CN113077312A - Hotel recommendation method, system, equipment and storage medium - Google Patents
Hotel recommendation method, system, equipment and storage medium Download PDFInfo
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Abstract
The invention provides a hotel recommendation method, a system, equipment and a storage medium, wherein the method comprises the following steps: acquiring all text evaluation data corresponding to a plurality of hotels; performing sentence segmentation on the text evaluation data, and extracting a target sentence; each target statement comprises an evaluation item and a corresponding evaluation keyword; establishing a first mapping table associating the evaluation keywords with the first scores; acquiring evaluation items and corresponding evaluation keywords in the target sentence, and obtaining a first score corresponding to each evaluation item according to the evaluation keywords and the first mapping table; obtaining a total score corresponding to each hotel according to the first score corresponding to the evaluation item of each target statement; sorting the hotels according to the sequence of the total scores from high to low, and recommending the hotels with the highest total scores to the user; the hotel recommendation effect is favorably improved.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a hotel recommendation method, a hotel recommendation system, hotel recommendation equipment and a storage medium.
Background
Existing commodity recommendation systems typically rely on a user's history score record for an item to predict the score of a new commodity, thereby enabling commodity recommendation. However, for some commodities such as hotels and restaurants, users only have written comment records of the commodities and do not score, and the prior art has no better method for processing the commodities, so that suitable hotels are recommended to the users.
Disclosure of Invention
Aiming at the problems in the prior art, the invention aims to provide a hotel recommendation method, a hotel recommendation system, hotel recommendation equipment and a storage medium, and solves the problem that in the prior art, a proper hotel cannot be recommended to a user by using a word comment record of the hotel.
In order to achieve the purpose, the invention provides a hotel recommendation method, which comprises the following steps:
s110, acquiring all text evaluation data corresponding to a plurality of hotels;
s120, performing sentence segmentation on the text evaluation data, and extracting a target sentence; each target statement comprises an evaluation item and a corresponding evaluation keyword;
s130, establishing a first mapping table associated with the evaluation keyword and the first score;
s140, obtaining the evaluation items in the target sentence and the corresponding evaluation keywords, and obtaining a first score corresponding to each evaluation item according to the evaluation keywords and the first mapping table;
s150, obtaining a total score corresponding to each hotel according to the first score corresponding to the evaluation item of each target statement; and sorting the hotels in the order of high total score to low total score, and recommending the hotels with the highest total score to the user.
Optionally, the method further comprises the step of:
s160, when a plurality of hotels with the highest total score exist, the hotels serve as the hotels to be screened, and historical text comment data of the user in historical hotel booking orders are obtained;
s170, extracting the evaluation item with the largest occurrence frequency from the historical text comment data of the user as a reference item;
and S180, acquiring a third score of the hotel to be screened on the reference item, and recommending the hotel with the highest third score in the hotel to be screened to the user.
Optionally, each evaluation keyword belongs to an evaluation category, and the evaluation category is positive or negative; in the first mapping table, a rating keyword, the rating category and the first score are associated;
the step S180 includes:
screening out hotels with negative evaluation categories corresponding to the reference items from the hotels to be screened, and using reserved hotels to be screened as alternative hotels;
and acquiring a third score of the alternative hotels on the reference item, and recommending the hotels with the highest third score in the hotels to be screened to the user.
Optionally, the step S120 includes:
s121, performing sentence segmentation on the text evaluation data according to punctuation marks to obtain an initial sentence library;
and S123, only the sentences containing the evaluation items and the corresponding evaluation keywords in the initial sentence library are reserved as target sentences.
Optionally, in the first mapping table, when the evaluation category corresponding to the evaluation keyword is negative, the first score corresponding to the evaluation keyword is a negative number; and when the evaluation category corresponding to the evaluation keyword is positive, the first score corresponding to the evaluation keyword is a positive number.
Optionally, a step is further included between step S121 and step S123:
and S122, preprocessing the sentences in the initial sentence library, and taking the sentences after preprocessing as the input of the trained deep learning model to obtain the evaluation items and/or the corresponding evaluation keywords contained in the sentences.
