CN111612590A - Scenic spot recommendation method and device based on artificial intelligence big data - Google Patents
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Abstract
The embodiment of the invention discloses a scenic spot recommendation method and device based on artificial intelligence big data, and belongs to the field of big data analysis. According to the scenic spot recommendation method based on the artificial intelligence big data, relevant factors influencing the scenic spot evaluation star level are determined by utilizing the website big data, grid data of the factors relevant to the scenic spot evaluation star level are extracted by utilizing the scenic spot evaluation star level data, the pixels in the local city are assigned by taking the local city as a unit, a large number of learning samples are obtained, then a BP neural network is carried out to construct an evaluation star level analysis model, the tourist feature information is matched with an obtained standard feature library, and recommended scenic spot sequencing is determined according to the evaluation star level dynamic analysis model, so that the tourist experience is improved.
Description
Technical Field
The disclosure relates to the field of big data analysis, in particular to a scenic spot recommendation method and device based on artificial intelligence big data.
Background
The tourist attraction recommendation means that the tourist can be provided with a suitable tourist attraction according to the personal condition of the tourist, so that the favorable travelling comment of the tourist is improved. At present, travel booking is basically completed on a network platform, and a traditional platform carries out scenic spot recommendation based on popular scenic spots and tourist search records, ignores the actual demands of travel and causes poor tourist experience of the tourists.
Disclosure of Invention
This disclosure is provided to introduce concepts in a simplified form that are further described below in the detailed description. This disclosure is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
The embodiment of the disclosure provides a scenic spot recommendation method and device based on artificial intelligence big data, which can accurately match potential users with scenic spots by using the big data, obtain scenic spot sequencing for the potential users to select, and accordingly improve the tourism experience.
In a first aspect, an embodiment of the present disclosure provides a scenic spot recommendation method based on artificial intelligence big data, where the method includes:
determining relevant factors influencing the scenic spot evaluation star level according to basic data, comment data and comment time in a website, determining static elements and dynamic elements according to time change relevance, and acquiring a standard feature library;
rasterizing the evaluation star level by taking a grade city as a unit so as to assign the evaluation star level of each scenic spot to each pixel in the grade city;
acquiring and obtaining a learning sample according to the evaluated tourist information;
normalizing the element data according to a maximum and minimum normalization method;
carrying out normalization processing on the evaluation star-level data;
constructing an evaluation star-level dynamic analysis model based on a BP neural network;
and acquiring the tourism historical data of the potential users, analyzing the tourism feature information in a specific period, matching the tourism feature information with the acquired standard feature library, and determining the recommended scenic spot sequence according to the evaluation star-level dynamic analysis model.
In combination with an embodiment of the first aspect, in some embodiments, the static elements include gender, age, occupation.
In combination with an embodiment of the first aspect, in some embodiments the dynamic elements include weather, temperature, traffic control, price.
In a second aspect, an embodiment of the present disclosure provides a scenic spot recommendation device based on artificial intelligence big data, including:
the determining unit is used for determining relevant factors influencing the scenic spot evaluation star level according to basic data, comment data and comment time in a website, determining static elements and dynamic elements according to the time change relevance and acquiring a standard feature library;
the grid unit is used for carrying out rasterization processing on the evaluation star level by taking a ground level city as a unit so as to assign the evaluation star level of each scenic spot to each pixel in the ground level city;
an acquisition unit configured to acquire a learning sample from the evaluated guest information;
the first normalization unit is used for normalizing the element data according to a maximum and minimum normalization method;
the second normalization unit is used for performing normalization processing on the evaluation star-level data;
the building unit is used for building an evaluation star-level dynamic analysis model based on the BP neural network;
and the recommending unit is used for acquiring the tourism historical data of the potential users, analyzing the tourism feature information in a specific period, matching the tourism feature information with the acquired standard feature library, and determining the recommended scenic spot sequence according to the evaluation star-level dynamic analysis model.
In combination with an embodiment of the second aspect, in some embodiments, the static elements include gender, age, occupation.
In combination with an embodiment of the second aspect, in some embodiments, the dynamic elements include weather, temperature, traffic control, price.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including: one or more processors; a storage device for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the artificial intelligence big data based tourist attraction recommendation method of the first aspect.
In a fourth aspect, the disclosed embodiments provide a computer readable medium, on which a computer program is stored, which when executed by a processor, implements the steps of the artificial intelligence big data based tourist attraction recommendation method according to the first aspect.
According to the scenic spot recommendation method and device based on the artificial intelligence big data, relevant factors influencing scenic spot evaluation star levels are determined by utilizing website big data, grid data of elements relevant to the scenic spot evaluation star levels are extracted by utilizing the scenic spot evaluation star level data, each pixel in the local city is assigned by taking the local city as a unit, a large number of learning samples are obtained, then a BP neural network is carried out to construct an evaluation star level analysis model, the tourist feature information is matched with an obtained standard feature library, and recommended scenic spot sequencing is determined according to the evaluation star level dynamic analysis model, so that tourist experience is improved.
