CN117522533A - Hotel intelligent searching method and system - Google Patents
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Abstract
The invention provides a hotel intelligent searching method and a system, the method comprises the steps of collecting data of each hotel officer network and data of an OTA platform to obtain hotel data sets of each hotel, and obtaining normalized hotel data sets; extracting key features in the hotel, generating feature representations of corresponding hotels, and constructing multi-feature indexes corresponding to the hotels; constructing a multi-feature index library according to the multi-feature index; analyzing and identifying the demand information to obtain demand characteristics and search intents; analyzing the search intention to obtain an intention sentence so as to output a structured dictionary; and carrying out multi-round query on the structured dictionary based on the multi-feature index library so as to identify and obtain a plurality of high-quality candidate hotels, and sequencing the plurality of high-quality candidate hotels to obtain a high-quality candidate hotel list. The invention can avoid neglecting various attributes of the hotel, so that the characteristics of the hotel are more comprehensive, and a user can select the hotel which is most matched with the requirements of the user.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an intelligent hotel searching method and system.
Background
With the development of economy and the improvement of living standard, the travel industry has been developed faster. The demands of people for travel continue to increase, and the demands for hotel accommodation services are also increasing.
However, most of the existing hotel searching methods still stay at the stage of searching by manually inputting keywords, if a certain area needs to be searched for a hotel meeting the needs of the user, the user needs to comprehensively consider various conditions such as geographic position, room, facility, price and the like of the hotel, and select and screen item by item, so that the operation is complex and time-consuming. The traditional manual searching mode is difficult to fully meet the increasing personalized demands of users, and intelligent hotel room booking service cannot be provided. In addition, in addition to the few simple attributes described above, some deep hotel attributes such as detailed hotel types, facilities, services, windows, user ratings, etc. are often ignored, and these drawbacks result in a failure to achieve a quick and efficient hotel search and in a failure to accurately match the personalized needs of the user.
In summary, in the prior art, when searching for a hotel, a user needs to select and screen item by item, which results in complex operation and time-consuming, and other attributes of the hotel can be ignored, so that the searched hotel is difficult to match with the user requirement.
Disclosure of Invention
Based on the above, the present invention aims to provide a hotel intelligent search method and system, which at least solve the above-mentioned shortcomings in the prior art.
In a first aspect, the present invention provides a hotel intelligent search method, the method comprising:
collecting data of each hotel officer network and data of an OTA platform to obtain hotel data sets of each hotel, and preprocessing the hotel data sets to obtain normalized hotel data sets;
extracting keywords and emotion words in the normalized hotel data set based on a natural language processing technology, extracting visual features in the normalized hotel data set based on an image recognition technology, acquiring statistical features in the normalized hotel data set, and constructing multi-feature indexes of each hotel based on the keywords, the emotion words, the visual features and the statistical features;
constructing a multi-feature index library based on the multi-feature indexes of the hotels;
the method comprises the steps of obtaining requirement information of a user, analyzing and identifying the requirement information to obtain requirement characteristics of the user on a hotel and search intention of the user respectively;
analyzing the search intention to obtain an intention sentence, and mapping the intention sentence to a high-dimensional vector space to output a structured dictionary;
and carrying out multi-round inquiry on the structured dictionary based on the multi-feature index library so as to identify and obtain a plurality of high-quality candidate hotels, and sequencing the plurality of high-quality candidate hotels so as to obtain a high-quality candidate hotel list.
Compared with the prior art, the invention has the beneficial effects that: by extracting key features in the normalized hotel data set, various attributes of hotels can be prevented from being ignored, the features of the hotels are more comprehensive, a multi-feature index library is built through multi-feature indexes corresponding to the hotels, and a structured dictionary obtained according to user requirements is queried for multiple rounds through the multi-feature index library, so that a plurality of high-quality candidate hotels matched with the user requirements can be obtained, and after sorting, a high-quality candidate hotel list is obtained, so that a user can select the hotels most matched with the user requirements.