Optionally, the step S110 includes:
and capturing all text evaluation data corresponding to the plurality of hotels by a crawler.
The invention also provides a hotel recommendation system, which is used for realizing the hotel recommendation method and comprises the following steps:
the evaluation text acquisition module is used for acquiring all text evaluation data corresponding to the plurality of hotels;
the target sentence extraction module is used for carrying out sentence segmentation on the text evaluation data and extracting a target sentence; each target statement comprises an evaluation item and a corresponding evaluation keyword;
the mapping table establishing module is used for establishing a first mapping table associated with the evaluation keyword and the first score;
the first score acquisition module is used for acquiring the evaluation items in the target sentence and the corresponding evaluation keywords and acquiring a first score corresponding to each evaluation item according to the evaluation keywords and the first mapping table;
the total evaluation recommending module is used for obtaining a total score corresponding to each hotel according to the first score corresponding to the evaluation item of each target statement; and sorting the hotels in the order of high total score to low total score, and recommending the hotels with the highest total score to the user.
The invention also provides hotel recommendation equipment, which comprises:
a processor;
a memory having stored therein executable instructions of the processor;
wherein the processor is configured to perform the steps of any of the hotel recommendation methods described above via execution of the executable instructions.
The invention also provides a computer readable storage medium for storing a program which when executed by a processor implements the steps of any of the hotel recommendation methods described above.
Compared with the prior art, the invention has the following advantages and prominent effects:
according to the hotel recommendation method, the hotel recommendation system, the hotel recommendation equipment and the hotel recommendation storage medium, only evaluation sentences which completely contain evaluation items and evaluation keywords are reserved, and are analyzed and converted into total scores corresponding to all hotels, so that the scores of the hotels can be converted into the scores of the hotels for recommendation based on network text comments, the hotel recommendation effect can be improved, and the user experience is also improved.
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Other features, objects and advantages of the present invention will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of a hotel recommendation method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a hotel recommendation method according to another embodiment of the present invention;
fig. 3 is a schematic structural diagram of a hotel recommendation system according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a hotel recommendation device according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a computer-readable storage medium according to an embodiment of the disclosure.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals in the drawings denote the same or similar structures, and thus their repetitive description will be omitted.
As shown in fig. 1, an embodiment of the present invention discloses a hotel recommendation method, which includes the following steps:
s110, all text evaluation data corresponding to the plurality of hotels are obtained. Specifically, this step may capture text evaluation data corresponding to each of the plurality of hotels by using a crawler technology in the prior art. The text evaluation data corresponding to each hotel is the word comment record of all the users who have used the hotel on the hotel.
And S120, performing sentence segmentation on the text evaluation data, and extracting a target sentence. Each of the target sentences includes an evaluation item and a corresponding evaluation keyword. Specifically, step S120 includes:
and S121, performing sentence segmentation on the text evaluation data according to the punctuation marks to obtain an initial sentence library. For example, a piece of text rating data of a user is: "the hotel sound insulation is very poor and the others are good" when the location is one or two hundred meters away from north gate of city. Because the data has two punctuations, the evaluation data of the text can be divided into three sentences: "the position is in one hundred meters of north gate of city", "the sound insulation of hotel is very bad and very bad", and "others are good". And all sentences obtained after segmenting the text evaluation data corresponding to all hotels form the initial sentence library.
And S122, preprocessing the sentences in the initial sentence library, and taking the sentences after preprocessing as the input of the trained deep learning model to obtain the evaluation items and/or the corresponding evaluation keywords contained in the sentences. The preprocessing can include removing duplicate, removing spam comments and part-of-speech tags, Chinese word segmentation, and the like. Because some spam comments, repeated sentences, water force comments and comments irrelevant to the hotel may appear in the obtained text evaluation data. After the preprocessing is carried out, the accuracy rate of algorithm recommendation is improved. This step can be implemented using existing techniques, and is not described in detail herein. The deep learning model can be a convolutional neural network model in the prior art, and the like.
