CN110750737A - Scenic spot recommendation method and device and storage medium - Google Patents

Scenic spot recommendation method and device and storage medium Download PDF

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CN110750737A
CN110750737A CN201910927256.XA CN201910927256A CN110750737A CN 110750737 A CN110750737 A CN 110750737A CN 201910927256 A CN201910927256 A CN 201910927256A CN 110750737 A CN110750737 A CN 110750737A
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马云峰
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Evergrande Intelligent Technology Co Ltd
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Abstract

The invention discloses a scenic spot recommendation method, which comprises the following steps: obtaining historical playing information of the tourists; clustering and calculating the recommended items of the tourists according to the historical playing information of the tourists; acquiring the starting position of the tourist and the positions of the recommended items; calculating the shortest distance between any two recommended items; and calculating the shortest path lengths of all the recommended items, performing clustering sequencing, and taking the shortest path lengths as recommended walking routes. Through the steps, the shortest items which accord with the tourists are dynamically searched, in order to further improve the matching degree of the search results and the tourists, the items which are not interested by the user and have been passed by the history can be eliminated, and the item facilities which accord with the search conditions are clustered. In addition, the invention also discloses a scenic spot recommendation device and a storage medium.

Description

Scenic spot recommendation method and device and storage medium
Technical Field
The invention relates to the technical field of computers, in particular to a scenic spot recommendation method, a scenic spot recommendation device and a storage medium.
Background
The comprehensive amusement park is always one of popular choices for leisure, entertainment and vacation of people, and no matter which city the large-scale comprehensive amusement park is in, people always feel full of the situation when the day of holiday comes.
At present, when a tourist plays projects in scenic spots, the tourist can manually select a forward or reverse visiting sequence according to a scenic spot distribution map of a garden or can play the designated scenic spot projects according to network visiting and attacking guidance, the jam of part of scenic spots is caused for both the garden and individuals, and the resource benefit maximization is not achieved. Often, for guests, a play facility is queued for a majority of an hour or even an hour. Therefore, the method brings inconvenience to people in playing, often consumes a lot of time in queuing, and cannot play the items which the people want to play.
Disclosure of Invention
The invention aims to provide a scenic spot recommendation method, a scenic spot recommendation device and a storage medium.
In order to achieve the above object, the present invention provides a scenic spot recommendation method, including:
obtaining historical playing information of the tourists;
clustering and calculating the recommended items of the tourists according to the historical playing information of the tourists;
acquiring the starting position of the tourist and the positions of the recommended items;
calculating the shortest distance between any two recommended items;
and calculating the shortest path lengths of all the recommended items, performing clustering sequencing, and taking the shortest path lengths as recommended walking routes.
Further, the calculation formula of the shortest path length of all the recommended items is as follows:
Figure BDA0002219252360000011
s [ i, j ] thereof]The Distances is the shortest distance between any two recommended items, and the Distances is the shortest path length of all the recommended items.
Further, the clustering and calculating the recommended items of the tourists according to the historical playing information of the tourists comprises:
and searching the tourists 'visit times of each item in the tourists' historical play information, and taking the scenic spots with the visit times smaller than a preset threshold value as recommended items.
Further, the calculation formula for calculating the recommended items of the tourists according to the historical playing information clusters of the tourists is as follows: s ═ CPH∩CPU and P are all open items of the current scenic spot park, H is an item which is historically visited by the user after the user enters the park, and U is an item which is not wanted by the user.
Further, the calculating the shortest distance between any two of the recommended items includes: and calculating the shortest distance between any two recommended items by using a Floyed algorithm.
Further, the calculating the shortest distance between any two of the recommended items includes: and calculating the shortest distance between any two recommended items by adopting a Dijkstra algorithm.
Further, the clustering and calculating the recommended items of the tourists according to the historical playing information of the tourists comprises:
and searching the tourist items of the tourists in each scenic spot according to the historical tourist playing information of the tourists, counting the types of the items with the highest tourist item ratio, acquiring the characteristics of the types of the items with the highest ratio, and matching the scenic spots with the best characteristic matching degree in the scenic spots as recommended items.
Further, the clustering and calculating the recommended items of the tourists according to the historical playing information of the tourists comprises:
and acquiring the age and the gender of the tourist, and matching the suitable playing item according to the age and the gender to serve as the recommended item.
