficient of the number of recommended tourist attractions according to the relation between i and a preset user age matrix R0 to correct the number of each preset recommended tourist attraction, and rounding up and taking the number as the number of the recommended tourist attraction when the number of the corrected recommended tourist attraction is not an integer;
when i is less than R01, a fourth preset recommended tourist attraction quantity correction coefficient B4 is selected to correct the first preset recommended tourist attraction quantity A1, and the corrected recommended tourist attraction quantity is A1 x B4;
when R01 is less than or equal to i and less than R02, selecting a third preset recommended tourist attraction quantity correction coefficient B3 to correct the second preset recommended tourist attraction quantity A2, wherein the corrected recommended tourist attraction quantity is A2 x B3;
when R02 is less than or equal to i and less than R03, selecting a second preset recommended tourist attraction quantity correction coefficient B2 to correct the third preset recommended tourist attraction quantity A3, wherein the corrected recommended tourist attraction quantity is A3;
When R03 is less than or equal to i and less than R04, a first preset recommended tourist attraction quantity correction coefficient B1 is selected to correct the fourth preset recommended tourist attraction quantity A4, and the corrected recommended tourist attraction quantity is A1 and B4.
The method has the beneficial effects that age is also an important basis for influencing the travel of the user, travel ranges related to the users in different age stages are different, so that the number of recommended tourist attractions can be effectively determined, the method is suitable for targeted recommendation of people in different age stages, and the accuracy of the number of recommended scenic spots is further improved.
In a specific embodiment of the present application, further comprising:
acquiring a month average consumption level value g of a user;
presetting a preset user month average consumption level value matrix W0 and a preset recommended tourist attraction number secondary correction coefficient matrix C, and setting C (C1, C2, C3 and C4) for the preset recommended tourist attraction number secondary correction coefficient matrix C, wherein C1 is a first preset recommended tourist attraction number secondary correction coefficient, C2 is a second preset recommended tourist attraction number secondary correction coefficient, C3 is a third preset recommended tourist attraction number secondary correction coefficient, C4 is a fourth preset recommended tourist attraction number secondary correction coefficient, and C1 is more than 1 and less than C2 and C3 is more than 1.2; setting W0 (W01, W02, W03 and W04) for a preset user month average consumption level matrix W0, wherein W01 is a first preset user month average consumption level value, W02 is a second preset user month average consumption level value, W03 is a third preset user month average consumption level value, W04 is a fourth preset user month average consumption level value, and W01 is less than W02 and less than W03 is less than W04;
Selecting a corresponding secondary correction coefficient of the number of recommended tourist attractions according to the relation between g and the average consumption level value matrix W0 of the preset user month so as to secondarily correct the number of each preset tourist attraction after correction, and when the number of the recommended tourist attractions after secondary correction is not an integer, rounding upwards to be used as the number of the recommended tourist attraction;
when g is less than W01, selecting a second correction coefficient C1 of the number of the first preset recommended tourist attractions to carry out second correction on the corrected number A1 of the first preset recommended tourist attractions, wherein the corrected number of the recommended tourist attractions is A1 x B4 x C1;
when W01 is less than or equal to g and less than W02, selecting a second preset recommended tourist attraction quantity secondary correction coefficient C2 to carry out secondary correction on the corrected second preset recommended tourist attraction quantity A2, wherein the corrected recommended tourist attraction quantity is A2 x B3 x C2;
when W02 is less than or equal to g and less than W03, selecting a second correction coefficient C3 of the number of the third preset recommended tourist attraction to carry out second correction on the corrected number A3 of the third preset recommended tourist attraction, wherein the corrected number of the recommended tourist attraction is A3 x B2 x C3;
when W03 is less than or equal to g and less than W04, selecting a second correction coefficient C4 of the number of the fourth preset recommended tourist attraction to carry out second correction on the corrected number A4 of the fourth preset recommended tourist attraction, wherein the corrected number A4 of the recommended tourist attraction is equal to or less than B1 of C4.
The method has the advantages that the consumption interval of the user can be intuitively determined by combining the average consumption level value of the user month, the travel demands of the user and the experience in the travel process can be better determined according to different consumption levels of the user for the travel scenic spot recommendation on the route, the recommendation quantity of the travel scenic spots can be adaptively improved, the experience of the user is met, and meanwhile, effective revenue generation can be carried out for the travel scenic spots.
In a specific embodiment of the present application, further comprising:
and acquiring the score increment value of each tourist attraction in the preset time, and recommending the tourist attractions with the same scores according to the number of the tourist attractions and the scores of the tourist attractions to the user when the scores of the tourist attractions are the same, and recommending the tourist attractions with the same scores in sequence according to the sequence from high to low according to the score increment value of each tourist attraction in the preset time.