Textual ratings data about a hotel typically contains the hotel's rating items and/or corresponding rating keywords. The evaluation items of the hotel relate to specific aspects of the hotel, such as the geographic location, environment, facilities, price, and health condition of the hotel. The evaluation keywords are words used for modifying the evaluation items in the text evaluation data and comprise emotional tendencies of a commentator, namely a user, to certain characteristics of the hotel. For example, in the above text evaluation data "hotel soundproofing is poor," hotel soundproofing "is an evaluation item," poor "represents an evaluation keyword, which indicates that a person who writes a comment is good at the price of the hotel. According to the relationship, the evaluation item and the evaluation keyword can be extracted according to the deep learning model.
And S123, only the sentences containing the evaluation items and the corresponding evaluation keywords in the initial sentence library are reserved as target sentences. Specifically, some sentences in the initial sentence library can only extract evaluation items, for example, an evaluation item corresponding to a text evaluation sentence "the position is one hundred meters in north and city," is a "geographical position," but since the commentator does not indicate that the position belongs to far or near, the sentence cannot extract evaluation keywords, and it cannot be determined whether the emotional tendency of the commentator belongs to positive or negative. Some sentences such as "hotel sound insulation is very poor and very poor", can extract both evaluation items and evaluation keywords. Some sentences such as "good others" can only extract the evaluation keyword "good others", and cannot extract the evaluation item.
Therefore, in the embodiment, not all sentences are retained, but only sentences with complete evaluation items and evaluation keywords are retained, so that the problems that part of evaluation keywords cannot be matched with the evaluation items or matching is wrong can be avoided as much as possible. Therefore, the accuracy of positioning the evaluation items and corresponding evaluation keywords in the target sentences subsequently can be improved, and the recommendation effect of the subsequent algorithm can be improved.
S130, establishing a first mapping table associating the evaluation keywords with the first scores. Specifically, each of the above-described evaluation keywords belongs to an evaluation category. The above evaluation categories are positive or negative. In the first mapping table, the evaluation keyword and the evaluation category are associated with the first score. For example, the evaluation category to which the evaluation keyword "new", "good", or the like belongs is positive. The evaluation keywords "bad", and the like are assigned to the evaluation categories of negative.
In the first mapping table, when the evaluation category corresponding to the evaluation keyword is negative, the first score corresponding to the evaluation keyword is negative. And when the evaluation category corresponding to the evaluation keyword is a positive number, the first score corresponding to the evaluation keyword is a positive number. Therefore, the situation that some hotel text evaluation data have evaluation data for some evaluation items and some hotel text evaluation data do not have evaluation data for the evaluation items can be avoided. For example, some hotels have an evaluation of sanitary items and evaluate the sanitary items as negative, for example, poor; there are hotels that do not have an assessment of a hygiene item and if the first score that evaluates to negative is still positive, then it will result in unfairness to hotels that do not have such item assessment data. Therefore, the technical scheme is favorable for improving the accuracy of hotel recommendation effect.
And S140, acquiring the evaluation items in the target sentence and the corresponding evaluation keywords, and obtaining a first score corresponding to each evaluation item according to the evaluation keywords and the first mapping table. It should be noted that, for a hotel, on an evaluation item, if the evaluation item is not involved in all target sentences of the hotel, a preset score, such as 60 scores, is set for the evaluation item of the hotel.
S150, obtaining a total score corresponding to each hotel according to the first score corresponding to the evaluation item of each target statement; and sorting the hotels in the order of high total score to low total score, and recommending the hotels with the highest total score to the user. Specifically, the method includes the steps of obtaining search conditions of a user on a search page, and screening hotels meeting the search conditions according to the search conditions. And summing the first scores corresponding to the evaluation items in all the target sentences extracted under each hotel to obtain a total score corresponding to each hotel. The hotel with the highest total score or the highest N hotels is then recommended to the user. Wherein N is a positive integer. And the hotels are ranked from high to low according to the total score and displayed in the search result of the user equipment.
