CN116720929A - Package recommendation method, device, equipment and storage medium - Google Patents

Package recommendation method, device, equipment and storage medium Download PDF

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CN116720929A
CN116720929A CN202311002066.XA CN202311002066A CN116720929A CN 116720929 A CN116720929 A CN 116720929A CN 202311002066 A CN202311002066 A CN 202311002066A CN 116720929 A CN116720929 A CN 116720929A
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travel
user
data
package
alternative
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黄琪钶
朱轩平
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Beijing Daye Smart Data Technology Service Co ltd
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Beijing Daye Smart Data Technology Service Co ltd
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    • G06Q30/00—Commerce
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    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
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Abstract

The application discloses a package recommending method, device, equipment and storage medium, and belongs to the technical field of computers. The method comprises the following steps: acquiring travel data of a designated user and travel product price data of a designated scenic spot; clustering treatment is carried out on the tourist data of the user so as to obtain the tourist group type of the user; determining at least one alternative travel package based on the guest group type of the user; determining a preference factor for each alternative travel package by the user based on the at least one alternative travel package and the travel consumption data; obtaining price data for each alternative travel package based on the travel product price data and the at least one alternative travel package; at least one alternative travel package, price data and preference coefficients are input into a preset genetic algorithm model to obtain at least one target travel package, so as to output at least one target travel package. The scheme disclosed by the application realizes the effectiveness of optimizing the travel package recommendation in the scenic spot.

Description

Package recommendation method, device, equipment and storage medium
Technical Field
The application relates to the technical field of computers, in particular to the technical field of big data, and particularly relates to a method, a device, equipment and a storage medium for recommending packages.
Background
In general, a scenic spot travel package service may include: scenic spot tour, accommodation, dining, travel, shopping, communication, value added services, and the like.
At present, the existing recommended schemes of tourist packages in scenic spots are mainly related to scenic spot tickets, catering, accommodation and the like, and other tourist demands of tourists, such as shopping, entertainment and the like, are rarely considered, so that deep personalized analysis on the tourist demands of the tourists is lacking. The tourist may not be able to obtain comprehensive travel information and may not find the most suitable scenic spot travel scheme.
Disclosure of Invention
The application provides a package recommending method, a device, equipment and a storage medium, which can solve the problem of poor recommending effect of scenic spot travel packages, and the technical scheme is as follows:
in a first aspect, a method of package recommendation is provided, the method comprising:
acquiring travel data of a designated user and travel product price data of a designated scenic spot; wherein, the travel data of the user comprises basic attribute data of the user, travel preference data of the user and consumption grade of the user;
clustering the tourist data of the user to obtain the tourist group type of the user;
Determining at least one alternative travel package based on the guest group type of the user;
determining a preference factor for each of the alternative travel packages by the user based on at least one of the alternative travel packages and the travel consumption data;
obtaining price data for each of the alternative travel packages based on the travel product price data and at least one of the alternative travel packages;
inputting the at least one alternative travel package, the price data and the preference coefficient into a preset genetic algorithm model to obtain at least one target travel package so as to output the at least one target travel package.
In one possible implementation manner, the acquiring travel data of the specified user includes:
acquiring mobile phone signaling data of the appointed user and mobile hotspot WiFi connection data of the appointed scenic spot;
and acquiring the travel data of the appointed user based on the mobile phone signaling data and the WiFi connection data.
In one possible implementation manner, the clustering processing is performed on the tourist data of the user to obtain the tourist group type of the user, including:
extracting travel preference characteristics of the user based on the travel preference data of the user;
And clustering the basic attribute data of the user, the travel preference characteristics of the user and the consumption level of the user by using a K-Means clustering algorithm to obtain the tourist group type of the user.
In one possible implementation manner, the extracting the travel preference feature of the user based on the travel preference data of the user includes:
extracting the travel preference data of the user based on the preset travel product type to obtain the travel preference characteristics of the user, wherein,
the number of the preset travel product types is a plurality, and the travel preference characteristics of the user comprise user preferences corresponding to each preset travel product type.