In another aspect, the present invention also provides a computer device, which includes a processor and a memory, the processor being coupled to the memory, and the processor executing instructions to implement the method described above when in operation.
In another aspect, the present invention also provides a computer-readable storage medium, on which a computer program is stored, wherein the computer program is executed by a processor to implement the above-mentioned method.
According to the invention, through the steps, the shortest item which accords with the tourists is dynamically searched, and in order to further improve the matching degree of the search result and the tourists, the items which are not interested by the user and have been passed by the history can be eliminated, and the item facilities which accord with the search conditions are clustered, so that the search result of the item facilities can be intelligently fed back according to the search conditions of the user.
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FIG. 1 is a flowchart illustrating a first embodiment of a scenic spot recommendation method according to the present invention;
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive work based on the embodiments of the present invention, are within the scope of the present invention.
In order to make the objects, technical solutions and advantageous technical effects of the present invention more clearly and completely apparent, the technical solutions in the embodiments of the present invention will be 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.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
Referring to fig. 1, fig. 1 is a flowchart illustrating a scenic spot recommendation method according to a first embodiment of the present invention. As shown in fig. 1, the scenic spot recommendation method of the present embodiment at least includes the following steps:
s1, obtaining historical playing information of the tourists;
the method comprises the steps that a tourist sends a path recommendation request to a client through an APP, and the client obtains historical tourist playing information of the tourist according to personal information of a user, wherein the historical tourist playing information comprises the age and the sex of the tourist, scenic spots visited and items experienced.
S2, calculating the recommendation items of the tourists according to the historical playing information clusters of the tourists;
the clustering calculation of the tourist recommended items according to the historical playing information of the tourists comprises the following steps:
and searching the tourists 'visit times of each item in the tourists' historical play information, and taking the scenic spots with the visit times smaller than a preset threshold value as recommended items.
The calculation formula for calculating the recommended items of the tourists according to the historical playing information of the tourists in a clustering mode is as follows: s ═ CPH∩CPU and P are all open items of the current scenic spot park, H is an item which is historically visited by the user after the user enters the park, and U is an item which is not wanted by the user.
S3, acquiring the starting position of the tourist and the position of each recommended item;
s4, calculating the shortest distance between any two recommended items;
further, the calculating the shortest distance between any two of the recommended items includes: and calculating the shortest distance between any two recommended items by using a Floyed algorithm.
s[i,j]：＝min{s[i,k]+s[k,j],s[i,j]}
The core is that a replacement equation of a Floyed algorithm is adopted, a node is inserted between a starting point i and an end point j for observation, if s [ i, k ] + s [ k, j ] ≦ s [ i, j ], the intermediate node is the shortest path, and the node is recorded in a journey.
Further, the calculating the shortest distance between any two of the recommended items includes: and calculating the shortest distance between any two recommended items by adopting a Dijkstra algorithm.
And S5, calculating the shortest path lengths of all the recommended items, performing clustering sequencing, and taking the shortest path lengths as recommended walking routes.
Further, all of said recommended itemsThe calculation formula of the shortest path length is as follows:
Figure BDA0002219252360000041
s [ i, j ] thereof]The Distances is the shortest distance between any two recommended items, and the Distances is the shortest path length of all the recommended items.
According to the invention, through the steps, the shortest item which accords with the tourists is dynamically searched, and in order to further improve the matching degree of the search result and the tourists, the items which are not interested by the user and have been passed by the history can be eliminated, and the item facilities which accord with the search conditions are clustered, so that the search result of the item facilities can be intelligently fed back according to the search conditions of the user.
Example 2
In this embodiment, a method for recommending scenic spots includes:
s1, obtaining historical playing information of the tourists;
s2, calculating the recommendation items of the tourists according to the historical playing information clusters of the tourists;
further, the clustering and calculating the recommended items of the tourists according to the historical playing information of the tourists comprises:
and searching the tourist items of the tourists in each scenic spot according to the historical tourist playing information of the tourists, counting the types of the items with the highest tourist item ratio, acquiring the characteristics of the types of the items with the highest ratio, and matching the scenic spots with the best characteristic matching degree in the scenic spots as recommended items.
