Content Recommendation Method, System, and Device Based on Cabin Passenger Behavior Data

Abstract

The present invention discloses a content recommendation method, system and device based on cabin passenger behavior data, the method comprising: obtaining basic passenger data and passenger behavior data; obtaining a recommendation list of passenger preferences for different cabin types in real time based on the basic passenger data and passenger behavior data; and displaying the obtained recommendation list of passenger preferences for different cabin types on public display screens of different cabin types according to priority. The present invention is applicable to an onboard cabin system, and draws a passenger portrait based on the passenger behavior data on a portal website, combined with the inherent information of the airline about the user, and then recommends the corresponding product information to the cabin public display screen, thereby improving the accuracy and reliability of matching.

Description

Technical Field

[0001] The present invention belongs to the technical field of civil aircraft cabin systems, and particularly relates to a content recommendation method, system and device based on cabin passenger behavior data. Background Art

[0002] Traveling by air has become an option for the general public, and the number of civil aviation passengers has been increasing significantly year by year, and the competition among airlines has gradually intensified. In order to improve the seat occupancy rate and passenger satisfaction of flights, airlines provide entertainment and information services during the flight by investing in in-flight entertainment of the cabin system. Airlines also hope that the in-flight entertainment system can bring corresponding value-added benefits to them.

[0003] The content of the in-flight entertainment system consists of information such as videos, news, music, games, in-flight shopping malls, advertisements, maps, etc. At present, it is only used to provide content to passengers unidirectionally by airlines. Both airlines and passengers can only passively become content providers and users, resulting in poor accuracy and reliability of recommendations.

[0004] Moreover, the in-flight entertainment system pushes content for a certain type of users (multiple users), so the existing technology for recommending products for a single user is not applicable to the in-flight entertainment system of aircraft cabins either. Summary of the Invention

[0005] In order to solve the problem of poor accuracy and reliability in the unidirectional recommendation of content in the current entertainment system, the present invention provides a content recommendation method based on cabin passenger behavior data.

[0006] The present invention is achieved through the following technical solutions:

[0007] A content recommendation method based on cabin passenger behavior data, comprising:

[0008] Obtaining passenger basic data and passenger behavior data;

[0009] According to the passenger basic data and the passenger behavior data, obtaining a passenger preference recommendation list for different cabin types in the cabin in real time;

[0010] Displaying the obtained passenger preference recommendation list for different cabin types on the public display screens of different cabin types according to the priority.

[0011] Preferably, after the step of obtaining the passenger basic data and the passenger behavior data, the present invention further includes:

[0012] Generating passenger basic portrait data according to the passenger basic data;

[0013] Storing the passenger behavior data in a unified storage format as passenger behavior historical data;

[0014] Obtain in-flight recommended content according to the operating airline.

[0015] Preferably, the steps of obtaining the preference recommendation list for passengers in different cabin types in real time in the present invention specifically include:

[0016] Draw a single passenger hobby portrait based on the basic portrait data and behavior history data of each passenger;

[0017] Match the single passenger hobby portrait with the in-flight recommended content to obtain a single passenger preference recommendation list;

[0018] Cluster and analyze the preference recommendation lists of all passengers according to the cabin type to obtain the preference recommendation lists of passengers within different cabin types.

[0019] Preferably, the clustering and analyzing of the preference recommendation lists of all passengers according to the cabin type in the present invention specifically is:

[0020] Classify according to first class, business class and economy class, and conduct classified statistics on the preference recommendations of passengers within different cabin types.

[0021] Preferably, the basic passenger data of the present invention is obtained from the ground through air-ground communication;

[0022] The passenger behavior data is obtained through the front-end web page java script.

[0023] Preferably, the basic passenger data of the present invention includes name, age, gender, occupation, flight mileage, frequent destinations, and flight class information of the flights taken.

[0024] Preferably, the passenger behavior data of the present invention includes logging in to the in-flight portal entertainment web page, on-demand movie content, music on-demand, viewing in-flight shopping information, and the time spent staying on the shopping items.

[0025] In a second aspect, the present invention proposes a content recommendation system based on in-cabin passenger behavior data, including a data collection layer, a data storage layer, a data calculation layer, and a content display layer;

[0026] The data collection layer is used to obtain basic passenger data and passenger behavior data;

[0027] The data storage layer is used to store basic passenger portraits, passenger behavior history data, and recommended content data; wherein, the basic passenger portrait is generated according to the basic passenger data; the passenger behavior history data is the passenger behavior data stored in a unified storage format;

[0028] The data calculation layer obtains the preference recommendation lists of passengers in different cabin types according to the data stored in the data storage layer;

[0029] The content display layer displays the passenger preference recommendation lists of different cabin types obtained by the data calculation layer on the public displays of different cabin types according to the priorities.

[0030] In a third aspect, the present invention provides a computer device, including a memory and a processor. The memory stores a computer program, and when the processor executes the computer program, the steps of the method of the present invention are implemented.

[0031] In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored. It is characterized in that when the computer program is executed by a processor, the steps of the method of the present invention are implemented.