In one possible implementation, the determining the preference coefficient of the user for each of the alternative travel packages based on at least one of the alternative travel packages and the travel consumption data includes:
obtaining the selection rate of the user for each alternative travel package based on the travel consumption data;
and determining preference coefficients of the user for each alternative travel package based on the selection rate.
In one possible implementation, the obtaining price data for each of the alternative travel packages based on the travel product price data and at least one of the alternative travel packages includes:
Obtaining price data for each of the alternative travel packages based on the travel product price data and at least one of the alternative travel packages;
according to a preset priority ordering strategy, ordering the price data of each alternative travel package;
and based on the result of the sorting processing, obtaining price data of each sorted alternative travel package.
In one possible implementation, the inputting the at least one alternative travel package, the price data, and the preference coefficient into a preset genetic algorithm model, to obtain at least one target travel package, to output the at least one target travel package, includes:
determining a price score and a preference score for each of the alternative travel packages based on price data and preference coefficients for each of the alternative travel packages;
inputting the price score and the preference score of each alternative travel package into a preset genetic algorithm model to obtain at least one target travel package and the price score and the preference score of each target travel package;
sorting the target travel packages based on the price score and the preference score of each target travel package;
Outputting the at least one target travel package after the sorting processing.
In a second aspect, there is provided a package recommendation apparatus, the apparatus comprising:
the acquisition unit is used for acquiring the travel data of the appointed user and the travel product price data of the appointed scenic spot; wherein, the travel data of the user comprises basic attribute data of the user, travel preference data of the user and consumption grade of the user;
the clustering unit is used for carrying out clustering processing on the tourist data of the user so as to obtain the tourist group type of the user;
a determining unit configured to determine at least one alternative travel package based on a guest group type of the user;
the determining unit is further configured to determine a preference coefficient of the user for each alternative travel package based on at least one of the alternative travel packages and the travel consumption data;
an obtaining unit configured to obtain price data for each of the alternative travel packages based on the travel product price data and at least one of the alternative travel packages;
and the output unit is used for inputting the at least one alternative travel package, the price data and the preference coefficient into a preset genetic algorithm model to obtain at least one target travel package so as to output the at least one target travel package.
In a third aspect, there is provided a computer readable storage medium having stored therein at least one instruction that is loaded and executed by a processor to implement the method of the aspects and any one possible implementation as described above.
In a fourth aspect, there is provided an electronic device comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the aspects and methods of any one of the possible implementations described above.
The technical scheme provided by the application has the beneficial effects that at least:
according to the technical scheme, the embodiment of the application can obtain the tourist data of the appointed user and the price data of the tourist product of the appointed scenic spot, so that the tourist data of the user can be clustered to obtain the tourist group class of the user, at least one alternative tourist package is determined based on the tourist group type of the user, the preference coefficient of the user for each alternative tourist package is determined based on at least one alternative tourist package and the tourist consumption data, and the price data of each alternative tourist package is obtained based on the price data of the tourist product and at least one alternative tourist package, so that the price data of each alternative tourist package can be input into a preset genetic algorithm model to obtain at least one target tourist package, and the at least one target tourist package is output to the user.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the application or to delineate the scope of the application. Other features of the present application will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow diagram of a method for package recommendation provided in one embodiment of the present application;
fig. 2 is a block diagram of a package recommendation apparatus according to still another embodiment of the present application.
FIG. 3 is a block diagram of an electronic device for implementing a method of package recommendation in accordance with an embodiment of the present application.
Detailed Description
Exemplary embodiments of the present application will now be described with reference to the accompanying drawings, in which various details of the embodiments of the present application are included to facilitate understanding, and are to be considered merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
It will be apparent that the described embodiments are some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that, the terminal device in the embodiment of the present application may include, but is not limited to, smart devices such as a mobile phone, a personal digital assistant (Personal Digital Assistant, PDA), a wireless handheld device, and a Tablet Computer (Tablet Computer); the display device may include, but is not limited to, a personal computer, a television, or the like having a display function.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
Referring to fig. 1, a flow chart of a method for recommending packages according to an embodiment of the application is shown. The package recommending method specifically comprises the following steps:
Step 101, acquiring travel data of a specified user and travel product price data of a specified scenic spot, wherein the travel data of the user comprises basic attribute data of the user, travel preference data of the user and consumption level of the user.