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The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. Throughout the drawings, the same or similar reference numbers refer to the same or similar elements. It should be understood that the drawings are schematic and that elements and features are not necessarily drawn to scale.
FIG. 1 is a flow diagram of one embodiment of a method for tourist attraction recommendation based on artificial intelligence big data according to the present disclosure;
fig. 2 is a schematic diagram of a basic structure of an electronic device provided according to an embodiment of the present disclosure.
Detailed Description
Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure are shown in the drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but rather are provided for a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the disclosure are for illustration purposes only and are not intended to limit the scope of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order, and/or performed in parallel. Moreover, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "include" and variations thereof as used herein are open-ended, i.e., "including but not limited to". The term "based on" is "based, at least in part, on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". Relevant definitions for other terms will be given in the following description.
It should be noted that the terms "first", "second", and the like in the present disclosure are only used for distinguishing different devices, modules or units, and are not used for limiting the order or interdependence relationship of the functions performed by the devices, modules or units.
It is noted that references to "a", "an", and "the" modifications in this disclosure are intended to be illustrative rather than limiting, and that those skilled in the art will recognize that "one or more" may be used unless the context clearly dictates otherwise.
The names of messages or information exchanged between devices in the embodiments of the present disclosure are for illustrative purposes only, and are not intended to limit the scope of the messages or information.
Referring to FIG. 1, a flow diagram of one embodiment of an artificial intelligence big data based tourist attraction recommendation method according to the present disclosure is shown. The scenic spot recommendation method based on artificial intelligence big data can be applied to providing scenic spot recommendation for potential users, but is not limited. As shown in FIG. 1, the scenic spot recommendation method based on artificial intelligence big data comprises the following steps:
Here, the relevant factors to forest coverage include sex, age, occupation, weather, temperature, traffic control, price.
These elements vary in degree with time, and some elements do not change in short term, such as gender, age, occupation, which are called static elements, and weather, temperature, traffic control, which are easily changed in short term, which are called dynamic elements.
And 102, rasterizing the evaluation star level by taking the grade city as a unit so as to assign the evaluation star level of each scenic spot to each pixel in the grade city.
The current travel evaluation is basically evaluated by scenic spots independently, and when a tourist actually travels, the tourist generally plays a plurality of scenic spots in the city of the level. The corresponding evaluation star levels are generally one, two, three, four and five stars. The rating star rating data may be derived from website user ratings.
And 103, acquiring and obtaining a learning sample according to the evaluated tourist information.
And 104, normalizing the element data according to a maximum and minimum normalization method. Because the units of all elements in the sample are various and the dimensions are not uniform, the order of magnitude difference among some data is large, the iteration is possibly slow, and the element data are normalized in order to reduce the influence of the value of the element.
And 105, normalizing the evaluation star-level data. And normalizing the evaluation star-level data, and performing inverse normalization on the output of the output layer after the model is established.
And step 106, evaluating a star-level analysis model based on a BP neural network structure, sequencing learning samples with errors exceeding a preset threshold value according to the error size after finishing learning once, removing the learning samples with larger errors to be no longer used as the learning samples, and then learning again by using the rest learning samples until the set learning times are finished or the errors meet the requirements, and then finishing learning.
And 107, acquiring the tourism historical data of the potential user, analyzing the tourism feature information in a specific period, matching the tourism feature information with the acquired standard feature library, and determining the recommended scenic spot sequence according to the evaluation star-level dynamic analysis model.
Here, the specific time period refers to an estimated travel time, and the sights generally recommended to the guest are different at different times (seasons).
In the embodiment, the scenic spot recommendation method and device based on the artificial intelligence big data determine relevant factors influencing the scenic spot evaluation star level by utilizing the website big data, extract the grid data of elements relevant to the scenic spot evaluation star level by utilizing the scenic spot evaluation star level data, assign each pixel in the city of the city.
As an implementation of the method shown above, the present disclosure provides a scenic spot recommendation device based on artificial intelligence big data, where an embodiment of the device corresponds to the method shown in fig. 1, and the device may be specifically applied to various electronic devices.
The tourist attraction recommendation device based on artificial intelligence big data of this embodiment includes: the device comprises:
the determining unit is used for determining relevant factors influencing the scenic spot evaluation star level according to basic data, comment data and comment time in a website, determining static elements and dynamic elements according to the time change relevance and acquiring a standard feature library;
the grid unit is used for carrying out rasterization processing on the evaluation star level by taking a ground level city as a unit so as to assign the evaluation star level of each scenic spot to each pixel in the ground level city;
an acquisition unit configured to acquire a learning sample from the evaluated guest information;
the first normalization unit is used for normalizing the element data according to a maximum and minimum normalization method;
the second normalization unit is used for performing normalization processing on the evaluation star-level data;
the building unit is used for building an evaluation star-level dynamic analysis model based on the BP neural network;
and the recommending unit is used for acquiring the tourism historical data of the potential users, analyzing the tourism feature information in a specific period, matching the tourism feature information with the acquired standard feature library, and determining the recommended scenic spot sequence according to the evaluation star-level dynamic analysis model.