It can be understood that when the score of the tourist attraction is increased within a certain time, the attraction can be effectively indicated to be the hot attraction, and the hot attraction can be predicted to meet the demands of more people, so that when a plurality of attraction with the same score appear, priority recommendation is performed according to the score increase value within a preset time, and the demands of more users can be better met.
In summary, the recommendation quantity of tourist attractions in the area is determined by combining the personalized information of the user and the travel days of the user, and the recommendation quantity is effectively corrected in a targeted manner by taking the age and consumption level of the user as correction parameters, so that the most suitable recommendation quantity of the scenic spots is ensured, the method is suitable for different crowds, the problem of redundancy of the traditional recommendation quantity is solved, the recommendation accuracy is improved, and the experience of the user is ensured. The invention has the advantages of intelligence, accuracy, high efficiency and the like.
The foregoing is merely an example of the present invention and is not intended to limit the scope of the present invention, and all changes made in the structure according to the present invention should be considered as falling within the scope of the present invention without departing from the gist of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, the specific working process of the system described above and the related description may refer to the corresponding process in the foregoing method embodiment, which is not repeated here.
It should be noted that, in the system provided in the foregoing embodiment, only the division of the foregoing functional modules is illustrated, in practical application, the foregoing functional allocation may be performed by different functional modules, that is, the modules or steps in the embodiment of the present invention are further decomposed or combined, for example, the modules in the foregoing embodiment may be combined into one module, or may be further split into multiple sub-modules, so as to complete all or part of the functions described above. The names of the modules and steps related to the embodiments of the present invention are merely for distinguishing the respective modules or steps, and are not to be construed as unduly limiting the present invention.
Those of skill in the art will appreciate that the various illustrative modules, method steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the program(s) corresponding to the software modules, method steps, may be embodied in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium known in the art. To clearly illustrate this interchangeability of electronic hardware and software, various illustrative components and steps have been described above generally in terms of their functionality. Whether such functionality is implemented as electronic hardware or software depends upon the particular application and design constraints imposed on the solution. Those skilled in the art may implement the described functionality using different approaches for each particular application, but such implementation is not intended to be limiting.
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/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/apparatus.
Thus far, the technical solution of the present invention has been described in connection with the preferred embodiments shown in the drawings, but it is easily understood by those skilled in the art that the scope of protection of the present invention is not limited to these specific embodiments. Equivalent modifications and substitutions for related technical features may be made by those skilled in the art without departing from the principles of the present invention, and such modifications and substitutions will fall within the scope of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the present invention.

Claims (10)

1. An intelligent travel market prediction and recommendation system, comprising:
the system comprises an acquisition unit, a storage unit and a storage unit, wherein the acquisition unit is used for acquiring position information of a user based on GPS positioning and acquiring travel information of the user, and the travel information comprises a plurality of historical travel distances, a plurality of historical travel days and a user age i;
the processing unit is used for calculating the average travel distance S of the user according to a plurality of the historical travel distances and calculating the average travel number d of the user according to a plurality of the historical travel numbers; wherein,,
the average travel distance S is obtained by calculating the average value of a plurality of historical travel distances, and the average travel number d is obtained by calculating the average value of a plurality of historical travel numbers;
The establishing unit is used for establishing a travel area model of the user according to the position information of the user and the average travel distance S; wherein,,
the travel area model is a circular area established by taking the position information of the user as an origin and taking the average travel distance S of the user as a radius;
the calculating unit is used for calculating the number of tourist attractions in the area according to the travel area model;
and the recommending unit is used for calculating the number of the tourist attractions recommended to the user according to the average travel days d, acquiring the scores of the tourist attractions, and sequentially recommending according to the number of the tourist attractions recommended to the user and the scores of the tourist attractions from high to low.