Step 102, clustering the travel data of the user to obtain the tourist group type of the user.
Step 103, determining at least one alternative travel package based on the guest group type of the user.
Step 104, determining preference coefficients of the user for each alternative travel package based on at least one of the alternative travel packages and the travel consumption data.
Step 105, obtaining price data of each alternative travel package based on the travel product price data and at least one alternative travel package.
Step 106, inputting the at least one alternative travel package, the price data and the preference coefficient into a preset genetic algorithm model to obtain at least one target travel package, so as to output the at least one target travel package.
It should be noted that, part or all of the execution body in steps 101 to 106 may be an application located in the local terminal, or may be a functional unit such as a plug-in unit or a software development kit (Software Development Kit, SDK) disposed in the application located in the local terminal, or may be a processing engine located in a server on the network side, or may be a distributed system located on the network side, for example, a processing engine or a distributed system in an offline data analysis platform on the network side, which is not limited in this embodiment.
It will be appreciated that the application may be a native program (native app) installed on the native terminal, or may also be a web page program (webApp) of a browser on the native terminal, which is not limited in this embodiment.
In this way, the tourist data of the appointed user and the tourist product price data of the appointed scenic spot can be obtained, and then the tourist data of the user can be clustered to obtain the tourist group class of the user, at least one alternative tourist package is determined based on the tourist group type of the user, and based on at least one alternative tourist package and the tourist consumption data, the preference coefficient of the user for each alternative tourist package is determined, and based on the tourist product price data and at least one alternative tourist package, the price data of each alternative tourist package is obtained, so that the at least one alternative tourist package, the price data and the preference coefficient can be input into a preset genetic algorithm model to obtain at least one target tourist package, and the at least one target tourist package is output to the user.
Optionally, in one possible implementation manner of this embodiment, in step 101, specifically, mobile phone signaling data of the specified user and WiFi connection data of the specified scenic spot may be obtained, and further, travel data of the specified user may be obtained based on the mobile phone signaling data and the WiFi connection data.
In this implementation, the designated user may include a guest who designates a attraction. The designated attraction may refer to an attraction for which a travel package is to be recommended.
In particular, the mobile phone signaling data based on the specified user may include, but is not limited to, stay time in the specified scenic spot, accessed web page data, commonly used application data, and the like.
In this implementation, the WiFi connection data for a given attraction may include, but is not limited to, identity data of guests connected to a WiFi hotspot deployed inside the attraction.
It will be appreciated that a given attraction may provide a wide variety of attractions of travel service types. The travel service types may include at least one of a dining service, an accommodation service, a attraction play type, a shopping type, an entertainment type, and a travel vehicle type.
Optionally, in one possible implementation manner of this embodiment, the user's travel data may include user basic attribute data, user's travel preference data and user's consumption level, and in step 102, specifically, the user's travel preference feature may be extracted based on the user's travel preference data, and the user's basic attribute data, the user's travel preference feature and the user's consumption level may be clustered by using a K-Means clustering algorithm to obtain the user's tourist group type.
In this implementation, the basic attribute data of the user may include, but is not limited to, data of the name, age, sex, occupation, etc. of the user.
In this implementation, the consumer level of the user may be determined from historical consumer data of the user. The consumer ratings may include high, medium, and low ratings. For example, a user with a monthly consumption level greater than 1 kilo yuan is a high-level user, a user with a monthly consumption level between 3 kilo yuan and 1 kilo yuan is a medium-level user, and a user with a monthly consumption level below 3 kilo yuan is a low-level user.