In some alternative embodiments, the static elements include gender, age, occupation.
In some alternative embodiments, the dynamic factors include weather, temperature, traffic control, price.
Referring now to FIG. 2, shown is a schematic diagram of an electronic device suitable for use in implementing embodiments of the present disclosure. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., car navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 2 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 2, the electronic device may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 901, which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)902 or a program loaded from a storage means 908 into a Random Access Memory (RAM) 903. In the RAM903, various programs and data necessary for the operation of the electronic apparatus 900 are also stored. The processing apparatus 901, the ROM 902, and the RAM903 are connected to each other through a bus 904. An input/output (I/O) interface 905 is also connected to bus 904.
Generally, the following devices may be connected to the I/O interface 905: input devices 906 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; an output device 907 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 908 including, for example, magnetic tape, hard disk, etc.; and a communication device 909. The communication means 909 may allow the electronic device to perform wireless or wired communication with other devices to exchange data. While fig. 2 illustrates an electronic device having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication device 909, or installed from the storage device 908, or installed from the ROM 6902. The computer program performs the above-described functions defined in the methods of the embodiments of the present disclosure when executed by the processing apparatus 901.
It should be noted that the computer readable medium of the present disclosure can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having 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. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network protocol, such as HTTP (HyperText transfer protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: determining relevant factors influencing the scenic spot evaluation star level according to basic data, comment data and comment time in a website, determining static elements and dynamic elements according to time change relevance, and acquiring a standard feature library; rasterizing the evaluation star level by taking a grade city as a unit so as to assign the evaluation star level of each scenic spot to each pixel in the grade city; acquiring and obtaining a learning sample according to the evaluated tourist information; normalizing the element data according to a maximum and minimum normalization method; carrying out normalization processing on the evaluation star-level data; constructing an evaluation star-level analysis model based on a BP neural network; and acquiring the tourism historical data of the potential users, analyzing the tourism feature information in a specific period, matching the tourism feature information with the acquired standard feature library, and determining the recommended scenic spot sequence according to the evaluation star-level dynamic analysis model.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Here, the name of the unit does not constitute a limitation of the unit itself in some cases, and for example, the acquisition unit may also be described as "a unit that acquires a learning sample from evaluated guest information".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, 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. A 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.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (8)
1. A scenic spot recommendation method based on artificial intelligence big data is characterized by comprising the following steps:
determining relevant factors influencing the scenic spot evaluation star level according to basic data, comment data and comment time in a website, determining static elements and dynamic elements according to time change relevance, and acquiring a standard feature library;
rasterizing the evaluation star level by taking a grade city as a unit so as to assign the evaluation star level of each scenic spot to each pixel in the grade city;
acquiring and obtaining a learning sample according to the evaluated tourist information;
normalizing the element data according to a maximum and minimum normalization method;
carrying out normalization processing on the evaluation star-level data;
constructing an evaluation star-level analysis model based on a BP neural network;
and acquiring the tourism historical data of the potential users, analyzing the tourism feature information in a specific period, matching the tourism feature information with the acquired standard feature library, and determining the recommended scenic spot sequence according to the evaluation star-level dynamic analysis model.
2. The method of claim 1, wherein static elements include gender, age, occupation.
3. The method of claim 1, wherein the dynamic factors include weather, temperature, traffic control, price.
4. An apparatus for recommending tourist attractions based on artificial intelligence big data, the apparatus comprising:
the determining unit is used for determining relevant factors influencing the scenic spot evaluation star level according to basic data, comment data and comment time in a website, determining static elements and dynamic elements according to the time change relevance and acquiring a standard feature library;
the grid unit is used for carrying out rasterization processing on the evaluation star level by taking a ground level city as a unit so as to assign the evaluation star level of each scenic spot to each pixel in the ground level city;
an acquisition unit configured to acquire a learning sample from the evaluated guest information;
the first normalization unit is used for normalizing the element data according to a maximum and minimum normalization method;
the second normalization unit is used for performing normalization processing on the evaluation star-level data;
the building unit is used for building an evaluation star-level dynamic analysis model based on the BP neural network;
and the recommending unit is used for acquiring the tourism historical data of the potential users, analyzing the tourism feature information in a specific period, matching the tourism feature information with the acquired standard feature library, and determining the recommended scenic spot sequence according to the evaluation star-level dynamic analysis model.
5. The apparatus of claim 4, wherein static elements include gender, age, occupation.
6. The apparatus of claim 4, wherein the dynamic factors include weather, temperature, traffic control, price.
7. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-3.
8. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-3.
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