2. The intelligent travel market prediction and recommendation system according to claim 1, wherein,
a preset average travel day matrix T0 and a preset recommended tourist attraction number matrix A are preset in the recommending unit, A (A1, A2, A3 and A4) are set for the preset recommended tourist attraction number matrix A, wherein A1 is the first preset recommended tourist attraction number, A2 is the second preset recommended tourist attraction number, A3 is the third preset recommended tourist attraction number and A4 is the fourth preset recommended tourist attraction number;
Setting T0 (T01, T02, T03 and T04) for the preset average travel day matrix T0, wherein T01 is a first preset average travel day, T02 is a second preset average travel day, T03 is a third preset average travel day, T04 is a fourth preset average travel day, and T01 is less than T02 and T03 is less than T04;
the recommending unit is used for selecting the corresponding recommended tourist attraction quantity as the tourist attraction quantity recommended to the user according to the relation between d and the preset average travel days matrix T0;
when d < T01, selecting the first preset recommended tourist attraction number A1 as the number of tourist attractions recommended to the user;
when T01 is less than or equal to K and less than T02, selecting the second preset recommended tourist attraction quantity A2 as the quantity of the tourist attraction recommended to the user;
when T02 is less than or equal to K and less than T03, selecting the third preset recommended tourist attraction quantity A3 as the quantity of the tourist attraction recommended to the user;
and when T03 is less than or equal to K and less than T04, selecting the fourth preset recommended tourist attraction quantity A4 as the quantity of the tourist attraction recommended to the user.
3. The intelligent travel market prediction and recommendation system according to claim 2, wherein,
The recommendation unit is also internally provided with a preset user age matrix R0 and a preset recommended tourist attraction quantity correction coefficient matrix B, B (B1, B2, B3 and B4) are set for the preset recommended tourist attraction quantity correction coefficient matrix B, wherein B1 is a first preset recommended tourist attraction quantity correction coefficient, B2 is a second preset recommended tourist attraction quantity correction coefficient, B3 is a third preset recommended tourist attraction quantity correction coefficient, B4 is a fourth preset recommended tourist attraction quantity correction coefficient, and B1 is more than 1 and less than B2 and B3 is more than 3 and less than 1.5;
setting R0 (R01, R02, R03, R04) for the preset user age matrix R0, wherein R01 is a first preset user age, R02 is a second preset user age, R03 is a third preset user age, R04 is a fourth preset user age, and R01 is less than R02 and less than R03 is less than R04;
the recommending unit is also used for selecting corresponding correcting coefficients of the number of the recommended tourist attractions according to the relation between i and the preset user age matrix R0 so as to correct the number of each preset recommended tourist attraction, and when the number of the corrected recommended tourist attraction is not an integer, the recommended tourist attraction is rounded upwards and used as the number of the recommended tourist attraction;
when i is less than R01, selecting the fourth preset recommended tourist attraction quantity correction coefficient B4 to correct the first preset recommended tourist attraction quantity A1, wherein the corrected recommended tourist attraction quantity is A1 x B4;
When R01 is less than or equal to i and less than R02, selecting the third preset recommended tourist attraction quantity correction coefficient B3 to correct the second preset recommended tourist attraction quantity A2, wherein the corrected recommended tourist attraction quantity is A2 x B3;
when R02 is less than or equal to i and less than R03, selecting the second preset recommended tourist attraction quantity correction coefficient B2 to correct the third preset recommended tourist attraction quantity A3, wherein the corrected recommended tourist attraction quantity is A3 x B2;
when R03 is less than or equal to i and less than R04, the first preset recommended tourist attraction quantity correction coefficient B1 is selected to correct the fourth preset recommended tourist attraction quantity A4, and the corrected recommended tourist attraction quantity is A1 x B4.
4. The intelligent travel market prediction and recommendation system according to claim 3, wherein,
the acquisition unit is also used for acquiring a month average consumption level value g of the user;
the recommendation unit is also internally provided with a preset user month average consumption level value matrix W0 and a preset recommended tourist attraction number secondary correction coefficient matrix C, and C (C1, C2, C3 and C4) is set for the preset recommended tourist attraction number secondary correction coefficient matrix C, wherein C1 is a first preset recommended tourist attraction number secondary correction coefficient, C2 is a second preset recommended tourist attraction number secondary correction coefficient, C3 is a third preset recommended tourist attraction number secondary correction coefficient, C4 is a fourth preset recommended tourist attraction number secondary correction coefficient, and C1 is more than 1 and less than C3 and less than 1.2; setting W0 (W01, W02, W03, W04) in the preset user month average consumption level matrix W0, wherein W01 is a first preset user month average consumption level value, W02 is a second preset user month average consumption level value, W03 is a third preset user month average consumption level value, W04 is a fourth preset user month average consumption level value, and W01 is less than W02 and less than W03 is less than W04;
The recommending unit is also used for selecting a corresponding secondary correction coefficient of the number of recommended tourist attractions according to the relation between g and the average consumption level value matrix W0 of the preset user month so as to carry out secondary correction on the number of each preset tourist attraction after correction, and when the number of the recommended tourist attraction after secondary correction is not an integer, the recommended tourist attraction is rounded upwards and used as the number of the recommended tourist attraction;
when g is smaller than W01, selecting a secondary correction coefficient C1 of the first preset recommended tourist attraction number to carry out secondary correction on the corrected first preset recommended tourist attraction number A1, wherein the corrected recommended tourist attraction number is A1 x B4 x C1;
when W01 is less than or equal to g and less than W02, selecting a secondary correction coefficient C2 of the number of the second preset recommended tourist attractions to secondarily correct the corrected number A2 of the second preset recommended tourist attractions, wherein the corrected number of the recommended tourist attractions is A2 x B3 x C2;
when W02 is less than or equal to g and less than W03, selecting a secondary correction coefficient C3 of the number of the third preset recommended tourist attraction to carry out secondary correction on the corrected number A3 of the third preset recommended tourist attraction, wherein the corrected number of the recommended tourist attraction is A3 x B2 x C3;
when W03 is less than or equal to g and less than W04, selecting a secondary correction coefficient C4 of the fourth preset recommended tourist attraction number to carry out secondary correction on the corrected fourth preset recommended tourist attraction number A4, wherein the corrected recommended tourist attraction number is A4 x B1 x C4.