In a specific implementation process of the implementation manner, the travel preference data of the user may be specifically extracted based on preset travel product types to obtain travel preference characteristics of the user, where the number of preset travel product types is a plurality of, and the travel preference characteristics of the user include user preferences corresponding to each preset travel product type.
In this implementation, the preset travel product type may be determined based on the travel service type. The preset travel product types may include at least one of accommodation type, travel tool type, attraction type, restaurant type, entertainment type, and shopping type.
In this implementation, the user's travel preference data may include preference data based on different travel product types. For example, accommodation type preferences may include child hotels, young hotels, scenic spots hotels, and the like; scenic spot type preferences may include historic cultural classes, natural scenery classes, experience classes, etc.; restaurant type preferences may include fast food, chinese meal, western meal, etc.; travel tool type preferences may include buses, airplanes, boarding, etc.; entertainment type preferences such as fishing, farmhouse, cafes, etc.; shopping type preferences such as specialty goods, household items, etc.
In this implementation, guest population types may include, but are not limited to, parent-child populations, luxury populations, centipede populations, economical populations, senior populations, and the like.
Specifically, tourist users of parent-child groups often focus on children's projects, pursuing parent-child interaction experiences. Tourist users of luxury groups often pursue high-end comfort, with importance attached to hotel, attraction and dining quality. The cost performance is usually paid attention to the tourist users of the economic community, and more tourist services are expected to be obtained through package and other modes. Cost factors are often valued by travel users of economic communities and travel budgets are limited. Elderly travel users are often more concerned about practical travel needs such as health in dining, comfort and safety in traffic, and priority.
In another specific implementation process of the implementation manner, a preset clustering algorithm can be utilized to analyze and process the sample user travel data, and a plurality of tourist group types are obtained based on the analysis and processing result.
It should be noted that, the specific implementation procedure provided in the present implementation manner may be combined with the various specific implementation procedures provided in the foregoing implementation manner to implement the package recommendation method of the present embodiment. The detailed description may refer to the relevant content in the foregoing implementation, and will not be repeated here.
Optionally, in one possible implementation manner of this embodiment, in step 103, specifically, based on the guest group type of the user, at least one alternative travel package corresponding to the user may be determined according to a correspondence between the guest group type and the travel package.
In a specific implementation process of the implementation manner, firstly, a corresponding relation between a predetermined tourist group type and a tourist package can be obtained, and at least one alternative tourist package corresponding to the user is determined according to the corresponding relation between the tourist group type and the tourist package based on the tourist group type of the user.
Illustratively, the guest group type of the user is parent-child group, and the alternative travel package corresponding to the user may include package 1: restaurant A1 with low price and high priority, hotel B1 with home housing and price between 200 and 400, travel selection bus C1, amusement facility D1 with children as scenic spot, toy commodity E1 in shopping; package 2: restaurant A2 with low price and high priority, hotel B2 with home housing and price between 200-400, etc., subway C2 for trip selection, child-parent item D2 for the scenic spot of children, commodity E2 for literary creation in shopping aspect; package 3: restaurant A3 with low price and high priority, hotel B3 with home housing and price between 200-400, travel selection walking C3, scenic spot of playing as parent-child item D3 of children, and shopping as commodity E3 of literature.
It will be appreciated that the corresponding relationship between the predetermined tourist group type and the tourist package may be implemented in a specific manner by using the user tourist data sample, the predetermined tourist product type and the predetermined tourist group type, which may not be limited herein.
It should be noted that, the specific implementation procedure provided in the present implementation manner may be combined with the various specific implementation procedures provided in the foregoing implementation manner to implement the package recommendation method of the present embodiment. The detailed description may refer to the relevant content in the foregoing implementation, and will not be repeated here.
Optionally, in one possible implementation manner of this embodiment, in step 104, a selection rate of each of the alternative travel packages by the user may be obtained specifically based on the travel consumption data, and further, a preference coefficient of the user for each of the alternative travel packages may be determined based on the selection rate.