Further, the step of collecting the data of each hotel network and the data of the OTA platform to obtain hotel data sets of each hotel, and preprocessing the hotel data sets to obtain normalized hotel data sets includes:
collecting hotel data, network text data, picture data and scoring data of each hotel officer network on an OTA platform by adopting a web crawler;
and cleaning the hotel data, the network text data, the picture data and the scoring data to remove invalid data to obtain the normalized hotel data set.
Further, the step of analyzing and identifying the requirement information to obtain the requirement characteristics of the user on the hotel and the search intention of the user respectively includes:
deep semantic analysis is carried out on the demand information based on a natural language processing technology so as to extract the geographical position demand, the house type demand and the price demand of the user for the hotel;
and identifying the search intention of the user based on the requirement information.
Further, the step of analyzing the search intent to obtain an intent statement and mapping the intent statement to a high-dimensional vector space to output a structured dictionary includes:
extracting an intention sentence based on the search intention, and mapping the intention sentence to a high-dimensional vector space through a text vectorization technology to output a structured dictionary;
the structured dictionary is converted into a personalized and recommended input set, wherein the output structured dictionary includes all the needs of the user for the hotel.
Further, the step of performing multiple rounds of query on the structured dictionary based on the multi-feature index library to identify a plurality of candidate hotels with good quality includes:
taking the structured dictionary as a query condition, and carrying out multi-round query indexing on the query condition based on the multi-feature index library so as to identify and obtain a plurality of high-quality candidate hotels;
and performing relevance scoring on the plurality of high-quality candidate hotels to obtain matching scores of the plurality of high-quality candidate hotels.
Further, the step of sorting the plurality of good candidate hotels to obtain a good candidate hotel list includes:
constructing an evaluation model so that the evaluation model evaluates the matching degree of a plurality of high-quality candidate hotels and the demand information;
and sorting the plurality of high-quality candidate hotels according to the matching degree to generate a high-quality candidate hotel list.
In a second aspect, the present invention provides a hotel intelligent search system, the system comprising:
the collection module is used for collecting data of each hotel officer network and data of the OTA platform to obtain hotel data sets of each hotel, and preprocessing the hotel data sets to obtain normalized hotel data sets;
the extraction module is used for extracting keywords and emotion words in the normalized hotel data set based on a natural language processing technology, extracting visual features in the normalized hotel data set based on an image recognition technology, acquiring statistical features in the normalized hotel data set, and constructing multi-feature indexes of each hotel based on the keywords, the emotion words, the visual features and the statistical features;
the construction module is used for constructing a multi-feature index library based on the multi-feature indexes of the hotels;
the acquisition module is used for acquiring the demand information of the user, analyzing and identifying the demand information to respectively acquire the demand characteristics of the user on the hotel and the search intention of the user;
the analysis module is used for analyzing the search intention to obtain an intention sentence and mapping the intention sentence to a high-dimensional vector space so as to output a structured dictionary;
and the query module is used for carrying out multi-round query on the structured dictionary based on the multi-feature index library so as to identify and obtain a plurality of high-quality candidate hotels, and sequencing the plurality of high-quality candidate hotels so as to obtain a high-quality candidate hotel list.
Drawings
Fig. 1 is a flowchart of a hotel intelligent search method in a first embodiment of the invention;
fig. 2 is a block diagram of a hotel intelligent search system in a second embodiment of the invention.
Description of main reference numerals:
10. a collection module;
20. an extraction module;
30. constructing a module;
40. an acquisition module;
50. an analysis module;
60. and a query module.
The invention will be further described in the following detailed description in conjunction with the above-described figures.
Detailed Description
In order that the invention may be readily understood, a more complete description of the invention will be rendered by reference to the appended drawings. Several embodiments of the invention are presented in the figures. This invention 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.