[0032] The present invention has the following advantages and beneficial effects:

[0033] The present invention is applicable to the aircraft cabin system. For the behavior data of passengers on the portal website, combined with the inherent information of users in the airline company, a passenger portrait is drawn, and then corresponding commodity information is recommended to the public display in the cabin. The present invention also recommends commodities for users in "first class", "business class" and "economy class" according to a specific group of people, improving the accuracy and reliability of matching. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of this application, and do not limit the embodiments of the present invention. In the drawings:

[0035] Figure 1 It is a schematic flow chart of the method of the present invention.

[0036] Figure 2 It is a schematic flow chart of the preference recommendation of the present invention.

[0037] Figure 3 It is a schematic block diagram of the device of the present invention.

[0038] Figure 4 It is a schematic diagram of the system architecture of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0039] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with the embodiments and the drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and do not limit the present invention.

[0040] Embodiment

[0041] This embodiment provides a content recommendation method based on cabin passenger behavior data. As Figure 1 shown, the method of this embodiment includes:

[0042] Step 101: Obtain passenger-related data.

[0043] Specifically, step 101 of this embodiment is as follows:

[0044] The obtained passenger-related data includes both static and dynamic data. Among them, static data refers to the basic data of cabin passengers, such as: name, age, gender, occupation, flight mileage, frequent destinations, flight class of the flight taken, etc. The static data is transmitted from the ground to the aircraft cabin system through air-ground communication.

[0045] Dynamic data refers to the key behavior data that passengers operate in the in-cabin entertainment system during the flight, such as: logging in to the on-board portal entertainment web page, the movie content ordered, music ordered, viewing in-flight shopping, and the length of time staying on the shopping items. All these operations are collected through the front-end web page JavaScript, and then the operation content is recorded in the background of the on-board server.

[0046] Step 102: Based on the passenger-related data and the recommended content data, obtain the preference recommendation lists for passengers in different cabin classes in real time.

[0047] After obtaining the basic passenger data and passenger behavior data in this embodiment, the obtained data is also preprocessed, specifically including:

[0048] Generate basic passenger portrait data through the basic passenger data for data preparation for real-time calculation of passenger portraits; the passenger behavior history data is the passenger operation data stored in a unified storage format.

[0049] As Figure 2 shown, step 102 of this embodiment specifically includes:

[0050] Step 201: Draw a single passenger preference portrait based on the basic portrait data and behavior history data of each passenger.

[0051] Step 202: Match the single passenger preference portrait with the in-flight recommended content to obtain a single passenger preference recommendation list.

[0052] The in-flight recommended content has different information according to different airline operations, mainly including: in-flight advertising information, in-flight shopping information, ground accommodation information, ground transportation information, ground tourism information, etc.

[0053] Step 203: Cluster and analyze the preference recommendation lists of all passengers according to the cabin class to obtain the preference recommendation lists of passengers within different cabin classes.

[0054] This embodiment classifies according to first class, business class, and economy class, and classifies and statistically analyzes the preference recommendations of passengers within these cabin classes.

[0055] Since it is impossible for the in-flight public display screen to recommend according to each passenger's preference, in this embodiment, corresponding content is recommended according to the hobbies of a certain type of passengers.

[0056] Step 103: Display the obtained recommended lists of passengers' preferences for different cabin types on the public display screens of different cabin types according to the priorities.

[0057] This embodiment also proposes a computer device for executing the above method of this embodiment.

[0058] Specifically, as Figure 3 shown, the computer device includes a processor, an internal memory, and a system bus; various device components including the internal memory and the processor are connected to the system bus. The processor is a hardware for executing computer program instructions through basic arithmetic and logical operations in a computer system. The internal memory is a physical device for temporarily or permanently storing computer programs or data (for example, program status information). The system bus can be any one of the following several types of bus structures, including a memory bus or a memory controller, a peripheral bus, and a local bus. The processor and the internal memory can communicate data through the system bus. Among them, the internal memory includes a read-only memory (ROM) or a flash memory (not shown in the figure), and a random access memory (RAM). The RAM usually refers to the main memory loaded with an operating system and computer programs.

[0059] The computer device generally includes an external storage device. The external storage device can be selected from a variety of computer-readable media. The computer-readable media refers to any available media that can be accessed by the computer device, including both mobile and fixed media. For example, the computer-readable media includes, but is not limited to, flash memory (micro SD card), CD-ROM, digital versatile disc (DVD) or other optical disc storage, tape cassette, tape, magnetic disk storage or other magnetic storage devices, or any other media that can be used to store the required information and can be accessed by the computer device.

[0060] The computer device can be logically connected to one or more network terminals in a network environment. The network terminals can be personal computers, servers, routers, smart phones, tablet computers or other public network nodes. The computer device is connected to the network terminals through a network interface (LAN interface of a local area network). A local area network (LAN) refers to a computer network interconnected within a limited area, such as a home, a school, a computer laboratory, or an office building using network media. WiFi and twisted pair wired Ethernet are the two most commonly used technologies for building a local area network.

[0061] It should be noted that other computer systems including more or fewer subsystems than the computer device can also be applicable to the invention.