In this implementation, the pick rate of the alternative travel packages may be determined based on historical consumption data of the user.
Illustratively, based on historical consumption data of a user, a probability that the user selects a restaurant with a low price and a large volume, i.e., a pick rate, is calculated. If the selection rate is 0.8, it may be determined that the user's preference factor for low priced and large restaurants may be 0.8.
It should be noted that, the specific implementation procedure provided in the present implementation manner may be combined with the various specific implementation procedures provided in the foregoing implementation manner to implement the package recommendation method of the present embodiment. The detailed description may refer to the relevant content in the foregoing implementation, and will not be repeated here.
Optionally, in one possible implementation manner of this embodiment, in step 105, price data of each alternative travel package may be obtained specifically based on the travel product price data and at least one alternative travel package, and the price data of each alternative travel package is ranked according to a preset priority ranking policy, and based on a result of the ranking processing, the price data of each alternative travel package after ranking is obtained.
In this implementation, the preset prioritization policy may be determined based on the number of offers in the alternative travel packages, with the number of offers in the packages having a higher priority level. The preset prioritization policy may also be a low to high ranking policy based on the total price of each alternative travel package.
It should be noted that, the specific implementation procedure provided in the present implementation manner may be combined with the various specific implementation procedures provided in the foregoing implementation manner to implement the package recommendation method of the present embodiment. The detailed description may refer to the relevant content in the foregoing implementation, and will not be repeated here.
Optionally, in one possible implementation manner of this embodiment, in step 106, the price score and the preference score of each alternative travel package may be specifically determined based on the price data and the preference coefficient of each alternative travel package, and then the price score and the preference score of each alternative travel package and each alternative travel package may be input into a preset genetic algorithm model to obtain at least one target travel package and the price score and the preference score of each target travel package, so that the target travel package may be ranked based on the price score and the preference score of each target travel package, so as to output at least one target travel package after the ranking process.
So far, after outputting the sorted multiple target travel packages to the user, the user can select based on the output multiple target travel packages, and the sorting of the target travel packages and/or the target travel packages can be adjusted in response to the selection of the user.
For example, first, if a user belongs to a parent-child group and is an economic group, focusing on parent-child interaction experience, a package corresponding to the user may include package 1: restaurant A1 with low price and high priority, hotel B1 with home housing and price between 200 and 400, travel selection bus C1, amusement facility D1 with children as scenic spot, toy commodity E1 in shopping; package 2: restaurant A2 with low price and high priority, hotel B2 with home housing and price between 200-400, etc., subway C2 for trip selection, child-parent item D2 for the scenic spot of children, commodity E2 for literary creation in shopping aspect; package 3: restaurant A3 with low price and high priority, hotel B3 with home housing and price between 200-400, travel selection walking C3, scenic spot of playing as parent-child item D3 of children, and shopping as commodity E3 of literature. And secondly, optimizing the price score and the preference score of each alternative travel package by using a preset genetic algorithm model to obtain at least one target travel package, and the price score and the preference score of each target travel package. For example, the target travel package obtained after the optimization process may include package 1: price score 7 and preference score 6; package 2: price score 8 and preference score 8. And thirdly, sorting the target travel packages based on the price scores and the preference scores of each target travel package, and outputting at least one sorted target travel package for selection by a user. For example, the target travel packages may be ranked from large to small according to the sum of the price score and the preference score for each of the target travel packages, resulting in a ranked result: package 2, package 1, output the ordered target travel package for selection by the user. Finally, the target travel packages, the ordering of the target travel packages, and the preference coefficients of the target travel packages may be adjusted based on the user's selection operations.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present application is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present application. Further, those skilled in the art will also appreciate that the embodiments described in the specification are all preferred embodiments, and that the acts and modules referred to are not necessarily required for the present application.
In the foregoing embodiments, the descriptions of the embodiments are emphasized, and for parts of one embodiment that are not described in detail, reference may be made to related descriptions of other embodiments.