Further, the clustering and calculating the recommended items of the tourists according to the historical playing information of the tourists comprises:
and acquiring the age and the gender of the tourist, and matching the suitable playing item according to the age and the gender to serve as the recommended item.
S3, acquiring the starting position of the tourist and the position of each recommended item;
s4, calculating the shortest distance between any two recommended items;
further, the calculating the shortest distance between any two of the recommended items includes: and calculating the shortest distance between any two recommended items by using a Floyed algorithm.
Further, the calculating the shortest distance between any two of the recommended items includes: and calculating the shortest distance between any two recommended items by adopting a Dijkstra algorithm.
And S5, calculating the shortest path lengths of all the recommended items, performing clustering sequencing, and taking the shortest path lengths as recommended walking routes.
Further, the calculation formula of the shortest path length of all the recommended items is as follows:s [ i, j ] thereof]The Distances is the shortest distance between any two recommended items, and the Distances is the shortest path length of all the recommended items.
The computer program is executed by a processor to implement the above-described method.
The invention relates to a scenic spot recommendation device corresponding to the first embodiment. The scenic spot recommendation device comprises a controller and a processor which are connected with each other. Wherein a Memory is disposed within the controller, wherein the Memory is configured to store a computer program, and the computer program includes program instructions, and the Memory may include a Random Access Memory (RAM) or may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The processor is configured to call the program instruction, and execute the sight recommendation method described in step S1-step S4.
The storage medium may be an internal storage device of the controller. The storage medium may also be an external storage device, such as a Smart Media Card (SMC) equipped on the wireless switch, a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the storage medium may also include both an internal storage unit and an external storage device of the wireless switch. The storage medium is used for storing the computer program and other programs and data required by the terminal. The storage medium may also be used to temporarily store data that has been output or is to be output. The computer program includes program instructions that, when executed by a processor, cause the processor to execute the sight recommendation method of steps S1-S3.
The scenic spot recommendation device according to the first embodiment is described above. The scenic spot recommendation device comprises a controller and a processor which are connected with each other. Wherein a memory is disposed within the controller, wherein the memory is configured to store a computer program, and the computer program includes program instructions, and the memory may include a high-speed Random Access Memory (RAM) and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory. The processor is configured to call the program instruction, and execute the sight recommendation method described in step S1-step S4.
The storage medium may be an internal storage device of the controller. The storage medium may also be an external storage device, such as a Smart Media Card (SMC) equipped on the wireless switch, a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the storage medium may also include both an internal storage unit and an external storage device of the wireless switch. The storage medium is used for storing the computer program and other programs and data required by the terminal. The storage medium may also be used to temporarily store data that has been output or is to be output. The computer program includes program instructions that, when executed by a processor, cause the processor to execute the sight recommendation method of steps S1-S4.
The invention relates to a scenic spot recommendation device corresponding to the second embodiment. The scenic spot recommendation device comprises a controller and a processor which are connected with each other. Wherein a Memory is disposed within the controller, wherein the Memory is configured to store a computer program, and the computer program includes program instructions, and the Memory may include a Random Access Memory (RAM) or may further include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The processor is configured to call the program instruction, and execute the sight recommendation method described in step S1-step S5.
The storage medium may be an internal storage device of the controller. The storage medium may also be an external storage device, such as a Smart Media Card (SMC) equipped on the wireless switch, a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the storage medium may also include both an internal storage unit and an external storage device of the wireless switch. The storage medium is used for storing the computer program and other programs and data required by the terminal. The storage medium may also be used to temporarily store data that has been output or is to be output. The computer program includes program instructions that, when executed by a processor, cause the processor to execute the sight recommendation method of steps S1-S3.
The scenic spot recommendation device according to the second embodiment is described above. The scenic spot recommendation device comprises a controller and a processor which are connected with each other. Wherein a memory is disposed within the controller, wherein the memory is configured to store a computer program, and the computer program includes program instructions, and the memory may include a high-speed Random Access Memory (RAM) and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory. The processor is configured to call the program instruction, and execute the sight recommendation method described in step S1-step S5.