As shown in fig. 2, another embodiment of the present application discloses another hotel recommendation method, which based on the above embodiment, further includes the steps of:
and S160, when a plurality of hotels with the highest total score exist, taking the hotels as the hotels to be screened, and acquiring historical text comment data of the user in historical hotel reservation orders. It should be noted that the historical hotel reservation orders are not limited to orders at the hotels to be screened, but are orders at all hotels. The historical text comment data also belongs to a text comment record.
And S170, extracting the evaluation item with the largest occurrence frequency from the historical text comment data of the user as a reference item. Specifically, for example, if the user has 100 records of the historical text comment data in which the evaluation item of the soundproofing of the hotel occurs the most frequently, the "soundproofing of the hotel" is used as the reference item. The reference item appears most frequently, which shows that the user is most interested in the condition of the facility in the hotel.
And S180, acquiring a third score of the hotel to be screened on the reference item, and recommending the hotel with the highest third score in the hotel to be screened to the user. Specifically, for example, there are three hotels to be screened, which are a first hotel, a second hotel and a third hotel, and third scores of the three hotels on the "hotel sound insulation" evaluation item are obtained, for example, the corresponding third scores are 60 scores, 70 scores and 80 scores, respectively, so that the third hotel corresponding to the 80 scores is recommended to the user. Therefore, the evaluation item facility, such as the hotel with the best sound insulation performance of the hotel, is recommended to the user on the basis of the evaluation item which the user intends to, the recommendation effect of the hotel is favorably improved, the recommendation result can be automatically adjusted according to the attention degree of the user to each evaluation item of the hotel, and the experience which is more in line with the individual requirements of the user is obtained.
Therefore, the historical word comment records of the user and the historical word comment records of the hotel can be combined, the historical word comment records of the user are effectively utilized, the hotel which is most suitable is recommended to the user, and user experience is improved.
Another embodiment of the present application discloses another hotel recommendation method, and on the basis of the above embodiment, step S180 includes:
s181, screening out the hotels to be screened from the evaluation categories corresponding to the reference items, wherein the hotels to be screened contain negative hotels, and reserving the hotels to be screened as alternative hotels. And
and S182, acquiring a third score of the alternative hotels on the reference item, and recommending the hotels with the highest third score in the hotels to be screened to the user.
Specifically, if the user is interested in "soundproofing of the hotel" in the historical text comment data, the hotel is screened out when a situation in which bad soundproofing of the hotel is reflected appears in the evaluation keyword. Therefore, the calculation amount of the algorithm in the recommendation process is reduced, and the system performance is improved.
Another embodiment of the application discloses another hotel recommendation method, which on the basis of the above embodiment, further comprises the steps of:
and after a preset time period, repeatedly executing the steps S110 to S150 to obtain a new hotel sequencing result. The hotel sequencing result is updated, and timeliness of the hotel sequencing result is guaranteed. The preset time period may be one week.
It should be noted that, when the hotel recommendation method disclosed in this embodiment is applied to other goods, such as restaurants, it is also within the scope of the present application. All the above embodiments of the present application can be freely combined, and the technical solutions obtained after combination are also within the protection scope of the present application.
As shown in fig. 3, an embodiment of the present invention further discloses a hotel recommendation system 3, which includes:
an evaluation text obtaining module 31, configured to obtain all text evaluation data corresponding to each of the multiple hotels.
A target sentence extraction module 32, configured to perform sentence segmentation on the text evaluation data to extract a target sentence; each of the target sentences includes an evaluation item and a corresponding evaluation keyword.
The mapping table establishing module 33 is configured to establish a first mapping table in which the evaluation keyword is associated with the first score.
The first score obtaining module 34 is configured to obtain the evaluation items in the target sentence and the corresponding evaluation keywords, and obtain a first score corresponding to each evaluation item according to the evaluation keywords and the first mapping table.