It will be understood that when an element is referred to as being "mounted" on another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. The terms "vertical," "horizontal," "left," "right," and the like are used herein for illustrative purposes only.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
Example 1
Referring to fig. 1, an intelligent search method for hotels in a first embodiment of the present invention is shown, and the method includes steps S1 to S6:
s1, collecting data of each hotel officer network and data of an OTA platform to obtain hotel data sets of each hotel, and preprocessing the hotel data sets to obtain normalized hotel data sets;
specifically, the step S1 includes steps S11 to S12:
s11, collecting hotel data, network text data, picture data and scoring data of each hotel officer network by adopting a web crawler;
in specific implementation, web crawlers and other technologies are used to collect hotel data of hotel officials, web text data, picture data and scoring data on an OTA platform, it is to be understood that the hotel data can be understood as rough descriptions of hotels, the OTA platform refers to an online travel platform, the web text data of each hotel can be acquired on the online travel platform, it is to be explained that the picture data can be acquired not only on the online travel platform but also on the hotel officials, and the scoring data can be acquired on the online travel platform.
S12, cleaning the hotel data, the network text data, the picture data and the scoring data to remove invalid data to obtain the normalized hotel data set;
in the specific implementation, the acquired hotel data, network text data, picture data and scoring data are cleaned and normalized to remove invalid information, so as to obtain a normalized hotel data set.
S2, extracting keywords and emotion words in the normalized hotel data set based on a natural language processing technology, extracting visual features in the normalized hotel data set based on an image recognition technology, acquiring statistical features in the normalized hotel data set, and constructing multi-feature indexes of each hotel based on the keywords, the emotion words, the visual features and the statistical features;
it should be explained that, after the normalized hotel data set is extracted, the semantic features are extracted from the normalized hotel data set by the natural language processing technology, and in this embodiment, the semantic features include, but are not limited to, keywords and emotion words.
It can be appreciated that, because the normalized hotel dataset includes picture data of the hotel, visual features are extracted from the picture dataset by image recognition technology, and statistical features are obtained from structured data in the normalized hotel dataset, that is, features of the hotel are extracted from three dimensions of web text data, image data and statistical features.
In specific implementation, multi-feature indexes of all hotels are built according to the extracted keywords, emotion words, visual features and the statistical features, specifically, multi-feature indexes of all hotels are built according to three types of features of geographic positions, network texts and semantic vectors of the hotels, wherein the network texts refer to introduction texts of the hotels, and the semantic vectors use tree index structures, so that approximate searching is facilitated.
It is worth to say that the geographic position features construct geographic indexes by longitude and latitude coordinates, the text features construct text indexes by extracting words and sentences, and the semantic vectors use tree index structures, so that approximate searching is facilitated.
S3, constructing a multi-feature index library based on the multi-feature indexes of each hotel;
it should be explained that, the geographic index, the text index and the tree index are respectively stored in the index storage systems such as PostgreSQL and Elastic Search to form an index cluster with strong expansibility, so as to complete the construction of the multi-feature index library. It is worth to say that in the index construction process, the distributed construction and storage of indexes are realized, the index construction efficiency is improved, the hotel data can be rapidly indexed and searched, and a foundation is provided for subsequent searching and sorting.
S4, acquiring requirement information of a user, analyzing and identifying the requirement information to respectively acquire requirement characteristics of the user on a hotel and search intention of the user;
specifically, the step S4 includes steps S41 to S42:
s41, carrying out deep semantic analysis on the demand information based on a natural language processing technology so as to extract the geographical position demand, the house type demand and the price demand of the user for the hotel;
in the specific implementation, the requirement information of the user, namely the search statement of the user during searching, is subjected to deep semantic analysis by a natural language processing technology, and the geographic position, the house type requirement, the price requirement and the like of the user for the hotel are extracted from the search statement.
S42, identifying the search intention of the user based on the requirement information;
it can be understood that the requirement information, that is, the search statement of the user during the search, can learn the search intention of the user by identifying the search statement.