Fig. 2 is a block diagram of an apparatus for recommending packages according to an embodiment of the present application, as shown in fig. 2. The package recommendation apparatus 200 of the present embodiment may include an acquisition unit 201, a clustering unit 202, a determination unit 203, an acquisition unit 204, and an output unit 205. Wherein, the obtaining unit 201 is configured to obtain travel data of a specified user and travel product price data of a specified scenic spot; wherein, the travel data of the user comprises basic attribute data of the user, travel preference data of the user and consumption grade of the user; a clustering unit 202, configured to perform clustering processing on the tourist data of the user, so as to obtain a tourist group type of the user; a determining unit 203, configured to determine at least one alternative travel package based on a guest group type of the user; the determining unit 203 is further configured to determine a preference coefficient of the user for each of the alternative travel packages based on at least one of the alternative travel packages and the travel consumption data; an obtaining unit 204 configured to obtain price data for each of the alternative travel packages based on the travel product price data and at least one of the alternative travel packages; and an output unit 205, configured to input the at least one alternative travel package, the price data, and the preference coefficient into a preset genetic algorithm model, and obtain at least one target travel package, so as to output the at least one target travel package.
It should be noted that, part or all of the package recommendation apparatus in this embodiment may be an application located at a local terminal, or may also be a functional unit such as a plug-in unit or a software development kit (Software Development Kit, SDK) disposed in the application located at the local terminal, or may also be a processing engine located in a server on a network side, or may also be a distributed system located on the network side, for example, a processing engine or a distributed system in an offline data analysis platform on the network side, which is not limited in this embodiment.
It will be appreciated that the application may be a native program (native app) installed on the native terminal, or may also be a web page program (webApp) of a browser on the native terminal, which is not limited in this embodiment.
Optionally, in one possible implementation manner of this embodiment, the obtaining unit 201 may be specifically configured to obtain mobile phone signaling data of the specified user and mobile hotspot WiFi connection data of the specified scenic spot; and acquiring the travel data of the appointed user based on the mobile phone signaling data and the WiFi connection data.
Alternatively, in one possible implementation manner of the present embodiment, the clustering unit 202 may be configured to extract the travel preference feature of the user based on the travel preference data of the user; and clustering the basic attribute data of the user, the travel preference characteristics of the user and the consumption level of the user by using a K-Means clustering algorithm to obtain the tourist group type of the user.
Optionally, in one possible implementation manner of this embodiment, the clustering unit 202 may be further configured to extract, based on a preset type of tourist product, the tourist preference data of the user to obtain a tourist preference feature of the user, where the number of preset types of tourist products is a plurality of, and the tourist preference feature of the user includes a user preference corresponding to each preset type of tourist product.
Optionally, in a possible implementation manner of this embodiment, the determining unit 203 may be configured to obtain, based on the travel consumption data, a selection rate of each of the alternative travel packages by the user; and determining preference coefficients of the user for each alternative travel package based on the selection rate.
Alternatively, in one possible implementation of the present embodiment, the obtaining unit 204 may be configured to obtain price data of each of the alternative travel packages based on the travel product price data and at least one of the alternative travel packages; according to a preset priority ordering strategy, ordering the price data of each alternative travel package; and based on the result of the sorting processing, obtaining price data of each sorted alternative travel package.
Alternatively, in one possible implementation of the present embodiment, the output unit 205 may be configured to determine a price score and a preference score for each of the alternative travel packages based on the price data and the preference coefficient for each of the alternative travel packages; inputting the price score and the preference score of each alternative travel package into a preset genetic algorithm model to obtain at least one target travel package and the price score and the preference score of each target travel package; sorting the target travel packages based on the price score and the preference score of each target travel package; outputting the at least one target travel package after the sorting processing.