The total evaluation recommending module 35 is configured to obtain a total score corresponding to each hotel according to the first score corresponding to the evaluation item of each target sentence; and sorting the hotels in the order of high total score to low total score, and recommending the hotels with the highest total score to the user.
It can be understood that the hotel recommendation system of the present invention further includes other existing functional modules that support the operation of the hotel recommendation system. The hotel recommendation system shown in fig. 3 is only an example and should not bring any limitations to the functionality and scope of use of embodiments of the present invention.
The hotel recommendation system in this embodiment is used to implement the hotel recommendation method, so for specific implementation steps of the hotel recommendation system, reference may be made to the description of the hotel recommendation method, and details are not described here.
The embodiment of the invention also discloses hotel recommendation equipment, which comprises a processor and a memory, wherein the memory stores executable instructions of the processor; the processor is configured to perform the steps of the hotel recommendation method described above via execution of executable instructions. Fig. 4 is a schematic structural diagram of the hotel recommendation device disclosed in the present invention. An electronic device 600 according to this embodiment of the invention is described below with reference to fig. 4. The electronic device 600 shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 4, the electronic device 600 is embodied in the form of a general purpose computing device. The components of the electronic device 600 may include, but are not limited to: at least one processing unit 610, at least one memory unit 620, a bus 630 connecting the different platform components (including the memory unit 620 and the processing unit 610), a display unit 640, etc.
Wherein the storage unit stores program code that may be executed by the processing unit 610 to cause the processing unit 610 to perform the steps according to various exemplary embodiments of the present invention as described in the hotel recommendation method section above in this specification. For example, processing unit 610 may perform the steps as shown in fig. 1.
The storage unit 620 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)6201 and/or a cache memory unit 6202, and may further include a read-only memory unit (ROM) 6203.
The memory unit 620 may also include a program/utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 600, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 600 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 650. Also, the electronic device 600 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via the bus 630. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, to name a few.
The invention also discloses a computer readable storage medium for storing a program, wherein the program realizes the steps of the hotel recommendation method when executed. In some possible embodiments, the various aspects of the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to perform the steps according to various exemplary embodiments of the invention described in the hotel recommendation method described above in this specification when the program product is run on the terminal device.
As shown above, when the program of the computer-readable storage medium of this embodiment is executed, data can be evaluated according to the text of the hotel, and only the evaluation sentences completely including the evaluation items and the evaluation keywords are retained, and are analyzed and converted into total scores corresponding to each hotel, so that the evaluation results can be converted into the scores of the hotels for recommendation based on web text comments, which is beneficial to improving the hotel recommendation effect and improving the user experience.
Fig. 5 is a schematic structural diagram of a computer-readable storage medium of the present invention. Referring to fig. 5, a program product 800 for implementing the above method according to an embodiment of the present invention is described, which may employ a portable compact disc read only memory (CD-ROM) and include program code, and may be run on a terminal device, such as a personal computer. However, the program product of the present invention is not limited in this regard and, in the present document, a 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.
The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A 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 (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, 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.
A computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, 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 readable storage medium may also be any readable medium that is not a 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 readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like 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 computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
According to the hotel recommendation method, the system, the equipment and the storage medium provided by the embodiment of the invention, the data can be evaluated according to the text of the hotel, and only the evaluation sentences completely containing the evaluation items and the evaluation keywords are reserved, so that the evaluation sentences are analyzed and converted into the total scores corresponding to each hotel, the recommendation can be carried out by converting the evaluation sentences into the scores of the hotel based on the network text comments, the hotel recommendation effect can be improved, and the user experience can be improved.
The foregoing is a more detailed description of the invention in connection with specific preferred embodiments and it is not intended that the invention be limited to these specific details. For those skilled in the art to which the invention pertains, several simple deductions or substitutions can be made without departing from the spirit of the invention, and all shall be considered as belonging to the protection scope of the invention.