5. The intelligent travel market prediction and recommendation system according to claim 4, wherein,
the recommendation unit is further configured to obtain a score increment value of each tourist attraction within a preset time, and when the user is recommended according to the number of the tourist attractions and the scores of the tourist attractions, and the scores of the tourist attractions are the same, sequentially recommend each tourist attraction with the same score according to the score increment value of each tourist attraction within the preset time from high to low.
6. A method for predicting and recommending intelligent travel market, applied to the intelligent travel market predicting and recommending system according to any one of claims 1 to 5, comprising:
acquiring position information of a user based on GPS positioning, and acquiring travel information of the user, wherein the travel information comprises a plurality of historical travel distances, a plurality of historical travel days and a user age i;
calculating an average travel distance S of the user according to the plurality of historical travel distances, and calculating an average travel number d of the user according to the plurality of historical travel numbers; wherein,,
the average travel distance S is obtained by calculating the average value of a plurality of historical travel distances, and the average travel number d is obtained by calculating the average value of a plurality of historical travel numbers;
Establishing a travel area model of the user according to the position information of the user and the average travel distance S; wherein,,
the travel area model is a circular area established by taking the position information of the user as an origin and taking the average travel distance S of the user as a radius;
calculating the number of tourist attractions in the area according to the travel area model;
calculating the number of tourist attractions recommended to the user according to the average travel days d, acquiring the scores of the tourist attractions, and recommending in sequence according to the number of the tourist attractions recommended to the user and the scores of the tourist attractions from high to low.
7. The intelligent travel market prediction and recommendation method according to claim 6, wherein,
presetting a preset average travel day matrix T0 and a preset recommended tourist attraction number matrix A, and setting A (A1, A2, A3 and A4) for the preset recommended tourist attraction number matrix A, wherein A1 is the first preset recommended tourist attraction number, A2 is the second preset recommended tourist attraction number, A3 is the third preset recommended tourist attraction number and A4 is the fourth preset recommended tourist attraction number;
Setting T0 (T01, T02, T03 and T04) for the preset average travel day matrix T0, wherein T01 is a first preset average travel day, T02 is a second preset average travel day, T03 is a third preset average travel day, T04 is a fourth preset average travel day, and T01 is less than T02 and T03 is less than T04;
selecting the corresponding recommended tourist attraction quantity as the tourist attraction quantity recommended to the user according to the relation between d and the preset average travel days matrix T0;
when d < T01, selecting the first preset recommended tourist attraction number A1 as the number of tourist attractions recommended to the user;
when T01 is less than or equal to K and less than T02, selecting the second preset recommended tourist attraction quantity A2 as the quantity of the tourist attraction recommended to the user;
when T02 is less than or equal to K and less than T03, selecting the third preset recommended tourist attraction quantity A3 as the quantity of the tourist attraction recommended to the user;
and when T03 is less than or equal to K and less than T04, selecting the fourth preset recommended tourist attraction quantity A4 as the quantity of the tourist attraction recommended to the user.