S5, analyzing the searching intention to obtain an intention sentence, and mapping the intention sentence to a high-dimensional vector space to output a structured dictionary;
specifically, the step S5 includes steps S51 to S52:
s51, extracting an intention sentence based on the search intention, and mapping the intention sentence to a high-dimensional vector space through a text vectorization technology to output a structured dictionary;
s52, converting the structured dictionary into a personalized and recommended input set, wherein the output structured dictionary comprises all requirements of the user for a hotel;
s6, carrying out multi-round inquiry on the structured dictionary based on the multi-feature index library to identify and obtain a plurality of high-quality candidate hotels, and sequencing the plurality of high-quality candidate hotels to obtain a high-quality candidate hotel list;
specifically, the step S6 includes steps S61 to S64:
s61, taking the structured dictionary as a query condition, and carrying out multi-round query indexing on the query condition based on the multi-feature index library so as to identify and obtain a plurality of high-quality candidate hotels;
in the specific implementation, the structured dictionary which is output by the requirement information of the user is used as a query condition, and a multi-round query index is carried out in combination with the multi-feature index library, and it is to be explained that multi-angle matching of the requirement of the user can be realized by utilizing multi-dimensional indexes such as geography, text, semantics and the like, and multi-dimensional joint recall is realized by integrating multi-feature search results such as geographic position query, room type matching query and the like in a multi-round query mode, so that a plurality of high-quality candidate hotels can be obtained.
S62, performing relevance scoring on the plurality of high-quality candidate hotels to obtain matching scores of the plurality of high-quality candidate hotels;
and in the specific implementation, carrying out relevance scoring on the plurality of high-quality candidate hotels according to the recall result of the multi-dimensional combined recall to obtain matching scores of the plurality of high-quality candidate hotels and the user requirement.
Further, topk results are selected as candidate hotel sets, and multi-feature query recognition and multi-round iterative recall supported by the multi-feature index library are utilized, so that accurate recognition of user requirements can be achieved, the recall fully covers the high-quality candidate hotel sets, and high-quality input is provided for subsequent sorting.
S63, constructing an evaluation model so that the evaluation model evaluates the matching degree of a plurality of high-quality candidate hotels and the requirement information;
when the method is implemented, the promtt is built firstly, the demand sentences in the demand information of the user and the candidate hotel information are converted into a format which can be processed by the large language model, then the built promtt is input into the large language model, and the large language model can evaluate the matching degree of the user demands and the hotel information in the promt.
S64, sorting a plurality of high-quality candidate hotels according to the matching degree to generate a high-quality candidate hotel list;
it can be appreciated that the matching scores of the plurality of high-quality candidate hotels and the user requirements are ranked according to the matching degree, so that a ranked hotel list with the matching degree from high to low is obtained.
In summary, according to the hotel intelligent search method in the embodiment of the invention, by extracting key features in the normalized hotel data set, various attributes of the hotels can be prevented from being ignored, so that the features of the hotels are more comprehensive, a multi-feature index library is constructed through multi-feature indexes corresponding to the hotels, and a structured dictionary obtained according to user requirements is queried for multiple rounds through the multi-feature index library, so that a plurality of high-quality candidate hotels matched with the user requirements can be obtained, and after sorting, a high-quality candidate hotel list is obtained, so that a user can select the hotels most matched with the user requirements.
Example two
Referring to fig. 2, a hotel intelligent search system according to a second embodiment of the present invention is shown, the system comprising:
the collection module 10 is configured to collect data of each hotel network and data of the OTA platform, so as to obtain hotel data sets of each hotel, and perform preprocessing on the hotel data sets to obtain normalized hotel data sets;
the extraction module 20 is configured to extract keywords and emotion words in the normalized hotel data set based on a natural language processing technology, extract visual features in the normalized hotel data set based on an image recognition technology, obtain statistical features in the normalized hotel data set, and construct a multi-feature index of each hotel based on the keywords, the emotion words, the visual features and the statistical features;
a building module 30, configured to build a multi-feature index library based on the multi-feature indexes of the hotels;
the acquiring module 40 is configured to acquire requirement information of a user, analyze and identify the requirement information, so as to obtain requirement characteristics of the user on a hotel and search intention of the user respectively;
an analysis module 50 for analyzing the search intent to obtain intent statements and mapping the intent statements to a high-dimensional vector space to output a structured dictionary;
and the query module 60 is configured to perform multiple rounds of queries on the structured dictionary based on the multi-feature index library to identify a plurality of candidate hotels, and rank the plurality of candidate hotels to obtain a list of candidate hotels.