In this embodiment, the obtaining unit may obtain the travel data of the specified user and the price data of the travel product of the specified scenic spot, and further the clustering unit may perform clustering processing on the travel data of the user to obtain the tourist group type of the user, the determining unit may determine at least one candidate travel package based on the tourist group type of the user, determine a preference coefficient of the user for each candidate travel package based on at least one candidate travel package and the travel consumption data, and the obtaining unit may obtain the price data of each candidate travel package based on the price data of the travel product and at least one candidate travel package, so that the output unit may input the at least one candidate travel package, the price data and the preference coefficient into a preset genetic algorithm model to obtain at least one target travel package, so as to output the at least one target travel package.
In the technical scheme of the application, related personal information of the user, such as collection, storage, use, processing, transmission, provision, disclosure and other processes of images, attribute data and the like of the user, accords with the regulations of related laws and regulations and does not violate the popular regulations.
According to embodiments of the present application, the present application also provides an electronic device, a readable storage medium and a computer program product.
FIG. 3 illustrates a schematic block diagram of an example electronic device 300 that may be used to implement an embodiment of the application. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the applications described and/or claimed herein.
As shown in fig. 3, the electronic device 300 includes a computing unit 301 that can perform various suitable actions and processes according to a computer program stored in a Read Only Memory (ROM) 302 or a computer program loaded from a storage unit 308 into a Random Access Memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 may also be stored. The computing unit 301, the ROM 302, and the RAM 303 are connected to each other by a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
Various components in the electronic device 300 are connected to the I/O interface 305, including: an input unit 306 such as a keyboard, a mouse, etc.; an output unit 307 such as various types of displays, speakers, and the like; a storage unit 308 such as a magnetic disk, an optical disk, or the like; and a communication unit 309 such as a network card, modem, wireless communication transceiver, etc. The communication unit 309 allows the electronic device 300 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The computing unit 301 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of computing unit 301 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the respective methods and processes described above, such as a package recommendation method. For example, in some embodiments, the package recommendation method may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 300 via the ROM 302 and/or the communication unit 309. When the computer program is loaded into RAM 303 and executed by computing unit 301, one or more steps of the method of package recommendation described above may be performed. Alternatively, in other embodiments, the computing unit 301 may be configured to perform the method of package recommendation in any other suitable way (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present application may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present disclosure may be performed in parallel, sequentially, or in a different order, so long as the desired result of the technical solution of the present disclosure is achieved, and the present disclosure is not limited herein.
The above embodiments do not limit the scope of the present application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of the present application.

Claims (10)

1. A method of package recommendation, the method comprising:
acquiring travel data of a designated user and travel product price data of a designated scenic spot; wherein, the travel data of the user comprises basic attribute data of the user, travel preference data of the user and consumption grade of the user;
clustering the tourist data of the user to obtain the tourist group type of the user;
Determining at least one alternative travel package based on the guest group type of the user;
determining a preference factor for each of the alternative travel packages by the user based on at least one of the alternative travel packages and travel consumption data;
obtaining price data for each of the alternative travel packages based on the travel product price data and at least one of the alternative travel packages;
inputting the at least one alternative travel package, the price data and the preference coefficient into a preset genetic algorithm model to obtain at least one target travel package so as to output the at least one target travel package.
2. The method of claim 1, wherein the obtaining travel data for the specified user comprises:
acquiring mobile phone signaling data of the appointed user and mobile hotspot WiFi connection data of the appointed scenic spot;
and acquiring the travel data of the appointed user based on the mobile phone signaling data and the WiFi connection data.
3. The method according to claim 1 or 2, wherein the clustering the travel data of the user to obtain the guest group type of the user includes:
Extracting travel preference characteristics of the user based on the travel preference data of the user;
and clustering the basic attribute data of the user, the travel preference characteristics of the user and the consumption level of the user by using a K-Means clustering algorithm to obtain the tourist group type of the user.
4. The method of claim 3, wherein extracting travel preference characteristics of the user based on the travel preference data of the user comprises:
extracting the travel preference data of the user based on the preset travel product type to obtain the travel preference characteristics of the user, wherein,
the number of the preset travel product types is a plurality, and the travel preference characteristics of the user comprise user preferences corresponding to each preset travel product type.