[0062] As described in detail above, the computer device applicable to this embodiment can perform the specified operations of the content recommendation method based on cabin passenger behavior data. The computer device executes these operations in the form of software instructions running on a processor in a computer-readable medium. These software instructions can be read into the memory from a storage device or from another device through a local area network interface. The software instructions stored in the memory cause the processor to execute the above-described processing method of group member information. In addition, the present invention can also be implemented by hardware circuits or a combination of hardware circuits and software instructions. Therefore, the implementation of this embodiment is not limited to any specific combination of hardware circuits and software.

[0063] Embodiment 2

[0064] This embodiment proposes a content recommendation system based on cabin passenger behavior data. The system architecture is as Figure 4 shown, and includes a data collection layer, a data storage layer, a data calculation layer, and a content display layer.

[0065] Among them, the data collection layer is used to obtain passenger basic data and passenger behavior data (passenger operation data);

[0066] The data storage layer is used to store passenger basic portraits, passenger behavior history data, and recommended content data; among them, the passenger basic portrait is generated according to the passenger basic data; the passenger behavior history data is the passenger behavior data stored in a unified storage format; the recommended content data varies according to different airline operations.

[0067] The data calculation layer is used to calculate the passenger hobby preferences in real time according to the data stored in the data storage layer, match the content or advertisements that can be recommended, and obtain the passenger preference recommendation list for different cabin types.

[0068] The content display layer displays the passenger preference recommendation list for different cabin types obtained by the data calculation layer on the public display screens of different cabin types according to the priority.

[0069] The specific embodiments described above further elaborate on the purpose, technical solutions, and beneficial effects of the present invention. It should be understood that the above description is only the specific embodiments of the present invention and is not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A content recommendation method based on cabin passenger behavior data, characterized in that, it includes: Obtain passenger basic data and passenger behavior data; According to the passenger basic data and passenger behavior data, obtain the passenger preference recommendation lists for different cabin types in the cabin in real time; Display the obtained passenger preference recommendation lists for different cabin types on the public displays of different cabin types according to the priority; After the step of obtaining passenger basic data and passenger behavior data, it further includes: Generate passenger basic portrait data according to the passenger basic data; Store the passenger behavior data in the unified storage format as passenger behavior history data; According to the operating airline, obtain the in-flight recommended content; The step of obtaining the passenger preference recommendation lists for different cabin types in the cabin in real time specifically includes: Draw a single passenger hobby portrait according to the basic portrait data and behavior history data of each passenger; Match the single passenger hobby portrait with the in-flight recommended content to obtain a single passenger preference recommendation list; Cluster and analyze the preference recommendation lists of all passengers according to the cabin type to obtain the passenger preference recommendation lists within different cabin types.

2. The content recommendation method based on cabin passenger behavior data according to claim 1, characterized in that, The step of clustering and analyzing the preference recommendation lists of all passengers according to the cabin type specifically is: Classify according to first class, business class and economy class, and classify and count the passenger preference recommendations within different cabin types.

3. The content recommendation method based on cabin passenger behavior data according to claim 1, characterized in that, The passenger basic data is obtained from the ground through the air-ground communication method; The passenger behavior data is obtained through the front-end web page java script.

4. The content recommendation method based on cabin passenger behavior data according to claim 1, characterized in that, The passenger basic data includes name, age, gender, occupation, flight mileage, frequent destinations, and flight class information of the flight taken.

5. The content recommendation method based on cabin passenger behavior data according to claim 1, characterized in that, The passenger behavior data includes logging in to the in-flight portal entertainment web page, on-demand movie content, music on-demand, viewing in-flight shopping information, and the time spent staying in the shopping items.

6. A content recommendation system based on cabin passenger behavior data, characterized in that, it includes a data collection layer, a data storage layer, a data calculation layer and a content display layer; The data collection layer is used to obtain passenger basic data and passenger behavior data; The data storage layer is used to store passenger basic portraits, passenger behavior history data and recommended content data; wherein, the passenger basic portrait is generated according to the passenger basic data; the passenger behavior history data is the passenger behavior data stored in the unified storage format. The data calculation layer obtains a passenger preference recommendation list for different cabin types based on the data stored in the data storage layer; the method for obtaining the passenger preference recommendation list for different cabin types is as follows: draw a single passenger hobby portrait according to the basic portrait data and behavior history data of each passenger; match the single passenger hobby portrait with the in-flight recommendation content to obtain a single passenger preference recommendation list; cluster and analyze the preference recommendation lists of all passengers according to the cabin type to obtain the passenger preference recommendation list within different cabin types; The content display layer displays the passenger preference recommendation list for different cabin types obtained by the data calculation layer on the public display screens of different cabin types according to the priority.

7. A computer device, including a memory and a processor, where the memory stores a computer program, characterized in that, when the processor executes the computer program, the steps of the method according to any one of claims 1-5 are implemented.

8. A computer-readable storage medium, on which a computer program is stored, characterized in that, when the computer program is executed by the processor, the steps of the method according to any one of claims 1-5 are implemented.

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