In some alternative embodiments, the collection module 10 includes:
the collecting unit is used for collecting hotel data of each hotel officnetwork, network text data, picture data and scoring data on the OTA platform by adopting the web crawler;
and the cleaning unit is used for cleaning the hotel data, the network text data, the picture data and the scoring data to remove invalid data so as to obtain the normalized hotel data set.
In some alternative embodiments, the acquisition module 40 includes:
the analysis unit is used for carrying out deep semantic analysis on the demand information based on a natural language processing technology so as to extract the geographical position demand, the house type demand and the price demand of the user for the hotel;
and the identification unit is used for identifying the search intention of the user based on the requirement information.
In some alternative embodiments, the analysis module 50 includes:
a mapping unit for extracting an intention sentence based on the search intention, and mapping the intention sentence to a high-dimensional vector space through a text vectorization technology to output a structured dictionary;
and the conversion unit is used for converting the structured dictionary into a personalized and recommended input set, wherein the output structured dictionary comprises all requirements of the user for the hotel.
In some alternative embodiments, the query module 60 includes:
the query unit is used for taking the structured dictionary as a query condition, and carrying out multi-round query indexing on the query condition based on the multi-feature index library so as to identify and obtain a plurality of high-quality candidate hotels;
the scoring unit is used for performing relevance scoring on the plurality of high-quality candidate hotels to obtain matching scores of the plurality of high-quality candidate hotels;
the second construction unit is used for constructing an evaluation model so that the evaluation model evaluates the matching degree of the plurality of high-quality candidate hotels and the requirement information;
and the sorting unit is used for sorting the plurality of high-quality candidate hotels according to the matching degree so as to generate a high-quality candidate hotel list.
The functions or operation steps implemented when the above modules and units are executed are substantially the same as those in the above method embodiments, and are not described herein again.
The above-described respective modules may be functional modules or program modules, and may be implemented by software or hardware. For modules implemented in hardware, the various modules described above may be located in the same processor; or the above modules may be located in different processors in any combination.
The hotel intelligent search system provided by the embodiment of the invention has the same implementation principle and technical effects as those of the foregoing method embodiment, and for the sake of brief description, reference may be made to corresponding contents in the foregoing method embodiment where the system embodiment part is not mentioned.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (7)
1. An intelligent search method for hotels is characterized by comprising the following steps:
collecting data of each hotel officer network and data of an OTA platform to obtain hotel data sets of each hotel, and preprocessing the hotel data sets to obtain normalized hotel data sets;
extracting keywords and emotion words in the normalized hotel data set based on a natural language processing technology, extracting visual features in the normalized hotel data set based on an image recognition technology, acquiring statistical features in the normalized hotel data set, and constructing multi-feature indexes of each hotel based on the keywords, the emotion words, the visual features and the statistical features;
constructing a multi-feature index library based on the multi-feature indexes of the hotels;
the method comprises the steps of obtaining requirement information of a user, analyzing and identifying the requirement information to obtain requirement characteristics of the user on a hotel and search intention of the user respectively;
analyzing the search intention to obtain an intention sentence, and mapping the intention sentence to a high-dimensional vector space to output a structured dictionary;
and carrying out multi-round inquiry on the structured dictionary based on the multi-feature index library so as to identify and obtain a plurality of high-quality candidate hotels, and sequencing the plurality of high-quality candidate hotels so as to obtain a high-quality candidate hotel list.