8. The intelligent travel market prediction and recommendation method according to claim 7, wherein,
Presetting a preset user age matrix R0 and a preset recommended tourist attraction quantity correction coefficient matrix B, and setting B (B1, B2, B3 and B4) for the preset recommended tourist attraction quantity correction coefficient matrix B, wherein B1 is a first preset recommended tourist attraction quantity correction coefficient, B2 is a second preset recommended tourist attraction quantity correction coefficient, B3 is a third preset recommended tourist attraction quantity correction coefficient, B4 is a fourth preset recommended tourist attraction quantity correction coefficient, and B1 is more than 1 and less than 2 and B3 and less than 1.5;
setting R0 (R01, R02, R03, R04) for the preset user age matrix R0, wherein R01 is a first preset user age, R02 is a second preset user age, R03 is a third preset user age, R04 is a fourth preset user age, and R01 is less than R02 and less than R03 is less than R04;
selecting a corresponding correction coefficient of the number of recommended tourist attractions according to the relation between i and the preset user age matrix R0 so as to correct the number of each preset recommended tourist attraction, and when the number of the corrected recommended tourist attraction is not an integer, rounding up and taking the number as the number of the recommended tourist attraction;
when i is less than R01, selecting the fourth preset recommended tourist attraction quantity correction coefficient B4 to correct the first preset recommended tourist attraction quantity A1, wherein the corrected recommended tourist attraction quantity is A1 x B4;
When R01 is less than or equal to i and less than R02, selecting the third preset recommended tourist attraction quantity correction coefficient B3 to correct the second preset recommended tourist attraction quantity A2, wherein the corrected recommended tourist attraction quantity is A2 x B3;
when R02 is less than or equal to i and less than R03, selecting the second preset recommended tourist attraction quantity correction coefficient B2 to correct the third preset recommended tourist attraction quantity A3, wherein the corrected recommended tourist attraction quantity is A3 x B2;
when R03 is less than or equal to i and less than R04, the first preset recommended tourist attraction quantity correction coefficient B1 is selected to correct the fourth preset recommended tourist attraction quantity A4, and the corrected recommended tourist attraction quantity is A1 x B4.
9. The intelligent travel market prediction and recommendation method according to claim 8, further comprising:
acquiring a month average consumption level value g of the user;
presetting a preset user month average consumption level value matrix W0 and a preset recommended tourist attraction number secondary correction coefficient matrix C, and setting C (C1, C2, C3 and C4) for the preset recommended tourist attraction number secondary correction coefficient matrix C, wherein C1 is a first preset recommended tourist attraction number secondary correction coefficient, C2 is a second preset recommended tourist attraction number secondary correction coefficient, C3 is a third preset recommended tourist attraction number secondary correction coefficient, C4 is a fourth preset recommended tourist attraction number secondary correction coefficient, and C1 is more than 1 and less than C2 and C3 is more than 1.2; setting W0 (W01, W02, W03, W04) for the preset user month average consumption level matrix W0, wherein W01 is a first preset user month average consumption level value, W02 is a second preset user month average consumption level value, W03 is a third preset user month average consumption level value, W04 is a fourth preset user month average consumption level value, and W01 is less than W02 and less than W03 is less than W04;
Selecting a corresponding secondary correction coefficient of the number of recommended tourist attractions according to the relation between g and the preset average consumption level value matrix W0 of the user month so as to secondarily correct the number of each preset tourist attraction after correction, and when the number of the recommended tourist attractions after secondary correction is not an integer, rounding upwards to be used as the number of the recommended tourist attraction;
when g is smaller than W01, selecting a secondary correction coefficient C1 of the first preset recommended tourist attraction number to carry out secondary correction on the corrected first preset recommended tourist attraction number A1, wherein the corrected recommended tourist attraction number is A1 x B4 x C1;
when W01 is less than or equal to g and less than W02, selecting a secondary correction coefficient C2 of the number of the second preset recommended tourist attractions to secondarily correct the corrected number A2 of the second preset recommended tourist attractions, wherein the corrected number of the recommended tourist attractions is A2 x B3 x C2;
when W02 is less than or equal to g and less than W03, selecting a secondary correction coefficient C3 of the number of the third preset recommended tourist attraction to carry out secondary correction on the corrected number A3 of the third preset recommended tourist attraction, wherein the corrected number of the recommended tourist attraction is A3 x B2 x C3;
when W03 is less than or equal to g and less than W04, selecting a secondary correction coefficient C4 of the fourth preset recommended tourist attraction number to carry out secondary correction on the corrected fourth preset recommended tourist attraction number A4, wherein the corrected recommended tourist attraction number is A4 x B1 x C4.
10. The intelligent travel market prediction and recommendation method as claimed in claim 9, further comprising:
and acquiring a score increasing value of each tourist attraction in a preset time, and when the tourist attraction is recommended to the user according to the number of the tourist attraction and the score of each tourist attraction and the scores of a plurality of tourist attraction are the same, recommending the tourist attraction with the same score in sequence from high to low according to the score increasing value of each tourist attraction in the preset time.
CN202311218926.3A 2023-09-21 2023-09-21 Intelligent travel market prediction and recommendation system and method Pending CN116955834A (en)

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