Claims (10)
1. A hotel recommendation method is characterized by comprising the following steps:
s110, acquiring all text evaluation data corresponding to a plurality of hotels;
s120, performing sentence segmentation on the text evaluation data, and extracting a target sentence; each target statement comprises an evaluation item and a corresponding evaluation keyword;
s130, establishing a first mapping table associated with the evaluation keyword and the first score;
s140, obtaining the evaluation items in the target sentence and the corresponding evaluation keywords, and obtaining a first score corresponding to each evaluation item according to the evaluation keywords and the first mapping table;
s150, obtaining a total score corresponding to each hotel according to the first score corresponding to the evaluation item of each target statement; and sorting the hotels in the order of high total score to low total score, and recommending the hotels with the highest total score to the user.
2. The hotel recommendation method as recited in claim 1, further comprising the steps of:
s160, when a plurality of hotels with the highest total score exist, the hotels serve as the hotels to be screened, and historical text comment data of the user in historical hotel booking orders are obtained;
s170, extracting the evaluation item with the largest occurrence frequency from the historical text comment data of the user as a reference item;
and S180, acquiring a third score of the hotel to be screened on the reference item, and recommending the hotel with the highest third score in the hotel to be screened to the user.
3. The hotel recommendation method as recited in claim 2, wherein each of said evaluation keywords is assigned to an evaluation category, said evaluation category being positive or negative; in the first mapping table, a rating keyword, the rating category and the first score are associated;
the step S180 includes:
screening out hotels with negative evaluation categories corresponding to the reference items from the hotels to be screened, and using reserved hotels to be screened as alternative hotels;
and acquiring a third score of the alternative hotels on the reference item, and recommending the hotels with the highest third score in the hotels to be screened to the user.
4. The hotel recommendation method of claim 1, wherein said step S120 comprises:
s121, performing sentence segmentation on the text evaluation data according to punctuation marks to obtain an initial sentence library;
and S123, only the sentences containing the evaluation items and the corresponding evaluation keywords in the initial sentence library are reserved as target sentences.
5. The hotel recommendation method of claim 3, wherein when the evaluation category corresponding to the evaluation keyword is negative in the first mapping table, the first score corresponding to the evaluation keyword is negative; and when the evaluation category corresponding to the evaluation keyword is positive, the first score corresponding to the evaluation keyword is a positive number.
6. The hotel recommendation method according to claim 4, further comprising, between step S121 and step S123, the steps of:
and S122, preprocessing the sentences in the initial sentence library, and taking the sentences after preprocessing as the input of the trained deep learning model to obtain the evaluation items and/or the corresponding evaluation keywords contained in the sentences.
7. The hotel recommendation method of claim 1, wherein said step S110 comprises:
and capturing all text evaluation data corresponding to the plurality of hotels by a crawler.
8. A hotel recommendation system for implementing the hotel recommendation method of claim 1, the system comprising:
the evaluation text acquisition module is used for acquiring all text evaluation data corresponding to the plurality of hotels;
the target sentence extraction module is used for carrying out sentence segmentation on the text evaluation data and extracting a target sentence; each target statement comprises an evaluation item and a corresponding evaluation keyword;
the mapping table establishing module is used for establishing a first mapping table associated with the evaluation keyword and the first score;
the first score acquisition module is used for acquiring the evaluation items in the target sentence and the corresponding evaluation keywords and acquiring a first score corresponding to each evaluation item according to the evaluation keywords and the first mapping table;
the total evaluation recommending module is used for obtaining a total score corresponding to each hotel according to the first score corresponding to the evaluation item of each target statement; and sorting the hotels in the order of high total score to low total score, and recommending the hotels with the highest total score to the user.
9. A hotel recommendation device, comprising:
a processor;
a memory having stored therein executable instructions of the processor;
wherein the processor is configured to perform the steps of the hotel recommendation method of any of claims 1-7 via execution of the executable instructions.
10. A computer readable storage medium storing a program, wherein the program when executed by a processor implements the steps of the hotel recommendation method of any of claims 1-7.
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