5. The method of claim 1 wherein said determining a preference factor for each of said alternative travel packages by said user based on at least one of said alternative travel packages and said travel consumption data comprises:
obtaining the selection rate of the user for each alternative travel package based on the travel consumption data;
And determining preference coefficients of the user for each alternative travel package based on the selection rate.
6. The method of claim 1 wherein said obtaining price data for each of said alternative travel packages based on said travel product price data and at least one of said alternative travel packages comprises:
obtaining price data for each of the alternative travel packages based on the travel product price data and at least one of the alternative travel packages;
according to a preset priority ordering strategy, ordering the price data of each alternative travel package;
and based on the result of the sorting processing, obtaining price data of each sorted alternative travel package.
7. The method of claim 1, wherein said inputting the at least one alternative travel package, the price data, and the preference coefficients into a predetermined genetic algorithm model results in at least one target travel package to output the at least one target travel package, comprising:
determining a price score and a preference score for each of the alternative travel packages based on price data and preference coefficients for each of the alternative travel packages;
Inputting the price score and the preference score of each alternative travel package into a preset genetic algorithm model to obtain at least one target travel package and the price score and the preference score of each target travel package;
sorting the target travel packages based on the price score and the preference score of each target travel package;
outputting the at least one target travel package after the sorting processing.
8. An apparatus for package recommendation, the apparatus comprising:
the acquisition unit is used for acquiring the travel data of the appointed user and the travel product price data of the appointed scenic spot; wherein, the travel data of the user comprises basic attribute data of the user, travel preference data of the user and consumption grade of the user;
the clustering unit is used for carrying out clustering processing on the tourist data of the user so as to obtain the tourist group type of the user;
a determining unit configured to determine at least one alternative travel package based on a guest group type of the user;
the determining unit is further configured to determine a preference coefficient of the user for each alternative travel package based on at least one of the alternative travel packages and travel consumption data;
An obtaining unit configured to obtain price data for each of the alternative travel packages based on the travel product price data and at least one of the alternative travel packages;
and the output unit is used for inputting the at least one alternative travel package, the price data and the preference coefficient into a preset genetic algorithm model to obtain at least one target travel package so as to output the at least one target travel package.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-7.
10. A non-transitory computer readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1-7.
CN202311002066.XA 2023-08-10 2023-08-10 Package recommendation method, device, equipment and storage medium Pending CN116720929A (en)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103020308A (en) * 2013-01-07 2013-04-03 北京趣拿软件科技有限公司 Method and device for recommending travel strategy project
CN106886911A (en) * 2015-12-15 2017-06-23 亿阳信通股份有限公司 A kind of travelling products method and device for planning based on user's telecommunications behavioural characteristic
CN113901329A (en) * 2021-12-07 2022-01-07 环球数科集团有限公司 Travel accommodation recommendation method and device and computer equipment
US20230169735A1 (en) * 2021-12-01 2023-06-01 Industry Academy Cooperation Foundation Of Sejong Universtiy Method and system of recommending accommodation for tourists using multi-criteria decision making and augmented reality
CN116561415A (en) * 2023-04-19 2023-08-08 南京睿弗鑫文化传媒有限公司 Travel recommendation system and method based on big data

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103020308A (en) * 2013-01-07 2013-04-03 北京趣拿软件科技有限公司 Method and device for recommending travel strategy project
CN106886911A (en) * 2015-12-15 2017-06-23 亿阳信通股份有限公司 A kind of travelling products method and device for planning based on user's telecommunications behavioural characteristic
US20230169735A1 (en) * 2021-12-01 2023-06-01 Industry Academy Cooperation Foundation Of Sejong Universtiy Method and system of recommending accommodation for tourists using multi-criteria decision making and augmented reality
CN113901329A (en) * 2021-12-07 2022-01-07 环球数科集团有限公司 Travel accommodation recommendation method and device and computer equipment
CN116561415A (en) * 2023-04-19 2023-08-08 南京睿弗鑫文化传媒有限公司 Travel recommendation system and method based on big data

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