2. The intelligent search method of claim 1, wherein the step of collecting data of each hotel network and data of an OTA platform to obtain a hotel data set of each hotel, and preprocessing the hotel data set to obtain a normalized hotel data set comprises:
collecting hotel data, network text data, picture data and scoring data of each hotel officer network on an OTA platform by adopting a web crawler;
and cleaning the hotel data, the network text data, the picture data and the scoring data to remove invalid data to obtain the normalized hotel data set.
3. The intelligent search method of hotels according to claim 1, wherein the step of analyzing and identifying the demand information to obtain the demand characteristics of the user for hotels and the search intents of the user respectively comprises:
deep semantic analysis is carried out on the demand information based on a natural language processing technology so as to extract the geographical position demand, the house type demand and the price demand of the user for the hotel;
and identifying the search intention of the user based on the requirement information.
4. The hotel intelligent search method of claim 1, wherein the step of analyzing the search intent to obtain an intent statement and mapping the intent statement to a high-dimensional vector space to output a structured dictionary comprises:
extracting an intention sentence based on the search intention, and mapping the intention sentence to a high-dimensional vector space through a text vectorization technology to output a structured dictionary;
the structured dictionary is converted into a personalized and recommended input set, wherein the output structured dictionary includes all the needs of the user for the hotel.
5. The intelligent hotel search method of claim 1, wherein the step of performing multiple rounds of queries on the structured dictionary based on the multi-feature index library to identify a number of good candidate hotels comprises:
taking the structured dictionary as a query condition, and carrying out multi-round query indexing on the query condition based on the multi-feature index library so as to identify and obtain a plurality of high-quality candidate hotels;
and performing relevance scoring on the plurality of high-quality candidate hotels to obtain matching scores of the plurality of high-quality candidate hotels.
6. The intelligent search method of hotels according to claim 1, wherein the step of sorting the plurality of good candidate hotels to obtain a list of good candidate hotels comprises:
constructing an evaluation model so that the evaluation model evaluates the matching degree of a plurality of high-quality candidate hotels and the demand information;
and sorting the plurality of high-quality candidate hotels according to the matching degree to generate a high-quality candidate hotel list.
7. An intelligent hotel search system, the system comprising:
the collection module is used for collecting data of each hotel officer network and data of the OTA platform to obtain hotel data sets of each hotel, and preprocessing the hotel data sets to obtain normalized hotel data sets;
the extraction module is used for extracting keywords and emotion words in the normalized hotel data set based on a natural language processing technology, extracting visual features in the normalized hotel data set based on an image recognition technology, acquiring statistical features in the normalized hotel data set, and constructing multi-feature indexes of each hotel based on the keywords, the emotion words, the visual features and the statistical features;
the construction module is used for constructing a multi-feature index library based on the multi-feature indexes of the hotels;
the acquisition module is used for acquiring the demand information of the user, analyzing and identifying the demand information to respectively acquire the demand characteristics of the user on the hotel and the search intention of the user;
the analysis module is used for analyzing the search intention to obtain an intention sentence and mapping the intention sentence to a high-dimensional vector space so as to output a structured dictionary;
and the query module is used for carrying out multi-round query on the structured dictionary based on the multi-feature index library so as to identify and obtain a plurality of high-quality candidate hotels, and sequencing the plurality of high-quality candidate hotels so as to obtain a high-quality candidate hotel list.
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| CN110807091A (en) * | 2019-03-01 | 2020-02-18 | 王涵 | Hotel intelligent question-answer recommendation and decision support analysis method and system |
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| CN116206318A (en) * | 2022-09-30 | 2023-06-02 | 携程计算机技术（上海）有限公司 | Hotel evaluation feedback method, system, equipment and storage medium |
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| CN110807091A (en) * | 2019-03-01 | 2020-02-18 | 王涵 | Hotel intelligent question-answer recommendation and decision support analysis method and system |
| CN112784156A (en) * | 2021-01-13 | 2021-05-11 | 携程旅游信息技术（上海）有限公司 | Search feedback method, system, device and storage medium based on intention recognition |
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