The storage medium may be an internal storage device of the controller. The storage medium may also be an external storage device, such as a Smart Media Card (SMC) equipped on the wireless switch, a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like. Further, the storage medium may also include both an internal storage unit and an external storage device of the wireless switch. The storage medium is used for storing the computer program and other programs and data required by the terminal. The storage medium may also be used to temporarily store data that has been output or is to be output. The computer program includes program instructions that, when executed by a processor, cause the processor to execute the sight recommendation method of steps S1-S4.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
When implemented in software and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention essentially or partially contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and various equivalent modifications and substitutions can be easily made by those skilled in the art within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. An attraction recommendation method, the method comprising:
obtaining historical playing information of the tourists;
clustering and calculating the recommended items of the tourists according to the historical playing information of the tourists;
acquiring the starting position of the tourist and the positions of the recommended items;
calculating the shortest distance between any two recommended items;
and calculating the shortest path lengths of all the recommended items, performing clustering sequencing, and taking the shortest path lengths as recommended walking routes.
2. The method of claim 1, wherein the shortest path length of all the recommended items is calculated by the following formula:
Figure FDA0002219252350000011
s [ i, j ] thereof]The Distances is the shortest distance between any two recommended items, and the Distances is the shortest path length of all the recommended items.
3. The method of claim 1, wherein the calculating of the guest recommendation items from the clustering of guest historical play information comprises:
and searching the tourists 'visit times of each item in the tourists' historical play information, and taking the scenic spots with the visit times smaller than a preset threshold value as recommended items.
4. The method of claim 3, wherein the calculation formula for calculating the recommended items of the tourists according to the clustering of the historical playing information of the tourists is as follows: s ═ CPH∩CPU and P are all open items of the current scenic spot park, H is an item which is historically visited by the user after the user enters the park, and U is an item which is not wanted by the user.
5. The method of claim 1, wherein calculating the shortest distance between any two of the recommended items comprises: and calculating the shortest distance between any two recommended items by using a Floyed algorithm.
6. The method of claim 1, wherein calculating the shortest distance between any two of the recommended items comprises: and calculating the shortest distance between any two recommended items by adopting a Dijkstra algorithm.
7. The method of claim 1, wherein the calculating of the guest recommendation items from the clustering of guest historical play information comprises:
and searching the tourist items of the tourists in each scenic spot according to the historical tourist playing information of the tourists, counting the types of the items with the highest tourist item ratio, acquiring the characteristics of the types of the items with the highest ratio, and matching the scenic spots with the best characteristic matching degree in the scenic spots as recommended items.
8. The method of claim 1, wherein the calculating of the guest recommendation items from the clustering of guest historical play information comprises:
and acquiring the age and the gender of the tourist, and matching the suitable playing item according to the age and the gender to serve as the recommended item.
9. A computer device comprising a processor and a memory, the processor being coupled to the memory and the processor executing instructions when in operation to implement the method of any one of claims 1 to 8.
10. A computer-readable storage medium, on which a computer program is stored, the computer program being executable by a processor for implementing the method according to any one of claims 1 to 8.
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CN112148978A (en) * 2020-09-24 2020-12-29 苏州七采蜂数据应用有限公司 Internet-based amusement park project recommendation method and system
CN113221028A (en) * 2021-05-17 2021-08-06 杭州快盈信息科技有限公司 Experiential route searching system based on optimal strategy
CN113642745A (en) * 2021-08-11 2021-11-12 余国立 Garden data acquisition method and system
CN117575117A (en) * 2023-10-23 2024-02-20 哈尔滨工业大学(深圳)(哈尔滨工业大学深圳科技创新研究院) Urban mature type recreation area slow space connection network recommendation method, system and medium

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Publication number Priority date Publication date Assignee Title
CN112148978A (en) * 2020-09-24 2020-12-29 苏州七采蜂数据应用有限公司 Internet-based amusement park project recommendation method and system
CN113221028A (en) * 2021-05-17 2021-08-06 杭州快盈信息科技有限公司 Experiential route searching system based on optimal strategy
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CN117575117A (en) * 2023-10-23 2024-02-20 哈尔滨工业大学(深圳)(哈尔滨工业大学深圳科技创新研究院) Urban mature type recreation area slow space connection network recommendation method, system and medium

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